Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their afford... more Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their affordable price and convenience. The increasing demand for DAR service has an impact on greenhouse gas emissions, but limited past studies in the relevant literature have considered this. In this paper, we present a green heterogeneous DAR problem inspired by Australian DAR service of elderly, patients and disabled individuals. The problem aims to route a fleet of heterogeneous vehicles to transport a set of users with different requirements, which include minimising the total routing cost and total CO2 emission simultaneously. To solve the problem, a Regional Multi-Objective Tabu Search (RMOTS) algorithm is proposed, taking the decision maker’s preferences of the objectives into account, and consequently concentrating on a specific area of the Pareto front. To evaluate the performance of RMOTS, it is compared with two algorithms from the literature developed for similar problems. Experimental results show that the proposed RMOTS is able to outperform these algorithms based on the performance measures considered.
International Journal of Production Research, Sep 3, 2014
ABSTRACT This paper deals the scheduling identical parallel batch-processing machines (BPMs) that... more ABSTRACT This paper deals the scheduling identical parallel batch-processing machines (BPMs) that each machine can be process a group of jobs as a batch simultaneously. The paper presents a new bi-objective-mixed integer linear programming model for BPM in which arbitrary job size, unequal release time and capacity limits are considered as realistic assumptions occur in the manufacturing environments. The objectives are to minimise the makespan and the total weighted earliness and tardiness of jobs (just in time). After developing a new bi-objective model, an ɛ-constraint method is proposed to solve the problem. This problem has been known as Np-hard. Therefore, two multi-objective optimisation methods, namely, fast non-dominated sorting genetic algorithm (NSGA-II) and multi-objective imperialist competitive algorithm (MOICA) are employed to find the pareto-optimal front for large-sized problems. The parameters of the proposed algorithms are calibrated using Response surface methodology (RSM) and the performances of the proposed algorithms on the problems of various sizes are analysed and the computational results clarify that MOICA outperform than NSGA-II in quality of solutions and computational time.
In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem wi... more In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem with simultaneous pickup and delivery. A non-homogenous fleet of vehicles and a number of drivers with different levels of capability are employed to service customers with pickup and delivery demands. The capability of drivers is considered to have a balanced distribution of travels. The objective is to minimize the total cost of routing, penalties for overworking of drivers and fix costs of drivers’ employment. As this problem is proven to be NP-hard, two meta-heuristic approaches based on Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) are employed to solve the generated problems. The parameter tuning is conducted by Taguchi experimental design method. The obtained results show the high performance of the proposed ICA in the quality of the solutions and computational time
Journal of Optimization in Industrial Engineering, 2013
This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multi... more This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. The objective is to minimize the weighted sum of makespan and maximum tardiness. Three meta-heuristic methods based on genetic algorithm (GA), imperialist competitive algorithm (ICA) and a hybrid approach of GA and ICA are proposed to solve the generated problems. The performances of algorithms are evaluated by computational time and Relative Percentage Deviation (RPD) factors. The results indicate that ICA solves the problems faster than other algorithms and the hybrid algorithm produced best solution based on RPD.
2019 IEEE Congress on Evolutionary Computation (CEC), 2019
Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their afford... more Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their affordable price and convenience. The increasing demand for DAR service has an impact on greenhouse gas emissions, but limited past studies in the relevant literature have considered this. In this paper, we present a green heterogeneous DAR problem inspired by Australian DAR service of elderly, patients and disabled individuals. The problem aims to route a fleet of heterogeneous vehicles to transport a set of users with different requirements, which include minimising the total routing cost and total CO2 emission simultaneously. To solve the problem, a Regional Multi-Objective Tabu Search (RMOTS) algorithm is proposed, taking the decision maker’s preferences of the objectives into account, and consequently concentrating on a specific area of the Pareto front. To evaluate the performance of RMOTS, it is compared with two algorithms from the literature developed for similar problems. Experimental results show that the proposed RMOTS is able to outperform these algorithms based on the performance measures considered.
