Processes · 2022 · 14 citations · 38 references
Natural SciencesOperations ManagementDesign OptimizationOptimizationProduction PlanningCombinatorial OptimizationEnergy-efficient OperationsHeuristic OptimizationMetaheuristic OptimizationProcess OptimizationProduction OptimizationDisassemblyEnergy ConsumptionInfiltrationLocal SearchLocal Search AlgorithmsMetaheuristic SearchOptimization Of Complex SystemsRemanufacturing
Nowadays, there is a great deal of interest in the development of practical optimization models and intelligent solution algorithms for solving disassembly-line balancing problems. Based on the importance of energy efficiency of product disassembly and the trend for green remanufacturing, this paper develops a new optimization model for the energy-efficient disassembly-line balancing problem where the goal is to minimize the energy consumption generated during the disassembly-line operations. Since the proposed model is a complex optimization problem known as NP-hard, this study develops an improved metaheuristic algorithm based on the water cycle algorithm as a recently developed successful metaheuristic inspired by the natural water cycle phenomena of diversion, rainfall, confluence, and infiltration operations. A local search operator is added to the main algorithm to improve its performance. The proposed algorithm is validated by the exact solver and compared with other state-of-the-art and recent metaheuristic algorithms. A case study in a turbine reducer with different parameters is solved to show the applicability of this paper. Finally, our results confirm the high performance of the proposed improved water cycle algorithm and the efficiency of our sensitivity analyses during some sensitivity analyses.
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Hadi Eskandar, Ali Sadollah, Ardeshir Bahreininejad et al. · Computers & Structures · 2012 · 1.7K citations
Natural Sciences, Environmental Engineering, Water Resources Management +13
Guangdong Tian, Yaping Ren, Yixiong Feng et al. · IEEE Transactions on Industrial Informatics · 2018 · 268 citations
Natural Sciences, Manufacturing Engineering, Operations Management +13