Abstract
This paper presents an early attempt to solve one-to-many-to-one dynamic pickup-and-delivery problem (DPDP) by proposing a multi-objective memetic algorithm called LSH-MOMA, which is a synergy of multi-objective evolutionary algorithm and locality-sensitive hashing (LSH) based local search. Three objectives namely route length, response time, and workload are optimized simultaneously in an evolutionary framework. In each generation of LSH-MOMA, LSH-based rectification and local search are imposed to repair and improve the individual solutions. LSH-MOMA is evaluated on four benchmark DPDPs and the experimental results show that LSH-MOMA is efficient in obtaining optimal tradeoff solutions of the three objectives.
Original language | English |
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Pages (from-to) | 73-89 |
Journal | Information Sciences |
Volume | 329 |
Early online date | 18 Sept 2015 |
DOIs | |
Publication status | Published - 1 Feb 2016 |
Keywords
- Memetic algorithm
- Multi-objective evolutionary algorithm
- Dynamic pickup and delivery problem
- Locality-sensitive hashing