Flexible Flow Shop with Storage: Complexity and Optimisation Methods

Fung, Joey; Zinder, Yakov; Singh, Gaurav

doi:10.1016/j.ifacol.2016.07.606

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Fung et al [42] pointed to the extended minimal covers, and the integer programming model with valid inequalities are the causes. The investigation of the structure of a typical schedule can be a future direction.…”

Section: Research Results and Recommendations On C Max For F F Mmentioning

confidence: 99%

Multi-objective permutation and non-permutation flow shop scheduling problems with no-wait: a systematic literature review

2021

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Flow shop scheduling is a type of scheduling where sequence follows for each job on a set of machines for processing. In practice, jobs in flow shops can arrive at irregular times, and the no-wait constraint allows the changes in the job order to flexibly manage such irregularity. The flexible flow shop scheduling problems with no-wait have mainly addressed for flow optimization on the shop floor in manufacturing, processing, and allied industries. The scope of this paper is to identify the literature available on permutation and non-permutation flow shop scheduling with no-wait constraint. This paper organizes scheduling problems based on performance measures of variability and shop environments. The extended summary of two/three-machine and m-machine problems has been compiled, including their objectives, algorithms, parametric considerations, and their findings. A systematic appearance of both conceptual and analytical results summarizes various advances of the no-wait constraint. The paper includes independently investigated problems and suggestions for future research directions.

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Section: Research Results and Recommendations On C Max For F F Mmentioning

confidence: 99%

Multi-objective permutation and non-permutation flow shop scheduling problems with no-wait: a systematic literature review

2021

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“…Denote by F2|storage, ω j , Ω(t), p i, j = 1|C max the restricted version of the F2|storage, ω j , Ω(t)|C max problem where all processing times are equal to one unit of time. In contrast to the case of constant storage availability, which is polynomially solvable when all processing times are equal to one unit of time [9], the F2|storage, ω j , Ω(t), p i, j = 1|C max problem is NP-hard in the strong sense. This will be proved below by a reduction from the Numerical Matching with Target Sums (NMTS) decision problem, which is NP-complete in the strong sense [10].…”

Section: Computational Complexitymentioning

confidence: 99%

“…The case where all jobs have the same processing time on one of the machines, or the same processing time on the first-stage machine and the same processing time on the second-stage machine was considered in a number of publications, including [2,9] and the mentioned above [13]. Besides the theoretical interest, this case has applications in star data gathering networks where different workstations are allocated datasetindependent time slots for data transfer, and in unloading and loading involving a crane.…”

Section: Introductionmentioning

confidence: 99%

“…The paper addresses this gap in the literature on scheduling by considering the two-machine flow shop with variable storage capacity. The paper (a) proves that the introduction of variable storage availability makes the problem NP-hard in the strong sense even for unit processing times (in contrast, the same problem with constant availability of the storage space is solvable in polynomial time [9]) (Sect. 3); (b) strengthens the result in [15] by establishing the existence of an optimal permutation schedule even for arbitrary storage requirements which are not necessarily proportional to the duration of the first operation (Sect.…”

Section: Introductionmentioning

confidence: 99%

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Two-machine flow shop with dynamic storage space

2020

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The publications on two-machine flow shop scheduling problems with job dependent storage requirements, where a job seizes a portion of the storage space for the entire duration of its processing, were motivated by various applications ranging from supply chains of mineral resources to multimedia systems. In contrast to the previous publications that assumed that the availability of the storage space remains unchanged, this paper is concerned with a more general case when the availability is a function of time. It strengthens the previously published result concerning the existence of an optimal permutation schedule, shows that the variable storage space availability leads to the NP-hardness in the strong sense even for unit processing times, and presents a polynomial-time approximation scheme together with several heuristic algorithms. The heuristics are evaluated by means of computational experiments.

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Automotive Paint Shop 4.0

Bysko

Krystek

2020

Computers & Industrial Engineering

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Flexible Flow Shop with Storage: Complexity and Optimisation Methods

Cited by 5 publications

References 9 publications

Multi-objective permutation and non-permutation flow shop scheduling problems with no-wait: a systematic literature review

Multi-objective permutation and non-permutation flow shop scheduling problems with no-wait: a systematic literature review

Two-machine flow shop with dynamic storage space

Automotive Paint Shop 4.0

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