Fuzzy scheduling optimization system for multi-objective transportation path based on ant colony algorithm

Issue title: Special Section: Iteration, Dynamics and Nonlinearity

Guest editors: Manuel Fernández-Martínez and Juan L.G. Guirao

Article type: Research Article

Authors: Wu, Gengrui^{a; c; *} | Bo, Niao^b | Wu, Husheng^a | Yang, Yong^c | Hassan, Nasruddin^{d; *}

Affiliations: [a] Material Management and Support College, Armed Police Force Engineering University, Xi’an, China | [b] Basic Department, Police officer college of Armed Police Force, Chengdu, China | [c] Department of Information and Communication, Police officer college of Armed Police Force, Chengdu, China | [d] School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia

Correspondence: [*] Corresponding author. Gengrui Wu. E-mail: [email protected] and Nasruddin Hassan. E-mail: [email protected].

Abstract: The key algorithm of the traditional system is aimed at the minimum of a certain factor, but does not consider the uncertain conditions and various modes of transportation, and the result of the scheduling is not excellent. To this end, a new fuzzy scheduling optimization system based on ant colony algorithm for multi-objective transportation path is designed. Based on the GPS module, a fuzzy scheduling optimization system based on ant colony algorithm for multi-objective transportation path is designed, and the overall structure of the system is given. The scheduling optimization problem of freight transport lines is described, and the volume of demand, the total volume of delivery and the remaining number of vehicles are made fuzzy processing. The goal is to minimize the total time of the advance or tardiness of the transportation and the total cost, so that the fuzzy scheduling model of transportation path is built. According to the principle of ant colony algorithm, the built multi-objective model will be transformed into a single objective model, and combined with the objective function, the index heuristic information and the performance of ant colony algorithm are set, and the optimal solution of that the deviation is minimum with the ideal solution is calculated by using ant colony algorithm, so as to achieve the multi-objective transportation path scheduling. The experimental results show that the total transportation distance of the designed system is short, the total cost is low, and the goods can be delivered in time.

Keywords: Ant colony algorithm, multi-objective, transportation path, fuzzy, scheduling

DOI: 10.3233/JIFS-169746

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4257-4266, 2018