Flowshop scheduling with limited temporary storage
TLDR
It is shown that all the intermediate (finite capacity) cases of the flowshop scheduling problem with 4 machines is NP-complete, and exact bounds for the relative improvement of execution times when a given buffer capacity is used are proved.Abstract:
We examine the problem of scheduling 2-machine flowshops in order to minimize makespan, using a limited amount of intermediate storage buffers. Although there are efficient algorithms for the extreme cases of zero and infinite buffer capacities, we show that all the intermediate (finite capacity) cases are NP-complete. We prove exact bounds for the relative improvement of execution times when a given buffer capacity is used. We also analyze an efficient heuristic for solving the 1-buffer problem, showing that it has a 3/2 worst-case performance. Furthermore, we show that the "no-wait" (i.e., zero buffer) flowshop scheduling problem with 4 machines is NP-complete. This partly settles a well-known open question, although the 3-machine case is left open here.read more
Citations
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Journal ArticleDOI
A Survey of Machine Scheduling Problems with Blocking and No-Wait in Process
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Chapter 9 Sequencing and scheduling: Algorithms and complexity
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Job-shop scheduling with blocking and no-wait constraints
TL;DR: It is shown that several key properties, used to design heuristic procedures, do not hold in the blocking and no-wait cases, while some of the most effective ideas used to develop branch and bound algorithms can be easily extended.
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A Review of Machine Scheduling: Complexity, Algorithms and Approximability
TL;DR: This work focuses on deterministic machine scheduling for which it is assumed that all data that define a problem instance are known with certainty.
Book ChapterDOI
Recent Developments in Deterministic Sequencing and Scheduling: A Survey
TL;DR: A survey of deterministic sequencing and scheduling can be found in this article, where the authors survey the state of the art with respect to optimization and approximation algorithms and interpret these in terms of computational complexity theory.
References
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