Production and process management: An optimal control approach
Author(s) -
Md. Haider Ali Biswas,
Ahad Ali
Publication year - 2016
Publication title -
yugoslav journal of operations research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.221
H-Index - 21
eISSN - 1820-743X
pISSN - 0354-0243
DOI - 10.2298/yjor141015008k
Subject(s) - optimal control , computer science , process (computing) , production (economics) , mathematical optimization , quality (philosophy) , constraint (computer aided design) , nonlinear system , control (management) , process control , key (lock) , production manager , state (computer science) , ode , industrial engineering , control engineering , engineering , mathematics , algorithm , mechanical engineering , philosophy , physics , computer security , epistemology , quantum mechanics , artificial intelligence , economics , macroeconomics , operating system
Optimal control and efficient management of industrial products are the key for sustainable development in industrial and process engineering. It is well-known that proper maintenance of process performance, ensuring the quality products after a long time operation of the system, is desirable in any industry. Nonlinear dynamical systems may play crucial role to appropriately design the model and obtain optimal control strategy in production and process management. This paper deals with a mathematical model in terms of ordinary differential equations (ODEs) that describe control of production and process arising in industrial engineering. The optimal control technique in the form of maximum principle, used to control the quality products in the operation processes, is applied to analyze the model. It is shown that the introduction of state constraint can be advantageous for obtaining good products during the longer operation process. We investigate the model numerically, using some known nonlinear optimal control solvers, and we present the simulation results to illustrate the significance of introducing state constraint onto the dynamics of the model
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