Bi-objective grey wolf optimization algorithm combined Levy flight mechanism for the FMC green scheduling problem

Zhou, Binghai; Lei, Yuanrui

doi:10.1016/j.asoc.2021.107717

Cited by 31 publications

(12 citation statements)

References 26 publications

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“…A large number of studies have shown that the movement trajectories of many creatures in nature during the foraging process satisfy Lévy flight. At present, Lévy flight is also widely used [23][24][25][26]. The mathematical model of Lévy flight is:…”

Section: Prey Search Stagementioning

confidence: 99%

Whale optimization algorithm based on domain search to solve 0-1 knapsack problem

Ning

2023

Preprint

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For the traditional whale optimization algorithm to solve the 0-1 knapsack combinatorial optimization problem, there are shortcomings of insufficient solution energy and easy to fall into local optima. A whale optimization algorithm based on domain search is proposed. This method focuses on the development of global search capabilities in the early stage of the evolution of the whale optimization algorithm, introduces the Lévy flight strategy, and uses a two-stage domain search method to enhance the diversity of the population and avoid the algorithm from falling into local optima; In the later stage, it focuses on the development of local search capabilities, and uses a greedy search method to perform fine search on some high-quality solutions to improve the accuracy of the solutions. Finally, the algorithm is used to conduct two sets of tests on the classic data set: the first group is a small and medium-scale data set, and the second group is a large-scale one. The test results show that the first group can find the optimal solution 100%, the second group has a strong advantage. The results show that the proposed algorithm has good robustness and optimization ability. It’s an effective method to solve the 0-1 knapsack problem.

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Section: Prey Search Stagementioning

confidence: 99%

Whale optimization algorithm based on domain search to solve 0-1 knapsack problem

Ning

2023

Preprint

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“…Nevertheless, there are a few exceptions. Some authors [37,38,85,[87][88][89][90][91][92] schedule transport and take into account the energy required to load/unload vehicles, and others [93][94][95][96][97][98] consider the energy required to transport the jobs between geographically distributed facilities.…”

Section: Pp = Maxmentioning

confidence: 99%

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

2022

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Energy efficiency has become a major concern for manufacturing companies not only due to environmental concerns and stringent regulations, but also due to large and incremental energy costs. Energy-efficient scheduling can be effective at improving energy efficiency and thus reducing energy consumption and associated costs, as well as pollutant emissions. This work reviews recent literature on energy-efficient scheduling in job shop manufacturing systems, with a particular focus on metaheuristics. We review 172 papers published between 2013 and 2022, by analyzing the shop floor type, the energy efficiency strategy, the objective function(s), the newly added problem feature(s), and the solution approach(es). We also report on the existing data sets and make them available to the research community. The paper is concluded by pointing out potential directions for future research, namely developing integrated scheduling approaches for interconnected problems, fast metaheuristic methods to respond to dynamic scheduling problems, and hybrid metaheuristic and big data methods for cyber-physical production systems.

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“…Chou et al (2020) designed an energy-aware scheduling algorithm under maximum power consumption constraints, where the energy consumption and production pattern of the machines were modelled in detail. Above all, there is a great opportunity in solving scheduling problem with both operational and energy-efficiency criteria (Zhou and Lei 2021).…”

Section: Introductionmentioning

confidence: 99%

An improved bi-objective salp swarm algorithm based on decomposition for green scheduling in flexible manufacturing cellular environments with multiple automated guided vehicles

Zhou

Zhang

2023

Preprint

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Energy-awareness in the industrial sectors has become a global consensus in recent decades. Green scheduling is acknowledged as an effective weapon to reduce energy consumption in the industrial sectors. Therefore, this paper is devoted to the green scheduling of flexible manufacturing cells (FMC) with auto-guided vehicle transportation, where conflict-free routing of the vehicles is considered. To deal with this problem, a bi-objective optimization model is proposed to achieve the minimization of the maximum completion time and the total energy consumption in an FMC. The studied problem is an extension of flexible job shop problem which is NP-hard. Thus, an improved bi-objective salp swarm algorithm based on decomposition (IMOSSA/D) is proposed and applied to the problem. The approach is based on the decomposition of the bi-objective problem. Salp swarm intelligence along with three stochastic-distribution-based operators are incorporated into the approach, to enhance and balance its exploring and exploiting ability. Computational experiments are performed to compare the proposed approach with two state-of-the-art algorithms. This study allows the decision makers to better trade-off between energy savings and production efficiency in flexible manufacturing cellular environment.

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Bi-objective grey wolf optimization algorithm combined Levy flight mechanism for the FMC green scheduling problem

Cited by 31 publications

References 26 publications

Whale optimization algorithm based on domain search to solve 0-1 knapsack problem

Whale optimization algorithm based on domain search to solve 0-1 knapsack problem

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

An improved bi-objective salp swarm algorithm based on decomposition for green scheduling in flexible manufacturing cellular environments with multiple automated guided vehicles

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