With the guidance of the advanced manufacturing philosophy, green scheduling and energy efficiency have received considerable attention from enterprises and countries. Meanwhile, distributed manufacturing is becoming ...
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With the guidance of the advanced manufacturing philosophy, green scheduling and energy efficiency have received considerable attention from enterprises and countries. Meanwhile, distributed manufacturing is becoming widespread due to the exploration of the business. Thus, this paper investigates the energy-efficient scheduling of the distributed flexible job shop problem (EEDFJSP) with the goal of minimizing the makespan and total energy consumption (TEC). Considering the difficulty of simultaneously optimizing both objectives, a knowledge-guided bi-population evolutionary algorithm (KBEA) is proposed to address this issue. Firstly, a problem-specific initialization strategy based on a four-vector representation is presented, which corresponds to four sub-problems including factory assignment, operation sequence, machine assignment, and speed assignment. Secondly, five different types of evolutionary operators with adaption strategy is designed to guide the bi-population to complete efficient evolution. Thirdly, a knowledge-guided local search strategy is used to enhance the exploitation capability of the algorithm. Furthermore, an elaborately-designed energy-saving strategy based on knowledge is developed to further reduce energy consumption. Additionally, to verify the effectiveness of the proposed KBEA, extensive experiments are conducted to compare with other 7 comparison algorithms on 39 instances. Experimental results manifest that KBEA is superior to its competitors.
The energy-efficient flexible job shop scheduling problem (FJSP) has attracted much attention in deterministic cases;however, uncertainty is seldom incorporated into energy-efficient FJSP and the neglecting of uncerta...
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The energy-efficient flexible job shop scheduling problem (FJSP) has attracted much attention in deterministic cases;however, uncertainty is seldom incorporated into energy-efficient FJSP and the neglecting of uncertainty will greatly diminish the application value of scheduling results. These make it necessary to handle uncertainty in the problem. In this study, energy-efficient fuzzy FJSP (EFFJSP) is considered and a bi-population evolutionary algorithm with feedback (FBEA) is proposed to minimize fuzzy makespan and fuzzy total energy consumption and maximize minimum agreement index. The computation of fuzzy energy consumption is given and four heuristics are proposed to produce the initial population. An effective method is presented to evaluate the quality of two populations and a feedback mechanism based on population quality is adopted to dynamically adjust the size of each population. A novel process of reproduction, crossover and mutation is developed based on feedback. An enhanced local search is also used to produce high-quality solutions. Extensive experiments are conducted to test the performance of FBEA. FBEA can provide promising results for EFFJSP.
There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling,but most scholars neglect the connection between them,which means the purpose of both models is...
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There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling,but most scholars neglect the connection between them,which means the purpose of both models is to simulate a more realistic factory *** this perspective,the solutions can be more precise and practical if both issues are considered ***,the deterioration effect is treated as a part of the fuzzy job shop scheduling problem in this paper,which means the linear increase of a certain processing time is transformed into an internal linear shift of a triangle fuzzy processing *** from that,many other contributions can be stated as follows.A new algorithm called reinforcement learning based biased bi-population evolutionary algorithm(RB2EA)is proposed,which utilizes Q-learning algorithm to adjust the size of the two populations and the interaction frequency according to the quality of population.A local enhancement method which combimes multiple local search stratgies is *** interaction mechanism is designed to promote the convergence of the *** experiments are designed to evaluate the efficacy of RB2EA,and the conclusion can be drew that RB2EA is able to solve energy-efficient fuzzy flexible job shop scheduling problem with deteriorating jobs(EFFJSPD)efficiently.
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