Results 251 to 260 of about 6,312 (313)

Variable iterated greedy for energy-efficient hybrid flow shop scheduling problem with blocking constraint

Jinli Liu, Haoxiang Qin, Yuyan Han, Yuting Wang   +3 more
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Centralized scheduling, decentralized scheduling or demand scheduling? How to more effectively allocate and recycle shared takeout lunch boxes. [PDF]

PLoS One
Bai Y, Liu D, Ma J.
europepmc   +1 more source

Dynamic job shop scheduling under multiple order disturbances using deep reinforcement learning. [PDF]

Sci Prog
Sun Z, Han W, Gao L, Zhu C, Lyu Q.
europepmc   +1 more source

Dual-self-learning co-evolutionary algorithm for energy-efficient flexible job shop scheduling problem with processing- transportation composite robots. [PDF]

Sci Rep
Zhang M, Zhou M, Zhang L, Zhang Z.
europepmc   +1 more source

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Multiobjective flow-shop scheduling

Naval Research Logistics, 1990
Summary: Previous research on the scheduling of multimachine systems has generally focused on the optimization of individual performance measures. This article considers the sequencing of jobs through a multimachine flow shop, where the quality of the resulting schedule is evaluated according to the associted levels of two scheduling criteria, schedule
Daniels, Richard L., Chambers, Robert J.
openaire   +2 more sources

Proportionate Flow Shop Scheduling with Rejection

Asia-Pacific Journal of Operational Research, 2017
We 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, Qian, De-Liang, Chen, Ren-Xia   +2 more
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Flow Shop Scheduling with Resource Flexibility

Operations Research, 1994
This paper explores the improvements in manufacturing efficiency that can be achieved by broadening the scope of production scheduling to include both the sequencing of work and the coordination of the resource inputs required to perform work. Recognizing that some resources are inherently flexible and thus can be reassigned dynamically to processing ...
Daniels, Richard L., Mazzola, Joseph B.
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FLOW SHOP SCHEDULING WITH REINFORCEMENT LEARNING

Asia-Pacific Journal of Operational Research, 2013
Reinforcement 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, WEIPING WANG, SHOUYAN ZHONG, KAISHUN HU   +3 more
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Heuristics for flow-shop scheduling

International Journal of Production Research, 1980
Existing methods are reviewed and new heuristics examined and developed for the flow-shop scheduling problem. Comparative tests are carried out using simulation methods on different sizes of problem and with different variability of processing time data.
J. R. KING, A. S. SPACHIS
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Flow Shop Scheduling

2019
Consider scheduling tasks on dedicated processors or machines. We assume that tasks belong to a set of n jobs, each of which is characterized by the same machine sequence.
Jacek Blazewicz, Klaus H. Ecker, Erwin Pesch, Günter Schmidt, Malgorzata Sterna, Jan Weglarz   +5 more
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