Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem

Li, Hongchan; Zhu, Haodong; Jiang, Tianhua

doi:10.3390/a13020044

Cited by 17 publications

(3 citation statements)

References 39 publications

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“…This paper took the instances in Tables A1 and A2 (refer to Appendix A) as the research object, and solved the green FJSP problem with the makespan, workshop energy consumption, and processing quality as the optimization goals. The transportation energy consumption of the AGV was generated randomly in [2.0, 2.5] [47]. Furthermore, from a cost-saving perspective, we aimed to use the minimum number of AGVs under the premise of achieving multi-objective optimization.…”

Section: Workhop Example Simulationmentioning

confidence: 99%

Modeling an Optimal Environmentally Friendly Energy-Saving Flexible Workshop

Zhang,

Wei,

Gao

2023

Applied Sciences

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From the perspective of energy efficiency and environmental sustainability, the scheduling problem in a flexible workshop with the utilization of automated guided vehicles (AGVs) was investigated for material transportation. Addressing the dual-constrained integrated scheduling challenge involving machining machines and AGVs, a scheduling optimization model was established with makespan, workshop energy consumption, and processing quality as the optimization objectives. To effectively solve this model, an enhanced whale optimization algorithm (IWOA) was proposed. Specifically, nonlinear convergence factors, adaptive inertia weights, and improved helix positions were introduced into the standard whale optimization algorithm to update the model. Furthermore, a loss function was constructed based on fuzzy membership theory to obtain the optimal compromise solution of the multi-objective model. The research results indicate that: (1) The IWOA obtained the optimal solutions on benchmark instances MK01, MK02, MK04, MK07, and MK08; (2) The IWOA outperformed the WOA(1), WOA(2), WOA-LEDE, and NSGA-II algorithms in the two instances provided in this paper, demonstrating strong robustness of the model; (3) Although the multi-objective model constructed in this paper could not surpass the single-objective optimal solution in individual objectives, it achieved compensation in other objectives, effectively balancing the trade-offs among the makespan, workshop energy consumption, and processing quality of the three objectives. This research offers an effective practical approach to address green flexible workshop scheduling with AGV transportation.

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Section: Workhop Example Simulationmentioning

confidence: 99%

Modeling an Optimal Environmentally Friendly Energy-Saving Flexible Workshop

Zhang,

Wei,

Gao

2023

Applied Sciences

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“…To calculate the total energy consumption of the manufacturing system, researchers consider three to five types of consumption, namely machine processing energy (P), machine idle energy (Id), machine setup energy (S), job transport energy (Tr), and indirect energy (In), as well as energy associated with auxiliary equipment [71][72][73][74][75][76], coolant and lubricant [77], and keeping room temperature constant and lighting [29,78].…”

Section: Energy Efficiency Objective Functionsmentioning

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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“…Traveling Salesman [92] Traveling Salesman [93] Multiple Traveling Salesman [94] Bottleneck Traveling Salesman [95] Cutting Stock [96] Cutting Stock [97] 2D Cutting [98] Packing [99] Packing [100] 2D Packing [101] Bin Packing [102] Knapsack [103] Knapsack [104] Subset Sum [105] Unbounded Knapsack [105] Bounded Knapsack [106] Multiple Knapsack [107] Quadratic Knapsack [108] Scheduling [109] Scheduling [110] Production Scheduling [111] Workforce Scheduling [112] Job-Shop Scheduling [113] Precedence Constrained Scheduling [114] Educational Timetabling [115] Educational Timetabling [116] Facility Location [117] Assignment [118] Quadratic Assignment [119] Spanning Tree [120] Maximum Leaf Spanning Tree [121] Degree Constrained Spanning Tree [122] Minimum Spanning Tree [123] Boolean Satisfiability [124] Boolean Satisfiability [125] Covering [126] Minimum Vertex Cover [127] Set Cover [128] Exact Cover [129] Minimum Edge Cover [130] Vehicle Routing [131] Vehicle Routing…”

Section: Type Problemmentioning

confidence: 99%

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

et al. 2020

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Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution. The problem changes; so does its state. As a consequence, for the next move, a different solver may be invoked. Hyper-heuristics have been around for almost 20 years. However, combinatorial optimization problems date from way back. Thus, it is paramount to determine whether the efforts revolving around hyper-heuristic research have been targeted at the problems of the highest interest for the combinatorial optimization community. In this work, we tackle such an endeavor. We begin by determining the most relevant combinatorial optimization problems, and then we analyze them in the context of hyper-heuristics. The idea is to verify whether they remain as relevant when considering exclusively works related to hyper-heuristics. We find that some of the most relevant problem domains have also been popular for hyper-heuristics research. Alas, others have not and few efforts have been directed towards solving them. We identify the following problem domains, which may help in furthering the impact of hyper-heuristics: Shortest Path, Set Cover, Longest Path, and Minimum Spanning Tree. We believe that focusing research on ways for solving them may lead to an increase in the relevance and impact that hyperheuristics have on combinatorial optimization problems.

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Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem

Cited by 17 publications

References 39 publications

Modeling an Optimal Environmentally Friendly Energy-Saving Flexible Workshop

Modeling an Optimal Environmentally Friendly Energy-Saving Flexible Workshop

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

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

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