Learn More
In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is required to complete a set of operations in a fixed order. Each operation is processed on a specific machine for a fixed duration. A machine can process no more than one job at a time and once a job initiates processing on a given machine it(More)
T his paper proposes and tests variants of GRASP (greedy randomized adaptive search procedure) with path relinking for the three-index assignment problem (AP3). GRASP is a multistart metaheuristic for combinato-rial optimization. It usually consists of a construction procedure based on a greedy randomized algorithm and of a local search. Path relinking is(More)
This papers describes a perl language program to create time-to-target solution value plots for measured CPU times that are assumed to fit a shifted exponential distribution. This is often the case in local search based heuristics for combinatorial optimization , such as simulated annealing, genetic algorithms, iterated local search, tabu search, WalkSAT,(More)
A GRASP (greedy randomized adaptive search procedure) is a multi-start metaheuristic for combinatorial optimization. We study the probability distributions of solution time to a sub-optimal target value in five GRASPs that have appeared in the literature and for which source code is available. The distributions are estimated by running 12,000 independent(More)
In this work, we propose a cooperative multi-thread parallel tabu search heuristic for the circuit partitioning problem. This procedure is based on the cooperation of multiple search threads. Each thread implements a diierent variant of a sequential tabu search algorithm, using a diierent combination of initial solution algorithm and move attribute(More)
A Greedy Randomized Adaptive Search Procedure (GRASP) is a metaheuris-tic for combinatorial optimization. It usually consists of a construction procedure based on a greedy randomized algorithm and local search. Path-relinking is an intensification strategy that explores trajectories that connect high quality solutions. We analyze two parallel strategies for(More)
  • 1