unsatisfied demand
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Natural or manmade disasters can bring havoc to the healthcare industry in terms of poor services, out of stocks, cost overruns, and loss of lives. Specifically, the aftermath of disasters can be brutal if not managed properly. The quicker the healthcare providers recover, the lesser the impact would be. A resilient system has the potential to reduce the recovery time significantly. Healthcare providers under emergency scenarios may realize out-of-stock situations for their critical medical supplies. The out-of-stock supplies potentially cause poor patient care including death. COVID 19 is an unfortunate example where critical medical supplies were completely out for many medical providers and that had a serious negative impact on healthcare service deliveries. Healthcare providers needed to replenish their supplies from the overseas manufacturing plants, or central distribution centers, or unaffected regional distribution centers. Most of the times healthcare authorities struggle to secure critical medical supplies from other distribution centers due to operational and transportation issues. Depending on the disaster condition, sometimes many health care providers are beyond reach due to damaged transportation networks. This is the perfect time to share critical medical supplies from unaffected regions. Proven techniques like operation research can alleviate this situation. There are very few works that have been done in the field of healthcare service deliveries in case of a disaster. This chapter discusses the modeling techniques using operations research to improve the service levels while minimizing unsatisfied demand in the natural disaster-affected zones.

Traditionally, the newsvendor problem is a single-period model for a retailer and can be applied in the replenishment decision for a product with a short life cycle. However, many fashionable commodities are seasonal; not all of these products must be sold within a single period of a selling season, and they can be replenished once in each cycle. This study develops a novel multi-period model to determine multiple ordering replenishment decisions for a product over a short selling season. This study not only demonstrates the profit function for a retailer, but also provides those for both the manufacturer and the entire channel in a supply chain problem. The proposed multi-period ordering model provides explicit insights into how the ordering decisions of the retailer are affected in a specific period by considering unsold inventory or unsatisfied demand from a previous period. A numerical analysis and the simulation results illustrate the feasibility of the proposed model.

Some retailers (e.g. pharmacies) are responsible for satisfying the demand for the minimum range of goods, which are generally unprofitable. With respect to such goods, the enterprise seeks to satisfy uncertain demand rather than to make profit. We assume that: (a) the vector of demand for goods of the minimum assortment in the planning period lies “between” the demand vectors of several previous periods (is a convex linear combination of these vectors); (b) the smaller the maximum unsatisfied demand (by product groups and possible vectors of demand), the greater is the reliability of meeting the demand. Under these assumptions, we address the problem of allocating a limited procurement budget among commodity groups to meet uncertain demand most reliably. The article shows that this problem is equivalent to finding an optimal strategy by Wald’s criterion in some game with nature and can be reduced to a linear programming problem. Using the problem features, we propose a fast (having quadratic complexity) algorithm for constructing an optimal procurement plan. The model can be used when planning the minimum assortment goods procurement in order to maximize the meeting demand reliability, achievable within the allocated budget. As far as we know, such a formulation of the problem has not been studied in the previous literature.

AbstractThis paper considers a retailer who sells perishable fresh products directly to customers through an online channel and encounters a transportation disruption. Products shipped during the disruption period come with an uncontrollable delivery lead time, resulting in product quality degradation. To balance the compensation price provided to customers because of quality losses, the retailer might employ freshness-keeping efforts to reduce the quality loss during transportation. Therefore, it raises several fundamental questions for the retailer in mitigating the disruption. Is it always optimal to satisfy those customers who are willing to purchase during disruption? If it is profitable to fulfill orders along with an extra delivery lead time, and with a quality loss compensation, what is the optimal freshness-keeping effort? If it is preferable to deliberately create unsatisfied demand by announcing shortages (rationing) to customers, when is the optimal time to do so? To answer these questions, we first present the dynamics of post-disruption inventory and demand, taking into account the demand learning effect facilitated from negative word-of-mouth during disruption and the demand recovery after disruption ends. Afterward, we develop a model to achieve the optimal selling strategy for maximizing post-disruption profit, identifying the joint decision of the rationing period and freshness-keeping effort. Finally, by numerical analysis, three types of selling strategies are visually provided to hedge against disruptions of different lengths.

Natural disasters represent a latent threat for every country in the world. Due to climate change and other factors, statistics show that they continue to be on the rise. This situation presents a challenge for the communities and the humanitarian organizations to be better prepared and react faster to natural disasters. In some countries, in-kind donations represent a high percentage of the supply for the operations, which presents additional challenges. This research proposes a Markov Decision Process (MDP) model to resemble operations in collection centers, where in-kind donations are received, sorted, packed, and sent to the affected areas. The decision addressed is when to send a shipment considering the uncertainty of the donations’ supply and the demand, as well as the logistics costs and the penalty of unsatisfied demand. As a result of the MDP a Monotone Optimal Non-Decreasing Policy (MONDP) is proposed, which provides valuable insights for decision-makers within this field. Moreover, the necessary conditions to prove the existence of such MONDP are presented.

This paper follows the previous effort of authors and builds the model of transit route network design for low-mobility individuals, proposing an appropriate solution methodology. Firstly, a desired objective, whose priority is to meet transit demands of low-mobility individuals followed by those of general public, is presented to minimize the weighted sum of direct traveler, transfer, and unsatisfied demand costs. Then, a hybrid metaheuristic approach based on ant colony and genetic algorithms is formulated to solve the proposed model in accordance with current conditions (i.e., existing routes that may need to undergo configuration adjustments to different degrees). Finally, the case study of Wenling is presented to highlight the performance and benefits of the proposed model and solution methodology.

The article defines the need for comprehensive research on the justification and development of requirements for this type of product on the part of both today’s consumer and production. As a result of such studies, it is necessary to establish indicators for a comprehensive assessment of school uniform providing a high level of its quality, as well as physiological and psychological comfort to the consumer. The study analyzes the opinions of customers and the product range in retail outlets in Moscow that specialize in sales of school uniforms. It was found that 40% of respondents had serious difficulties in purchasing school uniform kits. It is especially difficult to choose clothes for thin, low stature, and stout children having a body constitution significantly differing from the typical standard. Besides, the fabric properties and model designs do not fully meet the requirements of consumers. The information collected during the research has shown the presence of unsatisfied demand of a large group of consumers of school uniforms and indicated the need for additional research to take into account physiological, hygienic, psychological, ergonomic, aesthetic, and anthropological factors, as well as ensuring the quality of fabrics, and implementing image of school children when creating a school costume.

Under the third-party logistics management inventory model, the system dynamics method is used to establish a nonlinear supply chain system model with supply capacity limitation and nonpermissible return, which is based on unsatisfied demand nonaccumulation. The theory of singular value and the Jury Test are used to derive the stable interval of the model which is simplified. The Largest Lyapunov Exponent (LLE) of the system is calculated by the Wolf reconstruction method and used to analyze the influence of different parameters of system’s stability. Then, the most reasonable and unreasonable combination of decision parameters under different demand environment is found out. Next, this paper compared and analyzed the change of inventory or transportation volume of system members under the combination of rational and irrational decision parameters. All of these provided guidance for decision making, which shows an important practical significance.