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An Ordered Flow Shop with Two Agents
Asia-Pacific Journal of Operational Research, 2016In this paper, we consider a two-agent scheduling problem in an [Formula: see text]-machine ordered flow shop where each agent is responsible for his own set of jobs and wishes to minimize the makespan. Since the problem is NP-hard, we develop a pseudo-polynomial time approach for the case with a fixed number of machines and investigate the conditions
Byung-Cheon Choi, Myoung-Ju Park
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Journal of Global Optimization, 1996
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Flow Shops and Flexible Flow Shops (Deterministic)
2008In many manufacturing and assembly facilities each job has to undergo a series of operations. Often, these operations have to be done on all jobs in the same order implying that the jobs have to follow the same route. The machines are then assumed to be set up in series and the environment is referred to as a flow shop.
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Analysis of flow shop scheduling anomalies
European Journal of Operational Research, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
S. S. Panwalkar, Christos Koulamas
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The clustered flow-shop problem
Zeitschrift für Operations Research, 1988This paper considers a generalization of the classical flow-shop problem where n items, grouped into k fixed sequences (clusters) are processed on m machines. A permutation is being sought that minimizes the completion time of processing all items. This paper develops conditions when the clustered problem can be reduced to a classical case.
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2012
We introduce flow shops that revisit certain processors, and define the common patterns of flow: cyclic, chain, hub, and V-shaped. We show that even the simplest case, the (1,2,1)-reentrant shop, is NPhard, establish properties that facilitate a branch-and-bound algorithm, and present two simple but very effective heuristics.
Hamilton Emmons, George Vairaktarakis
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We introduce flow shops that revisit certain processors, and define the common patterns of flow: cyclic, chain, hub, and V-shaped. We show that even the simplest case, the (1,2,1)-reentrant shop, is NPhard, establish properties that facilitate a branch-and-bound algorithm, and present two simple but very effective heuristics.
Hamilton Emmons, George Vairaktarakis
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FLOW SHOP SCHEDULING WITH REINFORCEMENT LEARNING
Asia-Pacific Journal of Operational Research, 2013Reinforcement learning (RL) is a state or action value based machine learning method which solves large-scale multi-stage decision problems such as Markov Decision Process (MDP) and Semi-Markov Decision Process (SMDP) problems. We minimize the makespan of flow shop scheduling problems with an RL algorithm. We convert flow shop scheduling problems into
ZHICONG ZHANG +3 more
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Proportionate flow-shop scheduling with rejection
Journal of the Operational Research Society, 2016In many heavily loaded manufacturing systems, managers routinely make use of outsourcing options in order to maintain reasonable Quality of Service for customers. Thus, there is a strong need to provide tools for managers to economically coordinate sourcing and scheduling decisions.
Dvir Shabtay, Daniel Oron
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Lot sizing in a no-wait flow shop
Operations Research Letters, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hamilton Emmons, Kamlesh Mathur
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A Note on Permutation Flow Shop Problem
Annals of Operations Research, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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