Results 11  20
of
337
B.: A hybrid heuristic ordering and variable neighbourhood search for the nurse rostering problem
 European Journal of Operational Research
"... This paper is concerned with the development of intelligent decision support methodologies for nurse rostering problems in large modern hospital environments. We present an approach which hybridises heuristic ordering with variable neighbourhood search. We show that the search can be extended and th ..."
Abstract

Cited by 47 (26 self)
 Add to MetaCart
(Show Context)
This paper is concerned with the development of intelligent decision support methodologies for nurse rostering problems in large modern hospital environments. We present an approach which hybridises heuristic ordering with variable neighbourhood search. We show that the search can be extended and the solution quality can be significantly improved by the careful combination and repeated use of heuristic ordering, variable neighbourhood search and backtracking. The amount of computational time that is allowed plays a significant role and we analyse and discuss this. The algorithms are evaluated against a commercial Genetic Algorithm on commercial data. We demonstrate that this methodology can significantly outperform the commercial algorithm. This paper is one of the few in the scientific nurse rostering literature which deal with commercial data and which compare against a commercially implemented algorithm.
Ant colony optimization for the total weighted tardiness problem
 In Proceedings of the Parallel Problem Solving from Nature Conference
, 2000
"... Abstract. In this article we present an application of the Ant Colony Optimization (ACO) metaheuristic to the single machine total weighted tardiness problem. First, we briefly discuss the constructive phase of ACO in which a colony of artificial ants generates a set of feasible solutions. Then, we ..."
Abstract

Cited by 42 (5 self)
 Add to MetaCart
Abstract. In this article we present an application of the Ant Colony Optimization (ACO) metaheuristic to the single machine total weighted tardiness problem. First, we briefly discuss the constructive phase of ACO in which a colony of artificial ants generates a set of feasible solutions. Then, we introduce some simple but very effective local search. Last, we combine the constructive phase with local search obtaining a novel ACO algorithm that uses a heterogeneous colony of ants and is highly effective in finding the bestknown solutions on all instances of a widely used set of benchmark problems. 1
Randomized Heuristics for the MaxCut Problem
 Optimization Methods and Software
, 2002
"... Given an undirected graph with edge weights, the MAXCUT problem consists in finding a partition of the nodes into two subsets, such that the sum of the weights of the edges having endpoints in different subsets is maximized. ..."
Abstract

Cited by 40 (17 self)
 Add to MetaCart
(Show Context)
Given an undirected graph with edge weights, the MAXCUT problem consists in finding a partition of the nodes into two subsets, such that the sum of the weights of the edges having endpoints in different subsets is maximized.
A reactive variable neighborhood search for the vehicle routing problem with time windows
 INFORMS Journal on Computing
, 2003
"... The purpose of this paper is to present a new deterministic metaheuristic based on a modification of Variable Neighborhood Search of Mladenovic and Hansen (1997) for solving the vehicle routing problem with time windows. Results are reported for the standard 100, 200 and 400 customer data sets by So ..."
Abstract

Cited by 39 (1 self)
 Add to MetaCart
The purpose of this paper is to present a new deterministic metaheuristic based on a modification of Variable Neighborhood Search of Mladenovic and Hansen (1997) for solving the vehicle routing problem with time windows. Results are reported for the standard 100, 200 and 400 customer data sets by Solomon (1987) and Gehring and Homberger (1999) and two reallife problems by Russell (1995). The findings indicate that the proposed procedure outperforms other recent local searches and metaheuristics. In addition four new bestknown solutions were obtained. The proposed procedure is based on a new fourphase approach. In this approach an initial solution is first created using new route construction heuristics followed by route elimination procedure to improve the solutions regarding the number of vehicles. In the third phase the solutions are improved in terms of total traveled distance using four new local search procedures proposed in this paper. Finally in phase four the best solution obtained is improved by modifying the objective function to escape from a local minimum. (Metaheuristics; Vehicle Routing; Time Windows) 1.
Static Pickup and Delivery Problems: A Classification Scheme and Survey
, 2007
"... Pickup and delivery problems constitute an important class of vehicle routing problems in which objects or people have to be collected and distributed. This paper introduces a general framework to model a large collection of pickup and delivery problems, as well as a threefield classification schem ..."
Abstract

