Energy-efficient no-wait permutation flow shop scheduling by adaptive multi-objective variable neighborhood search

Wu, Xueqi; Che, Ada

doi:10.1016/j.omega.2019.102117

Cited by 80 publications

(32 citation statements)

References 49 publications

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“…The authors of [45] considered the makespan, tardiness, and energy consumption and assumed that the third objective is less important than other ones. The authors of [46] proposed an adaptive multi-objective variable neighborhood search algorithm to solve the no-wait flow shop problem, and the authors of [47] designed a multi-objective grey wolf optimization algorithm to solve the flexible job shop problem. The authors of [48] studied the flexible job shop scheduling problem considering the machines' on/off and speed level simultaneously.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint

Cui

2020

Sustainability

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With the growing concern of energy shortage and environment pollution, the energy aware operation management problem has emerged as a hot topic in industrial engineering recently. An integrated model consisting of production scheduling, preventive maintenance (PM) planning, and energy controlling is established for the flow shops with the PM constraint and peak demand constraint. The machine’s on/off and the speed level selection are considered to save the energy consumption in this problem. To minimize the makespan and the total energy consumption simultaneously, a multi-objective algorithm founded on NSGA-II is designed to solve the model effectively. The key decision variables are coded into the chromosome, while the others are obtained heuristically using the proposed decoding method when evaluating the chromosome. Numerical experiments were conducted to validate the effectiveness and efficiency by comparing the proposed algorithm and the traditional rules in manufacturing plant. The impacts of constraints on the Pareto frontier are also shown when analyzing the tradeoff between two objectives, which can be used to explicitly assess the energy consumption.

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Section: Literature Reviewmentioning

confidence: 99%

A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint

Cui

2020

Sustainability

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“…Scheduling is regarded as an effective tool to help make production plans (Wu and Che, 2020). Recently there is a trend to study machine scheduling under multitasking.…”

Section: Literature Reviewmentioning

confidence: 99%

Approximation Algorithms for Multitasking Scheduling Problems

et al. 2020

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In this work, we incorporate human factors and real-life operations into newly proposed multitasking scheduling problems with periodic shift activities. It is motivated by personnel resource scheduling with periodic work shifts under the requirement of providing continuous service to customers. We model the problem as two identical parallel machine scheduling with complementary non-available time periods, and consider two models with the objectives of the makespan, i.e. the maximum completion time and respectively the total completion time. We then prove that the Greedy algorithm and SPT rule are of asymptotic and parametric approximation ratios for the two models, respectively.

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“…Thus, green scheduling is more practical and has attracted much interest. For the no-wait permutation flow shop scheduling problem, Wu and Che [16] proposed an adaptive multiobjective variable neighborhood search to minimize both makespan and total energy consumption. For the hybrid flow shop scheduling problem, Liu et al [17] proposed an evolutionary algorithm based on weighted sum approach to minimize both makespan and total energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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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Energy-efficient no-wait permutation flow shop scheduling by adaptive multi-objective variable neighborhood search

Cited by 80 publications

References 49 publications

A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint

A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint

Approximation Algorithms for Multitasking Scheduling Problems

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

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