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Inyong Ham

Bio: Inyong Ham is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Null-move heuristic & Heuristics. The author has an hindex of 1, co-authored 1 publications receiving 2068 citations.

Papers
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Journal ArticleDOI
TL;DR: A simple algorithm is presented in this paper, which produces very good sequences in comparison with existing heuristics, and performs especially well on large flow-shop problems in both the static and dynamic sequencing environments.
Abstract: In a general flow-shop situation, where all the jobs must pass through all the machines in the same order, certain heuristic algorithms propose that the jobs with higher total process time should be given higher priority than the jobs with less total process time. Based on this premise, a simple algorithm is presented in this paper, which produces very good sequences in comparison with existing heuristics. The results of the proposed algorithm have been compared with the results from 15 other algorithms in an independent study by Park [13], who shows that the proposed algorithm performs especially well on large flow-shop problems in both the static and dynamic sequencing environments.

2,255 citations


Cited by
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01 Jan 1989
TL;DR: This survey focuses on the area of deterministic machine scheduling, and reviews complexity results and optimization and approximation algorithms for problems involving a single machine, parallel machines, open shops, flow shops and job shops.

1,401 citations

Book ChapterDOI
01 Jan 2003
TL;DR: Iterated Local Search (ILS) as mentioned in this paper is a general purpose metaheuristic for finding good solutions of combinatorial optimization problems, which is based on building a sequence of (locally optimal) solutions by perturbing the current solution and applying local search to that modified solution.
Abstract: This is a survey of "Iterated Local Search", a general purpose metaheuristic for finding good solutions of combinatorial optimization problems. It is based on building a sequence of (locally optimal) solutions by: (1) perturbing the current solution; (2) applying local search to that modified solution. At a high level, the method is simple, yet it allows for a detailed use of problem-specific properties. After giving a general framework, we cover the uses of Iterated Local Search on a number of well studied problems.

969 citations

Journal ArticleDOI
TL;DR: A fast and easily implementable approximation algorithm for the problem of finding a minimum makespan in a job shop is presented, based on a taboo search technique with a specific neighborhood definition which employs a critical path and blocks of operations notions.
Abstract: A fast and easily implementable approximation algorithm for the problem of finding a minimum makespan in a job shop is presented. The algorithm is based on a taboo search technique with a specific neighborhood definition which employs a critical path and blocks of operations notions. Computational experiments up to 2,000 operations show that the algorithm not only finds shorter makespans than the best approximation approaches but also runs in shorter time. It solves the well-known 10 × 10 hard benchmark problem within 30 seconds on a personal computer.

964 citations

Journal ArticleDOI
TL;DR: This work presents a new iterated greedy algorithm that applies two phases iteratively, named destruction, were some jobs are eliminated from the incumbent solution, and construction, where the eliminated jobs are reinserted into the sequence using the well known NEH construction heuristic.

923 citations

Journal ArticleDOI
TL;DR: A Genetic Algorithm is developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem and the performance of the algorithm is compared with that of a naive Neighbourhood Search technique and with a proven Simulated Annealing algorithm.

849 citations