Industrial Management Journal

Objective Waste collection poses a significant challenge for contemporary societies. Given the inevitability of ongoing human waste production, the organization of municipal waste collection holds paramount importance. Against the backdrop of escalating environmental pollutants over recent decades and crises induced by global warming, governments have increasingly prioritized addressing sustainability issues. The objective of this study was to formulate an urban waste collection network with a municipal sustainability perspective. To achieve this goal, we proposed a multi-objective mathematical model that incorporates economic, social, and environmental considerations pertaining to the routing of urban waste collection vehicles. Methods This study introduces an integer multi-objective mathematical model centred on stability components to address the routing problem of urban waste collection vehicles, to design an optimal network for urban waste collection. The model was addressed using real data from waste collection in Iran's Saveh city. GAMS software was employed for solving the model in small dimensions, while MATLAB software was utilized for solving the model in larger dimensions. The proposed model incorporates a robust approach to handle

Objective In the realm of green supply chain management, the pivotal responsibilities of companies involve supplier selection and order allocation. These tasks were treated as independent challenges in prior studies. This issue holds significant importance within the context of green supply chain management, demanding the simultaneous consideration of both quantitative and qualitative criteria. These criteria, at times conflicting, necessitate an indepth analysis of trade-offs. There is a pressing need for a comprehensive framework capable of concurrently addressing both aspects. In addition to accommodating green criteria, such a framework should enable the determination of order quantities allocated to suppliers.

Objective According to the studies conducted on New Technology-Based Firms (NTBFs), the biggest problem of these companies is their access to financial resources. Studies show that among the indirect intervention policies, credit guarantee schemes have been considered by governments for facilitating the financing of these enterprises due to the need for less budget and a higher leverage effect. The purpose of this article is to present the appropriate credit guarantee model for new technology-based Firms.

Objective
Designing a multi-level supply chain network with efficient product flow management is an important issue in supply chain management. Determining the price of products, which can be affected by different factors such as environmental uncertainty, will have a significant impact on the strategic decision of designing the supply chain network. In recent research on dynamic pricing, the majority focused on two-level supply chains, exploring the impact of advertising on the supply chain. In this study, the levels of the supply chain are upgraded to three levels, and the advertising and inventory variables as well as the pricing issue in the supply chain are investigated. Hence, the overarching objective is to scrutinize the coordination of a supply chain involving three variables: advertising, warehouse inventory, and a comparison between static and dynamic pricing. Given the accelerated expansion of e-commerce, dynamic pricing emerges as a potent strategy for profit augmentation within the supply chain, an aspect that has received comparatively less attention. The supply chain in this research includes three levels: producer, supplier, and retailer. In addition, many sources, including Chen et al, 2018, have indicated that dynamic pricing is being used in many innovative businesses, that 60% of market development managers around the world are familiar with dynamic pricing methods, and that 35% of CEOs and business managers plan to base all their pricing models on dynamic pricing methods in the next three years. This research initially aims to provide a stable model for dynamic pricing and compare it with static pricing in multi-level supply chains using a game theory approach. Secondly, it examines the theoretical foundations, thirdly, the research methodology is presented, and fourthly, the discussion and conclusions are discussed.

Methods
The present research falls under the category of applied research. It considers discount-oriented models to examine the planning horizon, the use of dynamic pricing in some actors at one level of the chain from the concept of dynamic pricing, and the use of static pricing by others at the same level and considering the demand in a non-deterministic (stable) manner. Finally, the use of Stackelberg’s game is presented to examine the core of the game and the Nash equilibrium point. The robust optimization model is presented by Malloy et al. Initially, the model introduces symbols: x as the vector of design variables, and y as the vector of control variables. Parameters A, B, and C are coefficient parameters, while b and e are parameter vectors. Values for A and B are predetermined, and B, C, and e involve uncertainty. A particular understanding of the parameter is called scenario uncertainty, which is assigned the symbol s, and its probability is specified by ps. K retailers operate in the supply chain, and each retailer supplies the desired product from only one manufacturer. In general, the outcome for each retailer is the disparity between their income and expenses. In such a situation, the retailer faces ordering, holding, and marketing costs for each product. The profit margin of each retailer is the difference between the price paid to the manufacturer for the bulk purchase and the selling price to the customer. It is worth noting that considering that the final product of each manufacturer is sent to only one retailer, the number of manufacturers and retailers is equal. For data analysis, a genetic algorithm, particle accumulation optimization, and MATLAB software were employed. Initially, the model underwent analysis in static mode, followed by dynamic mode analysis using Games software. Ultimately, the outcomes of the two pricing methods were compared.

Results
This article introduces the development of a stochastic demand function utilizing genetic algorithms and particle optimization. It presents two distinct single-period models designed for a competitive environment. In the first model, retailers have only an intermediary role and do not decide on pricing or optimal order quantity. In the mentioned model, it is assumed that retailers transfer the demand exactly and to the same extent as it is from the customers to the suppliers. Based on the amount of their production, suppliers face shortages and excess supply. The purpose of this model is to determine the optimal and desirable amount of production as well as the price for each supplier. In addition, routing costs are approximated. The results indicate that the presented approximation is very accurate and evaluates the equilibrium point in much less time than the original model. In the second model, retailers are assumed to incur shortage or maintenance costs according to the number of orders they send. Here, the suppliers determine the final price, and the goal of the retailers is to determine the optimal amount of the order. In this model, it is assumed that suppliers’ production is customized, and inventory costs are considered only for retailers. A noteworthy aspect of this model is the emphasis on dynamic pricing—an inherently crucial and intricate element within the domain of supply chain dynamics theory

