A hybrid approach for the dynamic vehicle routing problem with time windows

Alvarenga, Guilherme Bastos; Silva, R.M. de Abreu; Mateus, Geraldo Robson

doi:10.1109/ichis.2005.8

Cited by 14 publications

(15 citation statements)

References 5 publications

(12 reference statements)

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“…Alvarenga et al [1] extend a hybrid Column Generation Genetic Algorithm approach for solving the static VRP [2] towards the dynamic case. The algorithm is based on a set partitioning formulation of the VRP.…”

Section: Other Hybridization Approachesmentioning

confidence: 99%

Hybrid Metaheuristics for Dynamic and Stochastic Vehicle Routing

Ritzinger

Puchinger

2013

Hybrid Metaheuristics

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Abstract. Recent developments in telematics, such as the wide spread use of positioning services and mobile communication technologies, allow the exact monitoring of vehicles. These advances build the basis for automatic real-time fleet management systems. To be successful such systems have to rely on optimization algorithms for solving dynamic and stochastic vehicle routing problems based on ingredients such as historical data, stochastic modeling, machine learning, fast shortest-path calculation, fast construction heuristics, and exact and (meta)heuristic optimization methods. This book documents the growing interest in and success of hybrid metaheuristics. They are often used to solve complex and large real-world optimization problems, combining advantages from various fields of computer science and mathematical optimization. Within this chapter the application of such methods for the dynamic and stochastic vehicle routing problem is studied. After a general introduction in this field, the main commonalities of dynamic and stochastic vehicle routing problems are described and a short overview of classical algorithms for these problems is given. Then, in the third part hybrid metaheuristics for dynamic problems vehicle routing problems are be described. The third part focusses on stochastic problems. The fourth part examines the combination of dynamic and stochastic problems. The chapter is concluded with an outlook towards future developments in the field as well as promising open research areas. IntroductionIn todays globalized economy fast, reliable but also flexible supply chains are among the main factors for successful enterprises. In real-world applications it is most often the case that some information about the future is available (stochastic information) and that known information is revealed over time (dynamic information). For example, an estimation for the customer demand is given at the beginning, whereas

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Section: Other Hybridization Approachesmentioning

confidence: 99%

Hybrid Metaheuristics for Dynamic and Stochastic Vehicle Routing

Ritzinger

Puchinger

2013

Hybrid Metaheuristics

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“…When evaluating individual Ind i in the algorithm, we generate AM ants, and calculate the pareto-dominate relationship between ants and the set of Pareto candidate solutionΩ. The process is given below: 1)Rank=0; int count [2]; count [1] rank antj − AM + 1…”

Section: Evaluation Strategy Of Eamentioning

confidence: 99%

“…The researchers also have achieved huge progress [1][2][3][4]. The objective of DVRP is how to find out a perfect route for loaded vehicles when customers' requirements or traffic information keep changing, which means to minimize the total cost of all routes with minimum number of vehicles without violating any constraints.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multi-objective Algorithm for Dynamic Vehicle Routing Problems

Qin

Wang

2008

Computational Science – ICCS 2008

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Abstract. This paper analyzes firstly the limitation of traditional methods when used to solve Dynamic Vehicle Routing Problem (DVRP), and then constructs an adapted DVRP model named DVRPTW based on Multi-objective Optimization. In this model, we consider two subobjectives such as vehicle number and time cost as an independent objective respectively and simultaneously to coordinate the inherent conflicts between them. Also, a hybrid Multi-objective ant colony algorithm named MOEvo-Ant is proposed and some crucial techniques used by MOEvo-Ant algorithm are discussed too. In our ant colony algorithm, an EA is introduced into our ant colony algorithm to increase pheromone update. The main reason of the introduction is that we try to take advantage of the outstanding global searching capability of EA to speed up the convergence of our algorithm. Simulating experiments demonstrate that no matter when compared with the known best solutions developed by previous papers or when use it to solve dynamic vehicle routing problems generated randomly, our algorithm illustrates pretty good performance.

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“…In the literature, many approaches for solving DVRP can be found (Bertsimas and van Ryzin 1993;Papastavrou 1996;Savelsbergh and Sol 1998;Ichoua et al 2000;Caramia et al 2002;Krumke et al 2002;Tighe et al 2004;Alvarenga et al 2005;Montemanni et al 2005;Pankratz 2005;Potvin et al 2006;Gendreau et al 2006). Most of the authors of these research works have proposed tackling hard-constrained and self-generated instances, as well as real-world problems.…”

Section: Introductionmentioning

confidence: 99%

DVRP: a hard dynamic combinatorial optimisation problem tackled by an evolutionary hyper-heuristic

Garrido

Riff

2010

J Heuristics

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In this paper we propose and evaluate an evolutionary-based hyperheuristic approach, called EH-DVRP, for solving hard instances of the dynamic vehicle routing problem. A hyper-heuristic is a high-level algorithm, which generates or chooses a set of low-level heuristics in a common framework, to solve the problem at hand. In our collaborative framework, we have included three different types of low-level heuristics: constructive, perturbative, and noise heuristics. Basically, the hyper-heuristic manages and evolves a sophisticated sequence of combinations of these low-level heuristics, which are sequentially applied in order to construct and improve partial solutions, i.e., partial routes. In presenting some design considerations, we have taken into account the allowance of a proper cooperation and communication among low-level heuristics, and as a result, find the most promising sequence to tackle partial states of the (dynamic) problem. Our approach has been evaluated using the Kilby's benchmarks, which comprise a large number of instances with different topologies and degrees of dynamism, and we have compared it with some well-known methods proposed in the literature. The experimental results have shown that, due to the dynamic nature of the hyper-heuristic, our proposed approach is able to adapt to dynamic scenarios more naturally than low-level heuristics. Furthermore, the hyper-heuristic can obtain high-quality solutions when compared with other (meta) heuristic-based methods. Therefore, the findings of this contribution justify the employment of hyper-heuristic techniques in such changing environments, and we believe that further contributions could be successfully proposed in related dynamic problems.

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A hybrid approach for the dynamic vehicle routing problem with time windows

Cited by 14 publications

References 5 publications

Hybrid Metaheuristics for Dynamic and Stochastic Vehicle Routing

Hybrid Metaheuristics for Dynamic and Stochastic Vehicle Routing

A Hybrid Multi-objective Algorithm for Dynamic Vehicle Routing Problems

DVRP: a hard dynamic combinatorial optimisation problem tackled by an evolutionary hyper-heuristic

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