Abstract
Green manufacturing, which takes environmental effect and production benefit into consideration, has attracted increasing concern with the target of carbon peaking and carbon neutrality proposed. As a critical process in the manufacturing system, shop scheduling is also an important method for enterprises to achieve green manufacturing. Therefore, it is necessary to consider both production benefits and environmental objectives in shop scheduling, which are symmetrical and equally important. In addition, noise pollution has become an important environmental issue that cannot be ignored in the manufacturing processes, but which has been paid less attention in previous studies. Thus, the MODABC algorithm, with the optimization objectives of simultaneously minimizing lead-time/tardiness cost and job-shop noise pollution emission is proposed in this paper. We designed a discrete permutation-based two-layer encoding mechanism to generate the initial population. Then, three crossover methods were used to perform nectar update operations in the employed bee search phase, and three neighbourhood structures were used to improve the onlooker bee search operations. Finally, the MODABC algorithm was compared with other classical MOEAs. The results demonstrate that MODABC can provide non-dominated solution set with good convergence and distribution, and show significant superiority in solving green single-machine multi-objective scheduling problems.
Highlights
Shrouf et al [3] proposed a mathematical model for minimizing the cost of energy consumption in the single-machine production scheduling process by considering the variation of energy prices during a day by comparing the analytical and heuristic solutions using a genetic algorithm and demonstrated that avoiding high energy price cycles significantly reduced energy costs
The nectar source update method of the MODABC algorithm consists of three crossovers: partial map crossover, order crossover and position-based crossover
Scout Bee Search A scout bee search, i.e., a reinitialisation operation, is required during the iterations of the MODABC algorithm, when the exploitation level of the nectar source i is greater than the pre-set maximum exploitation level (Traili > Limit)
Summary
Single-machine scheduling is the most basic type of job-shop scheduling. It is commonly seen in production and serves as the basis for studying more complex job-shop scheduling problems. Rubaiee et al [9] studied the single-machine no-priority scheduling problem by building a mixed-integer multi-objective mathematical planning model They developed several new genetic algorithms and showed the possibility to optimize the production processes, improve energy efficiency and manage environmental challenges through operation management techniques. Noise pollution, which has a negative effect on employees’ health and emotion, has become an important environmental issue that cannot be ignored in the manufacturing processes [10,11] Considering both production benefits and environmental objectives in single-machine scheduling, a multi-objective discrete ABC algorithm, MODABC, with the optimization objectives of simultaneously minimizing lead-time/tardiness cost and job-shop noise pollution emission is proposed, aiming to coordinate and optimize the economic performance and environmental objectives, which were considered symmetrical and important. By sequencing the jobs to be processed on the machine, both objectives of minimizing lead time/tardiness costs and job-shop noise emissions are simultaneously achieved
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