AN EFFICIENTLY NOVEL MODEL FOR VEHICLE ROUTING PROBLEMS WITH STOCHASTIC DEMANDS

In this paper, the Vehicle Routing Problem with Stochastic Demands (VRPSD) is considered where customer demands are normally distributed. We propose a new model for computing the expected length of a tour. Monte Carlo simulation is used to demonstrate the accuracy of the model on randomly generated test problems. It is assumed that the service policy is non-divisible, meaning that the entire demand at each customer must be served in a single visit by a unique vehicle.

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INTEGRATING BIASED-RANDOMIZED GRASP WITH MONTE CARLO SIMULATION FOR SOLVING THE VEHICLE ROUTING PROBLEM WITH STOCHASTIC DEMANDS

2018 Winter Simulation Conference (WSC) ◽

10.1109/wsc.2018.8632348 ◽

2018 ◽

Cited By ~ 2

Author(s):

Paola Festa ◽

Tommaso Pastore ◽

Daniele Ferone ◽

Angel A. Juan ◽

Christopher Bayliss

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Vehicle Routing ◽

Vehicle Routing Problem ◽

Routing Problem ◽

Stochastic Demands

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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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A Hybrid Algorithm Based on Monte-Carlo Simulation for the Vehicle Routing Problem with Route Length Restrictions

Hybrid Algorithms for Service, Computing and Manufacturing Systems ◽

10.4018/978-1-61350-086-6.ch006 ◽

2011 ◽

pp. 122-135

Author(s):

Angel A. Juan ◽

Javier Faulin ◽

Tolga Bektas ◽

Scott E. Grasman

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Vehicle Routing ◽

Customer Service ◽

Vehicle Routing Problem ◽

Divide And Conquer ◽

Additional Restriction ◽

Routing Problem ◽

Random Behavior ◽

Route Length

This chapter describes an approach based on Monte Carlo Simulation (MCS) to solve the Capacitated Vehicle Routing Problem (CVRP) with route length restrictions and customer service times. The additional restriction introduces further challenges to the classical CVRP. The basic idea behind our approach is to combine direct MCS with an efficient heuristic, namely the Clarke and Wright Savings (CWS) algorithm, and a decomposition technique. The CWS heuristic provides a constructive methodology which is improved in two ways: (i) a special random behavior is introduced in the methodology using a geometric distribution; and (ii) a divide-and-conquer technique is used to decompose the original problem in smaller sub-problems that are easier to deal with. The method is tested using a set of well-known benchmarks. The chapter discusses the advantages and disadvantages of the proposed procedure in relation to other approaches for solving the same problem.

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