A hybrid heuristic method for the periodic inventory routing problem
| Autor: | Chih-Hung Chung, Ming-Che Lu, Shu-Chu Liu |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Inventory routing problem
Mathematical optimization 021103 operations research business.industry Heuristic (computer science) Mechanical Engineering 0211 other engineering and technologies Particle swarm optimization 02 engineering and technology Industrial and Manufacturing Engineering Tabu search Computer Science Applications Control and Systems Engineering Simulated annealing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Local search (optimization) Routing (electronic design automation) business Software Average cost Mathematics |
| Zdroj: | The International Journal of Advanced Manufacturing Technology. 85:2345-2352 |
| ISSN: | 1433-3015 0268-3768 |
| DOI: | 10.1007/s00170-015-8081-3 |
| Popis: | The periodic inventory routing problem (PIRP) determines the delivery routing and the inventory policies for retailers from a supplier in a periodic time based on the minimal cost criterion. Since it is a non-deterministic polynomial-time (NP)-hard problem, a heuristic method is needed for this problem. In the past, different global heuristic methods, such as tabu search (TS) and simulated annealing (SA), have been proposed; however, they seem ineffective. Particle swarm optimization (PSO) is known as resolving multidimensional combinatorial problems such as PIRP; however, it is easily trapped in local optimality. The authors of this paper propose a hybrid heuristic method for the PIRP. The hybrid method integrates a large neighborhood search (LNS) into PSO to overcome the drawbacks of PSO and LNS. The PSO is adopted first. A local search is applied to each particle in different iterations. Then, a local optimal solution (particle) for each particle is obtained. Last, the LNS is applied to the global best solution to avoid becoming trapped in local optimality. The results show that the proposed hybrid heuristic method is 10.93 % better than the existing method and 1.86 % better than the pure heuristic method in terms of average cost. |
| Databáze: | OpenAIRE |
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