Abstract
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.
Highlights
Simulation of a production–inventory system, even the simplest model comprising one manufacturer and one retailer, is a problem of multivariable input and multivariant output in mathematics
multi-objective particle swarm optimization (MOPSO) algorithm into simulation using the APIOBPCS model [22], once feeding the demand pattern and production lead time to the system, the automatic simulation process can produce a set of control parameters (Ti, Tw and time constant (Ta) ), in which each set can achieve the best balance between the variance ratio (Var ) and the integral of absolute error (IAE), in other words, between the bullwhip effect and customer service level (CSL)
Modelling production–inventory system is a problem of multivariable input and multivariant output in mathematics
Summary
Simulation of a production–inventory system, even the simplest model comprising one manufacturer and one retailer, is a problem of multivariable input and multivariant output in mathematics. A production–inventory system is a basic unit in supply chain that integrates inventory control policies with the production process. There are three major parts in APIOPBCS, the forecasting mechanism, the production lead time, and the controller strategy [15,16,17,18,19,20,21,22]. . The forecasting mechanism is a feed-forward loop designed to provide the estimated average sales (AVCONS ) and to set the desired work-in-process (WIP) level (DWIP ). The estimated average sales (AVCONS ) is commonly used to control the inventory steady-state error. Exponential smoothing with time constant (Ta ) representing the average age of the data is a forecasting method commonly used to smooth the demand because of its simplicity and comprehensibility in mathematics for practitioners
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
