A Hybrid (1+1)-Evolutionary Strategy for the Open Vehicle Routing Problem

Reinholz, Andreas; Schneider, Holger

doi:10.1007/978-1-4614-6322-1_8

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…MNS has been successfully applied in addressing logistic problems such as vehicle routing problems (VRP) (Sörensen et al 2008;Reinholz and Schneider 2013), and seems especially suitable to devise parallel algorithms which are adopted to solve real life problems (Jin et al 2012). Furthermore, because of its adaptability, MNS is very popular in the commercial VRP packages, where it constitutes the main mechanism of intensification and diversification in the search process (Sörensen et al 2008).…”

Section: Mns-ts For the Dynamic Memory Allocation Problemmentioning

confidence: 99%

A multiple neighborhood search for dynamic memory allocation in embedded systems

Soto

Rossi²,

Sevaux³

2015

J Heuristics

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International audienceMemory allocation has a significant impact on power consumption in embedded systems. We address the dynamic memory allocation problem, in which memory requirements may change at each time interval. This problem has previously been addressed using integer linear programming and iterative approaches which build a solution interval by interval taking into account the requirements of partial time intervals. A GRASP that builds a solution for all time intervals has been proposed as a global approach. Due to the complexity of this problem, the GRASP algorithm solution quality decreases for larger instances. In order to overcome this drawback, we propose a multiple neighborhood search hybridized with a Tabu Search and enhanced by complex ejection chains. The proposed approach outperforms all previously developed methods devised for the dynamic memory allocation problem

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Section: Mns-ts For the Dynamic Memory Allocation Problemmentioning

confidence: 99%

A multiple neighborhood search for dynamic memory allocation in embedded systems

Soto

Rossi²,

Sevaux³

2015

J Heuristics

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An Ant Colony-Based Matheuristic Approach for Solving a Class of Vehicle Routing Problems

Yıldırım

Çatay

2015

Lecture Notes in Computer Science

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Abstract. We propose a matheuristic approach to solve several types of vehicle routing problems (VRP). In the VRP, a fleet of capacitated vehicles visits a set of customers exactly once to satisfy their demands while obeying problem specific characteristics and constraints such as homogeneous or heterogeneous fleet, customer service time windows, single or multiple depots. The proposed matheuristic is based on an ant colony optimization (ACO) algorithm which constructs good feasible solutions. The routes obtained in the ACO procedure are accumulated in a pool as columns which are then fed to an integer programming (IP) optimizer that solves the set-partitioning (-covering) formulation of the particular VRP. The (near-)optimal solution found by the solver is used to reinforce the pheromone trails in ACO. This feedback mechanism between the ACO and IP procedures helps the matheuristic better converge to high quality solutions. We test the performance of the proposed matheuristic on different VRP variants using well-known benchmark instances from the literature. Our computational experiments reveal competitive results: we report six new best solutions and meet the best-known solution in 120 instances out of 193. Keywords: Vehicle routing problem · Matheuristic · Ant colony optimization IntroductionThere is an increasing trend towards matheuristics in the recent literature as they incorporate relatively fast and effective solutions while preserving the solution quality. Matheuristics can do so by combining heuristics/metaheuristics with exact solution approaches. According to Boschetti et al. (2009), the interoperation of metaheuristics and mathematical programming techniques yields the matheuristics. The metaheuristic further exploits the features derived by the mathematical model of the problem. Bertazzi and Speranza (2011) simply define a matheuristic as any heuristic that uses mathematical programming in one of its solution steps such as solving sub-problems, solving parts of an instance, restricting the search space and exploring neighborhoods.

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A Bumble Bees Mating Optimization algorithm for the Open Vehicle Routing Problem

Marinakis

Marinaki

2014

Swarm and Evolutionary Computation

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A Hybrid (1+1)-Evolutionary Strategy for the Open Vehicle Routing Problem

Cited by 5 publications

References 15 publications

A multiple neighborhood search for dynamic memory allocation in embedded systems

A multiple neighborhood search for dynamic memory allocation in embedded systems

An Ant Colony-Based Matheuristic Approach for Solving a Class of Vehicle Routing Problems

A Bumble Bees Mating Optimization algorithm for the Open Vehicle Routing Problem

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