Conclusion
The results underscore the substantial efficacy of the proposed combination in addressing the multi-level supply chain network design model with both dynamic and static pricing. The sensitivity analysis of the manufacturer’s profit showed that the changes indicating an increase in the market base of a manufacturer will lead to an increase in profit for both manufacturers. The graph of the change in the producer’s profit compared to the change in the costs showed that a decrease in the production cost leads to an increase in the profit for the producer, while the profit of the next producer decreases in these conditions. Like the producer’s profit, the distributor’s profit also increases with the market base. Expanding the market base not only leads to an increase in the distributor's profit through collaboration with producers but also occurs through two distinct mechanisms. Firstly, there's a profit increase due to raising retailer prices. Although wholesale prices increase with the expanded market base and heightened demand, the distributor, to maintain balance, can elevate retail prices at a higher ratio. Consequently, the value of the pi-wi term increases. Secondly, there's a profit increase resulting from boosting sales volume. The distributor's profit is positively influenced by the increase in sales volume, thereby reinforcing the overall financial outcome.

Objective Short product life cycles, unpredictable demand patterns, and ever-decreasing time-tomarket have put manufacturing companies under pressure. To face all complex production scenarios, these companies tend to the implementation of Cellular Manufacturing Systems (CMS) to reduce production costs, increase flexibility, and respond quickly to market demands. The cellular manufacturing system facilitates the control and management of the production system by dividing a large system into several small subsystems. The design of a cellular manufacturing system includes cell formation, group layout, group scheduling, and resource allocation. The first issue in designing a cellular system is the grouping of machines to produce a family of parts in production cells referred to as cell formation. The creation of efficient cells aims to achieve maximum performance of intracellular machines (intra-cellular processes) and minimize the transfer of parts from one cell to another (inter-cellular processes). The second problem is the problem of group layout, which includes the placement of cells in the workshop relative to each other (intercellular layout) and the layout of machines within the cells (intracellular layout). The optimal grouping of machines in cells, the efficient placement of cells relative to each other, as well as the machines inside cells affect the cost of intracellular movements and intercellular movements of parts. Considering that in a static cellular manufacturing system, the demand is considered constant in all periods, however, due to the advancement of technology, the environment of an industry is faced with turbulence in the types of products and demand. To overcome these problems, a dynamic cellular manufacturing system was introduced, which forms optimal cells in each period according to the demand conditions and changes in product composition. This study addresses the simultaneous challenges of a dynamic cellular manufacturing system (DCMS) with unreliable machines and production planning and intercellular layout problems. The proposed model seeks to minimize the costs of Inter and intracellula movement, reconfiguration, machine breakdown, part production, keeping parts in the warehouse, and back-ordering parts in production cycles. Methods At first, a mixed integer nonlinear programming mathematical model for the considered problem was presented, Next, linearized and validated with a case study in GAMS software with a GUROBI solver. In the following, the impact of moving machines between periods and the sensitivity analysis of the MTBF parameter were discussed. Results Flexibility in routing, optimal location of cells, and optimal grouping of machines reduced production costs, and also by moving two machines m1 and m3, production costs improved by 353,870 Tomans. Conclusion The reconfiguration of machine cells in the new period improves the cost of production and also the model is flexible in routing part production. In this study, MTBF sensitivity analysis showed that the number of failures affects the system's performance.

Objective Supply chain management is a modern organizational management mode that organizes and plans information, capital flow, and business partnerships in the supply chain and requires complete business and market information (Quinn et al., 2012). However, the cost of acquiring supply chain companies and product information by traditional methods is very high. Information technology provides the power for companies to implement supply chain management and share the supply chain easily, and all companies in the supply chain can create value through information management (Shawaki et al., 2023). The utilization of intelligent approaches to predict prices and demand quantities enhances supplier delivery performance. It also refines demand forecasting accuracy, improves factory planning precision, forecasts demand for new products, and minimizes supplier risks, transportation costs, inventory, operational expenses, and time (Tirklai et al., 2021). In supply chain management, accurate forecasting of demand reflects the price. It is a critical issue that can reduce inventory costs and achieve the desired service level (Zouqaq et al., 2020). Intelligent supply chain pricing approaches can help supply chain companies to adapt the quality of their product offerings in supply chain management according to the knowledge gained (Kotsiopoulos et al., 2021). Identifying and modeling steel market fluctuations is very important in the steel industry and supply chain management. Considering the vertical chain in this industry and the interaction between the players of this industry, game theory has been used to model the optimal price. Neural network models were employed to replicate the game, as interaction and repeated

Objective Railway cargo transportation has gained popularity as a preferred mode of conveyance, owing to its benefits over alternative methods. These advantages include reduced pollution in comparison to road transport, shorter transit times compared to maritime shipping, and cost-effectiveness in contrast to air transport. As a result, transport companies are more willing to use rail freight transport, especially for international transportation. One of the challenges facing the Islamic Republic of Iran Railways is to rank these companies, which are influential in various decisions such as granting discounts and facilities, handing over foreign wagons, prioritizing requests, and handling complaints. Therefore, this paper aims to develop a dynamic multi-criteria decisionmaking model to rank Iran's international rail transport companies.

