Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU

A parallel genetic algorithm (GA) implemented on GPU clusters is proposed to solve the Uncapacitated Single Allocation p-Hub Median problem. The GA uses binary and integer encoding and genetic operators adapted to this problem. Our GA is improved by generated initial solution with hubs located at middle nodes. The obtained experimental results are compared with the best known solutions on all benchmarks on instances up to 1000 nodes. Furthermore, we solve our own randomly generated instances up to 6000 nodes. Our approach outperforms most well-known heuristics in terms of solution quality and time execution and it allows hitherto unsolved problems to be solved

Managing facility disruption in hub-and-spoke networks: formulations and efficient solution methods

Hub disruption result in substantially higher transportation cost and customer dissatisfaction. In this study, first a mathematical model to design hub-and-spoke networks under hub failure is presented. For a fast and inexpensive recovery, the proposed model constructs networks in which every single demand point will have a backup hub to be served from in case of disruption. The problem is formulated as a mixed integer quadratic program in a way that could be linearized without significantly increasing the number of variables. To further ease the model’ computational burden, indicator constraints are employed in the linearized model. The resulting formulation produced optimal solutions for small and some medium size instances. To tackle large problems, three efficient particle swarm optimisation-based metaheuristics which incorporate efficient solution representation, short-term memory and special crossover operator are proposed. We present the results for two scenarios relating to high and low probabilities of hub failures and provide managerial insight. The computational results, using problem instances with various sizes taken from CAB and TR datasets, confirm the effectiveness and efficiency of the proposed problem formulation and our new solution techniques

Improving performances of the genetic algorithm by caching

In this paper we optimize run-time performance of the genetic algorithm by caching. We are caching the genetic algorithm procedure for evaluation of an objective function. Least Recently Used (LRU) caching strategy is used, that is simple but effective. This approach is good for problems that have a relatively small length of item string, and a large evaluation time of objective function. We present results of the caching to genetic algorithm for solving one such problem - the simple plant location problem (SPLP)

A Genetic Algorithm for the Index Selection Problem

This paper considers the problem of minimizing the response time for a given database workload by a proper choice of indexes. This problem is NP-hard and known in the literature as the Index Selection Problem (ISP)

A Hybrid GA for the Edge-Biconnectivity Augmentation Problem

In the design of communication networks, robustness against failures in single links or nodes is an important issue. This paper proposes a new approach for the edge-biconnectivity augmentation (E2AUG) problem, in which a given graph G0 (V, E0 ) needs to be augmented by the cheapest possible set of edges AUG so that a single edge deletion does not disconnect G0 . The new approach is based on a preliminary reduction of the problem and a genetic algorithm (GA) using a binary vector to represent a set of augmenting edges and therefore a candidate solution. Two strategies are proposed to deal with infeasible solutions that do not lead to edge-biconnectivity. In the first, more traditional variant, infeasible solutions are detected and simply discarded

An Evolutionary Algorithm with Solution Archive for the Generalized Minimum Spanning Tree Problem

We consider the recently proposed concept of enhancing an evolutionary algorithm (EA) with a complete solution archive. It stores evaluated solutions during the optimiza-tion in order to detect duplicates and to efficiently transform them into yet unconsidered solutions. For this approach we introduce the so-called bounding extension in order to identify and prune branches in the trie-based archive which only contain inferior solutions. This extension enables the EA to concentrate the search on promising areas of the so-lution space. Similarly to the classical branch-and-bound technique, bounds are obtained via primal and dual heuris-tics. As an application we consider the generalized min-imum spanning tree problem where we are given a graph with nodes partitioned into clusters and exactly one node from each cluster must be connected in the cheapest way. As the EA uses operators based on two dual representa-tions, we exploit two corresponding tries that complement each other. Test results on TSPlib instances document the strength of this concept and that it can compete with the leading metaheuristics for this problem in the literature