Energy-efficient scheduling for multi-objective two-stage flow shop using a hybrid ant colony optimisation algorithm

鄭, 旭光; Zhou, Shengchao; Xu, Rui; Chen, Huaping

doi:10.1080/00207543.2019.1642529

Cited by 58 publications

(19 citation statements)

References 41 publications

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“…The growing number of published articles, especially in the past 5 years, makes energy-efficiency scheduling a hot research topic. In the review process of this review work, there are several newest publications related to the scope of this review paper [95][96][97][98][99] which shows the importance and high relevancy of the studied subject. Intelligent strategies are used by many researchers in scientific community.…”

Section: Discussionmentioning

confidence: 99%

A review of energy-efficient scheduling in intelligent production systems

Gao

Huang

Sadollah

et al. 2019

Complex Intell. Syst.

182

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Recently, many manufacturing enterprises pay closer attention to energy efficiency due to increasing energy cost and environmental awareness. Energy-efficient scheduling of production systems is an effective way to improve energy efficiency and to reduce energy cost. During the past 10 years, a large amount of literature has been published about energy-efficient scheduling, in which more than 50% employed swarm intelligence and evolutionary algorithms to solve the complex scheduling problems. This paper aims to provide a comprehensive literature review of production scheduling for intelligent manufacturing systems with the energy-related constraints and objectives. The main goals are to summarize, analyze, discuss, and synthesize the existing achievements, current research status, and ongoing studies, and to give useful insight into future research, especially intelligent strategies for solving the energy-efficient scheduling problems. The scope of this review is focused on the journal publications of the Web of Science database. The energy efficiency-related publications are classified and analyzed according to five criteria. Then, the research trends of energy efficiency are discussed. Finally, some directions are pointed out for future studies.

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Section: Discussionmentioning

confidence: 99%

A review of energy-efficient scheduling in intelligent production systems

Gao

Huang

Sadollah

et al. 2019

Complex Intell. Syst.

182

View full text Add to dashboard Cite

show abstract

“…When R N(S j ) � 0, the solutions in S j are all dominated by other solutions. Also, when R N(S j ) � 1, the solutions in S j are all nondominated solutions in S. N N(S j ) is the number of solutions in S j which are not dominated by other solutions (shown in equation (18)). e larger the value of N N(S j ), the greater the number of nondominated solutions.…”

Section: Evaluation Indexmentioning

confidence: 99%

“…For the energy-optimized multiobjective green scheduling problem of the production process, the existing research has largely focused on optimizing the machining process with methods of machine speed control, turn off/on machine strategy, etc. [14][15][16][17][18]. However, the transport of workpieces is ignored in the production process.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Estimation of Distribution Algorithm for Solving Three‐Stage Multiobjective Integrated Scheduling Problem

Deng

Qian

et al. 2021

Complexity

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Aiming at reducing the total energy consumption of three stages processing-transportation-assembly in the assembly manufacturing industry, a three-stage multiobjective integrated scheduling problem with job batch transportation considering the energy consumption (3sMISP_JBTEC) is proposed, and a comprehensive energy consumption model of multistage of 3sMISP_JBTEC with an improved turn off/on strategy in the processing stage and considering speed in the transportation stage is formulated. Then, a hybrid estimation of distribution algorithm with variable neighborhood search (HEDA_VNS) is developed to solve the scheduling problem. In the HEDA_VNS, the reasonable coding/decoding rules and speed scheduling scheme (SSS) are designed. Moreover, two local search strategies are designed to further enhance the performance of HEDA_VNS. Among them, three types of neighborhood search strategies are devised in Local Search I to improve the search efficiency while retaining the structure of the original high-quality solution. A variable neighborhood hybrid operation based on the speed scheduling set is designed in Local Search II to further improve the quality of the solution while balancing the optimization goals. Finally, simulations and comparisons show the efficiency of the proposed HEDA_VNS.

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“…Thus, we adopt the meta-heuristic to solve this model instead of using exact algorithms. According to the research results in [55], meta-heuristics such as genetic algorithm (GA) [56], ant colony optimization algorithm (ACO) [57], and particle swarm optimization algorithm (PSO) [58] are very effective to solve the combinatorial problems such as flow shop, job shop and open shop scheduling problems to get the near optimal solutions.…”

Section: Algorithm Designingmentioning

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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Energy-efficient scheduling for multi-objective two-stage flow shop using a hybrid ant colony optimisation algorithm

Cited by 58 publications

References 41 publications

A review of energy-efficient scheduling in intelligent production systems

A review of energy-efficient scheduling in intelligent production systems

Hybrid Estimation of Distribution Algorithm for Solving Three‐Stage Multiobjective Integrated Scheduling Problem

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

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