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A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under
Yanhui Li1, Hao Guo1, Lin Wang2
1School of Information Management, Central China Normal University, Wuhan 430079, China.
Thescientificworldjournal
|February 4, 2014
Summary
This study addresses e-commerce logistics challenges by developing a model for location, inventory, and routing problems with non-defective returns. A hybrid algorithm significantly improves upon traditional methods for efficient supply chain management.
Area of Science:
- Operations Research
- Supply Chain Management
- E-commerce Logistics
Background:
- E-commerce logistics involves complex, interrelated decisions in facility location, inventory control, and vehicle routing.
- Internet sales exhibit higher return rates than traditional retail, with many items being resalable after repackaging.
Purpose of the Study:
- To formulate a location-inventory-routing problem model that specifically accounts for returns with no quality defects in e-commerce.
- To develop an efficient algorithm for solving this complex optimization problem.
Main Methods:
- Formulation of a novel location-inventory-routing problem model.
- Development and application of a hybrid genetic simulated annealing algorithm (HGSAA) to solve the NP-hard problem.
Main Results:
- The proposed HGSAA demonstrates superior performance compared to the genetic algorithm (GA).
- HGSAA achieves better results in terms of computing time, solution optimality, and computational stability.
- Numerical examples validate the effectiveness of the proposed approach.
Conclusions:
- The developed model and HGSAA provide a valuable tool for e-commerce managers.
- Effective decision-making in e-supply chain management is enhanced by considering non-defective returns.
- Optimizing logistics operations is crucial for the success of e-commerce businesses.
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