A traditional logistics decision model mainly studies the location of distribution centers, storage inventory management, vehicle scheduling, and transportation routes. The location-inventory-routing problem (LIRP) is an integrated optimization three problems—a comprehensive for whole system. This review paper uses PRISMA (Preferred Reporting Items Systematic Reviews Meta-Analysis) method to li...

The multi-depot fleet size and mix vehicle routing problem, also known as the multi-depot routing with heterogeneous vehicles, is investigated. A mathematical formulation is given and lower as well as upper bounds are produced using a three hour execution time of CPLEX. An efficient implementation of variable neighborhood search that incorporates new features in addition to the adaptation of se...

This paper presents a unified exact method for solving an extended model of the well-known Capacitated Vehicle Routing Problem (CVRP), called the Heterogenous Vehicle Routing Problem (HVRP), where a mixed fleet of vehicles having different capacities, routing and fixed costs is used to supply a set of customers. The HVRP model considered in this paper contains as special cases: the Single Depot...

The efficient design/redesign of the logistics network is essential for companies to maintain their competitive advantage. This paper presents a novel non-linear mixedinteger programming model for the multi-product capacitated location routing inventory problem (MPCLRIP), where a two-layer distribution system consisting of depots and customers was studied. The supply decisions such as location ...

Abstract: The Open Vehicle Routing Problem (OVRP) is one of the most important extensions of the vehicle routing problem (VRP) that has many applications in industrial and service. In the VRP, a set of customers with a specified demand of goods are given and a depot where a fleet of identical capacitated vehicles is located. We are also given the ‘‘traveling costs’’ between the depot and all th...

The multi-depot vehicle routing problem (MDVRP) is a very challenging part of supply chain optimization. We propose here a simple yet powerful heuristic for the MDVRP with an integrated look-ahead. Compared to other state-of-the-art approaches, our heuristic is significantly faster, but yields competitive results and even found several new best solutions on a set of standard benchmark problems.