Optimization of Routing of After-Sales Service Technicians with Probable Demand and Capacity Constraints Using Clustering‎: ‎A Case Study in Isfahan

‎Regard to daily increasing of customer services share in all over the world‎, ‎one of most effective parameters on customer satisfaction would be service delivery with the least delay‎. ‎work allocation method‎, ‎planning‎, ‎organizing‎, ‎prioritizing and service delivery routing have always been one of the main concerns of service providing centers and lack of proper planning in this regard will cause service network traffic‎, ‎environmental and noise pollution‎, ‎waste of time and fuel and eventually dissatisfaction of consumers and technicians‎.
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‎On the other hand‎, ‎daily division of labor in order to deliver delightful services by considering man’s opinion would not be an optimal choice‎. ‎In this research‎, ‎with case study on a home appliance service company and by considering customer demands in city of Isfahan and by data analysis‎, ‎geographic points of customer’s demands have clustered by k-mean algorithm‎.

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‎It has been tried to reduce the search space by clustering geographic areas and then by using simulated annealing‎, ‎the optimum path for customer’s probable demands present to the technicians with observance of daily working capacity per cluster‎.

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‎The computational results show that after clustering by k-means algorithm‎, ‎routing probable demands with observance of daily working capacity for technicians‎, ‎the objective function has better improvement in compare with non-clustering case‎.

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‎Service technician routing by clustering‎, ‎while being responsive in shortest time‎, ‎has more repeatability test and cause more order and responsibility sense and more domination on service areas and also has an effective role in reducing time to handle a consumer and getting their satisfaction‎.