Optimal generator maintenance scheduling using integer programming

Dopazo, J.F.; Merrill, H.M.

doi:10.1109/t-pas.1975.31996

Cited by 180 publications

(64 citation statements)

References 4 publications

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“…The major efforts, perhaps, are those that have been devoted to studying the traditional optimisation-based techniques such as integer programming [1,2,5,6,22], dynamic programming [5,6,9,10] and branch-and-bound [10,15,16,27] which have been proposed to solve small problems. These methods give an exact optimal solution.…”

Section: Important Fields Of Msmentioning

confidence: 99%

Maintenance Scheduling: Description, Status and Future Directions

Oke

2012

The South African Journal of Industrial Engineering

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In the past several decades, there have been diverse and significant research activities on maintenance scheduling (MS) due to the multi-disciplinary dimension of the field. As a consequence of this diversity and multi-disciplinary involvement, the MS literature is at present fragmented, somewhat misleading, and deficient in informative articles that cover the length, breadth, and depth of scientific investigations in the field. This paper reviews the scientific discipline of MS, covering general surveys, models, and significant empirical studies and cases in MS research. The research reflects the multidisciplinary nature of work in the field and suggests areas for future investigation. OPSOMMINGIn die afgelope aantal dekades was daar uiteenlopende en aansienlike navorsingsbedrywigheid op die gebied van instandhoudingskedulering (IS) weens die multidissiplinêre dimensie van die gebied. As gevolg van hierdie diversiteit en multidissiplinêre betrokkenheid is die ISliteratuur tans gefragmenteer, ietwat misleidend en gebrekkig aan insiggewende artikels wat die lengte, breedte en diepte van wetenskaplike ondersoeke op die gebied dek. Hierdie artikel bied 'n oorsig oor die wetenskaplike vakgebied van IS en dek algemene opnames, modelle en aansienlike empiriese ondersoeke en gevalle in IS-navorsing. Die navorsing weerspieël die multidissiplinêre uitkyk van werk op dié gebied en open gebiede vir toekomstige navorsing.

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Section: Important Fields Of Msmentioning

confidence: 99%

Maintenance Scheduling: Description, Status and Future Directions

Oke

2012

The South African Journal of Industrial Engineering

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“…The use of these variables instead of the variables associated with answers to "When does maintenance occur?" reduces the number of variables [1]. The first formulation satisfy the constraints on the periods and duration of maintenance.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The answer to the first question automatically provides the answer to the second. Several deterministic mathematical methods and heuristic techniques are reported in the literature for solving these problems [1,2,3]. General solution methods are based on integer programming, branch-and-bound techniques, dynamic programming, etc.…”

Section: Problem Descriptionmentioning

confidence: 99%

Generational and Steady-State Genetic Algorithms for Generator Maintenance Scheduling Problems

Dahal

McDonald

1998

Artificial Neural Nets and Genetic Algorithms

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“…In fact, the exact solution methods such as branch and bound (Egan et al, 1976;Bagchi et al, 1987;Dorn & Kerr, 1994), dynamic programming (Zurn & Quintana, 1975;Abdul-Razaq & Potts, 1988), integer programming (Dopazo & Merrill, 1975;Edwin & Curtius, 1990) and Lagrangian relaxation (Li, 1997) generate optimal schedules. Practically, these methods are generally inefficient in computational time terms that they are not suggested for scheduling of flexible industrial systems under dynamically changing conditions.…”

Section: Introductionmentioning

confidence: 99%

Scheduling a maintenance activity under skills constraints to minimize total weighted tardiness and late tasks

Hedjazi

2015

10.5267/j.ijiec

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Skill management is a key factor in improving effectiveness of industrial companies, notably their maintenance services. The problem considered in this paper concerns scheduling of maintenance tasks under resource (maintenance teams) constraints. This problem is generally known as unrelated parallel machine scheduling. We consider the problem with a both objectives of minimizing total weighted tardiness (TWT) and number of tardiness tasks. Our interest is focused particularly on solving this problem under skill constraints, which each resource has a skill level. So, we propose a new efficient heuristic to obtain an approximate solution for this NP-hard problem and demonstrate his effectiveness through computational experiments. This heuristic is designed for implementation in a static maintenance scheduling problem (with unequal release dates, processing times and resource skills), while minimizing objective functions aforementioned.

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Optimal generator maintenance scheduling using integer programming

Cited by 180 publications

References 4 publications

Maintenance Scheduling: Description, Status and Future Directions

Maintenance Scheduling: Description, Status and Future Directions

Generational and Steady-State Genetic Algorithms for Generator Maintenance Scheduling Problems

Scheduling a maintenance activity under skills constraints to minimize total weighted tardiness and late tasks

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