In dealing with model predictive controllers (MPC), controller tuning is a key design step. Various tuning methods are proposed in the literature which can be categorized as heuristic, numerical and analytical methods. Among the available tuning methods, analytical approaches are more interesting and useful. This paper is based on a proposed analytical MPC tuning approach for plants can be appr...

this paper develops a single-vendor single-retailer supply chain for multi-product. the proposed model is based on vendor managed inventory (vmi) approach and vendor uses the retailer's data for better decision making. number of orders and available capital are the constraints of the model. in this system, shortages are backordered; therefore, the vendor’s warehouse capacity is another limitati...

The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristi...

This paper proposes a new PI/PID controller tuning method within filtered Smith predictor (FSP) configuration in order to deal with various types of time delay processes including stable, unstable and integrating delay dominant and slow dynamic processes. The proposed PI/PID controller is designed based on the IMC principle and is tuned using a new constraint and without requiring any approxima...

Parameter tuning is an optimization problem with the objective of finding good static parameter settings before the execution of a metaheuristic on a problem at hand. The requirement of tuning multiple control parameters, combined with the stochastic nature of the algorithms, make parameter tuning a non-trivial problem. To make things worse, one parameter vector allowing the algorithm to solve ...