Journal of Optimization in Industrial Engineering, 2013
This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multi... more This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. The objective is to minimize the weighted sum of makespan and maximum tardiness. Three meta-heuristic methods based on genetic algorithm (GA), imperialist competitive algorithm (ICA) and a hybrid approach of GA and ICA are proposed to solve the generated problems. The performances of algorithms are evaluated by computational time and Relative Percentage Deviation (RPD) factors. The results indicate that ICA solves the problems faster than other algorithms and the hybrid algorithm produced best solution based on RPD.
This paper considers a two-stage assembly flow shop problem (TAFSP) where m machines are in the f... more This paper considers a two-stage assembly flow shop problem (TAFSP) where m machines are in the first stage and an assembly machine is in the second stage. The objective is to minimize a weighted sum of earliness and tardiness time for n available jobs. JIT seeks to identify and eliminate waste components including over production, waiting time, transportation, inventory, movement and defective products.Two-stage assembly flow shop is a combinational production system in which different parts are manufactured on parallel machines independently. This system can be used as a method to produce a variety of products through assembling and combining different set of parts. We apply e-constraint method as an exact approach to validate the proposed model and to obtain fronts of the solutions in the solution spaceThe goal of the proposed problem is trade off between two objectives, minimization makespan and total weighted tardiness and earliness. To analyze effects of n and m factors on ...
Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors suc... more Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously. Design/methodology/approach Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions. Findings Finally, experimental study and analysis of variance (ANO...
Purpose The purpose of this paper is to present a new mathematical model for the unrelated parall... more Purpose The purpose of this paper is to present a new mathematical model for the unrelated parallel machine scheduling problem with aging effects and multi-maintenance activities. Design/methodology/approach The authors assume that each machine may be subject to several maintenance activities over the scheduling horizon and a machine turn into its initial condition after maintenance activity and the aging effects start anew. The objective is to minimize the weighted sum of early/tardy times of jobs and maintenance costs. Findings As this problem is proven to be non-deterministic polynomial-time hard (NP-hard), the authors employed imperialist competitive algorithm (ICA) and genetic algorithm (GA) as solution approaches, and the parameters of the proposed algorithms are calibrated by a novel parameter tuning tool called Artificial Neural Network (ANN). The computational results clarify that GA performs better than ICA in quality of solutions and computational time. Originality/value ...
International Journal of Industrial and Systems Engineering, 2016
This paper considers a multi-server-vehicle routing problem where vehicles could exist and enter ... more This paper considers a multi-server-vehicle routing problem where vehicles could exist and enter the service depot several times. The central branch of bank has a number of nurses to service the failures. The objective is to find efficient routes for the nurses to service each task for each customer in order to minimise the total cost of routing and lateness/earliness penalties. In this paper, a mixed integer programming model is presented and two meta-heuristics approaches namely hybrid of genetic and simulated algorithms (HGSA) and imperialist competitive algorithm (ICA) are developed for solving the random generated problems. In HGSA, simulated annealing (SA) is employed with a certain probability to avoid being trapped in a local optimum. Furthermore, Taguchi experimental design method is applied to set the proper values of the algorithm's parameters. The available results show the higher performance of proposed HGSA compared with ICA, in quality of solutions within comparatively shorter periods of time.
Journal of Industrial and Production Engineering, 2016
This paper presents a new bi-objective mixed integer programming model for the two-stage assembly... more This paper presents a new bi-objective mixed integer programming model for the two-stage assembly flow shop scheduling problem with preventive maintenance (PM) activities, in which the reliability/availability approach is employed to model the maintenance concepts of a problem. PM activities carry out the operations on machines and tools before the breakdown takes place. Therefore, it helps to prevent failures before they happen. After developing a new bi-objective model, an Epsilon-constraint method is proposed to solve the problem. This problem has been known as Np-hard. Therefore, three multi-objective optimization methods, namely fast non-dominated sorting genetic algorithm, Multi-objective imperialist competitive algorithm, and non-dominated ranking genetic algorithm (NRGA) are employed to find the pareto-optimal front for large sized problems. The parameters of the proposed algorithms are calibrated using artificial neural network (ANN) and the performances of the proposed algorithms on the problems of various sizes are analyzed and the computational results reveal that NRGA outperform than two other proposed algorithms in quality of solutions and computational time.
Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their afford... more Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their affordable price and convenience. The increasing demand for DAR service has an impact on greenhouse gas emissions, but limited past studies in the relevant literature have considered this. In this paper, we present a green heterogeneous DAR problem inspired by Australian DAR service of elderly, patients and disabled individuals. The problem aims to route a fleet of heterogeneous vehicles to transport a set of users with different requirements, which include minimising the total routing cost and total CO2 emission simultaneously. To solve the problem, a Regional Multi-Objective Tabu Search (RMOTS) algorithm is proposed, taking the decision maker’s preferences of the objectives into account, and consequently concentrating on a specific area of the Pareto front. To evaluate the performance of RMOTS, it is compared with two algorithms from the literature developed for similar problems. Experimental results show that the proposed RMOTS is able to outperform these algorithms based on the performance measures considered.
International Journal of Production Research, Sep 3, 2014
ABSTRACT This paper deals the scheduling identical parallel batch-processing machines (BPMs) that... more ABSTRACT This paper deals the scheduling identical parallel batch-processing machines (BPMs) that each machine can be process a group of jobs as a batch simultaneously. The paper presents a new bi-objective-mixed integer linear programming model for BPM in which arbitrary job size, unequal release time and capacity limits are considered as realistic assumptions occur in the manufacturing environments. The objectives are to minimise the makespan and the total weighted earliness and tardiness of jobs (just in time). After developing a new bi-objective model, an ɛ-constraint method is proposed to solve the problem. This problem has been known as Np-hard. Therefore, two multi-objective optimisation methods, namely, fast non-dominated sorting genetic algorithm (NSGA-II) and multi-objective imperialist competitive algorithm (MOICA) are employed to find the pareto-optimal front for large-sized problems. The parameters of the proposed algorithms are calibrated using Response surface methodology (RSM) and the performances of the proposed algorithms on the problems of various sizes are analysed and the computational results clarify that MOICA outperform than NSGA-II in quality of solutions and computational time.
In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem wi... more In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem with simultaneous pickup and delivery. A non-homogenous fleet of vehicles and a number of drivers with different levels of capability are employed to service customers with pickup and delivery demands. The capability of drivers is considered to have a balanced distribution of travels. The objective is to minimize the total cost of routing, penalties for overworking of drivers and fix costs of drivers’ employment. As this problem is proven to be NP-hard, two meta-heuristic approaches based on Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) are employed to solve the generated problems. The parameter tuning is conducted by Taguchi experimental design method. The obtained results show the high performance of the proposed ICA in the quality of the solutions and computational time
Journal of Optimization in Industrial Engineering, 2013
This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multi... more This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. The objective is to minimize the weighted sum of makespan and maximum tardiness. Three meta-heuristic methods based on genetic algorithm (GA), imperialist competitive algorithm (ICA) and a hybrid approach of GA and ICA are proposed to solve the generated problems. The performances of algorithms are evaluated by computational time and Relative Percentage Deviation (RPD) factors. The results indicate that ICA solves the problems faster than other algorithms and the hybrid algorithm produced best solution based on RPD.
2019 IEEE Congress on Evolutionary Computation (CEC), 2019
Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their afford... more Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their affordable price and convenience. The increasing demand for DAR service has an impact on greenhouse gas emissions, but limited past studies in the relevant literature have considered this. In this paper, we present a green heterogeneous DAR problem inspired by Australian DAR service of elderly, patients and disabled individuals. The problem aims to route a fleet of heterogeneous vehicles to transport a set of users with different requirements, which include minimising the total routing cost and total CO2 emission simultaneously. To solve the problem, a Regional Multi-Objective Tabu Search (RMOTS) algorithm is proposed, taking the decision maker’s preferences of the objectives into account, and consequently concentrating on a specific area of the Pareto front. To evaluate the performance of RMOTS, it is compared with two algorithms from the literature developed for similar problems. Experimental results show that the proposed RMOTS is able to outperform these algorithms based on the performance measures considered.