Objective Organizational structures are purposefully crafted around the strategic objectives of companies, incorporating well-defined processes that serve as the foundation for employee activities and communication. The processes and activities of organizations and companies are implemented through organizational roles and positions that are assigned to employees based on the structure and goals. However, the crucial question lies in how effectively official structures can address people's communication needs and facilitate the implementation of activities. Within numerous organizations, an informal structure emerges in parallel to the official hierarchy, leading to the execution of various activities based on the relationships established within this informal framework. Because of various reasons, employees in some organizations are forced to carry out organizational processes through informal communication and in temporary work teams. The presence of an informal structure in numerous organizations is an unavoidable phenomenon. However, the primary concern lies in effectively monitoring and appropriately managing this structure and the relationships that arise from it. This study mainly seeks to measure the degree of similarity and difference between the formal and informal structures of the organization. Methods In the present study, the official structures were extracted based on the organizational chart. The informal structures were based on data logs and information from different sources and bases. Reports of events taking place via the work interactions of people in the process of supplier selection in a project-oriented company were collected in one of the complex construction projects. The gathered data was used as an informal network using the social network analysis method. The degree of similarity and difference between the formal and informal structure of the company was measured through social network criteria and the Rand index. Results Based on the information obtained, the informal working groups or communities resulting from the real interactions of people were identified and analyzed. Also, these communities were compared with departments and formal units based on the hierarchical structure of the organization. In addition, by using the Rand index, the similarity of these two structures was estimated to stand at 0.76%. This method can be a basis for comparing the official structure and the real structure of companies. The ideal degree of similarity or difference between the two structures varies depending on the nature, culture, and mission of each company. Conclusion Using this method and identifying and analyzing the causes of the differences between these two groups of communication structures can help organizations improve their structures, make them more agile, and compensate for some structural deficiencies. By using this way of analyzing and comparing the results in these two groups, it is possible to examine the organizational structure, strengthen effective working groups and teams, support effective communication, and even prevent weak communication.

Objective: The swift expansion of intricate projects in the global construction industry has prompted numerous investigations in the last twenty years, highlighting the significance of comprehending project intricacy for the triumph of construction project management. Identifying, assessing, and ranking procedures in response to risk is a crucial yet currently difficult aspect of project management to handle intricate projects at every phase of their existence effectively. Project managers have consistently focused on complexity and its linked hazards since it is a significant factor in project cost and time delays. This study explores the correlation between project complexity and modeling its outcomes' risks. Methods: The study employed a deductive, positivistic methodology. The literature review examined the history and definition of complex projects. The risk factors were identified based on the underlying causes of the project's complexity. To achieve the best possible outcome in financial terms, a comprehensive model was proposed that considered the type of project contract with different risk response activities. The model was then tested by analyzing the risks associated with a sample project, and cost-response index graphs were generated for each risk individually and aggregated. Results: This research aimed to examine the current state and developments in project complexity research and to provide valuable insights for scholars and practitioners. The study's findings indicated that risks do not impact all projects equally. It was found that the actual effects of a risk event depend not only on the event itself but also on the management actions taken to address the contingency and their timing. These factors can influence the severity of the problems caused by the event and its ripple effects throughout the project organization. Conclusion: According to the results of this field study, risks do not uniformly affect all projects. The actual impact of a risk event is contingent not only on the nature of the event itself but also on the managerial response to the contingency and its timing. These factors can influence the severity of problems caused by the event and the cascading effects within the project organization. While no single set of guidelines can guarantee project success, it is essential to recognize that the process is not random. A better understanding of the organizational dynamics that affect project performance and the factors contributing to risks in complex projects is a crucial precondition for creating a cross-functional solid team capable of managing risks before they negatively impact project outcomes. Therefore, this study can represent the first attempt to investigate the relationship between project complexity, risk consequences, and financial goals in complex construction projects. Among the various criteria contributing to complexity, the project's content, organization, and external environment were identified as the most significant risk generators in complex construction projects.

Objective: Despite the presence of legislation aimed at promoting the development of oil-rich and less-developed regions in Iran, the Western Ilam province continues to face significant deprivation in various areas, despite its abundant oil and gas resources. The primary focus of this study revolves around the intricate management of poverty through the implementation of a comprehensive intervention plan. The objective of this study is to identify policies for reducing deprivation through sustainable development in the oil and gas regions of Ilam province.

As economic enterprises that seek to earn a profit, banks collect and accept financial resources from depositors in the form of financial intermediaries. They allocate the resources to economic activities (investors) by providing various facilities. Therefore, banks play a very sensitive and important role in the economic system of a country and have a significant effect in regulating the economic relationships in society. Banks need to pay attention to the accurate allocation of facilities and resources. Accordingly, in today's financial landscape, effective asset and debt management has become a crucial component for the success of banks and other financial institutions. It is now essential to optimize the provision and allocation of financial resources in the banking system to reduce the costs of collecting financial resources and increase income via the distribution of financial resources in the form of various facilities. Introducing a multi-objective mathematical model and considering the limitations of the problem, this study seeks to present a mathematical model for equipping and optimal allocation of financial resources.

Objective: Numerous variables are involved in green supply chain management and investigating any intervening variable can influence a diverse set of others. Blockchain, as an emerging technology of the fourth generation of the industry, comes with a variety of distinctive characteristics. The analysis of the integration between blockchain and green supply chain management will be highly intricate. Therefore, it is essential to establish a model to comprehend it effectively. Methods: This study was carried out in two phases. In the first phase, the required information was collected, extracted, analyzed, and combined through a systematic review with a focus on the key questions. After identifying the primary existing sources, based on the entry conditions, some items were removed according to the determined conditions. Finally, 18 articles were thoroughly reviewed, and the variables were extracted. In the second phase, the validation, refinement, and examination of the relationship among variables were discussed through questionnaires. The combined method of DEMATEL and Interpretive Structural Modeling (ISM) were used. Results: Variables from the green supply chain, likely to be influenced by blockchain were extracted, refined, and modeled in 10 levels, ranging from the most influential to the least influential ones. Environment-related variables, along with business viability, are the most influential. Conclusion: According to the achieved model and the degree of influence and impact of the variables, the main driver for moving towards green practices and utilizing blockchain include "the need to obtain environmental certification (government pressure)," "buying green products (inclination)," and "giving importance to the social responsibility of the green company." The outcome also depends on "business continuity" and environmental variables. An important aspect regarding the relationship between blockchain and the green supply chain is the location of the variables influenced by blockchain in the interpretive structural model. These variables were placed in the middle levels of the model, indicating that before implementing blockchain technology, it is crucial to assess the influencing variables and determine their value. The assessment can determine whether the establishment or non-establishment of blockchain technology is justified. If these variables indicate the need for changes in the intermediate variables of the model, blockchain technology can be used as a tool or enabler in the supply chain to drive the desired changes based on the specified influencing variables. Ultimately, as the intermediate variables in the model change, the dependent or influencing variables in the model can also be affected.

