Mathematical programming representations of the dynamics of continuous-flow production systems

Tan, Barış

doi:10.1080/0740817x.2014.892232

Cited by 20 publications

(6 citation statements)

References 33 publications

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“…In relation to representative models of continuous flow systems, Ref. [16] develops a mathematical programming model of discrete events in continuous time with a discrete and continuous mixed state space. The research focuses on optimally controlling the flow of the product through the system with high efficiency.…”

Section: A Short Literature Reviewmentioning

confidence: 99%

Mathematical Modeling of Manufacturing Lines with Distribution by Process: A Markov Chain Approach

2021

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Today, there are a wide variety of ways to produce goods in a manufacturing company. Among the most common are mass or line production and process production, both of which are antagonists. In an online production system, materials move from station to station, receiving added value on a well-defined layout. In a production line by process, the materials randomly visit a set of machines strategically located in order to receive a treatment, almost always through metalwork machines, according to the final product of which they will be part. In this case, there is not a predefined layout, as the incoming materials are sectioned and each piece forms a continuous flow through different workstations to receive some process. This activity depends on the function of the product and its final destination as a component of a finished product. In this proposal, Markov chain theory is used to model a manufacturing system by process in order to obtain the expected values of the average production per machine, the total expected production in all the facilities, the leisure per machine and the total productive efficiency of the system, among other indicators. In this research, we assume the existence of historical information about the use of the equipment, its failures, the causes of failure and their repair times; in any factory, this information is available in the area of manufacturing engineering and plant engineering. From this information, statistical frequency indicators are constructed to estimate transition probabilities, from which the results presented here are derived. The proposal is complemented with a numerical example of a real case obtained from a refrigerator factory established in Mexico in order to illustrate the results derived from this research. The results obtained show their feasibility when successfully implemented in the company.

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Section: A Short Literature Reviewmentioning

confidence: 99%

Mathematical Modeling of Manufacturing Lines with Distribution by Process: A Markov Chain Approach

2021

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“…To date, performance evaluation and parameter optimization have been studied as separate problems for the most part. One exception is a recent stream of research that uses mathematical programming approaches for the simultaneous simulation and optimization of discrete event dynamic systems, based on the seminal work of Chan andSchruben (2008) (e.g., Helber et al 2011;Matta 2012, 2013;Weiss and Stolletz 2015;Tan 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of optimal buffer allocation in flow lines under installation buffer, echelon buffer, and CONWIP policies

Liberopoulos

2019

Flex Serv Manuf J

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We compare the optimal buffer allocation of a manufacturing flow line operating under three different production control policies: installation buffer (IB), echelon buffer (EB), and CONWIP (CW). IB is the conventional policy where each machine may store the parts that it produces only in its immediate downstream buffer if the next machine is occupied. EB is a more flexible policy where each machine may store the parts that it produces in any of its downstream buffers. CW is a special case of EB where the capacities of all buffers, except the last one, are zero. The optimization problem that we consider is to maximize the average gross profit (AGP) minus the average cost (AC), subject to a minimum average throughput constraint. AGP is defined as the average throughput of the line weighted by the gross marginal profit (selling price minus production cost per part), and AC is the sum of the average WIP plus total buffer capacity plus transfer rate of parts to remote buffers, weighted by the inventory holding cost rate, the cost of storage space, and the marginal cost of transferring parts to remote buffers, respectively. Numerical results show that the optimal EB policy generally outperforms the optimal IB and CW policies. They also show that as the production rates of the machines decrease, the relative advantage in performance of the EB policy over the other two policies increases. When the cost of transferring parts to remote buffers increases, the dominance of the EB policy over the IB policy decreases while the dominance of the EB policy over CW increases.

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“…For an optimal production control and pricing problem of an assembly system, Keblis and Feng [31] proved that the optimal production control follows a state-dependent base-stock policy and the optimal pricing follows a threshold switching policy. Tan [32] proposed a mathematical programming model for the optimal production flow rate control problem of a continuous material flow system with an unreliable machine and deterministic demand, based which the structure of optimal production control policy and the optimal control parameters are obtained.…”

Section: B Optimal Production Controlmentioning

confidence: 99%

Heuristic Production and Sale Policy for a Two-Product-Type Manufacturing System With Downward Substitution

Wang

Gershwin

2015

IEEE Trans. Syst. Man Cybern, Syst.

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In this paper, we study a manufacturing system with downward substitution in sales, i.e., selling superior products as inferior ones when the demand for the latter cannot be met immediately, in order to reduce the backlog cost. However, downward substitution in sales also causes profit loss. Therefore, controlling a manufacturing system with downward substitution in sales in order to minimize total production cost is a problem worthy of investigation. In this paper, we focus on the production and sale control problem for a two-product-type manufacturing system with downward substitution in sales and time-delay between the releasing of raw materials and the output of final goods. The system is decomposed into two subsystems. In the first subsystem, the state variables are total production surpluses (i.e., the sum of work-in-process and final goods surpluses), and a hedging point policy with a new structure is constructed to control the raw material releasing rates of the two product-types. In the second subsystem, the state variables are the final goods surpluses, and a new switching curve policy is constructed to control the downward substitution rate in sales. Numerical experiments show that downward substitution in sales contributes to the reduction of total production cost, especially when the time-delay is large.

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Mathematical programming representations of the dynamics of continuous-flow production systems

Cited by 20 publications

References 33 publications

Mathematical Modeling of Manufacturing Lines with Distribution by Process: A Markov Chain Approach

Mathematical Modeling of Manufacturing Lines with Distribution by Process: A Markov Chain Approach

Comparison of optimal buffer allocation in flow lines under installation buffer, echelon buffer, and CONWIP policies

Heuristic Production and Sale Policy for a Two-Product-Type Manufacturing System With Downward Substitution

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