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In This paper a two stages Hybrid Flow Shop (HFS) problem with sequence dependent set up times is considered in which the preemption is also allowed. The objective is to minimize the weighted sum of completion time and maximum tardiness. Since this problem is categorized as an NP-hard one, metaheuristic algorithms can be utilized to obtain high quality solutions in a reasonable amount of time. In this paper a Genetic algorithm (GA) approach is used and for parameter tuning the Response Surface Method (RSM) is applied to increase the performance of the algorithm. Computational results show the high performance of the proposed algorithm to solve the generated problems.

This paper investigates a scheduling combined manpower-vehicle routing problem with a central depot in and a set of multi-skilled manpower for serving to customers. Teams are in different range of competencies 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 minimize the total cost of servicing, routing, and lateness penalties. This paper presents a mixed integer programming model and two meta-heuristic approaches of genetic algorithm (GA) and artificial bee colony algorithm (ABC) 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 ABC, in quality of solutions.

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; manpower; competency of working team; genetic algorithm; particle swarm optimisation; PSO.Reference to this paper should be made as follows: Kiani, M., Seidgar, H. and Mahdavi, I. (2017) 'Scheduling multi-skilled manpower with considering teams replacement and site-dependent vehicles routing', Int.