Abstract
New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously minimize carbon emission and total late work criterion as sustainability-based and classical-based objective functions, respectively, in the multiobjective job shop scheduling environment. In order to solve the presented problem more effectively, a new multiobjective imperialist competitive algorithm imitating the behavior of imperialistic competition is proposed to obtain a set of non-dominated schedules. In this work, a three-fold scientific contribution can be observed in the problem and solution method, that are: (1) integrating carbon dioxide into the operational decision level of job shop scheduling, (2) considering total late work criterion in multi-objective job shop scheduling, and (3) proposing a new multi-objective imperialist competitive algorithm for solving the extended multi-objective optimization problem. The elements of the proposed algorithm are elucidated and forty three small and large sized extended benchmarked data sets are solved by the algorithm. Numerical results are compared with two well-known and most representative metaheuristic approaches, which are multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II, in order to evaluate the performance of the proposed algorithm. The obtained results reveal the effectiveness and efficiency of the proposed multi-objective imperialist competitive algorithm in finding high quality non-dominated schedules as compared to the other metaheuristic approaches
Highlights
In the last decades, various reports on environmental issues have been released, in which they are stating the escalating deterioration of the environment that is mainly caused by the world population activities
This paper will address the following research questions: (1) How an eco-efficient-based objective function and a classicalbased objective function can be simultaneously minimized in the job shop scheduling environment? (2) How this multi-objective job shop scheduling problem can be solved? Due to the scarcity of research done on carbon footprint reduction in the operational decision level, the main motivation of this paper is to model a green multi-objective job shop scheduling problem that is aimed at optimizing two objectives, simultaneously
The first and second columns show the instance name and instance size while the remaining columns present the values of Quality Metric (QM), Mean Ideal Distance (MID), Diversification Metric (DM), Spacing Metric (SM), and Non-Dominated Solutions (NNDS) for multi-objective imperialist competitive algorithm (MOICA), multi-objective particle swarm optimization (MOPSO), and NSGAII, respectively
Summary
Various reports on environmental issues have been released, in which they are stating the escalating deterioration of the environment that is mainly caused by the world population activities. Due to the scarcity of research done on carbon footprint reduction in the operational decision level, the main motivation of this paper is to model a green multi-objective job shop scheduling problem that is aimed at optimizing two objectives, simultaneously It reduces carbon footprint as the green or eco-efficient objective. Based on the fact that the scheduling objectives are usually in conflict with each other, a new multi-objective imperialist competitive algorithm (MOICA) is proposed to generate a set of non-dominated solutions for the presented problem This is the first attempt at solving job shop scheduling problems with carbon footprint and total late work criterion objective functions by applying MOICA, i.e. a three-fold scientific contribution.
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