An Optimization-Based Decision Support System for Strategic Planning in a Process Industry: The Case of an Aluminum Company in India

<div align="justify">We describe how a generic multi-period optimization-based decision support system (DSS) can be used for strategic planning in process industries. The DSS is built on five fundamental elements . materials, facilities, activities, storage areas and time periods. It requires little direct knowledge of optimization techniques to be used effectively. Results based on real data from an aluminum company in India demonstrate significant potential for improvement in profits.</div>

Database Structure for a Class of Multi-Period Mathematical Programming Models

We describe how a generic multi-period optimization-based decision support system can be used for strategic and operational planning in a company whose processes can be described in terms of five fundamental elements: Materials, Facilities, Activities, Times and Storage-Areas. We discuss the issues of interface design, data reporting and updating, and production and profit planning. We also compare the performances of two different types of database structures with respect to optimization

A modern approach to computer systems for linear programming

At head of title: Center for Computational Research in Economics and Management Science

XML modeling language for linear programming : specification and examples

"Center for Computational Research in Economics and Management Science.

Database Structure for a Class of Multi-Period Mathematical Programming Models (Revised-May 07)

We describe how a generic multi-period optimization-based decision support system can be used for strategic and operational planning in a company whose processes can be described in terms of five fundamental elements: Materials, Facilities, Activities, Times and Storage-Areas. We discuss the issues of interface design, data reporting and updating, and production and profit planning. We also compare the performances of two different types of database structures with respect to optimization. [This paper is a revised version of an earlier working paper (No.2000-01-06)]

An expanded database structure for a class of multi-period, stochastic mathematical programming models for process industries

We introduce a multiple scenario, multiple period, optimization-based decision support system (DSS) for strategic planning in a process industry. The DSS is based on a two stage stochastic linear program (SLP) with recourse for strategic planning. The model can be used with little or no knowledge of Management Sciences. The model maximizes the expected contribution (to profit), subject to constraints of material balance, facility capacity, facility input, facility output, inventory balance constraints, and additional constraints for non-anticipativity. We describe the database structure for a SLP based DSS in contrast to the deterministic linear programming (LP) based DSS. In the second part of this paper, we compare a completely relational database structure with a hierarchical one using multiple criteria. We demonstrate that by using completely relational databases, the efficiency of model generation can be improved by 60% compared to hierarchical databases

An Optimization-Based Decision Support System for Strategic Planning in a Process Industry: The Case of a Pharmaceutical Company in India

We describe how a generic multi-period optimization-based decision support system (DSS) can be used for strategic planning in process industries. Built on five fundamental elements – materials, facilities, activities, time periods and storage areas – this DSS requires little direct knowledge of optimization techniques to be used effectively. It is user friendly and requires little knowledge of optimization. Results based on real data from a pharmaceuticals company in India demonstrate significant potential for improvements in revenues and profits.

Constraints and AI Planning

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the authorâ s and shouldnâ t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.Tackling real-world problems often requires to take various types of constraints into account. Such constraint types range from simple numerical comparators to complex resources. This article describes how planning techniques can be integrated with general constraint-solving frameworks, like SAT, IP and CP. In many cases, the complete planning problem can be cast in these frameworks