Journal of Optimization in Industrial Engineering, 2013
This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multi... more This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. The objective is to minimize the weighted sum of makespan and maximum tardiness. Three meta-heuristic methods based on genetic algorithm (GA), imperialist competitive algorithm (ICA) and a hybrid approach of GA and ICA are proposed to solve the generated problems. The performances of algorithms are evaluated by computational time and Relative Percentage Deviation (RPD) factors. The results indicate that ICA solves the problems faster than other algorithms and the hybrid algorithm produced best solution based on RPD.
This paper considers a two-stage assembly flow shop problem (TAFSP) where m machines are in the f... more This paper considers a two-stage assembly flow shop problem (TAFSP) where m machines are in the first stage and an assembly machine is in the second stage. The objective is to minimize a weighted sum of earliness and tardiness time for n available jobs. JIT seeks to identify and eliminate waste components including over production, waiting time, transportation, inventory, movement and defective products.Two-stage assembly flow shop is a combinational production system in which different parts are manufactured on parallel machines independently. This system can be used as a method to produce a variety of products through assembling and combining different set of parts. We apply e-constraint method as an exact approach to validate the proposed model and to obtain fronts of the solutions in the solution spaceThe goal of the proposed problem is trade off between two objectives, minimization makespan and total weighted tardiness and earliness. To analyze effects of n and m factors on ...
Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors suc... more Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously. Design/methodology/approach Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions. Findings Finally, experimental study and analysis of variance (ANO...
Purpose The purpose of this paper is to present a new mathematical model for the unrelated parall... more Purpose The purpose of this paper is to present a new mathematical model for the unrelated parallel machine scheduling problem with aging effects and multi-maintenance activities. Design/methodology/approach The authors assume that each machine may be subject to several maintenance activities over the scheduling horizon and a machine turn into its initial condition after maintenance activity and the aging effects start anew. The objective is to minimize the weighted sum of early/tardy times of jobs and maintenance costs. Findings As this problem is proven to be non-deterministic polynomial-time hard (NP-hard), the authors employed imperialist competitive algorithm (ICA) and genetic algorithm (GA) as solution approaches, and the parameters of the proposed algorithms are calibrated by a novel parameter tuning tool called Artificial Neural Network (ANN). The computational results clarify that GA performs better than ICA in quality of solutions and computational time. Originality/value ...
International Journal of Industrial and Systems Engineering, 2016
This paper considers a multi-server-vehicle routing problem where vehicles could exist and enter ... more This paper considers a multi-server-vehicle routing problem where vehicles could exist and enter the service depot several times. The central branch of bank has a number of nurses to service the failures. The objective is to find efficient routes for the nurses to service each task for each customer in order to minimise the total cost of routing and lateness/earliness penalties. In this paper, a mixed integer programming model is presented and two meta-heuristics approaches namely hybrid of genetic and simulated algorithms (HGSA) and imperialist competitive algorithm (ICA) are developed for solving the random generated problems. In HGSA, simulated annealing (SA) is employed with a certain probability to avoid being trapped in a local optimum. Furthermore, Taguchi experimental design method is applied to set the proper values of the algorithm's parameters. The available results show the higher performance of proposed HGSA compared with ICA, in quality of solutions within comparatively shorter periods of time.
Journal of Industrial and Production Engineering, 2016
This paper presents a new bi-objective mixed integer programming model for the two-stage assembly... more This paper presents a new bi-objective mixed integer programming model for the two-stage assembly flow shop scheduling problem with preventive maintenance (PM) activities, in which the reliability/availability approach is employed to model the maintenance concepts of a problem. PM activities carry out the operations on machines and tools before the breakdown takes place. Therefore, it helps to prevent failures before they happen. After developing a new bi-objective model, an Epsilon-constraint method is proposed to solve the problem. This problem has been known as Np-hard. Therefore, three multi-objective optimization methods, namely fast non-dominated sorting genetic algorithm, Multi-objective imperialist competitive algorithm, and non-dominated ranking genetic algorithm (NRGA) are employed to find the pareto-optimal front for large sized problems. The parameters of the proposed algorithms are calibrated using artificial neural network (ANN) and the performances of the proposed algorithms on the problems of various sizes are analyzed and the computational results reveal that NRGA outperform than two other proposed algorithms in quality of solutions and computational time.
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