Integrated maintenance and production planning with endogenous uncertain yield

•We introduce a novel integrated decision making framework for production planning and maintenance problems.•Stochastic endogenous (decision dependent) yield is considered.•The integrated model lets the decision maker evaluate the trade-offs involved among production related decisions and uncertain...

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Vydáno v:Reliability engineering & system safety Ročník 179; s. 52 - 61
Hlavní autor: Ekin, Tahir
Médium: Journal Article
Jazyk:angličtina
Vydáno: Barking Elsevier Ltd 01.11.2018
Elsevier BV
Témata:
Augmented probability simulation
Computer simulation
Conduction
Conductors
Decision making
Decisions
Endogenous uncertainty
Integrated decision making
Maintenance
Mathematical models
Probability
Production planning
Reliability engineering
Repair & maintenance
Sensitivity analysis
Stochastic models
Stochastic programs
Uncertainty
Stochastic programs
Augmented probability simulation
Maintenance
Endogenous uncertainty
Integrated decision making
Production planning
ISSN:0951-8320, 1879-0836
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Shrnutí:•We introduce a novel integrated decision making framework for production planning and maintenance problems.•Stochastic endogenous (decision dependent) yield is considered.•The integrated model lets the decision maker evaluate the trade-offs involved among production related decisions and uncertain machine yield in a two-stage setting.•The model is solved by augmented probability simulation based optimization. The relationships among production planning, maintenance decisions and machine yield are crucial in a number of manufacturing environments such as the semi-conductor industry. This paper presents an integrated maintenance and production decision making framework with stochastically proportional endogenous yield rate and random demand. Finding the solution for this two-stage nonlinear stochastic program with endogenous uncertainty is not straightforward, and has not been considered previously. An augmented probability simulation based method is utilized to solve for the proposed decision model. We demonstrate the use of the proposed approach by conducting a numerical study and sensitivity analysis. We discuss the trade-offs involved among the yield, and simultaneous decisions of production planning and maintenance.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2017.07.011