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Accelerated Cuckoo Optimization Algorithm for Capacitated Vehicle Routing Problem in Competitive Conditions

Alireza Goli, Adel Aazami, Armin Jabbarzadeh


Transportation represents one of the major human activities all over the world; besides, it is an important part of economy, the improvement of which results in a considerable reduction in costs. Routing is one of the most well-known problems in the field of transportation optimization, which is of high complicacy due to being categorized as an NP-hard problem. In this research, in order to approximate this problem to real conditions, the customer satisfaction is considered in the model along with cost reduction. The main innovation of this study is to consider the competitive conditions as well as customer satisfaction in vehicle routing; besides, another innovation is to present a developed meta-heuristic algorithm based on cuckoo optimization algorithm (COA) in order to solve the problem in a short time and with a high quality. COA is a subset of the evolved computations, which is directly related with the artificial intelligence (AI); in fact, this algorithm is a subset of AI. In the proposed algorithm, instead of k-means clustering, the simulated annealing algorithm (SAA) is used to accelerate the cuckoo clustering. The results show that the proposed algorithm can accurately solve the problem with large dimensions in a reasonable time and with minimum errors. In this regard, a case study on dairy products distribution is conducted and solved using the proposed algorithm, and accordingly the efficacy and effectiveness of the developed algorithm and model are proved by sensitivity analysis of the main parameters.


Vehicle Routing, Accelerated Cuckoo Optimization Algorithm (ACOA), Simulated Annealing Algorithm (SAA), Increasing of Satisfaction Level, Competitive Conditions.

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