A Model-Data Framework for Dynamic Vehicle Routing Models

Abstract

We propose a Model-Data Framework (MoDaF) to consider delivery-related static and dynamic contexts to support delivery service. In general, Static Vehicle Routing Models (SVRMs) usually use Static Modeling-Context (SMOC) such as Immediate Pickup and Delivery Requirements (IPDR) and resources related to idle vehicles at depots. On the other hand, Dynamic Vehicle Routing Models (DVRMs) need to consider Dynamic Modeling-Context (DMOC) such as unfulfilled Advanced Pickup and Delivery Requirements (APDR) on advanced vehicle routes. Therefore, it is getting more important to manage Modeling-Context (MOC) including both SMOC and DMOC and to specify the MOC into input data used in DVRMs to find a solution. Recently, the generation of input data for a specific IP (Integer Programming) model becomes a significant issue as the MOC is getting dynamic. Hence, it needs systematic approach. In this research, we focus on the generation of input data from the MOC and Input Data Inference Rules (IDIRs) based on forward chaining for a dynamic-MDPDPTW as an illustration of a specific DVRM. To prove the availability of a MoDaF, we implement a prototype to generate input data from MOC effectively and show that it is significantly useful to solve DVRM in the realistic vehicle routing system through the experimentation.