Large-Scale Vehicle Routing Problem with Massive Precedence and Cluster Constraints

This study investigates a multidepot heterogeneous vehicle routing problem for a variety of hazardous materials with risk analysis, which is a practical problem in the actual industrial field. The objective of the problem is to design a series of routes that minimize the total cost composed of transportation cost, risk cost, and overtime work cost. Comprehensive consideration of factors such as transportation costs, multiple depots, heterogeneous vehicles, risks, and multiple accident scenarios is involved in our study. The problem is defined as a mixed integer programming model. A bidirectional tuning heuristic algorithm and particle swarm optimization algorithm are developed to solve the problem of different scales of instances. Computational results are competitive such that our algorithm can obtain effective results in small-scale instances and show great efficiency in large-scale instances with 70 customers, 30 vehicles, and 3 types of hazardous materials.

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A practical heuristic for a large scale vehicle routing problem

European Journal of Operational Research ◽

10.1016/0377-2217(92)90303-q ◽

1992 ◽

Vol 57 (1) ◽

pp. 32-38 ◽

Cited By ~ 8

Author(s):

Anirudh Raghavendra ◽

T.S. Krishnakumar ◽

R. Muralidhar ◽

D. Sarvanan ◽

B.G. Raghavendra

Keyword(s):

Vehicle Routing ◽

Vehicle Routing Problem ◽

Large Scale ◽

Routing Problem

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APPLICATION OF GENETIC ALGORITHMS TO A LARGE-SCALE MULTIPLE-CONSTRAINT VEHICLE ROUTING PROBLEM

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026803000835 ◽

2003 ◽

Vol 03 (01) ◽

pp. 1-21 ◽

Cited By ~ 3

Author(s):

GEORGE MOURKOUSIS ◽

MATHEW PROTONOTARIOS ◽

THEODORA VARVARIGOU

Keyword(s):

Vehicle Routing ◽

Vehicle Routing Problem ◽

High Performance ◽

Large Scale ◽

Hybrid Genetic Algorithm ◽

Real Life ◽

Search Space ◽

Genetic Operators ◽

Actual Problem ◽

Routing Problem

This paper presents a study on the application of a hybrid genetic algorithm (HGA) to an extended instance of the Vehicle Routing Problem. The actual problem is a complex real-life vehicle routing problem regarding the distribution of products to customers. A non homogenous fleet of vehicles with limited capacity and allowed travel time is available to satisfy the stochastic demand of a set of different types of customers with earliest and latest time for servicing. The objective is to minimize distribution costs respecting the imposed constraints (vehicle capacity, customer time windows, driver working hours and so on). The approach for solving the problem was based on a "cluster and route" HGA. Several genetic operators, selection and replacement methods were tested until the HGA became efficient for optimization of a multi-extrema search space system (multi-modal optimization). Finally, High Performance Computing (HPC) has been applied in order to provide near-optimal solutions in a sensible amount of time.

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A Solution Approach from an Analytic Model to Heuristic Algorithm for Special Case of Vehicle Routing Problem with Stochastic Demands

Mathematical Problems in Engineering ◽

10.1155/2008/571414 ◽

2008 ◽

Vol 2008 ◽

pp. 1-16

Author(s):

Selçuk K. İşleyen ◽

Ö. Faruk Baykoç

Keyword(s):

Vehicle Routing ◽

Vehicle Routing Problem ◽

Large Scale ◽

Iterated Local Search ◽

Routing Problem ◽

Solution Approach ◽

Stochastic Demands ◽

Expected Length ◽

The Traveling Salesman Problem ◽

Special Case

We define a special case for the vehicle routing problem with stochastic demands (SC-VRPSD) where customer demands are normally distributed. We propose a new linear model for computing the expected length of a tour in SC-VRPSD. The proposed model is based on the integration of the “Traveling Salesman Problem” (TSP) and the Assignment Problem. For large-scale problems, we also use an Iterated Local Search (ILS) algorithm in order to reach an effective solution.

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