Application of Vehicle Routing Optimization in Improving the Flow of Mail to a Processing Plant (original) (raw)
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Vehicle routing problem (VRP) is a Nondeterministic Polynomial Hard combinatorial optimization problem to serve the consumers from central depots and returned back to the originated depots with given vehicles. Furthermore, two of the most important extensions of the VRPs are the open vehicle routing problem (OVRP) and VRP with simultaneous pickup and delivery (VRPSPD). In OVRP, the vehicles have not return to the depot after last visit and in VRPSPD, customers require simultaneous delivery and pickup service. The aim of this paper is to present a combined effective ant colony optimization (CEACO) which includes sweep and several local search algorithms which is different with common ant colony optimization (ACO). An extensive numerical experiment is performed on benchmark problem instances addressed in the literature. The computational result shows that suggested CEACO approach not only presented a very satisfying scalability, but also was competitive with other meta-heuristic algorithms in the literature for solving VRP, OVRP and VRPSPD problems. 1. Introduction The capacitated vehicle routing problem (CVRP) is one of the most important combinatorial optimization problems, which recently has been receiving much attention by researchers and scientists (Yousefikhoshbakht & Dolatnezhad, 2016). CVRP deals with servicing a set of delivery customers or a set of pickup customers by a set of vehicles stationed at a central depot. Each vehicle, visits a set of customers such that every customer is visited exactly once and by exactly one vehicle. Furthermore, the capacity of each vehicle must not be violated. The objective of CVRP is to plan a set of routes to service all customers while minimizing the total travel distance (Yousefikhoshbakht et al., 2014). The open vehicle routing problem (OVRP) is one of the most important variant of VRP which has recently attracted the attention of scientists and researchers because of many applications in industrial and service firms specially delivering packages and newspapers to homes. This problem similar to VRP involves routing a homogeneous fleet of vehicles that start to move simultaneously from the depot but do not come back to the depot after visiting customers. Each vehicle has a fixed capacity and perhaps a route-length restriction which limits the maximum distance it can travel. Each customer has a known demand and is serviced by only one visit of a single vehicle. The objective is to design a set of minimum cost routes to serve all customers so that the load on a vehicle is below vehicle capacity at each point on the route. In addition, we need to find the minimum number of vehicles which are required to service all customers. The description of this important variant of the VRP appeared in the literature over 30 years ago in which contractors who are not employees of the delivery company use their own vehicles and do not return to the depot. As a result, researcher interest in the OVRP has increased dramatically and a
Variants of VRP to Optimize Logistics Management Problems
Logistics Management and Optimization Through Hybrid Artificial Intelligence Systems, 2012
The Vehicle Routing Problem (VRP) is a key to the efficient transportation management and supply-chain coordination. VRP research has often been too focused on idealized models with non-realistic assumptions for practical applications. Nowadays the evolution of methodologies allows that the classical problems could be used to solve VRP problems of real life. The evolution of methodologies allows the creation of variants of the VRP which were considered too difficult to handle by their variety of possible restrictions. ...
The multiple traveling salesman problem: an overview of formulations and solution procedures
The multiple traveling salesman problem (mTSP) is a generalization of the well-known traveling salesman problem (TSP), where more than one salesman is allowed to be used in the solution. Moreover, the characteristics of the mTSP seem more appropriate for real-life applications, and it is also possible to extend the problem to a wide variety of vehicle routing problems (VRPs) by incorporating some additional side constraints. Although there exists a wide body of the literature for the TSP and the VRP, the mTSP has not received the same amount of attention. The purpose of this survey is to review the problem and its practical applications, to highlight some formulations and to describe exact and heuristic solution procedures proposed for this problem.
The Vehicle Routing Problem: An overview of exact and approximate algorithms
In this paper, some of the main known results relative to the Vehicle Routing Problem are surveyed. The paper is organized as follows: (1) definition; (2) exact algorithms; (3) heuristic algorithms; (4) conclusion. The Vehicle Routing Problem (VRP) can be described as the problem of designing optimal delivery or collection routes from one or several depots to a number of geographically scattered cities or customers, subject to side constraints. The VRP plays a central role in the fields of physical distribution and logistics. There exists a wide variety of VRPs and a broad literature on this class of problems (see, for example, the surveys of Bodin et al., 1983, Christofides, 1985a, Laporte and Nobert, 1987, Laporte, 1990, as well as the recent classification scheme proposed by Desrochers, Lenstra and Savelsbergh, 1990). The purpose of this paper is to survey the main exact and approximate algorithms developed for the VRP, at a level appropriate for a first graduate course in combinatorial optimization. 1. Definition Let G = (V, A) be a graph where V = {1 .... , n} is a set of vertices representing cities with the depot located at vertex 1, and A is the set of arcs. With every arc (i, j) i 4=j is associated a non-negative distance matrix C = (cii). In some contexts, ci~ can be interpreted as a travel cost or as a travel time. When C is symmetrical, it is often convenient to replace A by a set E of undirected edges. In addition, assume there are m available vehicles based at the depot, where m L < m < m U. When m L = mrs, m is said to be fixed. When m L = 1 and m U = n-1, m is said to be free. When m is not fixed, it often makes sense to associate a fixed cost f on the use of a vehicle. For the sake of simplicity, we will ignore these costs and unless otherwise specified, we assume that all vehicles are identical and have the same capacity D. The VRP consists of designing a set of least-cost vehicle routes in such a way that (i) each city in V\{1} is visited exactly once by exactly one vehicle; (ii) all vehicle routes start and end at the depot; (iii) some side constraints are satisfied.
A Survey on the Vehicle Routing Problem and Its Variants
In this paper, we have conducted a literature review on the recent developments and publications involving the vehicle routing problem and its variants, namely vehicle routing problem with time windows (VRPTW) and the capacitated vehicle routing problem (CVRP) and also their variants. The VRP is classified as an NP-hard problem. Hence, the use of exact optimization methods may be difficult to solve these problems in acceptable CPU times, when the problem involves real-world data sets that are very large. The vehicle routing problem comes under combinatorial problem. Hence, to get solutions in determining routes which are realistic and very close to the optimal solution, we use heuristics and meta-heuristics. In this paper we discuss the various exact methods and the heuristics and meta-heuristics used to solve the VRP and its variants.