scholarly journals Robust Optimisation Approach for Vehicle Routing Problems with Uncertainty

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Liang Sun ◽  
Bing Wang
Keyword(s):  
Vehicle Routing ◽  
Numerical Experiments ◽  
Transportation Cost ◽  
Solution Procedure ◽  
Vehicle Routing Problems ◽  
Robust Optimisation ◽  
Solution Quality ◽  
Routing Problems ◽  
Optimisation Model ◽  
Future Demand

We formulated a solution procedure for vehicle routing problems with uncertainty (VRPU for short) with regard to future demand and transportation cost. Unlike E-SDROA (expectation semideviation robust optimisation approach) for solving the proposed problem, the formulation focuses on robust optimisation considering situations possibly related to bidding and capital budgets. Besides, numerical experiments showed significant increments in the robustness of the solutions without much loss in solution quality. The differences and similarities of the robust optimisation model and existing robust optimisation approaches were also compared.

scholarly journals Novel Encoding and Routing Balance Insertion Based Particle Swarm Optimization with Application to Optimal CVRP Depot Location Determination

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Yin-Mou Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Vehicle Routing ◽  
Particle Swarm ◽  
Transportation Cost ◽  
Vehicle Routing Problems ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Routing Problems ◽  
Vehicle Routes

A depot location has a significant effect on the transportation cost in vehicle routing problems. This study proposes a hierarchical particle swarm optimization (PSO) including inner and outer layers to obtain the best location to establish a depot and the corresponding optimal vehicle routes using the determined depot location. The inner layer PSO is applied to obtain optimal vehicle routes while the outer layer PSO is to acquire the depot location. A novel particle encoding is suggested for the inner layer PSO, the novel PSO encoding facilitates solving the customer assignment and the visiting order determination simultaneously to greatly lower processing efforts and hence reduce the computation complexity. Meanwhile, a routing balance insertion (RBI) local search is designed to improve the solution quality. The RBI local search moves the nearest customer from the longest route to the shortest route to reduce the travel distance. Vehicle routing problems from an operation research library were tested and an average of 16% total routing distance improvement between having and not having planned the optimal depot locations is obtained. A real world case for finding the new plant location was also conducted and significantly reduced the cost by about 29%.

scholarly journals An Inverse Robust Optimisation Approach for a Class of Vehicle Routing Problems under Uncertainty

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Liang Sun ◽  
Bing Wang
Keyword(s):  
Vehicle Routing ◽  
Transportation Cost ◽  
Test Problems ◽  
Vehicle Routing Problems ◽  
Road Transportation ◽  
Robust Optimisation ◽  
Routing Problems ◽  
Routing Problem ◽  
Trade Off ◽  
Total Transportation Cost

There is a trade-off between the total penalty paid to customers (TPC) and the total transportation cost (TTC) in depot for vehicle routing problems under uncertainty (VRPU). The trade-off refers to the fact that the TTC in depot inevitably increases when the TPC decreases andvice versa. With respect to this issue, the vehicle routing problem (VRP) with uncertain customer demand and travel time was studied to optimise the TPC and the TTC in depot. In addition, an inverse robust optimisation approach was proposed to solve this kind of VRPU by combining the ideas of inverse optimisation and robust optimisation so as to improve both the TPC and the TTC in depot. The method aimed to improve the corresponding TTC of the robust optimisation solution under the minimum TPC through minimising the adjustment of benchmark road transportation cost. According to the characteristics of the inverse robust optimisation model, a genetic algorithm (GA) and column generation algorithm are combined to solve the problem. Moreover, 39 test problems are solved by using an inverse robust optimisation approach: the results show that both the TPC and TTC obtained by using the inverse robust optimisation approach are less than those calculated using a robust optimisation approach.

Enhancements to the Localized Genetic Algorithm for Large Scale Capacitated Vehicle Routing Problems

2013 ◽  
Vol 4 (1) ◽  
pp. 17-38 ◽  
Author(s):  
Ziauddin Ursani ◽  
Daryl Essam ◽  
David Cornforth ◽  
Robert Stocker
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Large Scale ◽  
Computational Time ◽  
Small Scale ◽  
Problem Decomposition ◽  
Vehicle Routing Problems ◽  
Solution Quality ◽  
Routing Problems ◽  
Capacitated Vehicle

This paper is a continuation of two previous papers where the authors used Genetic Algorithm with automated problem decomposition strategy for small scale capacitated vehicle routing problems (CVRP) and vehicle routing problem with time windows (VRPTW). In this paper they have extended their scheme to large scale capacitated vehicle routing problems by introducing selective search version of the automated problem decomposition strategy, a faster genotype to phenotype translation scheme, and various search reduction techniques. The authors have shown that genetic algorithm used with automated problem decomposition strategy outperforms the GAs applied on the problem as a whole not only in terms of solution quality but also in terms of computational time on the large scale problems.

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