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

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.

scholarly journals Stochastic multi-depot capacitated vehicle routing problem with pickup and delivery: heuristic approaches

2021 ◽  
Author(s):  
Brenner H. O. Rios ◽  
Eduardo C. Xavier ◽  
Flávio K. Miyazawa ◽  
Pedro Amorim
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Iterated Local Search ◽  
Neighborhood Search ◽  
Pickup And Delivery ◽  
Data Set ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Expected Length ◽  
Capacitated Vehicle

We present a natural probabilistic variation of the multi-depot vehicle routing problem with pickup and delivery. We denote this variation by Stochastic multi-depot capacitated vehicle routing problem with pickup and delivery (SMCVRPPD). We present an algorithm to compute the expected length of an apriori route under general probabilistic assumptions. To solve the SMCVRPPD we propose an Iterated Local Search (ILS) and a Variable Neighborhood Search(VNS). We evaluate the performance of these heuristics on a data set adapted from TSPLIB instances. The results show that the ILS is effective to solve SMCVRPPD.

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.

scholarly journals Combining Heuristics with Simulation and Fuzzy Logic to Solve a Flexible-Size Location Routing Problem under Uncertainty

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 45
Author(s):  
Rafael D. Tordecilla ◽  
Pedro J. Copado-Méndez ◽  
Javier Panadero ◽  
Carlos L. Quintero-Araujo ◽  
Jairo R. Montoya-Torres ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Local Search ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Iterated Local Search ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Fuzzy Pattern ◽  
Reasonable Computing

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.

A Fast and Scalable Heuristic for the Solution of Large-Scale Capacitated Vehicle Routing Problems

2021 ◽  
Author(s):  
Luca Accorsi ◽  
Daniele Vigo
Keyword(s):  
Vehicle Routing ◽  
Large Scale ◽  
Computing Time ◽  
Search Space ◽  
Iterated Local Search ◽  
Solution Quality ◽  
Routing Problem ◽  
Acceptance Criterion ◽  
Acceleration Techniques ◽  
Capacitated Vehicle

In this paper, we propose a fast and scalable, yet effective, metaheuristic called FILO to solve large-scale instances of the Capacitated Vehicle Routing Problem. Our approach consists of a main iterative part, based on the Iterated Local Search paradigm, which employs a carefully designed combination of existing acceleration techniques, as well as novel strategies to keep the optimization localized, controlled, and tailored to the current instance and solution. A Simulated Annealing-based neighbor acceptance criterion is used to obtain a continuous diversification, to ensure the exploration of different regions of the search space. Results on extensively studied benchmark instances from the literature, supported by a thorough analysis of the algorithm’s main components, show the effectiveness of the proposed design choices, making FILO highly competitive with existing state-of-the-art algorithms, both in terms of computing time and solution quality. Finally, guidelines for possible efficient implementations, algorithm source code, and a library of reusable components are open-sourced to allow reproduction of our results and promote further investigations.

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