Objective: One of the important issues in project management is project scheduling. Since projects are faced with high uncertainty, project scheduling is of high importance under uncertain conditions. The purpose of this research is to present a robust multiobjective model to optimize project scheduling with limited resources by considering uncertainty, in which activities have several execution modes with uncertain duration, costs, and resources. Methods: After reviewing the extant theoretical literature on project scheduling, the assumptions, parameters, and variables of the mathematical model were determined. Then, considering the goals and limitations of the project scheduling problem in deterministic conditions, a mathematical model was developed. This model was transformed into a single-objective model by the epsilon constraint method. To consider the uncertainty in the parameters of the problem, robust optimization, and Bertsimas and Sim's approach were used. Also, Robust optimization of the single-objective model was developed to consider the uncertainty. Results: According to the obtained results, while the duration of the project increases with the increase in the tolerance of non-deterministic parameters; the percentage of project duration changes decreases for higher values of robust parameters. Conclusion: The robust parameters are with negative coefficient in the objective function, so their increase leads to a decrease in the objective function value. The highest reduction of the objective function is when the robust parameter is changed from two to one. This coefficient decreases with the increasing value of the corresponding parameter i.e., as the value of stability parameters increases, its effect on changing the value of the objective function decreases. The cost parameter was changed between-40% to +40% for the value of gamma 10 (the state where 10 activities of the project are non-deterministic) for different variation values of parameters of non-deterministic activities. Its effect on the values of the objective function indicated that the variation of the cost parameter in the range of 0 to +40% turns it into an unnecessary constraint and that its change has no effect on the value of the objective function (project duration). Also, in the range of 0 to-40%, the cost reduction caused a decrease in the value of the objective function (increasing the project duration) and the maximum impact of the reduction of the project budget related to the situation where the uncertain parameters of time and cost change by 40% and 50%.

Objective: In recent years, industries worldwide have been striving to adopt a groundbreaking accomplishment of the fourth industrial revolution known as smart manufacturing. This advanced manufacturing system is based on Industry 4.0 technologies to improve systems' reliability, efficiency, and productivity. To have the highest level of performance and success, organizations need to pay constant attention to some variables and conditions, which are called Critical Success Factors (CSFs). Critical Success Factors play a significant role in all projects. Implementing smart manufacturing requires the identification of Critical Success Factors to be implemented effectively. Managers need to know the relationship between components of technology and the critical success factors, as well as the stakeholders who are involved or exert influence over these components. Therefore, the purpose of this study is to collect the Critical Success Factors of smart manufacturing implementation and also to determine the Critical Success Factors' relationship with components of technology and stakeholders of organizations, based on the extant research. Methods: Collecting all the Critical Success Factors will be possible through a comprehensive and systematic literature review. In a systematic literature review, in addition to answering research questions, gaps in a field can be identified and introduced to researchers. To conduct a systematic literature review in this study, keywords were first selected based on the research objectives and questions. Then, articles were collected by searching selected databases. After collecting the articles, relevant articles were identified according to the selected criteria. Finally, the research questions were answered by using the selected related articles. In addition to answering the research questions, the citation analysis of the selected articles was also done. Results: In this study, 62 relevant research studies were identified. Through the examination of the selected research, 22 critical success factors were recognized. These factors were further classified into the domains of Technology-Organization Environment (TOE Model). By conducting a thorough analysis of the content in the selected studies, the interconnections between the critical success factors, stakeholders, and components of technology were thoroughly investigated. Conclusion: The findings of this study can provide valuable assistance to researchers in gaining insights into existing gaps and addressing them appropriately. Moreover, the research outcomes can prove beneficial to managers as they endeavor to implement intelligent production methods. By effectively harnessing the identified critical success factors, managers can ensure that their endeavors are impactful and successful. This study offers a comprehensive understanding of the crucial factors that contribute to success in the chosen domain. It serves as a guide for future research endeavors and provides actionable insights for practitioners aiming to optimize their strategies. The study underscores the importance of considering stakeholder engagement and technological aspects in conjunction with organizational structures.

Objective: The purpose of this study is to present a new mathematical model to design a supply network by considering the strategic alliance and the relationships between the supply chain members under uncertainty. This study attempts to create a suitable decision-making environment for managers to optimize the network and make appropriate strategic decisions accordingly. Since the mathematical model of network design has computational complexity, providing a suitable solution method for the proposed model is another goal of this research. between the risk and benefits of the strategic alliance are other research findings. Considering the different levels of strategic alliance and risk for each partner, the results of the current research show that a strategic alliance reduces the cost, and this cost reduction depends on the risk level of the partners. In addition, the computational results show the efficiency of the accelerated Benders decomposition algorithm for solving mathematical models in large-scale problems. In some problems that the Gams software is not able to provide the right answer in the appropriate time, the algorithm based on benders methods provided acceptable answers in a shorter time frame. Conclusion: Applying the industry data shows the effectiveness of the model in creating a decision-making environment for managers and decision-makers. Also, the results show the appropriate performance of the solution method. Therefore, the finding of this research indicates a new research viewpoint in the field of network design under strategic alliance for the production and distribution of products.

