Vehicle routing problem with stochastic demand (VRPSD): optimisation by neighbourhood search embedded adaptive ant algorithm (ns-AAA)
Taking into account the real world applications, this paper considers a vehicle routing problem with stochastic demand (VRPSD) in which the customer demand has been modelled as a stochastic variable. Considering the computational complexity of the problem and to enhance the algorithm performance, a neighbourhood search embedded adaptive ant algorithm (ns-AAA) is proposed as an improvement to the existing ant colony optimisation. The proposed metaheuristic adapts itself to maintain an adequate balance between exploitation and exploration throughout the run of the algorithm. The performance of the proposed methodology is benchmarked against a set of test instances that were generated using design of experiment (DOE) techniques. Besides, analysis of variance (ANOVA) is performed to determine the impact of various factors on the objective function value. The robustness of the proposed algorithm is authenticated against ant colony optimisation and genetic algorithm over which it always demonstrated better results thereby proving its supremacy on the concerned problem.
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