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

2018 ◽  
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

AN EFFICIENTLY NOVEL MODEL FOR VEHICLE ROUTING PROBLEMS WITH STOCHASTIC DEMANDS

2009 ◽  
Vol 26 (02) ◽  
pp. 185-197 ◽  
Author(s):  
SELÇUK KÜRŞAT İŞLEYEN ◽  
ÖMER FARUK BAYKOÇ
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Test Problems ◽  
Routing Problem ◽  
Stochastic Demands ◽  
Expected Length ◽  
Service Policy ◽  
Novel Model

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.

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

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.

Combining Monte Carlo simulation with heuristics to solve a rich and real-life multi-depot vehicle routing problem

2016 ◽  
Author(s):  
Gabriel Alemany ◽  
Jesica de Armas ◽  
Angel A. Juan ◽  
Alvaro Garcia-Sanchez ◽  
Roberto Garcia-Meizoso ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Real Life ◽  
Routing Problem
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