805 research outputs found
A Computational Study of Genetic Crossover Operators for Multi-Objective Vehicle Routing Problem with Soft Time Windows
The article describes an investigation of the effectiveness of genetic
algorithms for multi-objective combinatorial optimization (MOCO) by presenting
an application for the vehicle routing problem with soft time windows. The work
is motivated by the question, if and how the problem structure influences the
effectiveness of different configurations of the genetic algorithm.
Computational results are presented for different classes of vehicle routing
problems, varying in their coverage with time windows, time window size,
distribution and number of customers. The results are compared with a simple,
but effective local search approach for multi-objective combinatorial
optimization problems
A comparison of recombination operators for capacitate vehicle routing problem
The Vehicle Routing Problem (VRP) deals with the assignment of a set of transportation orders to a fleet of vehicles, and the sequencing of stops for each vehicle to minimize transportation costs. In this paper we study the Capacitated VRP (CVRP), which is mainly characterized by using vehicles of the same capacity. Taking a basic GA to solve the CVRP, we propose a new problem dependent recombination operator, called Best Route Better Adjustment recombination (BRBAX). A comparison of its performance is carried out with respect to other two classical recombination operators. Also we conduct a study of different mutations in order to determine the best combination of genetic operators for this problem. The results show that the use of our specialized BRBAX recombination outperforms the others more generic on all problem instances used in this work for all the metrics tested.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI
Metaheuristics and cooperative approaches for the Bi-objective Ring Star Problem
International audienceThis paper presents and investigates different approaches to solve a new bi-objective routing problem called the ring star problem. It consists of locating a simple cycle through a subset of nodes of a graph while optimizing two kinds of cost. The first objective is the minimization of a ring cost that is related to the length of the cycle. The second one is the minimization of an assignment cost from non-visited nodes to visited ones. In spite of its obvious bi-objective formulation, this problem has always been investigated in a single-objective way. To tackle the bi-objective ring star problem, we first investigate different stand-alone search methods. Then, we propose two cooperative strategies that combine two multi-objective metaheuristics: an elitist evolutionary algorithm and a population-based local search. We apply these new hybrid approaches to well-known benchmark test instances and demonstrate their effectiveness in comparison to non-hybrid algorithms and to state-of-the-art methods
Workload Equity in Vehicle Routing Problems: A Survey and Analysis
Over the past two decades, equity aspects have been considered in a growing
number of models and methods for vehicle routing problems (VRPs). Equity
concerns most often relate to fairly allocating workloads and to balancing the
utilization of resources, and many practical applications have been reported in
the literature. However, there has been only limited discussion about how
workload equity should be modeled in VRPs, and various measures for optimizing
such objectives have been proposed and implemented without a critical
evaluation of their respective merits and consequences.
This article addresses this gap with an analysis of classical and alternative
equity functions for biobjective VRP models. In our survey, we review and
categorize the existing literature on equitable VRPs. In the analysis, we
identify a set of axiomatic properties that an ideal equity measure should
satisfy, collect six common measures, and point out important connections
between their properties and those of the resulting Pareto-optimal solutions.
To gauge the extent of these implications, we also conduct a numerical study on
small biobjective VRP instances solvable to optimality. Our study reveals two
undesirable consequences when optimizing equity with nonmonotonic functions:
Pareto-optimal solutions can consist of non-TSP-optimal tours, and even if all
tours are TSP optimal, Pareto-optimal solutions can be workload inconsistent,
i.e. composed of tours whose workloads are all equal to or longer than those of
other Pareto-optimal solutions. We show that the extent of these phenomena
should not be underestimated. The results of our biobjective analysis are valid
also for weighted sum, constraint-based, or single-objective models. Based on
this analysis, we conclude that monotonic equity functions are more appropriate
for certain types of VRP models, and suggest promising avenues for further
research.Comment: Accepted Manuscrip
Evolutionary computation applied to combinatorial optimisation problems
This thesis addresses the issues associated with conventional genetic algorithms (GA) when applied to hard optimisation problems. In particular it examines the problem of selecting and implementing appropriate genetic operators in order to meet the validity constraints for constrained optimisation problems. The problem selected is the travelling salesman problem (TSP), a well known NP-hard problem.
Following a review of conventional genetic algorithms, this thesis advocates the use of a repair technique for genetic algorithms: GeneRepair. We evaluate the effectiveness of this operator against a wide range of benchmark problems and compare these results with conventional genetic algorithm approaches. A comparison between GeneRepair and the conventional GA approaches is made in two forms: firstly a handcrafted approach compares GAs without repair against those using GeneRepair. A second automated approach is then presented. This meta-genetic algorithm examines different configurations of operators and parameters. Through the use of a cost/benefit (Quality-Time Tradeoff) function, the user can balance the computational effort against the quality of the solution and thus allow the user to specify exactly what the cost benefit point should be for the search.
Results have identified the optimal configuration settings for solving selected TSP problems. These results show that GeneRepair when used consistently generates very good TSP solutions for 50, 70 and 100 city problems. GeneRepair assists in finding TSP solutions in an extremely efficient manner, in both time and number of evaluations required
A comparison of recombination operators for capacitate vehicle routing problem
The Vehicle Routing Problem (VRP) deals with the assignment of a set of transportation orders to a fleet of vehicles, and the sequencing of stops for each vehicle to minimize transportation costs. In this paper we study the Capacitated VRP (CVRP), which is mainly characterized by using vehicles of the same capacity. Taking a basic GA to solve the CVRP, we propose a new problem dependent recombination operator, called Best Route Better Adjustment recombination (BRBAX). A comparison of its performance is carried out with respect to other two classical recombination operators. Also we conduct a study of different mutations in order to determine the best combination of genetic operators for this problem. The results show that the use of our specialized BRBAX recombination outperforms the others more generic on all problem instances used in this work for all the metrics tested.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI
A genetic algorithm to minimise number of vehicles in an electric vehicle routing problem
National Research Foundation (NRF) Singapore under Corp Lab @ University scheme; Fujitsu Lt
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