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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Deep Reinforcement Learning Approach To Solve Dynamic Vehicle Routing Problem With Stochastic Customers, Waldy Joe, Hoong Chuin Lau
Deep Reinforcement Learning Approach To Solve Dynamic Vehicle Routing Problem With Stochastic Customers, Waldy Joe, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In real-world urban logistics operations, changes to the routes and tasks occur in response to dynamic events. To ensure customers’ demands are met, planners need to make these changes quickly (sometimes instantaneously). This paper proposes the formulation of a dynamic vehicle routing problem with time windows and both known and stochastic customers as a route-based Markov Decision Process. We propose a solution approach that combines Deep Reinforcement Learning (specifically neural networks-based TemporalDifference learning with experience replay) to approximate the value function and a routing heuristic based on Simulated Annealing, called DRLSA. Our approach enables optimized re-routing decision to be generated …
Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou
Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou
Research Collection School Of Computing and Information Systems
This article presents the modeling and solution of an extended type of split delivery vehicle routing problem (SDVRP). In SDVRP, the demands of customers need to be met by efficiently routing a given number of capacitated vehicles, wherein each customer may be served multiple times by more than one vehicle. Furthermore, in many real-world scenarios, consumption of vehicles en route is the same as the goods being delivered to customers, such as food, water and fuel in rescue or replenishment missions in harsh environments. Moreover, the consumption may also be in virtual forms, such as time spent in constrained tasks. …
A Mathematical Programming Model For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiwan, Aldy Gunawan, Audrey Tedja Widjaja
A Mathematical Programming Model For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiwan, Aldy Gunawan, Audrey Tedja Widjaja
Research Collection School Of Computing and Information Systems
A green mixed fleet vehicle routing with realistic energy consumption and partial recharges problem (GMFVRP-REC-PR) is addressed in this paper. This problem involves a fixed number of electric vehicles and internal combustion vehicles to serve a set of customers. The realistic energy consumption which depends on several variables is utilized to calculate the electricity consumption of an electric vehicle and fuel consumption of an internal combustion vehicle. Partial recharging policy is included into the problem to represent the real life scenario. The objective of this problem is to minimize the total travelled distance and the total emission produced by internal …