Time-optimal control of parts-routing in a manufacturing system with failure-prone machines

Olsder, Geert Jan; Suri, Rajan

doi:10.1109/cdc.1980.271895

Cited by 88 publications

(35 citation statements)

References 4 publications

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“…Olsder and Suri [7] exploited the formalism of Richel [8] and developed the dynamic programming equation of the optimal control policy for a manufacturing system operating in an uncertain environment according to a homogeneous Markov process. They focus on the production planning of a manufacturing system composed of a single machine that produces one part type and whose dynamics is described by a homogeneous Markov Chain (constant transition rates).…”

Section: Bureau Of Labor Statistics (Bls) Classifies Pharmaceutical Cmentioning

confidence: 99%

Joint production and subcontracting planning of unreliable multi-facility multi-product production systems

Assid

Gharbi

Dhouib

2015

Omega

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This article addresses the problem of joint optimization of production and subcontracting of unreliable production systems. The production system considered presents a common problem in the pharmaceutical industry. It is composed of multiple production facilities with different capacities, each of which is capable of producing two different classes of medications (brand name and generic). The resort to subcontracting is double: first, it involves the quantity of products received on a regular basis in order to compensate for insufficient production capacity in existing facilities, second, when needed, urgent orders are also launched in order to reduce the risk of shortages caused by breakdowns of manufacturing facilities. Failure, repair and urgent delivery times may be represented by any probability distributions.The objective is to propose a general control policy for the system under consideration, and to obtain, in the case of two facilities, optimal control parameters that minimize the total incurred cost for a specific level of the customer service provided. Given the complexity of the problem considered, an experimental optimization approach is chosen in order to determine the optimal control parameters. This approach includes experimental design, analysis of variance, response surface methodology and simulation modeling. It allows the accurate representation of the dynamic and stochastic behaviours of the production system and the assessment of optimal control parameters. Other control parameters which represent the subcontracting are introduced and three joint production / subcontracting control policies (general, urgent, regular) This policy offers not only cost savings, but is also easier to manage, as compared to that proposed by Dror et al. [1]. Numerical examples and a sensitivity analysis are also performed to illustrate the robustness of the proposed control policy and the solution approach.

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Section: Bureau Of Labor Statistics (Bls) Classifies Pharmaceutical Cmentioning

confidence: 99%

Joint production and subcontracting planning of unreliable multi-facility multi-product production systems

Assid

Gharbi

Dhouib

2015

Omega

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“…Rishel [14] has identified the sufficient and necessary conditions for optimizing a finite-state Markov process using dynamic programming. Older and Suri [15] have extended Rishel's formalism by presenting FMS production planning optimality as a problem of stochastic control subject to random failure. They obtained dynamic programming equations of optimal control, while the differential equations remained unsolved due to their complexity.…”

Section: Introductionmentioning

confidence: 99%

Considering human error in optimizing production and corrective and preventive maintenance policies for manufacturing systems

Emami-Mehrgani

Neumann

Nadeau

et al. 2016

Applied Mathematical Modelling

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This article extends previous work on co-optimization of production and corrective and preventive maintenance including lockout/tagout. We study the impact of human error on repairable manufacturing systems subject to random failure over an infinite planning horizon and its implications for system capacity and inventory policies, and we derive an optimal policy for minimizing production cost based on machinery maintenance and inventory management while meeting market demand over an infinite horizon. A numerical example is provided to demonstrate the usefulness of the proposed approach and a sensitivity analysis is presented to confirm the efficiency of the control policy.

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“…The problem of controlling manufacturing systems with unreliable machines was formulated as a stochastic control problem by Older and Suri (1980). Failure and repair processes were supposed to be described using homogeneous Markov processes.…”

Section: Introductionmentioning

confidence: 99%

Maintenance scheduling and production control of multiple-machine manufacturing systems

Gharbi

Kenné

2005

Computers & Industrial Engineering

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This paper deals with the production and preventive maintenance control problem for a multiple-machine manufacturing system. The objective of such a problem is to find the production and preventive maintenance rates for the machines so as to minimize the total cost of inventory/backlog, repair and preventive maintenance. A two-level hierarchical control model is presented, and the structure of the control policy for both identical and non-identical manufacturing systems is described using parameters, referred to here as input factors. By combining analytical formalism with simulation-based statistical tools such as experimental design and response surface methodology, an approximation of the optimal control policies and values of input factors are determined. The results obtained extend those available in existing literature to cover non-identical machine manufacturing systems. A numerical example and a sensitivity analysis are presented in order to illustrate the robustness of the proposed approach. The extension of the proposed production and preventive maintenance policies to cover large systems (multiple machines, multiple products) is discussed.

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Time-optimal control of parts-routing in a manufacturing system with failure-prone machines

Cited by 88 publications

References 4 publications

Joint production and subcontracting planning of unreliable multi-facility multi-product production systems

Joint production and subcontracting planning of unreliable multi-facility multi-product production systems

Considering human error in optimizing production and corrective and preventive maintenance policies for manufacturing systems

Maintenance scheduling and production control of multiple-machine manufacturing systems

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