The green capacitated multi-item lot sizing problem with parallel machines

•We propose a mixed integer programming model for the green capacitated multi-item lot sizing problem with parallel machines.•The mathematical model can support the reduction of carbon emissions for manufacturing companies.•The green lot sizing problem is NP-hard and is therefore difficult to solve....

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Veröffentlicht in:Computers & operations research Jg. 98; S. 149 - 164
Hauptverfasser: Wu, Tao, Xiao, Fan, Zhang, Canrong, He, Yan, Liang, Zhe
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.10.2018
Pergamon Press Inc
Schlagworte:
Carbon
Carbon emissions
Column generation
Computation
Customer satisfaction
Emissions control
Energy consumptions
Green manufacturing
Heuristic
Integer programming
Lagrangian relaxation
Linear programming
Lot sizing
Lower bounds
Manufacturing
Mixed integer
Operations research
Parallel processing
Problem solving
Production planning
Studies
Green manufacturing
Column generation
Production planning
Lagrangian relaxation
Lot sizing
Energy consumptions
Carbon emissions
Heuristic
ISSN:0305-0548, 1873-765X, 0305-0548
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Zusammenfassung:•We propose a mixed integer programming model for the green capacitated multi-item lot sizing problem with parallel machines.•The mathematical model can support the reduction of carbon emissions for manufacturing companies.•The green lot sizing problem is NP-hard and is therefore difficult to solve.•We apply a progressive selection heuristic to find feasible solutions and upper bounds for the problem.•We also apply Lagrangian-relaxation, Dantzig–Wolfe decomposition, and column generation to enhance lower bounds for quantifying the solution quality. Carbon emissions related to energy consumptions from the manufacturing industry have become a substantial part of environmental burdens. To reduce carbon emissions, we introduce carbon emission constraints into the capacitated multi-item lot sizing problem with nonidentical parallel machines. The problem aims to satisfy customer demand for various items over the planning horizon, with an objective to minimize total costs without violating the capacity and carbon emission constraints. We formulate the problem with a mixed integer programming model and propose Lagrangian relaxation and column generation methods to improve lower bounds over the linear programming relaxation. Furthermore, we apply a heuristic named progressive selection to solve the problem and compare the heuristic with other state-of-the-art approaches in the literature. Computational results indicate that the progressive selection heuristic is computationally tractable and can obtain superior results under the same computational resources.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2018.05.024