Vehicle Routing Problem with Multiple Commodities and Mixed-Fleet Leasing

Clarissa Choon Kai Ting

Abstract

The need to safely and effectively allocate resources (staffing and equipment) to fulfill military and commercial operations was a challenge. Optimally allocating the appropriate resources to the customer from different locations falls into the vehicle routing problem (VRP). Increasingly, many companies have shown interest in vehicle routing problems. The vehicle routing problem and vehicle with time windows and multi-commodity constraint are the basis variations built upon the company problems. Therefore, to stay competitive in the market, it is necessary to look for optimal routing solutions for their operation efficiency. The goal of this project is to develop a mathematical model in addressing the company problem which computed a route optimality from the company depot to the customer. The model developed will be applied to ST-Airport Services operation processes to demonstrate the effectiveness in addressing the resource allocation issues. Vehicle routing problem are presented and solved with Python Programming with Gurobi Optimizer.  For the Vehicle Routing Problem, a Mixed Integer Programming (MIP) model is modelled to determine the optimal solution for the company. Other than addressing the current supply demand constraints, the model also look into various other constraints such as time windows request from customer, vehicle capacity, vehicle multi-commodity and the optimality of the distance travelled. Beside addressing the existing operation constraint, the mixture of fleet analysis was conducted to tackle the resource optimality for the company by outsourcing the vehicle.   The Vehicle Routing Problem was also compared to a traditional (existing) planning approach to determine the cost savings of the model for the company.  From the result, the base model and the new model demonstrates an effective approach in solving the vehicle routing problem for the company. It is relatively rapid in terms of the running time to generate the optimal solution. With the data provided by the company, the best optimal vehicle routing plan are found and satisfied the objective function of minimizing the lateness of delivery and all other specific constraints.