Gautam Sardar

Technical Communique: Template generation for continuous transfer functions using interval analysis

This paper presents a template generation algorithm for transfer functions that are continuous in the uncertain system parameters. The algorithm is developed using interval mathematics. The generated templates are of arbitrary accuracy, safe, and reliable. Further, if the magnitude and phase are continuously differentiable functions of the parameters, then the algorithm can be speeded up considerably with the help of an additional step based on the interval Gauss-Seidel method. The convergence, finite termination, safety, and reliability properties of the algorithm are proven, and an example is provided to demonstrate its versatility.

Control and optimization in cement plants

Energy consumption is a crucial parameter in plant performance and a critical factor for optimization. From a control point of view, factors such as nonlinear, multi-variable dynamics and process constraints need to be handled effectively. In addition, several critical process variables and product quality parameters are not measured online in an industrial cement plant. An effective online estimation mechanism along with a knowledge-based control algorithm is thus essential for achieving consistent closed-loop performance. An appropriately designed solution with these components ensures that process variability is significantly reduced along with reduction in specific energy consumption

A template generation algorithm for non-rational transfer functions in QFT designs

In several applications of quantitative feedback theory (QFT) approach to robust controller synthesis, templates of uncertain non-rational transfer functions are required to be numerically generated. An algorithm is proposed paper for generating templates of such transfer functions. The main features of the algorithm are: (i) it is applicable to transfer functions expressible in terms of most standard FORTRAN functions, (ii) nonlinear correlated parametric dependencies are permitted, (iii) it yields templates that are guaranteed to include the actual ones, and (iv) it is simple to implement using any interval arithmetic compiler. Examples are given to demonstrate the capabilities of the proposed algorithm.

Reducing inventory cost for a medical device manufacturer using simulation

Seeking to enter new geographic markets where expected margins are relatively tight, a manufacturer of medical devices must reduce inventory and related costs in its finished goods supply chain. The manufacturers supply chain includes four echelons - factories, distribution centers, regional salespeople (also known as “vans”), and customers. The amount of inventory typically held and corresponding reorder policies near the customer end of this supply chain are not known. A simulation approach was selected to provide insight into those inventory levels based on assumed reorder policies. Analysis conducted using a simulation model implemented using SimPy point to significant potential savings, with the value of inventory-related savings over a four year period approaching

Supply Chain Planning under Uncertainty: A Chance Constrained Programming Approach

200 million.

Challenges in Achieving Optimal Asset Performance Based on Total Cost of Ownership

Uncertainty issues associated with a multi-site, multi-product supply chain planning problem has been analyzed in this paper using the chance constraint programming approach. In literature, such problems have been addressed using the two stage stochastic programming approach. While this approach has merits in terms of decomposition, computational complexity even for small size planning problem is large. This problem is overcome in our paper by adopting the chance constraint programming approach for solving the mid term planning problem. It is seen that this approach is generic, relatively simple to use, and can be adapted for bigger size planning problems as well. We demonstrate the proposed approach on a relatively moderate size planning problem taken from the work of McDonald and Karimi (1997) and discuss various aspects of uncertainty in context of this problem.

Production Scheduling For an Auto-Component Manufacturing Plant Using Model Predictive Control

Ensuring optimal performance of assets in capital-intensive industry sectors is crucial towards sustained growth in terms of returns on investment (ROI). The primary challenge is to achieve optimal utilization of the assets over their productive lifecycle and identify the need and timing for replacement and/or enhancement. Such decisions are not trivial and can affect the ROI considerably. Operational and planning decisions related to the assets need to be based on the concept of Total Cost of Ownership (TCO) if they have to be economically optimal. The technical challenges posed are in terms of selection of a mix of techniques and methods for extracting useful information/knowledge from raw operational data, its use to construct predictive models for asset performance, and use of the models to achieve continuous optimization of asset performance with TCO consideration. It requires a model based approach to achieve asset performance over multiple lifecycles. Technology enablers are also a crucial element in achieving the maturity of asset management processes and practices in the organization. The paper discusses an EAM maturity model and its technological requirements that would help organizations to achieve sustained asset performance based on TCO.

Taking the Grunt Work Out of Tolerance Optimization

A two-level approach for production scheduling is suggested. The first level uses model predictive control (MPC) to allocate resource capacities, while the second level does the sequencing. A plant-wide linear optimization problem is formulated for the first level. Next, smaller independent clusters within the plant are identified and production is sequenced for these. This approach has the advantages of centralized allocation and being linear can solve large size problems. The sequencer takes care of local effects, that otherwise would make the MPC problem complex. The rolling horizon feature of MPC helps working under uncertain conditions.

Towards resilient supply chains: Uncertainty analysis using fuzzy mathematical programming

The activity of tolerance allocation and optimization is a critical step in the product design process. This inherent trade-off between design objectives and process capability poses challenges in achieving right tolerances, both technically and effort-wise. Traditional methods in tolerance allocation are mostly regressive and are constrained by selection of the manufacturing processes. A progressive approach to tolerance allocation that does not assume these processes helps in achieving optimality of the tolerances and selection of manufacturing processes to realize the design. The two-stage process suggested in this paper formulates an optimization problem that allocates the tolerances based on sensitivities of tolerance values at the first stage followed by manufacturing process selection and further optimization to adhere to the processes selected in the second stage. The approach aims at achieving optimal allocation of tolerances and assignment of the manufacturing processes, while keeping the optimization problem computationally simple, although iterative.Copyright