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Optimization of Routing of After-Sales Service Technicians with Probable Demand and Capacity Constraints Using Clustering: A Case Study in Isfahan
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Mohammad Jafari Aminabadi *   , Javid Jowzadani , Hadi Shiroyeh Zad , Khalegh Behrooz Dehkordi |
| Islamic Azad University Najaf Abad |
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Abstract: (2716 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.
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.
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. |
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| Keywords: Vehicle Routing, Simulated Annealing algorithm, k-means Algorithm. |
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Full-Text [PDF 516 kb]
(964 Downloads)
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Type of Study: Applicable |
Subject:
Special
Received: 2019/07/12 | Accepted: 2019/09/21 | Published: 2019/10/22
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