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An effective hybrid self-adapting differential evolution algorithm for the joint replenishment and location-inventory
1School of Management, Huazhong University of Science and Technology, Wuhan 430074, China.
Thescientificworldjournal
|January 24, 2014
Summary
This study introduces a hybrid self-adapting differential evolution algorithm (HSDE) to solve the complex joint replenishment and location-inventory problem (JR-LIP). The HSDE algorithm demonstrates superior performance and robustness compared to genetic algorithms and hybrid DE for optimizing supply chain decisions.
Area of Science:
- Operations Research
- Supply Chain Management
- Computational Intelligence
Background:
- Integrating supply chain decisions is crucial for avoiding suboptimal outcomes.
- The joint replenishment and location-inventory problem (JR-LIP) is an NP-hard problem that is challenging to solve effectively.
- Existing methods struggle with the mathematical complexity of JR-LIP.
Purpose of the Study:
- To develop an effective intelligent algorithm for the modified JR-LIP.
- To determine optimal distribution center (DC) locations, customer assignments, and DC replenishment policies.
- To minimize overall costs in complex supply chain networks.
Main Methods:
- A novel hybrid self-adapting differential evolution algorithm (HSDE) was designed.
- The HSDE algorithm was benchmarked against genetic algorithms (GA) and hybrid DE (HDE).
- Performance was evaluated using benchmark functions and randomly generated JR-LIP instances.
Main Results:
- The HSDE algorithm significantly outperformed both GA and HDE in solving the JR-LIP.
- Sensitivity analysis provided valuable managerial insights into cost parameters.
- HSDE demonstrated enhanced stability and robustness, particularly for large-scale JR-LIP instances.
Conclusions:
- The proposed HSDE is an effective and robust solution for the JR-LIP.
- The algorithm offers a significant advancement in optimizing integrated supply chain decisions.
- HSDE provides a stable approach for complex, large-scale supply chain optimization problems.
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