Integration of scheduling and control can improve the overall performance of a manufacturing process. However, the integration leads to a mixed-integer dynamic optimization problem (MIDO), which could be challenging to solve. We propose a novel algorithm based on the generalized Bender decomposition method that takes advantage of the special structure of the integrated problem. It decomposes th...

Robust control systems may successfully be designed by ~'=-optimization, in particular, by reformulating the design problem as a mixed sensitivity problem. Abstract-The paper presents a tutorial exposition of ~=-optimal regulation theory, emphasizing the relevance of the mixed sensitivity problem for robust control system design.

This paper considers the design of robust`1 estimators based on multiplier theory (which is intimately related to the mixed structured singular value theory) and the application of robust`1 estimators to robust fault detection. The key to estimator-based, robust fault detection is to generate residuals which are robust against plant uncertainties and external disturbance inputs, which in turn r...

—The do-not-exceed (DNE) limit method was proposed to accommodate more variable renewable generation (VRG) securely. However, the lack of involving discrete recourse control precludes this method from gaining more flexibility for better VRG integration. This letter formulates a multi-period DNE limit model considering continuous and discrete recourse controls. This model belongs to two-stage ro...

Cell Formation (CF) is the initial step in the configuration of cell assembling frameworks. This paper proposes a new mathematical model for the CF problem considering aspects of production planning, namely inventory, backorder, and subcontracting. In this paper, for the first time, backorder is considered in cell formation problem. The main objective is to minimize the total fixed and variable...

optimization models have been used to support decision making in production planning for a long time. however, several of those models are deterministic and do not address the variability that is present in some of the data. robust optimization is a methodology which can deal with the uncertainty or variability in optimization problems by computing a solution which is feasible for all possible ...

in this paper, we introduce a model  to optimization of milk run system that is one of vrp problem with time window and uncertainty in inventory. this approach led to the routes with minimum cost of transportation while satisfying all inventory in a given bounded set of uncertainty .the problem is formulated as a robust optimization problem. since the resulted problem illustrates that grows up ...

In this paper, a mixed-integer linearized programming (MINLP) model is presented to design a group layout (GL) of a cellular manufacturing system (CMS) in a dynamic environment with considering production planning (PP) decisions. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. There are also some features that make the presented...

This work introduces a robust formulation of the uncertain set covering problem combining the concepts of robust and probabilistic optimization and defines ’Γ-robust α-covers’. It is shown that the proposed robust uncertain set covering problem can be stated as a compact mixed-integer linear programming model which can be solved with modern computer software. This model is a natural extension o...