Control of a production-inventory system using a PID controller and demand prediction

Tosetti, Santiago; Patiño, H. Daniel; Capraro, Flavio; Gambier, Adrián

doi:10.3182/20080706-5-kr-1001.00319

Cited by 9 publications

(12 citation statements)

References 12 publications

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“…In the same way, the forecasting method is an exponential smoothing where time constant T represents the smoothing time constant [15] [17] [30]. The APIOBPCS model utilizes three policies (demand, inventory level, pipeline policies) to determine the Order Rate (ORATE) [25]. The average consumption rate AV based on exponential smoothing forecasts with time constant T is the forward control policy.…”

Section: Mathematical Modellingmentioning

confidence: 99%

“…Cannella and Ciancimino [24] investigated how different smoothing parameters of the APIOBPCS model impact on the performance of the system under progressive information sharing strategies. Tosetti et al [25] assessed the performance of adding the Proportional, Integrative and Derivative (PID) controller to the APIOBPCS model. AL-Khazraji et al [26] used simulation experiments to investigate the impact of applying two classical controller strategies on the performance of the production-inventory control system based on the state space approach.…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Feedback Information on Dynamics Performance of Production-Inventory Control Systems

Al-Khazraji¹,

Cole²,

Guo³

2021

Preprint

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The aim of this paper is to examine the beneficial impact of feedback information in the dynamics of production-inventory control systems. Two production-inventory control system models are analyzed: APIOBPCS and 2APIOBPCS models. The simulation-based experiment designs were conducted by using the state-space equations of the two models. The bullwhip effect as measured by the variance ratio between the order rate and the consumption rate, and inventory responsiveness as measured by the Integral of Absolute Error between the actual and the target levels of inventory, are two metrics used to evaluate the performance of the production-inventory control system in response to a random customer demand. To ensure that both models work under optimal performance, multi-objective particle swarm optimization (MOPSO) is employed to address the problem of tuning the controller’s parameters. The simulation results show the 2APIOBPCS model outperforms the APIOBPCS model at achieving the desired bullwhip effect and being able to provide better inventory responsiveness. The improvement in the inventory responsiveness becomes more significant when the system operates under mismatched lead time and/or an initial condition.

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Section: Mathematical Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Impact of Feedback Information on Dynamics Performance of Production-Inventory Control Systems

Al-Khazraji¹,

Cole²,

Guo³

2021

Preprint

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“…Silva 2017 A própria dinâmica da indústria do vestuário contribui para algumas ações como a antecipação de processos e a compra de materiais por meio de previsões de demanda produtiva e venda (SILVA et al, 2018). Deste modo para atender aos prazos referentes a produção e entrega dos produtos a compra por previsão é um fato comum -independente do porte ou tipo de comercialização -existente nas empresas de vestuário (POZO, 2007;TOSETTI et al, 2008;SILVA, 2017). Talvez essa seja uma das maiores atribuições do setor de compras, no contexto do vestuário, adequar índices de materiais por meio de previsões.…”

Section: O Processo De Compras No Contexto Do Vestuáriounclassified

“…Livro Tosetti et al, (2008) Trazem uma abordagem sobre a aquisição de materiais frente a um ambiente de negócios com mudanças de demanda e de mercado. Não sendo uma abordagem específica para indústrias de vestuário.…”

Section: Livrounclassified

O processo de compras: um estudo teórico de sua relação com o ciclo produtivo da indústria do vestuário

Silva

Pinheiro

Menegassi

2020

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

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A área de compras tem importância estratégica para os negócios dos diversos setores, inclusive para as indústrias de vestuário, que exige um olhar sob suas especificidades. Este trabalho tem como objetivo analisar como o processo de compras está inserido no ciclo produtivo da indústria do vestuário identificando as relações existentes com outros processos. O delineamento utilizado foi uma pesquisa exploratória de caráter qualitativo, a partir da busca em publicações existentes sobre o tema. Descobriu-se que há lacunas sobre o processo de compras em publicações da área de moda, que o setor de compras atua juntamente com outros setores com a finalidade de suprir o curto ciclo produtivo, que esse setor é responsável por criar informações importantes para o processo produtivo. A partir disso o estudo apresenta um esquema que contempla a inserção do processo de compras no ciclo produtivo da indústria do vestuário. Esse resultado é importante por destacar a importância da área de compras para o setor de vestuário e consequentemente beneficia as discussões em torno da cadeia de suprimentos.

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“…The inventory and order based production control system (IOBPCS) model proposed by Towill [13] has been recognized as a common framework for modeling the control of a production-inventory system. John et al [14] made an important extension to the IOBPCS model by including the work-in-process (WIP) feedback and proposed the automatic pipeline, inventory, and order based production control system (APIOBPCS) model, which has been widely used for modelling production-inventory systems since then [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Optimization and Simulation of Dynamic Performance of Production–Inventory Systems with Multivariable Controls

2021

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The production–inventory system is a problem of multivariable input and multivariant output in mathematics. Selecting the best system control parameters is a crucial managerial decision to achieve and dynamically maintain an optimal performance in terms of balancing the order rate and stock level under dynamic influence of many factors affecting the system operations. The dynamic performance of the popular APIOBPCS model and the newly modified 2APIOBPCS model for optimal control of production–inventory systems is examined in the study. This examination is based on the leveled ground with a new simulation scheme that incorporates a designated multi-objective particle swarm optimization (MOPSO) algorithm into the simulation, which enables the optimal set of system control parameters to be selected for achieving the situational best possible performance of the production–inventory system under study. The dynamic performance is measured by the variance ratio between the order rate and the sales rate related to the bullwhip effect, and the integral of absolute error related to the inventory responsiveness in response to a random customer demand. Our simulation indicates that the 2APIOBPCS model performed better than or at least no worse than, and more robust than the APIOBPCS model under different conditions.

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Control of a production-inventory system using a PID controller and demand prediction

Cited by 9 publications

References 12 publications

The Impact of Feedback Information on Dynamics Performance of Production-Inventory Control Systems

The Impact of Feedback Information on Dynamics Performance of Production-Inventory Control Systems

O processo de compras: um estudo teórico de sua relação com o ciclo produtivo da indústria do vestuário

Optimization and Simulation of Dynamic Performance of Production–Inventory Systems with Multivariable Controls

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