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:: Volume 24, Issue 1 (9-2019) ::
Andishe 2019, 24(1): 103-116 Back to browse issues page
Optimization of Routing of After-Sales Service Technicians with Probable Demand and Capacity Constraints Using Clustering‎: ‎A Case Study in Isfahan
Mohammad Jafari Aminabadi *, Javid Jowzadani, Hadi Shiroyeh Zad, Khalegh Behrooz Dehkordi
Islamic Azad University Najaf Abad
Abstract:   (684 Views)
‎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.

‎‎

‎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.

‎‎

‎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.

Keywords: Vehicle Routing‎, Simulated Annealing algorithm‎, ‎k-means Algorithm‎.
Full-Text [PDF 516 kb]   (123 Downloads)    
Type of Study: Applicable | Subject: Special
Received: 2019/07/12 | Accepted: 2019/09/21 | Published: 2019/10/22
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Jafari Aminabadi M, Jowzadani J, Shiroyeh Zad H, Behrooz Dehkordi K. Optimization of Routing of After-Sales Service Technicians with Probable Demand and Capacity Constraints Using Clustering‎: ‎A Case Study in Isfahan. Andishe. 2019; 24 (1) :103-116
URL: http://andisheyeamari.irstat.ir/article-1-763-en.html


Volume 24, Issue 1 (9-2019) Back to browse issues page
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