Results 151 to 160 of about 468,839 (205)
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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 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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2012
We introduce four types of flexibility encountered in a flow shop: job routing through a hybrid shop, machine assignment, allocation of a scarce resource over the tasks to speed up processing, and the composition of daily production batches to satisfy requirements for a finite set of parts over a finite horizon.
Hamilton Emmons, George Vairaktarakis
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We introduce four types of flexibility encountered in a flow shop: job routing through a hybrid shop, machine assignment, allocation of a scarce resource over the tasks to speed up processing, and the composition of daily production batches to satisfy requirements for a finite set of parts over a finite horizon.
Hamilton Emmons, George Vairaktarakis
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Flow Shops, Job Shops and Open Shops (Stochastic)
2008The results for stochastic flow shops, job shops, and open shops are somewhat less extensive than those for their deterministic counterparts.
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2012
When job parameters are uncertain or unpredictable, new types of policies become possible. Besides static policies, we now should consider dynamic policies, with or without preemption. Objectives too have more variety. The makespan, for example, is now random; we usually choose to minimize its expectation.
Hamilton Emmons, George Vairaktarakis
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When job parameters are uncertain or unpredictable, new types of policies become possible. Besides static policies, we now should consider dynamic policies, with or without preemption. Objectives too have more variety. The makespan, for example, is now random; we usually choose to minimize its expectation.
Hamilton Emmons, George Vairaktarakis
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Permutation flow‐shop theory revisited
Naval Research Logistics Quarterly, 1978AbstractThe paper provides a new theoretical framework for generating dominance conditions and lower bounds and for solving special cases. All existing and new results have been derived in a routine and simple manner.
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Inequalities for stochastic flow shops and job shops
Applied Stochastic Models and Data Analysis, 1986AbstractConsider an m‐machine flow shop with n jobs. The processing time of job j, j = 1,…, n, on each one of the m machines is equal to the random variable Xj and is distributed according to Fj. We show that, under certain conditions, more homogeneous distributions F1,…, Fn result in a smaller expected makespan.
Pinedo, Michael, Wie, Sung-Hwan
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Routing open shop and flow shop scheduling problems
European Journal of Operational Research, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu, Wei +3 more
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Proportionate Flow Shop Scheduling with Rejection
Asia-Pacific Journal of Operational Research, 2017We consider the problem of scheduling [Formula: see text] jobs with rejection on a set of [Formula: see text] machines in a proportionate flow shop system where the job processing times are machine-independent. The goal is to find a schedule to minimize the scheduling cost of all accepted jobs plus the total penalty of all rejected jobs.
Li, Shi-Sheng +2 more
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2012
In this chapter, job processing requirements are considered to be uncertain. They are no longer assumed to be deterministically known. One modeling approach would be to consider processing time probability distributions, and indeed this is done in a later chapter.
Hamilton Emmons, George Vairaktarakis
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In this chapter, job processing requirements are considered to be uncertain. They are no longer assumed to be deterministically known. One modeling approach would be to consider processing time probability distributions, and indeed this is done in a later chapter.
Hamilton Emmons, George Vairaktarakis
openaire +1 more source