Cited by 36 (3 self)
 Add to MetaCart
Pickup and delivery problems constitute an important class of vehicle routing problems in which objects or people have to be collected and distributed. This paper introduces a general framework to model a large collection of pickup and delivery problems, as well as a threefield classification scheme for these problems. It surveys the methods used for solving them.
Particle Swarm Optimization Algorithm for Makespan and Maximum Lateness Minimization in Permutation Flowshop Sequencing Problem
"... In this paper, a particle swarm optimization algorithm (PSO) is presented to solve the permutaion flowshop sequencing problem (PFSP) with the objectives of minimizing makespan and maximum lateness of jobs, respectively. Simple but very efficient local search based on variable neighborhood search ( ..."
Abstract

Cited by 32 (2 self)
 Add to MetaCart
In this paper, a particle swarm optimization algorithm (PSO) is presented to solve the permutaion flowshop sequencing problem (PFSP) with the objectives of minimizing makespan and maximum lateness of jobs, respectively. Simple but very efficient local search based on variable neighborhood search (VNS) is embedded in the particle swarm optimization algorithm to solve the well known benchmark suites in the literature. The proposed PSO algorithm is applied to both 40 benchmark problems for makespan criterion and 160 problem instances for maximum lateness
Applying Iterated Local Search to the Permutation Flow Shop Problem
, 1998
"... Iterated local search (ILS) is a general and powerful metaheuristic that can significantly improve the performance of local search algorithms. We give a short overview of ILS approaches and widen the range of successful ILS applications to the permutation flow shop problem (FSP). We discuss the e ..."
Abstract

Cited by 32 (9 self)
 Add to MetaCart
Iterated local search (ILS) is a general and powerful metaheuristic that can significantly improve the performance of local search algorithms. We give a short overview of ILS approaches and widen the range of successful ILS applications to the permutation flow shop problem (FSP). We discuss the effect of specific choices for our ILS algorithm and give an experimental analysis of its performance. Even with our use of a very simple local search, ILS compares very to other metaheuristic approaches proposed for the FSP, and in particular we find new best solutions among Taillard's benchmark problems. Additionally, we show that the so called acceptance criterion has a considerable influence on ILS performance and therefore should receive more attention in future ILS applications.
Heuristics for the Mirrored Traveling Tournament Problem
 European Journal of Operational Research
, 2004
"... Professional sports leagues are a major economic activity around the world. Teams and leagues do not want to waste their investments in players and structure in consequence of poor schedules of games. ..."
Abstract

Cited by 28 (4 self)
 Add to MetaCart
(Show Context)
Professional sports leagues are a major economic activity around the world. Teams and leagues do not want to waste their investments in players and structure in consequence of poor schedules of games.
An interior point algorithm for minimum sum of squares clustering
 SIAM J. Sci. Comput
, 1997
"... Abstract. An exact algorithm is proposed for minimum sumofsquares nonhierarchical clustering, i.e., for partitioning a given set of points from a Euclidean mspace into a given number of clusters in order to minimize the sum of squared distances from all points to the centroid of the cluster to wh ..."
Abstract

Cited by 27 (10 self)
 Add to MetaCart
(Show Context)
Abstract. An exact algorithm is proposed for minimum sumofsquares nonhierarchical clustering, i.e., for partitioning a given set of points from a Euclidean mspace into a given number of clusters in order to minimize the sum of squared distances from all points to the centroid of the cluster to which they belong. This problem is expressed as a constrained hyperbolic program in 01 variables. The resolution method combines an interior point algorithm, i.e., a weighted analytic center column generation method, with branchandbound. The auxiliary problem of determining the entering column (i.e., the oracle) is an unconstrained hyperbolic program in 01 variables with a quadratic numerator and linear denominator. It is solved through a sequence of unconstrained quadratic programs in 01 variables. To accelerate resolution, variable neighborhood search heuristics are used both to get a good initial solution and to solve quickly the auxiliary problem as long as global optimality is not reached. Estimated bounds for the dual variables are deduced from the heuristic solution and used in the resolution process as a trust region. Proved minimum sumofsquares partitions are determined for the first time for several fairly large data sets from the literature, including Fisher’s 150 iris. Key words. classification and discrimination, cluster analysis, interiorpoint methods, combinatorial optimization