Solving energy-efficient distributed job shop scheduling via multi-objective evolutionary algorithm with decomposition

Jiang, Enda; Wang, Ling; Peng, Zhiping

doi:10.1016/j.swevo.2020.100745

Cited by 73 publications

(35 citation statements)

References 43 publications

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“…3 NMOEA/D for EADHFSPMT To solve MOPs, MOEA/D is an effective approach [20,21,28] . It decomposes an MOP into a number of single-objective sub-problems using decomposition function, and then optimizes these sub-problems simultaneously.…”

Section: Problem Description and Formulationmentioning

confidence: 99%

“…Jiang and Wang [20] proposed a modified MOEA/D for the flexible job shop scheduling problem under time-of-use electricity prices to minimize the makespan and total electricity cost. Jiang et al [21] proposed a collaborative MOEA/D for the distributed job shop to minimize makespan and total energy consumption. However, to the best of our knowledge, there is no existing work about the MOEA/D for the EADHFSPMT.…”

Section: Problem Description and Formulationmentioning

confidence: 99%

“…There are three sub-problems, i.e., job assignment total electricity cost. Jiang et al [21] proposed a collaborative MOEA/D for the distributed job shop to minimize makespan and total energy consumption. However, to the best of our knowledge, there is no existing work about the MOEA/D for the EADHFSPMT.…”

Section: Encoding and Decodingmentioning

confidence: 99%

“…For flexible job shop scheduling problem under time-of-use electricity prices, Jiang and Wang [20] proposed a Modified multi-Objective Evolutionary Algorithm (MOEA/D) to minimize both makespan and total electricity cost. For the distributed job shop scheduling problem, Jiang et al [21] proposed a collaborative MOEA/D to minimize both makespan and total energy consumption. Recently, for the distributed parallel machines scheduling problem, Pan et al [22] proposed a knowledge-based two-population optimization algorithm to minimize both total energy consumption and total tardiness.…”

Section: Introductionmentioning

confidence: 99%

“…Evolutionary algorithms have been proved to be effective in solving a variety of large-scale optimization problems in different fields [23][24][25][26] . For the multi-objective optimization problems, Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) [27] is an effective technique with many successful applications [20,21] . In this paper, we will propose a Novel MOEA/D (NMOEA/D) with some special designs according to characteristics of the EADHFSPMT.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks

Jiang

Wang

2021

Tsinghua Sci. Technol.

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This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks (EADHFSPMT) by considering two objectives simultaneously, i.e., makespan and total energy consumption. It consists of three sub-problems, i.e., job assignment between factories, job sequence in each factory, and machine allocation for each job. We present a mixed inter linear programming model and propose a Novel Multi-Objective Evolutionary Algorithm based on Decomposition (NMOEA/D). We specially design a decoding scheme according to the characteristics of the EADHFSPMT. To initialize a population with certain diversity, four different rules are utilized. Moreover, a cooperative search is designed to produce new solutions based on different types of relationship between any solution and its neighbors. To enhance the quality of solutions, two local intensification operators are implemented according to the problem characteristics. In addition, a dynamic adjustment strategy for weight vectors is designed to balance the diversity and convergence, which can adaptively modify weight vectors according to the distribution of the non-dominated front. Extensive computational experiments are carried out by using a number of benchmark instances, which demonstrate the effectiveness of the above special designs. The statistical comparisons to the existing algorithms also verify the superior performances of the NMOEA/D.

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Section: Problem Description and Formulationmentioning

confidence: 99%

Section: Problem Description and Formulationmentioning

confidence: 99%

Section: Encoding and Decodingmentioning

confidence: 99%