An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints

[EN] Nowadays, the manufacturing industry faces the challenge of reducing energy consumption and the associated environmental impacts. Production scheduling is an effective approach for energy-savings management. During the entire workshop production process, both the processing and transportation operations consume large amounts of energy. To reduce energy consumption, an energy-efficient job-shop scheduling problem (EJSP) with transportation constraints was proposed in this paper. First, a mixed-integer programming model was established to minimize both the comprehensive energy consumption and makespan in the EJSP. Then, an enhanced estimation of distribution algorithm (EEDA) was developed to solve the problem. In the proposed algorithm, an estimation of distribution algorithm was employed to perform the global search and an improved simulated annealing algorithm was designed to perform the local search. Finally, numerical experiments were implemented to analyze the performance of the EEDA. The results showed that the EEDA is a promising approach and that it can solve EJSP effectively and efficiently.This work was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 17KJB460018), the Innovation Foundation for Science and Technology of Yangzhou University (No. 2016CXJ020 and No. 2017CXJ018), Science and Technology Project of Yangzhou under (No. YZ2017278), Research Topics of Teaching Reform of Yangzhou University under (No. YZUJX2018-28B), and the Spanish Government (No. TIN2016-80856-R and No. TIN2015-65515-C4-1-R).Dai, M.; Zhang, Z.; Giret Boggino, AS.; Salido, MA. (2019). An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints. Sustainability. 11(11):1-23. https://doi.org/10.3390/su11113085S1231111Wu, X., & Sun, Y. (2018). 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Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

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A Simple Differential Evolution Algorithm to Solve the Flexible Job Shop Scheduling Problem

This paper addresses the Flexible Job Shop Scheduling Problem (FJSSP) where the objective is to minimize the makespan. We develop a parallel hybrid Differential Evolution (DE) algorithm to tackle this problem. A random key representation of the FJSSP is adopted, which requires a very simple conversion mechanism to obtain a feasible schedule. This allows the DE algorithm to work on the continuous domain to explore the problem space of the discrete FJSSP. Moreover, a simple local search algorithm is embedded in the DE framework to balance the exploration and exploitation by enhancing the local searching ability. In addition, parallelism of the DE operations is included to improve the efficiency of whole algorithm. Experiments confirm the significant improvement achieved by integrating the propositions introduced in this study. Additional, test results show that our algorithm is competitive when compared with most existing approaches for the FJSSP.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

A Simple Differential Evolution Algorithm to Solve the Flexible Job Shop Scheduling Problem

This paper addresses the Flexible Job Shop Scheduling Problem (FJSSP) where the objective is to minimize the makespan. We develop a parallel hybrid Differential Evolution (DE) algorithm to tackle this problem. A random key representation of the FJSSP is adopted, which requires a very simple conversion mechanism to obtain a feasible schedule. This allows the DE algorithm to work on the continuous domain to explore the problem space of the discrete FJSSP. Moreover, a simple local search algorithm is embedded in the DE framework to balance the exploration and exploitation by enhancing the local searching ability. In addition, parallelism of the DE operations is included to improve the efficiency of whole algorithm. Experiments confirm the significant improvement achieved by integrating the propositions introduced in this study. Additional, test results show that our algorithm is competitive when compared with most existing approaches for the FJSSP.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic