In this paper, we consider a real-life supply chain optimization problem concerned with supplying a product from multiple warehouses to multiple geographically dispersed retailers. Each retailer faces a deterministic and period-dependent demand over some ﬁnite planning horizon. The demand of each retailer is satisﬁed by the supply from some predetermined warehouse through a ﬂeet of vehicles which are only available within certain time windows at each period. Our goal is to identify a combined inventory and routing schedule such that the system-wide total cost over the planning horizon is minimised. This problem in essence is an amalgamation of two classical NP-hard optimizatin problems: the Dynamic Lotsizing problem and the Vehicle Routing problem. In this paper, we propose an efﬁcient rolling horizon heuristic that combines two heuristics to solve this problem. Numerical experiment results show that our approach can achieve, on average, within 10% of the lower-bound proposed by Chan, Federgruen and Simchi-Levi (1998) for some speciﬁc instances generated from Solomon benchmarks.
Meta-Heuristics, Planning, Scheduling, Search
Computer Sciences | Operations and Supply Chain Management
Information Systems and Management
15th European Conference on Artificial Intelligence (ECAI), July 21-26, 2002, Lyon France
City or Country
Lau, Hoong Chuin and SONG, Yuyue.
Combining Two Heuristics to solve a Supply Chain Optimization Problem. (2002). 15th European Conference on Artificial Intelligence (ECAI), July 21-26, 2002, Lyon France. 581-585. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1120
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