Objective: This study focuses on analyzing a pricing problem within a two-echelon supply chain, comprising a manufacturer and multiple retailers. It also explores the integration of various channels, including traditional, electronic, and omni-channels such as "Buy-Online-Pickup-in-Store", "Buy-Online-Deliver-to-Home", and "Order-in-Store-Deliver-to-Home". In addition, it seeks to develop a demand function dependent on product price and return policy in electronic channels, as well as delivery times for products. Accordingly, the present study aims to investigate and evaluate a pricing model considering various distribution channels in a single-period single-product environment and to provide a solution approach with valuable performance that can be used in real problems as a decision-making tool. It also tries to investigate the impact of various factors on the number of decision variables and the profit of the entire supply chain. Methods: The problem was formulated as a non-linear mathematical programming model and coded and implemented using GAMS software. Furthermore, several numerical examples were solved to investigate the effect of changes in some parameters on the values of decision variables, retailers' demand, and the total profit of the supply chain. Results: The results showed that the two parameters of price sensitivity of demand and return sensitivity of demand have a critical impact on decision variables including the retailer's sales price, return price, delivery time, and profit of the entire supply chain. Conclusion: This study proposed an integrated approach to evaluate the impact of pricing decisions and return policy in the supply chain. Therefore, it can serve as a valuable resource for companies in making operational decisions regarding the concurrent implementation of pricing decisions, product return policy, and delivery time. Online retailers can use the achieved results to make operational decisions regarding the application of the product return policy according to the presented analysis.

Objective: Today, productivity is one of the most important factors in economic growth. At the organizational level, a high level of productivity indicates optimal performance to gain a competitive advantage. Despite the important role of productivity of service organizations in economic growth, few studies have been conducted in this context. The purpose of this study is to evaluate service productivity by a combined fuzzy best-worst method and data envelopment analysis. Methods: In the first stage, carrying out a comprehensive review of the research literature, indicators of service productivity evaluation were identified. Then, the evaluation indicators were weighed using the fuzzy best-worst method. After weighing the indicators and determining the importance of each of them, using the data envelopment analysis approach, the efficiency, effectiveness, and productivity of Mazandaran Electricity Distribution Company were evaluated for five consecutive years from 2016 to 2020. Results: In this study, efficiency, effectiveness, and productivity were simultaneously evaluated using quantitative and qualitative indicators affecting the evaluation of the productivity of electricity distribution companies. Conclusion: The obtained results would help to assess service productivity by emphasizing both quantitative and qualitative aspects of services and paying attention to the dimensions of efficiency and effectiveness simultaneously. Productivity improvement strategies were also suggested based on the achieved results of the current study.

Objective: Food production and supply chains contribute significantly to carbon emissions. The beef supply chain is of those food industry sectors with significant carbon emissions in its entire supply chain. In this study, the beef supply chain is studied by considering carbon emissions under the coordination contracts and the key decisions of supply chain players. Methods: The need to reduce emissions in energy consumption of upstream suppliers and downstream distributors encourages suppliers to do promotional work for manufacturing low-carbon products. Accordingly, the retailers would have the incentive to offer some encouraging schemes to the manufacturers, which stimulates the manufacturer's motivation to reduce emissions. In this research, price sensitivity, level of greenery, and the amount of advertising are considered to investigate the effects of these parameters on chain profitability of the chain and the decisions of the channel members. Cooperative Advertising contracts (CA), Revenue Sharing contracts (RS), and Cost Sharing contracts (CS) are also examined. Examining the decisions of supply chain actors and analyzing the results, the researchers proved that the increase in the effectiveness of green product advertisements pushes up the number of applicants for buying green products and makes the supply chain provide products with higher levels of greening. Results: Based on the achieved results, corporate advertising contracts increase retail advertising investment and total supply chain profit. They also increase channel effectiveness. This study also discussed the key role of advertising and its effects on the profitability of the channel. The results shied that the best way to increase the demand for green products is to raise the awareness of customers about the environment through advertising. When consumers' sensitivity to the level of the greening of a product increases, they tend to pay higher prices for low-carbon products. In addition, this study asserts that a cost-sharing agreement is also beneficial for companies and the supply chain, since it increases the level of greenery in products, brings higher profits for individual companies, and increases supply chain profits. Conclusion: This study comes with practical implications that can be used as support for decision-making. It indicated that a revenue-sharing contract with a low-performance difference after cost-sharing is a suitable option for the supply chain. Unlike the costsharing contract, almost all retail stores have computer systems and barcodes to track the sales of each meat product, so monitoring and verifying revenue is not a difficult task for the producer. A revenue-sharing contract not only helps the manufacturer and retailer achieve a "win-win" situation but also leads to a greater supply chain profit than a decentralized policy.

Objective: While the closed-loop supply chain network was created launch, to design, and exploit the flow of materials between chain centers, supply chains face different risks, including operational ones and disruption. Each of such risks can lead to irreparable damage. Therefore, designing or redesigning supply chains to make them resilient against different risks is one of the most important programs that potentially affect the supply chain's performance. The purpose of this research is to design a resilient closed-loop supply chain network under the operational risks and disruption conditions by the Mulvey approach in Tehran's food industry companies with multi-products. Methods: In this research, the problem of the resilient closed-loop supply chain is considered as a network of nodes (production sites) that are connected by arcs (paths). The model is formulated as an integer programming, the objective function of which involves maximizing the number of nodes in estimating demand and minimizing costs based on a series of scenarios developed by the Mulvey approach. Results: In the practical phase, first, a closed-loop supply chain consisting of 10 manufacturers, 300 distributors, and two types of products was considered for modeling.

