A Combinatorial Optimization Model for Customer Routing Problem Considering Cost Discount

One of the most important combinatorial optimization problems is the transport problem, which has been associated with many variants such as the HVRP and dynamic problem. The authors propose in this study a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that the authors call the Multi-Depot HVRPTW by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles..., and the authors solve this problem by proposing a new scheme based on a genetic algorithm heuristics that they will specify later. Computational experiments with the benchmark test instances confirm that their approach produces acceptable quality solutions compared with previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that the method of genetic algorithm heuristics is effective in solving the MDHVRPTW problem and hence has a great potential.

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The Traveling Salesman Problem, the Vehicle Routing Problem, and Their Impact on Combinatorial Optimization

International Journal of Strategic Decision Sciences ◽

10.4018/jsds.2010040104 ◽

2010 ◽

Vol 1 (2) ◽

pp. 82-92 ◽

Cited By ~ 2

Author(s):

Gilbert Laporte

Keyword(s):

Combinatorial Optimization ◽

Vehicle Routing ◽

Traveling Salesman Problem ◽

Vehicle Routing Problem ◽

Heuristic Algorithms ◽

Optimization Problems ◽

Traveling Salesman ◽

Routing Problem ◽

Combinatorial Optimization Problems ◽

The Traveling Salesman Problem

The Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two of the most popular problems in the field of combinatorial optimization. Due to the study of these two problems, there has been a significant growth in families of exact and heuristic algorithms being used today. The purpose of this paper is to show how their study has fostered developments of the most popular algorithms now applied to the solution of combinatorial optimization problems. These include exact algorithms, classical heuristics and metaheuristics.

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Discrete Optimization Model for Vehicle Routing Problem with Scheduling Side Cosntraints

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/300/1/012024 ◽

2018 ◽

Vol 300 ◽

pp. 012024 ◽

Cited By ~ 1

Author(s):

Dedy Juliandri ◽

Herman Mawengkang ◽

F. Bu’ulolo

Keyword(s):

Vehicle Routing ◽

Discrete Optimization ◽

Optimization Model ◽

Vehicle Routing Problem ◽

Routing Problem

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Flow Merging and Hub Route Optimization in Collaborative Transportation

Journal of Applied Mathematics ◽

10.1155/2014/621487 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 4

Author(s):

Kerui Weng ◽

Zi-hao Xu

Keyword(s):

Integer Programming ◽

Lagrangian Relaxation ◽

Benders Decomposition ◽

Programming Model ◽

Route Optimization ◽

Mixed Integer ◽

Routing Problem ◽

Fixed Charges ◽

Postal Services ◽

Cost Discount

This paper studies the optimal hub routing problem of merged tasks in collaborative transportation. This problem allows all carriers’ transportation tasks to reach the destinations optionally passing through 0, 1, or 2 hubs within limited distance, while a cost discount on arcs in the hub route could be acquired after paying fixed charges. The problem arises in the application of logistics, postal services, airline transportation, and so forth. We formulate the problem as a mixed-integer programming model, and provide two heuristic approaches, respectively, based on Lagrangian relaxation and Benders decomposition. Computational experiments show that the algorithms work well.

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