크로스도킹 환경에서의 차량경로계획을 위한 진화 알고리즘

다국어 초록 (Multilingual Abstract)

Cross-docking is one of the effective ways applied in supply chain management in order to minimize total transportation cost while satisfying the customer demands. This paper addresses the transportation problem of cross-docking network design where products are transferred from suppliers to customers through distribution center without storing them for long. The objective of the problem is to find vehicle routings that minimize total transportation cost with heterogeneous vehicles of different capacities. Evolutionary algorithms (EA) are designed to solve the problem. The EA needs three types of chromosomes. The first is employed for representing the assignment of vehicles to travel to suppliers or customers, the second for representing suppliers to be assigned to a vehicle, and the third for representing customers to be assigned to a vehicle. Permutation representation is used for encoding the individuals of all the three types. Depending on how to evaluate the individuals of different types, EAs can be implemented in different forms: genetic algorithm (GA), cooperative coevolutionary algorithm with two populations (CCEA-2p), or CCEA with three populations (CCEA-3P). A computational study is carried out to compare the performances of these approaches. Numerical experiments show that both CCEA-2P and CCEA-3P tend to outperform GA in terms of the quality of the best solutions generated, while the performance difference between CCEA-2P and CCEA-3P looks insignificant.

목차 (Table of Contents)

• 1. 서론
• 2. 선행 연구
• 3. 문제 정의 및 수리 모형
• 4. 진화 알고리즘 설계
• 5. 해법의 성능 실험 및 분석
• 6. 결론
• References

참고문헌 (Reference)

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