Objective: The influence of climate change on the frequency and severity of natural disasters is ever-increasing on a global scale. While natural disasters are unpredictable, emerging technologies can help with their prevention. Technologies allow responders to act sooner rather than later. The importance of the humanitarian supply chain in reducing the suffering and injuries caused by natural disasters is also undeniable. Developing emerging technologies offers an opportunity to improve the efficiency and effectiveness of responses of the humanitarian supply chain to disasters. Since Iran is one of the most natural catastrophe-prone countries in the world, this study aims to identify and prioritize emerging technologies suitable for its humanitarian supply chain. This study could provide solutions for responders and decision-makers to achieve an effective humanitarian supply chain. Methods: This is descriptive survey research and its statistical population consists of five emerging technology experts in the Iranian humanitarian supply chain. To identify and prioritize applications of emerging technologies in the humanitarian supply chain, the Best-Worst method (BWM) was used. Results: Reviewing the extant academic literature, 12 emerging technologies in the humanitarian supply chain were investigated. After assessing their content validity, experts selected eight relevant technologies for Iran's humanitarian supply chain; including "Big Data", "Internet of Things", "Augmented Reality", "Smart Robots", "Artificial Intelligence", "Gamification", "3D Printing", and "Smart Advisors". In the next step, two sets of questionnaires were distributed among the experts. The questionnaires were collected and analyzed to measure the attractiveness and capabilities of emerging technologies in Iran's humanitarian supply chain. The weights of technologies' attractiveness were calculated based on five filled-in questionnaires and by the Best-Worst Method and LINGO software. Next, to assess the technical capability of each emerging technology, eight Likert scale questionnaires were distributed. Finally, the gathered research data was analyzed using SPSS 26. Capability-Attractiveness Matrix was then formed. Analyzing areas of the Matrix helped the researchers determine specific actions suitable for each emerging technology. Based on the findings, "Smart Robots", "3D Printing" and "Smart Advisors" technologies were placed in the "Position protection/Development" area with the highest capability and attractiveness. "Artificial Intelligence" was placed in the "improvement" area with high attractiveness and relatively low capability. "Internet of Things" and "Gamification" had a high capability and relatively low attractiveness. "Big Data" and "Augmented Reality" with relatively low capability and attractiveness had the least priority in Iran's Humanitarian Supply Chain. Conclusion: The critical role that emerging technologies play in disaster preparedness and recovery is increasingly becoming recognized. The findings of the BWM Method and Capability-Attractiveness Matrix can be a good help for decision-makers and key players in the humanitarian supply chain. Natural disaster preparedness and collaboration to reduce impact across the humanitarian supply chain have never been more important. Utilizing these emerging technologies can minimize fatalities and injuries caused by natural disasters. Emerging technologies need to be adapted to various situations that arise during disaster relief.

Objective: Smart product-service systems (SPSS) as a new paradigm was presented recently, which created considerable changes in the industry and production, especially in the production of smart home appliances. Despite many achievements in implementing SPSS, this system faces many challenges in the development and implementation processes. This study aims to identify and analyze the challenges of implementing an SPSS and evaluate and prioritize them as one of the most basic initial steps in implementing the system. Methods: In this paper, first, the challenges of implementing SPSS are identified through literature review, document review, interviews with experts, and the fuzzy Delphi method. Then the best-worst rough-fuzzy method is used to prioritize the identified challenges.

Objective: Highly unstable social and economic ecosystems have inevitably resulted in severe momentums and adverse environmental changes. To overcome the mentioned challenge and also increase their competitiveness, most businesses have accepted a resilience approach. Moreover, the presence of so called techno-social Complex Systems has given rise to serious challenges in developing Business Plans. To have accurate planning for the business, social issues, technical processes, and their correlation should be simultaneously addressed. In this work, following a comprehensive review of the previous efforts, a more detailed and sophisticated explanation of how techno-social factors impact resilience Business model will be investigated. In other words, we aim to develop an improved Business model model for companies dealing with the mentioned challenges. Methods: Meta-Synthesis method have been used for gathering necessary resources. A complete review of all related references has been carried out. 78 outstanding articles have been selected from all the papers published since 2000. The study and analysis of these 78 references has built the framework of our work. Results: In this work 85 sub-codes, indicating the impacts on resilience business model, have been successfully extracted. Using these codes and their proximity in definition, 21 core themes were defined in the next part. Finally, the developed themes have been categorized into four main elements of Business Model Ontology: customer, management, infrastructure, financial flows, and recommended value. Conclusion: The finally presented model, enjoying simultaneous analysis of social parameters and corporate technical processes, has shown to provide more generality in modeling. The model would be a great help for senior managers to desing resilient business model. In addition, this model can efficiently be used in thorough assessment of resilience in techno-social companies.

Objective: To evaluate the achievement degree of an organization to its goals, its productivity must be measured. Since an organization is composed of different groups, the productivity of its groups should be examined. This paper aims to introduce a suitable index to evaluate group cost productivity changes and identify factors affecting it. To explain the applicability of the proposed index, a study on the productivity evaluation of a bank's branches in different regions is presented. Methods: A suitable distance function is created to measure the cost efficiency of a group, and based on that, the group cost Malmquist index is introduced. Then, the influencing factors on the productivity growth of the groups are examined by focusing on intra-group and extra-group sections. Results: An indicator was presented to evaluate the cost productivity changes of a group of decision-making units and the factors that affect the cost productivity changes of the groups were identified. The proposed group Malmquist index breaks down into four components: Pure efficiency changes, scale efficiency changes, allocative efficiency changes, and cost technological changes. The pure efficiency change measures the optimal use of inputs to produce output indicators. The scale efficiency changes reflect the effect of changes in the size of branches of a region on its productivity growth. The allocative efficiency changes indicate the achieved changes in the optimal combinations of inputs considering the prices of each period. The cost technological changes component reflects changes in cost technology frontiers during two periods. All of these components were examined from two perspectives: intra-group and extra-group. In the intra-group perspective, the internal group frontier is considered, and in the extra-group perspective, the common frontier of all groups is focused. To explain the applicability of the proposed index and calculate the impact of its components on productivity growth, a real case study was presented. This case study evaluated the cost productivity changes of a bank's branches in eight different regions. The results showed that three regions have cost productivity growth. One area has almost no changes in cost productivity, and the other regions have productivity regressions. The results of measuring the group cost Malmquist productivity index on bank data provide meaningful and useful information about bank productivity changes in different areas. Conclusion: Most organizations consist of different groups and departments. Sometimes, it is necessary to examine a group of decision-making units instead of evaluating the performance of several decision-making units to analyze the role of environmental conditions on the performance assessment of the decision-making units. The results are shown that the proposed group cost Malmquist productivity index provides a suitable tool for evaluating the cost productivity changes of groups. Also, it prepares a clear perspective for managers of organizations for future policies by identifying the intragroup and extra-group factors affecting group cost productivity changes.

