The Weighted Independent Domination Problem: ILP Model and Algorithmic Approaches

This work deals with the so-called weighted independent domination problem, which is an $NP$-hard combinatorial optimization problem in graphs. In contrast to previous work, this paper considers the problem from a non-theoretical perspective. The first contribution consists in the development of three integer linear programming models. Second, two greedy heuristics are proposed. Finally, the last contribution is a population-based iterated greedy metaheuristic which is applied in two different ways: (1) the metaheuristic is applied directly to each problem instance, and (2) the metaheuristic is applied at each iteration of a higher-level framework---known as construct, merge, solve \& adapt---to sub-instances of the tackled problem instances. The results of the considered algorithmic approaches show that integer linear programming approaches can only compete with the developed metaheuristics in the context of graphs with up to 100 nodes. When larger graphs are concerned, the application of the populated-based iterated greedy algorithm within the higher-level framework works generally best. The experimental evaluation considers graphs of different types, sizes, densities, and ways of generating the node and edge weights

The weighted independent domination problem: ILP model and algorithmic approaches

This work deals with the so-called weighted independent domination problem, which is an N P -hard combinatorial optimization problem in graphs. In contrast to previous theoretical work from the liter- ature, this paper considers the problem from an algorithmic perspective. The first contribution consists in the development of an integer linear programming model and a heuristic that makes use of this model. Sec- ond, two greedy heuristics are proposed. Finally, the last contribution is a population-based iterated greedy algorithm that takes profit from the better one of the two developed greedy heuristics. The results of the compared algorithmic approaches show that small problem instances based on random graphs are best solved by an efficient integer linear programming solver such as CPLEX. Larger problem instances are best tackled by the population-based iterated greedy algorithm. The experimental evaluation considers random graphs of different sizes, densities, and ways of generating the node and edge weights

Data for: Models and an exact method for the unrelated parallel machine scheduling problem with setups and resources

-Instances generator for problems of Unrelated Parallel Machines: Simple or with setups, and/or with resources (needed in setup and/or processing)-Executable for all data presented in the paper of UPMSR problem-Tables with all results-Explanation of all these online Material

Data for: Models and an exact method for the unrelated parallel machine scheduling problem with setups and resources

-Instances generator for problems of Unrelated Parallel Machines: Simple or with setups, and/or with resources (needed in setup and/or processing)-Executable for all data presented in the paper of UPMSR problem-Tables with all results-Explanation of all these online MaterialsTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

The Weighted Independent Domination Problem: ILP Model and Algorithmic Approaches

This work deals with the so-called weighted independent domination problem, which is an NP-hard combinatorial optimization problem in graphs. In contrast to previous theoretical work from the literature, this paper considers the problem from an algorithmic perspective. The first contribution consists in the development of an integer linear programming model and a heuristic that makes use of this model. Second, two greedy heuristics are proposed. Finally, the last contribution is a population-based iterated greedy algorithm that takes profit from the better one of the two developed greedy heuristics. The results of the compared algorithmic approaches show that small problem instances based on random graphs are best solved by an efficient integer linear programming solver such as CPLEX. Larger problem instances are best tackled by the population-based iterated greedy algorithm. The experimental evaluation considers random graphs of different sizes, densities, and ways of generating the node and edge weights. © Springer International Publishing AG 2017.This work was supported by project TIN2012-37930-C02-02 (Spanish Ministry for Economy and Competitiveness, FEDER funds from the European Union)Peer Reviewe

Application of the simulated annealing algorithm to minimize the makespan on the unrelated parallel machine scheduling problem with setup times

In this paper, the unrelated parallel machine scheduling problem considering machine-dependent and job sequence-dependent setup times is addressed. This problem involves the scheduling of n jobs on m unrelated machines with setup times in order to minimize the makespan. The Simulated Annealing algorithm is used to solve four sets of small scheduling problems, from the literature, on two unrelated machines: the first one has six jobs, the second has seven jobs and the third and fourth has eight and nine jobs, respectively. The results seem promising when compared with other methods referred in literature.This project is funded by COMPETE: POCI-01-0145-FEDER-007043 and FCT –Fundação para a Ciência e Tecnologia within the project scope: UID/CEC/00319/201