Title: Scheduling multi-skilled manpower with considering teams replacement and site-dependent vehicles routing

Authors: Morteza Kiani; Hany Seidgar; Iraj Mahdavi

Addresses: Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran ' Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran ' Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran

Abstract: In this paper, a combined manpower-vehicle routing problem (CMVRP) is presented that a central depot is considered in which a set of vehicles and a set of multi-skilled teams originate from it to move toward each customer's site for servicing tasks. This problem deals with scheduling of multi-skilled manpower to service a set of tasks with due dates and at the same, routing of the vehicles which are used for moving this manpower. Teams are in different range of competency that it will affect the service time duration. Vehicles are in different moving speeds and costs and not all the vehicles are capable to move toward all the customers' sites. The objective is to find an efficient schedule for the teams and vehicles movement in order to minimise the total cost of servicing, routing and lateness penalties. In this paper, a mixed integer programming model is presented and two meta-heuristics approaches of genetic algorithm (GA) and particle swarm optimisation (PSO) are developed to solve the generated problems. Furthermore, Taguchi experimental design method is applied to set the proper values of parameters. The available results show the higher performance of proposed GA compared with PSO, in quality of solutions within comparatively shorter periods of time.

Keywords: vehicle routing problem; VRP; team competence; genetic algorithms; particle swarm optimisation; PSO; scheduling; multi-skilled teams; team replacement; site-dependent vehicle routing; due dates; servicing costs; routing costs; lateness penalties; mixed integer programming; MIP; metaheuristics; Taguchi methods; experimental design.

DOI: 10.1504/IJMOR.2017.080744

International Journal of Mathematics in Operational Research, 2017 Vol.10 No.1, pp.49 - 68

Available online: 07 Nov 2016 *

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