A novel mathematical model and multi-objective method for the low-carbon flexible job shop scheduling problem

2017 ◽  
Vol 13 ◽  
pp. 15-30 ◽  
Author(s):  
Lvjiang Yin ◽  
Xinyu Li ◽  
Liang Gao ◽  
Chao Lu ◽  
Zhao Zhang
Keyword(s):  
Mathematical Model ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Objective Method ◽  
Low Carbon ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

scholarly journals Designing a Multi-Objective Mathematical Model for Flexible Job Shop Scheduling Problem With the Earliness/Tardiness Penalty

2019 ◽  
pp. 1375-1382 ◽  
Author(s):  
Mir Hossein Seyyedi ◽  
Amir Mohammad Fakoor Saghih ◽  
Alireza Pooya ◽  
Zahra Naji Azim
Keyword(s):  
Mathematical Model ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Actual Situation ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective

The scheduling of flexible job shop systems is one of the most important issues in the various fields of production and is currently being addressed by many researchers in the field of optimization issues. The present research includes flexible job shop scheduling problem (FJSSP) with multi-objective, minimizing maximum completion time (makespan), maximum machine workload, total machines workload and also, earliness/tardiness penalty with different constraints. In this research, the researcher is looking to design a mathematical model that can cover all the constraints and assumptions related to the problem. Therefore, the mathematical model was designed with multi-objective and different constraints with exact details and different assumptions that are consistent with the actual situation of the problem and implemented at Comex Company. What that distinguishes this research from other similar researches is the approach of multi-objective with different constraints, which, at the same time, it raises the complexity of the problem but the problem gets closer to the actual situation, with less research done. Finally, the results of the study showed that this mathematical model designed, as well as in the real environment which has the flexible job shop system, can be implemented within a reasonable time with the highest efficiency before the implementation of the model.

Discrete African Buffalo Optimization Algorithm for the Low-carbon Flexible Job Shop Scheduling Problem

2020 ◽  
Vol 19 (04) ◽  
pp. 837-854
Author(s):  
Huiqi Zhu ◽  
Tianhua Jiang ◽  
Yufang Wang
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
African Buffalo ◽  
Low Carbon ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Neighborhood Structures

In the area of production scheduling, some traditional indicators are always treated as the optimization objectives such as makespan, earliness/tardiness and workload, and so on. However, with the increasing amount of energy consumption, the low-carbon scheduling problem has gained more and more attention from scholars and engineers. In this paper, a low-carbon flexible job shop scheduling problem (LFJSP) is studied to minimize the earliness/tardiness cost and the energy consumption cost. In this paper, a low-carbon flexible job shop scheduling. Due to the NP-hard nature of the problem, a swarm-based intelligence algorithm, named discrete African buffalo optimization (DABO), is developed to deal with the problem under study effectively. The original ABO was proposed for continuous problems, but the problem is a discrete scheduling problem. Therefore, some individual updating methods are proposed to ensure the algorithm works in a discrete search domain. Then, some neighborhood structures are designed in terms of the characteristics of the problem. A local search procedure is presented based on some neighborhood structures and embedded into the algorithm to enhance its searchability. In addition, an aging-based population re-initialization method is proposed to enhance the population diversity and avoid trapping into the local optima. Finally, several experimental simulations have been carried out to test the effectiveness of the DABO. The comparison results demonstrate the promising advantages of the DABO for the considered LFJSP.

scholarly journals Modified Multi-Crossover Operator NSGA-III for Solving Low Carbon Flexible Job Shop Scheduling Problem

Processes ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 62
Author(s):  
Xingping Sun ◽  
Ye Wang ◽  
Hongwei Kang ◽  
Yong Shen ◽  
Qingyi Chen ◽  
...  
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Low Carbon ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Decision Space ◽  
Shop Scheduling ◽  
Multi Objective

Low carbon manufacturing has received increasingly more attention in the context of global warming. The flexible job shop scheduling problem (FJSP) widely exists in various manufacturing processes. Researchers have always emphasized manufacturing efficiency and economic benefits while ignoring environmental impacts. In this paper, considering carbon emissions, a multi-objective flexible job shop scheduling problem (MO-FJSP) mathematical model with minimum completion time, carbon emission, and machine load is established. To solve this problem, we study six variants of the non-dominated sorting genetic algorithm-III (NSGA-III). We find that some variants have better search capability in the MO-FJSP decision space. When the solution set is close to the Pareto frontier, the development ability of the NSGA-III variant in the decision space shows a difference. According to the research, we combine Pareto dominance with indicator-based thought. By utilizing three existing crossover operators, a modified NSGA-III (co-evolutionary NSGA-III (NSGA-III-COE) incorporated with the multi-group co-evolution and the natural selection is proposed. By comparing with three NSGA-III variants and five multi-objective evolutionary algorithms (MOEAs) on 27 well-known FJSP benchmark instances, it is found that the NSGA-III-COE greatly improves the speed of convergence and the ability to jump out of local optimum while maintaining the diversity of the population. From the experimental results, it can be concluded that the NSGA-III-COE has significant advantages in solving the low carbon MO-FJSP.

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