scholarly journals Adaptive large neighborhood search for the vehicle routing problem with synchronization constraints at the delivery location

Networks ◽  
2019 ◽  
Vol 75 (1) ◽  
pp. 64-85 ◽  
Author(s):  
Briseida Sarasola ◽  
Karl F. Doerner
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Adaptive Large Neighborhood Search ◽  
Routing Problem ◽  
Large Neighborhood ◽  
Synchronization Constraints ◽  
Delivery Location

scholarly journals An adaptive large neighborhood search heuristic for multi-commodity two-echelon vehicle routing problem with satellite synchronization

2022 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Shengyang Jia ◽  
Lei Deng ◽  
Quanwu Zhao ◽  
Yunkai Chen
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Adaptive Large Neighborhood Search ◽  
Routing Problem ◽  
Large Neighborhood ◽  
Multiple Depots ◽  
Synchronization Constraints

<p style='text-indent:20px;'>In considering route optimization from multiple distribution centers called depots via some intermediate facilities called satellites to final customers with multiple commodities request, we introduce the Multi-Commodity Two-Echelon Vehicle Routing Problem with Satellite Synchronization (MC-2E-VRPSS). The MC-2E-VRPSS involves the transportation from multiple depots to satellites on the first echelon and the deliveries from satellites to final customers on the second echelon. The MC-2E-VRPSS integrates satellite synchronization constraints and time window constraints for satellites on the two-echelon network and aims to determine cost-minimizing routes for the two echelons. The satellite synchronization constraints which trucks from the multiple depots to some satellites need to be coordinated guarantee the efficiency of the second echelon network. In this study, we develop a mixed-integer programming model for the MC-2E-VRPSS. For validating the model formulation, we conduct the computational experiments on a set of small-scale instances using GUROBI and an adaptive large neighborhood search (ALNS) heuristic which we develop for the problem. Furthermore, the computation experiments for evaluating the applicability of the ALNS heuristic compared with large neighborhood search (LNS) on a set of large-scale instances are also conducted, which proved the effectiveness of the ALNS.</p>

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