Results 301 to 310 of about 14,109,241 (348)

Scheduling of Rolling Ingots Production

2003
We consider a real-world scheduling problem arising in the context of a rolling ingots production. We review the production process and discuss peculiarities that have to be observed when scheduling a given set of production orders on the production facilities.
Christoph Schwindt, Norbert Trautmann
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Random and evolution algorithms of the tasks scheduling and of the production scheduling

2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2005
This work presents contemporary artificial intelligence tools - evolution algorithms and random algorithms designed for the optimalisation of the production scheduling problem for multi-assortment short-series production. The essential idea of SZEZA method has been presented. SZEZA method has the ability of part control of the sequence of tasks.
Tadeusz Witkowski, Arkadiusz Antczak, Pawel Antczak   +2 more
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Production planning and scheduling in the context of cyclic delivery schedules

Computers & Industrial Engineering, 1999
Abstract This paper considers a factory which produce n items on a single facility and which is confronted to a cyclic demand: each item is ordered relatively to a cyclic delivery schedule with a mean quantity for each delivery. This kind of « contract-order a is found very frequently, especially in the big distribution and the automotive sectors. So,
Bahroun, Zied, Baptiste, P., Campagne, Jean Pierre, Moalla, Mohamed   +3 more
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Integrated production and distribution scheduling with a perishable product

European Journal of Operational Research, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Priyantha Devapriya, William G. Ferrell, H. Neil Geismar   +2 more
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Production Scheduling and Rescheduling with Genetic Algorithms

Evolutionary Computation, 1999
A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations.
Christian Bierwirth, Dirk C. Mattfeld
openaire   +2 more sources

Optimal Production Scheduling in the Dairy Industries

Industrial & Engineering Chemistry Research, 2019
This work presents the application of a Mixed-Integer Linear Programming (MILP) model that was previously developed in our group for the lot-sizing and production scheduling problem in a real-life industry.
G. Georgiadis, G. Kopanos, Antonis Karkaris, Harris Ksafopoulos, M. Georgiadis   +4 more
semanticscholar   +1 more source

On Inventory, Production and Employment Scheduling

Management Science, 1959
The paper extends results of Holt, Modigliani, Muth and Simon in scheduling over time to minimize costs. It studies generalizations of the cost function which are possible without spoiling the essential simplicity of the decision rules. This is illustrated by a costing model which includes: (a) inventories of individual products, (b) a more realistic ...
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Controlling a Production Scheduling System

Operational Research Quarterly (1970-1977), 1972
When using an automatic production scheduling system management require the system to respond to different overall policies, such as clear all arrears or minimize average lateness weighted by job importance. In many commercial scheduling packages there is no quantitative explanation of how to achieve this response. A successfully implemented scheduling
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A Common Error in Production Scheduling

Journal of the Operational Research Society, 1974
Many production scheduling systems use schedules of planned start/finish times for jobs up to a given planning horizon which includes the period between rescheduling operations. The integrity and usefulness of such schedules depends on the accuracy of the estimated time data which is available on the operations to be performed.
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The Production Schedule vs. The Actual Schedule

Anti-AI scan ceiling: 0.0 (compile-v3 enforced). Sources cited: 32; facts indexed: 32 (research.json in deposition bundle). Voice profile: voice/hibranwar.yml. Imprint: groundwrk. Tier: typical. Thesis-driven outline (thesis.yml in deposition bundle).
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