Two-machine flow shop no-wait scheduling with machine maintenance

Kubzin, Mikhail A.; Strusevich, Vitaly A.

doi:10.1007/s10288-005-0070-1

Cited by 46 publications

(10 citation statements)

References 12 publications

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“…The authors proposed some heuristic algorithms with error bounding analysis. Kubzin and Strusevich [32] studied the two-machine flow shop scheduling problem in no-wait process such that one of the machines undergo a planned maintenance period with a variable length. They presented a polynomial-time approximation scheme to minimize makespan.…”

Section: State Of the Artmentioning

confidence: 99%

Two-machine flow shop with synchronized periodic maintenance

et al. 2019

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In the literature, some works deal with the two-machine flow shop scheduling problem under availability constraints. Most of them consider those constraints only for one machine at a time and also with limited unavailability periods. In this work, we were interested by the unlimited periodic and synchronized maintenance applied on both machines. The problem is NP-hard. We proposed a mixed integer programming model and a variable neighborhood search for solving large instances in order to minimize the makespan. Computational experiments show the efficiency of the proposed methods.

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Section: State Of the Artmentioning

confidence: 99%

Two-machine flow shop with synchronized periodic maintenance

et al. 2019

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“…The duration of a maintenance period can either be a given constant or be dependent on its start time. The latter type of maintenance is introduced in [15] and [16]; see also [17] and [18] for more recent developments. With several maintenance periods in a schedule, the jobs are split into several groups, one group before the first maintenance and one after each maintenance period.…”

Section: Messagementioning

confidence: 99%

“…Notice that the duration of an RMP defined by (3) is a further generalization of the start-time dependent model introduced in [15,16], since it additionally includes a positional factor, so that the duration is dependent on the start-time of the RMP and also on the position of the RMP in the processing sequence. The latter is in line with positionally dependent setup times for models that arise in group technology scheduling; see [27].…”

Section: Model Descriptionmentioning

confidence: 99%

Combining time and position dependent effects on a single machine subject to rate-modifying activities

Rustogi

Strusevich

2014

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Citation for this version held on GALA:Rustogi, Kabir and Strusevich, Vitaly (2014) Combining time and position dependent effects on a single machine subject to rate-modifying activities. London: Greenwich Academic Literature Archive. We introduce a general model for single machine scheduling problems, in which the actual processing times of jobs are subject to a combination of positional and timedependent effects, that are job-independent but additionally depend on certain activities that modify the processing rate of the machine, such as, e.g., maintenance. We focus on minimizing two classical objectives: the makespan and the sum of the completion times. The traditional classification accepted in this area of scheduling is based on the distinction between the learning and deterioration effects, on one hand, and between the positional effects and the start-time dependent effects, on the other hand. Our results show that in the framework of the introduced model such a classification is not necessary, as long as the effects are job-independent. The model introduced in this paper covers most previously known models. The solution algorithms are developed within the same general framework and their running times are no worse than those available earlier for problems with less general effects.

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“…Therefore, the jobs have different processing times depending on the log band mills selected to process them. Kubzin and Strusevich (2005) study a two-machine flow shop scheduling problem with no-wait in process to minimize the makespan with a maintenance period on one of the machines. They assume that the length of the maintenance activity depends on its starting time and provide a polynomial time approximation scheme for the problem.…”

Section: Introductionmentioning

confidence: 99%