Objective: The purpose of this research was to provide a mathematical model for smart supply chains with Vendor Managed Inventory (VMI) through the Internet of Things (IoT) technology to overcome traditional supply chain challenges and solve problems such as uncertainty, high costs, and changes in customer demands. Creating an intelligent and agile supply chain is essential to tackle these challenges and problems. Generally, customers are still skeptical and doubtful of the smart supply chain, and this skepticism is more likely to arise in the financial context. To improve efficiency, all organizations and institutions need to adapt themselves to external and internal changes, maintain supply chain inventory, and be flexible with customer demands. Methods: Previous studies discussed intelligent supply chains, IoT, and their combination and described the advantages and disadvantages of using these technologies. Despite much research in this field, no mathematical modeling has been done for a smart supply chain that incorporates technology and automation. Based on such needs and objectives, this study sought to design a two-objective mathematical model, with a fourlevel supply chain. In this model, there is a direct relationship from suppliers to manufacturers, from producers to retailers, and from retailers to customers. The supply chain was intelligently designed, and the chain levels used Wireless Sensor Network (WSN) technologies, Radio Frequency Identification (RFID), blockchain, and Internet sales. Results: The designed model in this research was validated by GAMS software. The researchers identified 10 problems of small and medium dimensions in the model. They presented the results in the form of objective function values and solution time. The basic problem was solved using the ɛ-constraint method. The results obtained from solving the model and the Pareto diagram were presented in detail. The optimal implementation of the mentioned technologies, as well as the time and cost response to this implementation, proved the efficiency of the model. Conclusion: The present study put forward a two-objective mathematical planning model. The first and second objectives of this model were to minimize the cost and decrease the time of the supply chain, respectively. By solving the basic problem, using the ɛ-constraint method, the values of the objective functions and the Pareto diagram showed desired results. Solving the model in small and medium-sized dimensions using GAMS software, the values of the objective functions and the solution time were achieved. Also, the results proved the validity of the provided model suggesting that it can be used in larger dimensions. However, due to the lack of memory in GAMS software, for large dimensions, the suggested mathematical model for the smart supply chain needs to be checked using MATLAB software and meta-heuristic algorithms.

Objective: This research has been carried out to identify the effective factors in the formation of the technology-based entrepreneurship ecosystem in the technical faculties of the University of Tehran. Results: A six-level model was developed. The first level included the infrastructure components of entrepreneurship ecosystem development, policy and management of financial resources, intensity and level of design preparation, development and commercialization, creation and development of supporting institutions, and business development in the region. The second level included the university policy and rules component. The market component stood in the third level. The fourth level embraced the economic factors and the business environment. The fifth level included the governance factors and the last level included the cultural component of the academic technological entrepreneurship ecosystem. It was also shown that the cultural component is the most influential factor and the infrastructure components of entrepreneurship ecosystem development, policy, and financial resource management, intensity and level of preparation for design, development, and commercialization, creation and development of supporting institutions, and business development in the region were the most effective factors in the formation of the technology-based academic entrepreneurship ecosystem in the University of Tehran. Conclusion: The development of entrepreneurial culture and spirit is the base of the formation of the university entrepreneurship ecosystem. Relying on technology, it can be implemented by applying correct policies and competent management at both micro and macro levels.

Objective: Venture capitalists have unique capabilities in dealing with uncertain conditions. Despite the increasing level of risk in the development of innovative startups based on emerging technologies, investing in these emerging areas and startups is a strategic measure for venture capitalists. This highlights the need for comprehensive carrying out evaluation and analysis in this regard. The need for a benchmark and a model to identify high-potential success options among the masses of startups is a challenging issue for which this research seeks to provide a solution. The main purpose of this research is to develop a benchmark for identifying high-potential success options. Methods: The main research question of the present study was answered by cooperating and interacting with ten experts in the venture capital industry. First, the components of collaboration were extracted through library studies. Next, the extracted components were evaluated and analyzed based on Markov analysis, the average duration of multidimensional analysis in decision-making. Then, based on the findings of Markov analysis, two-dimensional evaluation and screening of identified agents were performed based on the fuzzy Delphi method. Results: This study showed that out of 62 indicators of mutual participation between investors and startups, 41 indicators were key, and 21 indicators were not applicable. In the next step, the indicators were evaluated based on the FBWM method. Conclusion: The achieved model was tested on six successful startups in capital attraction using fuzzy multi Moora. The model could rank the investment priority in these six startups.

Objective: People give inputs to the organizations and receive outputs instead. Each individual compares his/her inputs and received outputs with others and may feel dissatisfied. This comparison forms the foundation of the equity theory introduced by Adams. Despite the widespread acceptance of Adams' equity theory and the consensus on its ability to explain the distributive equity perceived by employees, the difficulty in quantifying the concept of equity has made its use challenging to some extent. This study seeks to propose an approach to estimate the degree of equity perceived by each employee compared to others and determine the number of changes in inputs/outputs of the employees to increase their sense of equity while causing the least dissatisfaction.

