Abstract
This paper aims to find the optimal depot locations and vehicle routings for spare parts of an automotive company considering future demands. The capacitated location-routing problem (CLRP), which has been practiced by various methods, is performed to find the optimal depot locations and routings by additionally using the artificial neural network (ANN). A novel multi-stage approach, which is performed to lower transportation cost, is carried out in CLRP. Initially, important factors for customer demand are tested with an univariate analysis and used as inputs in the prediction step. Then, genetic algorithm (GA) and ANN are hybridized and applied to provide future demands. The location of depots and the routings of the vehicles are determined by using the variable neighborhood descent (VND) algorithm. Five neighborhood structures, which are either routing or location type, are implemented in both shaking and local search steps. GA-ANN and VND are applied in the related steps successfully. Thanks to the performed VND algorithm, the company lowers its transportation cost by 2.35% for the current year, and has the opportunity to determine optimal depot locations and vehicle routings by evaluating the best and the worst cases of demand quantity for ten years ahead.
Highlights
Nowadays, it is obvious that companies should make strategic and operational decisions to optimize and operate their systems more efficiently
In light of the above, we aim to predict the demand to find out the best possible depots and routes by considering the genetic algorithm artificial neural networks (GA-ANN) approach considering capacity constraint of depot(s) and vehicles for ten years ahead
First four instances consist of 20 customers and 5 depots, and the applied variable neighborhood descent (VND) algorithm found the optimal solutions for these test instances
Summary
It is obvious that companies should make strategic and operational decisions to optimize and operate their systems more efficiently. The capacitated location-routing problem (CLRP) deals with the placement of facility locations and routing between the customer and facilities or depots simultaneously by considering that the capacities of depots and vehicles are not violated [1]. The important steps in forming the distribution network are the placement of the locations, such as warehouses, depots, and distribution centers, and routing in a way that considers some given depot or vehicle capacity constraints in order to satisfy customer demands and minimize routing costs, vehicle fixed costs, and depot fixed and operating costs [2]. The capacity constraint has been taken into higher consideration because papers dealing with the location-routing problem (LRP) are generally based on real cases [3, 4]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
