Fleet Size And Mix Vehicle Routing Problem Research Articles

Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study

Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study

Simple heuristics for the multi-period fleet size and mix vehicle routing problem

ABSTRACTMost of the research on vehicle routing problems has focused on the operational or the tactical level. However, any decision made on a strategic level will affect the lower level decisions. One type of strategic decision that has been considered in the literature is to compose an appropriate fleet of different vehicles to perform transportation tasks, which yields the fleet size and mix vehicle routing problem (FSMVRP). This work defines a new variant of the FSMVRP where the customer demands may vary over a set of periods, for example by having different demands on each day of the week. The objective is to find the best fleet composition that can be used to repeatedly fulfil the customer demands, as well as the best routing with that fleet for each of the different periods in the planning horizon. Simple heuristics incorporating a tabu search are implemented to find the fleet composition. Computational results are reported on instances created from a set of standard FSMVRP test instances.

Differential approximation algorithm of FSMVRP

We study the fleet size and mix vehicle routing problem with constraints on the capacity of each vehicle. The objective is to minimize the total cost including fixed utilization cost of vehicles and traveling cost by vehicles. We give differential approximation algorithms for the fleet size and mix vehicle routing problem (FSMVRP) with two kinds of vehicles, the capacities of which are respectively n 1 k and n 2 k, n 2 > n 1 ≥ 1, k ≥ 1. Using existing theories for vehicle routing problems and feature of the algorithms represented in the paper, we also prove that the algorithms give \(\left( {1 - \frac{{6n + 3}}{{{{\left( {n + 1} \right)}^2}k + n + 1}}} \right)\) differential approximation ratio for (k, nk)VRP, n > 1 and \(\left( {1 - \frac{{6{n_2} + 3{n_1}}}{{{{\left( {{n_1}k + {n_2}k} \right)}^2}k}}} \right)\) differential approximation ratio for (n 1 k, n 2 k)VRP, n 2 > n 1 > 1.

A Bio-Inspired Algorithm for the Fleet Size and Mix Vehicle Routing Problem

The fleet size and mix vehicle routing problem (FSMVRP) is a class of combinatorial optimization problem with wide-ranging applications in practice. The purpose of this problem is to manage the routes as well as the type of vehicles. In this paper, we apply a bio-inspired algorithm based on the concept of membrane computing (named BIBMA) to solve FSMVRP. In BIBMA, a cell-like framework and rules such as updating rules, communication rules, recording rules and selecting rules, are combined to improve the algorithm’s global search capacities. We tested our method on several benchmark functions of FSMVRP, including 12 instances with fixed cost and 8 instances without fixed cost. Simulation results show that the proposed algorithm is valid to solve FSMVRP for high quality results.

A hybrid heuristic method for the fleet size and mix vehicle routing problem

Numerous heuristic methods have been proposed for the fleet size and mix vehicle routing problem (FSMVRP), since the FSMVRP is an NP-hard problem. However, the vehicle type information is always ignored in the local improvement approaches of these heuristic methods. Hence, the solutions found by these heuristic methods without the vehicle type information are worse than those with the vehicle type information. In this paper, a hybrid heuristic method, incorporating the vehicle type information into variable neighborhood search (VNS), is proposed. The experimental results indicate that the proposed method is better than other reported methods and VNS without the vehicle type information in terms of average percentage deviation.

The Shrinking and Expanding Heuristic for the Fleet Size and Mix Vehicle Routing Problem

The FSMVRP (Fleet Size and Mix Vehicle Routing Problem) is a variant of the classical Capacitated Vehicle Routing Problem, CVRP. We suggest a new methodology, called the Shrinking and Expanding Heuristic (SEH) which is incorporated in a standard tabu search. To determine an appropriate fleet mix is a major challenge in this type of problem and the SEH technique is especially developed to find a good combination of vehicles by introducing a mechanism for changing the existing fleet mix during the search, thus also changing the underlying route structure. The SEH utilizes the concept of depletion and expansion of routes depending upon the filling degree of a vehicle. This strategy is tested on standard problem instances and good quality solutions are obtained.

Research on Contract Logistics Mathematical Model and System Based on Lindo and Baidu Map

Based on fleet size and mix vehicle routing problem (FSMVRP), the article analyzes the contract logistics transport demand and characteristics. The authors establish a fleet combination and route optimization model with multiple transfer centers and multiple transport methods. Furthermore, according to the webgis service of Baidu Map, the authors develop a corresponding logistics system according with J2EE framework. Finally, Lindo solver figure out the minimum cost and solution for the dedicated situation, and results display in the Baidu Map.

An effective genetic algorithm for the fleet size and mix vehicle routing problems

This paper studies the fleet size and mix vehicle routing problem (FSMVRP), in which the fleet is heterogeneous and its composition to be determined. We design and implement a genetic algorithm (GA) based heuristic. On a set of twenty benchmark problems it reaches the best-known solution 14 times and finds one new best solution. It also provides a competitive performance in terms of average solution.

A new heuristic for the fleet size and mix vehicle routing problem

In this paper we address the problem of simultaneously selecting the composition and routing of a fleet of vehicles in order to service efficiently customers with known demands from a central depot. This problem is called the fleet size and mix vehicle routing problem (FSMVRP). The vehicle fleet may be heterogeneous. The objective is to find the fleet composition and a set of routes with minimum total cost, which includes routing cost and vehicle cost. We present a new savings heuristic based on successive route fusion. At each iteration, the best fusion is selected by solving a weighted matching problem. This provides a less myoptic criteria than the usual savings heuristics. This algorithm is also very easy to implement. Computational results are provided for a number of benchmark problems in order to compare its performance to that of various other methods.