Vehicle Routing and Scheduling Problem with Time Windows and Stochastic Demand

Author(s):  
Mei-Shiang Chang ◽  
Yi-Chen Lin ◽  
Che-Fu Hsueh
Author(s):  
Hsiao-Fan Wang

One key role along green supply chain is the distribution center which has the responsibility to deliver the commodities to the customers and collect the end-used products back to the center for further process. This activity requires a distributor to determine how many vehicles with what sizes along which routes to deliver commodities so that the demands from all customers will be satisfied within customers’ available time with minimum operation cost. This problem can be classified into a vehicle routing and scheduling problem with multiple vehicle types and service time windows. In practice, the complexity of the problem requires a structural model to facilitate general analysis and applications. However, also because of its complexity, an efficient solution procedure is equivalently important. Therefore, in this study, we have first developed a model for a distribution center to support the decisions on vehicle types and numbers; as well as the routing route and schedule so that the overall operation cost will be minimized. Since this model of vehicle routing and scheduling problem with multiple vehicle types and multiple time windows (VRSP-MVMT) is a nondeterministic polynomial time (NP)-hard problem, we have developed a genetic algorithm (GA) for efficient solution. The efficiency and accuracy of the algorithm will be evaluated and illustrated with numerical examples.


Author(s):  
J. Desrosiers ◽  
F. Soumis ◽  
M. Desrochers ◽  
M. Sauvé

2019 ◽  
Vol 11 (15) ◽  
pp. 4248 ◽  
Author(s):  
Jinghua Li ◽  
Hui Guo ◽  
Qinghua Zhou ◽  
Boxin Yang

Timeliness of steel distribution centers can effectively ensure the smooth progress of ship construction, but the carbon emissions of vehicles in the distribution process are also a major source of pollution. Therefore, when considering the common cost of vehicle distribution, taking the carbon emissions of vehicles into account, this paper establishes a Mixed Integer Linear Programming (MILP) model called green vehicle routing and scheduling problem with simultaneous pickups and deliveries and time windows (GVRSP-SPDTW). An intelligent water drop algorithm is designed and improved, and compared with the genetic algorithm and traditional intelligent water drop algorithm. The applicability of the improved intelligent water drop algorithm is proven. Finally, it is applied to a specific example to prove that the improved intelligent water drop algorithm can effectively reduce the cost of such problems, thereby reducing the carbon emissions of vehicles in the distribution process, achieving the goals of reducing environmental pollution and green shipbuilding.


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