Objective: Since the obstacles to executing directional contracts have not been identified by previous research, the present study seeks to examine executive obstacles to carrying them out in supply chains. The purpose of this research is to investigate, identify and prioritize implementation obstacles of bi-directional contracts in the supply chain both in production and distribution contexts. Methods: At the end of this study, barriers to the implementation of bi-directional contracts in the supply chain were examined. Ten criteria were identified including lack of employee training (with a weight of 57.64 percent), lack of motivation and employee involvement (48.82 percent), unwillingness to change (81.3 percent), lack of corporate social responsibility (63.09 percent), management skills and knowledge (63.18 percent), lack of less perceived benefits (22.6 percent), fear of failure (61.5 percent), unclear organizational objective responsibility (32.36 percent), lack of integration and coordination benefits (20.73 percent), and political instability (22.3 percent). Results: As a case study of five car companies in Iran, the current study identified barriers to the implementation of bi-directional contracts in the supply chain. Examining the criteria, the study could identify which of the companies was ready to implement such types of contracts. Conclusion: In this study, the effective factors and criteria in bi-directional contracts, as well as their prioritization and degree of importance in automotive companies, were identified and analyzed in a fuzzy environment. Finally, the results were analyzed by applying other decision-making methods. It was shown that the results are not much different from each other. This means that more experienced and up-to-date companies are always in the first ranks and are always better prepared to implement contracts of various types in the supply chain.

Objective: Purchasing portfolio models have received a great deal of attention in both academic and practice fields as suitable purchasing strategies. Purchasing portfolio applies as a diagnostic and prescriptive purchasing tool. The core purpose of this study is to introduce a quantified portfolio for developing purchasing strategies that are aligned with competitive priorities. The quantitative method of this study relies on data mining (ordered clustering) and MADM (Best Worst method) to classify purchased items with the aim of creating a strategic fit in the supply chain and developing purchasing strategies in accordance with the competitive priorities of organizations.

Objective: Applying the system dynamics approach in businesses requires specialized knowledge, in particular, of defining mathematical relationships among variables. This stud seeks to make the use of this approach easier by providing a method for using linguistic variables and the fuzzy inference system in the systems dynamics approach. To evaluate the ease of use and efficiency of the presented method, this method would be used to define the relationship among variables in the purchasing department of a distribution company.
Methods: To carry out this research, a literature review was first conducted in the field of fuzzy logic and system dynamics. Next, with the cooperation of an expert from the purchasing department of the distribution company under study, some fuzzy linguistic variables as well as their rules were determined. Finally, the SD model was obtained by using the fuzzy inference system.
Results: The proposed approach can reflect the business dynamics of the distribution company in accordance with what is happening in practice. According to the feedback model feedback and based on the modified linguistic variables, appropriate values were obtained for decision making. In order to evaluate the hybrid approach, a fuzzy inference system was used to calculate the purchase rate according to the two factors of inventory and base sales. These two factors were expressed through linguistic variables by the words "low", "medium", and "high", while the purchase price, as the output of the inference system, was expressed through the five words "very low", "low", "medium", "much", and "too much", according to the expert. After implementing the model, the presented approach (by modifying the fuzzy linguistic variables) was found capable of changing the output to achieve the desired results, as the expert confirmed.
Conclusion: The combined approach can be used in simulating similar cases (where human factor perception and decision-making play a significant role) and can easily reduce the complexity of the required formulas in the system dynamics approach. An important function of the hybrid approach used in this study was to model and simulate the real world in accordance with what is happening in practice.

Objective: Iran's oil reservoirs are operated in cooperation with international oil companies under a contract titled Iran Petroleum Contract (IPC). During the life of the concluded contracts, Iran and oil companies seek to maximize the economic value as well as the value of the cash flow. In this paper, IPC was modeled and the sensitivity of parameters was illustrated. Methods: Contract financial flow was mathematically modeled considerin the physical characteristics of the oil well. This model was simulated using Matlab to evaluate the effect of different values of two parameters of production rate and wages in IPC contracts on the field production process. Results: Results show that contractors were inclined to lower production rates if the fee per barrel is not set in the proper range (3$-7$). Furthermore, for very low oil prices (under 30$) contractor is at the risk of investment and for higher prices contractor's share saturates. Conclusion: All the parameters of the problem including contractual, reservoir, field parameters, oil price, investment costs, and operating costs play a role in the profitability of the project, and knowing contract parameters sensitivity can give Iran a clear view of negotiating the contract.

Objective: This study introduces Metaphorical Operational Research (MOR) as a new insight into decision making and answers to some criticisms of classical Operational Research (OR). It also shows the ability of MOR to describe pairs trading problems.
Methods: The basic assumption of the study is that classic OR is still in the crisis phase before the paradigm shift and no competing paradigm has ever been able to overcome it and provide a complete response to the criticisms. Therefore, in order to respond to some of the criticisms, this study tried to introduce MOR as a new subject in the field of OR. The main focus of this paper was to introduce the problem representation process (as a classic OR critical point) using MOR as a novel approach to problem structuring. Also, in order to show the ability of MOR, this study used pairs trading problems as a case study and binary stars as a source domain. This study used MOR to redefine the pairs trading problems in the form of binary stars and theorize in this field.
Results: The findings of this study show that there are six principles as the main theories of this research in pairs trading problems by looking at it through the window of binary stars. These principles include the principle of the common center of gravity, the principle of historical records, the principle of divergence convergence, the principle of external conditions neutrality, the property of being a pair, and the existence of different classes of pairs.
Conclusion: Two general conclusions can be drawn from the study. First, it is possible to communicate between binary stars and paired shares through MOR. Second, MOR can solve some problems that classical OR cannot address.