Integrated production-inventory-routing problem for multi-perishable products under uncertainty by meta-heuristic algorithms

The present study aims to introduce an integrated production-inventory-routing problem (PIRP) with a mixed-integer linear programming model, remarking a multi-perishable product, multi-period, and heterogeneous fleets with time windows in a distribution network. The objective of the proposed model i...

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Veröffentlicht in:International journal of production research Jg. 60; H. 9; S. 2766 - 2786
Hauptverfasser: Ghasemkhani, Ahmad, Tavakkoli-Moghaddam, Reza, Rahimi, Yaser, Shahnejat-Bushehri, Sina, Tavakkoli-Moghaddam, Haed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Taylor & Francis 03.05.2022
Taylor & Francis LLC
Schlagworte:
Algorithms
Constraints
Evolutionary algorithms
Evolutionary computation
food industry
fuzzy chance-constrained model
Heuristic methods
Integer programming
Linear programming
meta-heuristics
Mixed integer
perishable product
production-inventory-routing problem
Route planning
Vehicle routing
ISSN:0020-7543, 1366-588X
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Zusammenfassung:The present study aims to introduce an integrated production-inventory-routing problem (PIRP) with a mixed-integer linear programming model, remarking a multi-perishable product, multi-period, and heterogeneous fleets with time windows in a distribution network. The objective of the proposed model is to maximise the total profit, which equals the selling revenue subtract the aggregation of the holding, production, transportation, and utility preference costs. At the production level, a multi-period production system with production capacity constraints is considered, in which the inventory at each stage of production is intended to compute the related holding costs and schedule more appropriate planning. The vehicle routing problem is tackled at the distribution level regarding vehicles with various capacities in a multi-period condition. Consequently, a fuzzy chance-constrained programming model is used to deal with fuzzy parameters. Furthermore, two evolutionary algorithms, namely a hybrid imperialist competitive algorithm (HICA) and self-adaptive differential evolution (SADE), are proposed to solve the given problem. Subsequently, several numerical examples with managerial insights are solved to evaluate the performances of the proposed algorithms and show their effectiveness and efficiency. Computational results demonstrate the superiority of the proposed algorithms for this problem. Finally, the applicability of the proposed algorithms is investigated by a real-case study.
Bibliographie:ObjectType-Article-1
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2021.1902013