Comparison of iterative improvement techniques for schedule optimization

Dorn, Jürgen; Girsch, Mario; Skele, Günther; Slany, Wolfgang

doi:10.1016/0377-2217(95)00162-x

Cited by 34 publications

(18 citation statements)

References 10 publications

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“…The Iterative Maximization (IM) techniques are a variation of the iterative deepening and random search techniques used in [16]. The partition/merge methods used in [26] are adapted to our environment to find the set of machines to be used for the Greedy Iterative Maximization heuristic.…”

Section: Related Workmentioning

confidence: 99%

Robust Processor Allocation for Independent Tasks When Dollar Cost for Processors is a Constraint

Sugavanam

Siegel

Maciejewski

et al. 2005

2005 IEEE International Conference on Cluster Computing

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Section: Related Workmentioning

confidence: 99%

Robust Processor Allocation for Independent Tasks When Dollar Cost for Processors is a Constraint

Sugavanam

Siegel

Maciejewski

et al. 2005

2005 IEEE International Conference on Cluster Computing

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“…The MaxMax is a variation of the Min-Min that has proven to be a good heuristic for static and dynamic mapping problems (e.g., [11,23,49]). The iterative maximization (IM) techniques are a variation of the iterative deepening and random search techniques used in [16]. The Genitor-style genetic algorithm used here is an adaptation of [48].…”

Section: Related Workmentioning

confidence: 99%

Robust static allocation of resources for independent tasks under makespan and dollar cost constraints

Sugavanam

Siegel

Maciejewski

et al. 2007

Journal of Parallel and Distributed Computing

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Heterogeneous computing (HC) systems composed of interconnected machines with varied computational capabilities often operate in environments where there may be inaccuracies in the estimation of task execution times. Makespan (defined as the completion time for an entire set of tasks) is often the performance feature that needs to be optimized in such systems. Resource allocation is typically performed based on estimates of the computation time of each task on each class of machines. Hence, it is important that makespan be robust against errors in computation time estimates. In this research, the problem of finding a static mapping of tasks to maximize the robustness of makespan against the errors in task execution time estimates given an overall makespan constraint is studied. Two variations of this basic problem are considered: (1) where there is a given, fixed set of machines, (2) where an HC system is to be constructed from a set of machines within a dollar cost constraint. Six heuristic techniques for each of these variations of the problem are presented and evaluated.

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“…The actually scheduled batches are determined by the schedule builder. Dorn et al (1996) describe an experimental comparison of four iterative improvement techniques for schedule optimisation including iterative deepening, random search, tabu search and genetic algorithms. They apply these techniques on the data of a steel making plant in Austria.…”

Section: Genetic Algorithm Applications In Realistic Schedulingmentioning

confidence: 99%

Heuristics and metaheuristics for heavily constrained hybrid flowshop problems

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Es ist nicht genug zu wissen, man muss auch anwenden.Es ist nicht genug zu wollen, man muss auch tun.Knowing is not enough, we must apply.Willing is not enough, we must do.Johann Wolfgang von Goethe, Wilhelm Meisters Wanderjahre, 1821. I ABSTRACT HEURISTICS AND METAHEURISTICS FOR HEAVILY CONSTRAINED HYBRID FLOWSHOP PROBLEMSDue to the current trends in business as the necessity to have a large catalogue of products, orders that increase in frequency but not in size, globalisation and a market that is increasingly competitive, the production sector faces an ever harder economical environment. All this raises the need for production scheduling with maximum efficiency and effectiveness.The first scientific publications on production scheduling appeared more than half a century ago. However, many authors have recognised a gap between the literature and the industrial problems. Most of the research concentrates on optimisation problems that are actually a very simplified version of reality. This allows for the use of sophisticated approaches and guarantees in many cases that optimal solutions are obtained. Yet, the exclusion of real-world restrictions harms the applicability of those methods. What the industry needs are systems for optimised production scheduling that adjust exactly to the conditions in the production plant and that generates good solutions in very little time. This is exactly the objective in this thesis, that is, to treat more realistic scheduling problems and to help closing the gap between the literature and practice.The considered scheduling problem is called the hybrid flowshop problem, which consists in a set of jobs that flow through a number of production stages. At each of the stages, one of the machines that belong to the stage is visited. III IVA series of restriction is considered that include the possibility to skip stages, non-eligible machines, precedence constraints, positive and negative time lags and sequence dependent setup times. In the literature, such a large number of restrictions has not been considered simultaneously before. Briefly, in this thesis a very realistic production scheduling problem is studied.Various optimisation methods are presented for the described scheduling problem. A mixed integer programming model is proposed, in order to obtain optimal solutions for limited cases and in order to analyse the complexity of each of the problem restrictions. In continuation, seven constructive heuristics are presented with the purpose of obtaining fast solutions to the problem in more general cases. Advanced metaheuristic methods are studied in detail, starting with five genetic algorithms that allow studying the effect of the solution representation. Three local search based algorithms are proposed, which is very novel if the elevated complexity of the problem is taken into account. In addition, novel methods are presented that shift the solution representation during the search process in order to acquire near-optimal solutions. The obtained results endorse the use of thes...

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Comparison of iterative improvement techniques for schedule optimization

Cited by 34 publications

References 10 publications

Robust Processor Allocation for Independent Tasks When Dollar Cost for Processors is a Constraint

Robust Processor Allocation for Independent Tasks When Dollar Cost for Processors is a Constraint

Robust static allocation of resources for independent tasks under makespan and dollar cost constraints

Heuristics and metaheuristics for heavily constrained hybrid flowshop problems

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