Computers and Operations Research
A Decomposition Approach for the Inventory-Routing Problem
Transportation Science
A memetic algorithm and a tabu search for the multi-compartment vehicle routing problem
Computers and Operations Research
An integrated local search method for inventory and routing decisions
Expert Systems with Applications: An International Journal
Solving a rich vehicle routing and inventory problem using column generation
Computers and Operations Research
Invited Review: Industrial aspects and literature survey: Combined inventory management and routing
Computers and Operations Research
A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands
Computers and Operations Research
A heuristic method for the inventory routing and pricing problem in a supply chain
Expert Systems with Applications: An International Journal
A heuristic method for the inventory routing problem with time windows
Expert Systems with Applications: An International Journal
Solving the ship inventory routing and scheduling problem with undedicated compartments
Computers and Industrial Engineering
Variable neighborhood search for the inventory routing and scheduling problem in a supply chain
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper we observe the extension of the vehicle routing problem (VRP) in fuel delivery that includes petrol stations inventory management and which can be classified as the Inventory Routing Problem (IRP) in fuel delivery. The objective of the IRP is to minimize the total cost of vehicle routing and inventory management. We developed a Variable Neighborhood Search (VNS) heuristic for solving a multi-product multi-period IRP in fuel delivery with multi-compartment homogeneous vehicles, and deterministic consumption that varies with each petrol station and each fuel type. The stochastic VNS heuristic is compared to a Mixed Integer Linear Programming (MILP) model and the deterministic ''compartment transfer'' (CT) heuristic. For three different scale problems, with different vehicle types, the developed VNS heuristic outperforms the deterministic CT heuristic. Also, for the smallest scale problem instances, the developed VNS was capable of obtaining the near optimal and optimal solutions (the MILP model was able to solve only the smallest scale problem instances).