Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0952-1976(02)00011-8
Title: Artificial intelligence heuristics in solving vehicle routing problems with time window constraints
Authors: Tan, K.C. 
Lee, L.H. 
Ou, K.
Keywords: Artificial intelligence
Genetic algorithms
Simulated annealing
Tabu search
Vehicle routing problems with time windows
Issue Date: Dec-2001
Source: Tan, K.C., Lee, L.H., Ou, K. (2001-12). Artificial intelligence heuristics in solving vehicle routing problems with time window constraints. Engineering Applications of Artificial Intelligence 14 (6) : 825-837. ScholarBank@NUS Repository. https://doi.org/10.1016/S0952-1976(02)00011-8
Abstract: This paper describes the authors' research on various heuristics in solving vehicle routing problem with time window constraints (VRPTW) to near optimal solutions. VRPTW is NP-hard problem and best solved to near optimum by heuristics. In the vehicle routing problem, a set of geographically dispersed customers with known demands and predefined time windows are to be served by a fleet of vehicles with limited capacity. The optimized routines for each vehicle are scheduled as to achieve the minimal total cost without violating the capacity and time window constraints. In this paper, we explore different hybridizations of artificial intelligence based techniques including simulated annealing, tabu search and genetic algorithm for better performance in VRPTW. All the implemented hybrid heuristics are applied to solve the Solomon's 56 VRPTW with 100-customer instances, and yield 23 solutions competitive to the best solutions published in literature according to the authors' best knowledge. © 2002 Elsevier Science Ltd. All rights reserved.
Source Title: Engineering Applications of Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/55148
ISSN: 09521976
DOI: 10.1016/S0952-1976(02)00011-8
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