Optimal Selection of Ingot Sizes Via Set Covering

Vasko, Francis J.; Wolf, Floyd E.; Stott, Kenneth L.

doi:10.1287/opre.35.3.346

Cited by 80 publications

(15 citation statements)

References 16 publications

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“…Game-based learning simulator framework, system architecture of Project Cul, storyboarding, wireframing, prototyping, status quo of Project Cul, ExLean development methodology, heuristic evaluation [15,16,24], scenario-based evaluation [28], and usability evaluation [4] were addressed. Game-based learning simulator framework, system architecture of Project Cul, storyboarding, wireframing, prototyping, status quo of Project Cul, ExLean development methodology, heuristic evaluation [15,16,24], scenario-based evaluation [28], and usability evaluation [4] were addressed.…”

Section: Resultsmentioning

confidence: 99%

HCI International 2015 - Posters’ Extended Abstracts

Stephanidis¹

2015

Communications in Computer and Information Science

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Abstract. We present an approach to solve the Resource Constrained Project Scheduling Problem. This problem consists on executing a group of activities limited by constraints. Precedence relationships force to some activities to begin after the finalization of others. In addition, processing every activity requires a predefined amount of limited resources. The target of this problem is to minimize the duration of whole project. In this paper, an approach based on Artificial Bee Colony algorithm for the Resource Constrained Project Scheduling Problem is presented. That algorithm is one of the most recent algorithms in the domain of the collective intelligence who was motivated by the intelligent behavior observed in the domestic bees to take the process of forage. Thus, ABC combines methods of local search and global search, trying to balance the process of the exploration and exploitation of the space of search.

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Section: Resultsmentioning

confidence: 99%

HCI International 2015 - Posters’ Extended Abstracts

Stephanidis¹

2015

Communications in Computer and Information Science

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“…The set covering problem is a well-known combinatorial optimization problem with variety of real time applications in different fields, in particular crew scheduling in railway and airlines (Housos and Elmoth 1997), location problem of facility (Vasko and Wilson 1984) and in industry production planning (Vasko and Wolf 1987). The description of the SCP is as follows:…”

Section: Set Covering Problemmentioning

confidence: 99%

A new approach for solving set covering problem using jumping particle swarm optimization method

Balaji

Revathi

2015

Nat Comput

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The set covering problem (SCP) is a well known classic combinatorial NP-hard problem, having practical application in many fields. To optimize the objective function of the SCP, many heuristic, meta heuristic, greedy and approximation approaches have been proposed in the recent years. In the development of swarm intelligence, the particle swarm optimization is a nature inspired optimization technique for continuous problems and for discrete problems we have the well known discrete particle swarm optimization (DPSO) method. Aiming towards the best solution for discrete problems, we have the recent method called jumping particle swarm optimization (JPSO). In this DPSO the improved solution is based on the particles attraction caused by attractor. In this paper, a new approach based on JPSO is proposed to solve the SCP. The proposed approach works in three phases: for selecting attractor, refining the feasible solution given by the attractor in order to reach the optimality and for removing redundancy in the solution. The proposed approach has been tested on the benchmark instances of SCP and compared with best known methods. Computational results show that it produces high quality solution in very short running times when compared to other algorithms.

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“…There have been a number of previous studies of location problems (Domschke and Drexl, 1985;Bhatnagar and Sohal, 2005). Several surveys of the applications of location models have been performed (Eiselt, 1992;Jacobsen and Madsen, 1980;Marks and Liebman, 1971;Wirasinghe and Waters, 1983;Hopmans, 1986;Kimes and Fitzsimmons, 1990;Huxley, 1982;Tryfos, 1986;Vasko et al, 1987;Hogan, 1990;Azadeh et al, 2008). In this article, DEA was used as a multi-criteria method for location of solar plants.…”

Section: Introductionmentioning

confidence: 99%