A dynamic closed-loop location-inventory problem under disruption risk

•Presenting a novel bi-objective dynamic closed-loop location-inventory problem.•Considering the effectiveness of returned products on the ordering patterns of the forward logistics.•Presenting a developed hybrid multi-objective meta-heuristic algorithm.•Applying the proposed model on a real case-st...

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Bibliographic Details
Published in:Computers & industrial engineering 2015-12, Vol.90, p.414-428
Main Authors: Asl-Najafi, Javad, Zahiri, Behzad, Bozorgi-Amiri, Ali, Taheri-Moghaddam, Alireza
Format: Article
Language:English
Subjects:
Algorithms
Closed-loop location-inventory
Decision analysis
Decisions
Disruption
Dynamics
Facility disruption
Genetic algorithms
Heuristic
Inventory
Inventory management
Location analysis
Mathematical models
Meta-heuristics
Multi-period location–allocation
Networks
Optimization algorithms
Risk
Studies
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Summary:•Presenting a novel bi-objective dynamic closed-loop location-inventory problem.•Considering the effectiveness of returned products on the ordering patterns of the forward logistics.•Presenting a developed hybrid multi-objective meta-heuristic algorithm.•Applying the proposed model on a real case-study in Iran. In this paper, a dynamic closed-loop location-inventory problem is addressed that optimizes strategic decisions (i.e., facility location in terms of contracting/selection of distribution centers and reworking centers) along with tactical ones (i.e., allocation of centers, inventory management) under facility disruption risks. The presented model seeks to minimize total cost as the first objective function, and time as the second one in the considered network. Due to the NP-Hard nature of the model, a hybrid meta-heuristic algorithm based on Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is presented to solve the problem in large scales. Finally, applicability of the proposed model is tested via a real case study and the results are analyzed in depth.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2015.10.012