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

a r t i c l e i n f o 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. 1. Introduction and motivation We introduce a user friendly, model data independent, model solver independent, stochastic optimization based DSS for strategic planning in a process industry. This research is an extension of an earlier work by Dutta [12], and Dutta & Fourer [14,24] where a multi-period optimization based DSS was developed for process industries. Fourer [24], in his seminal work, showed that the fundamental principles of relational database construction could be used to represent a linear program. This work was carried out for a single period deterministic optimization. Dutta [12] and Dutta & Fourer [13,14] extended the research of single period planning to multiple period planning. These applications ranged from a steel company in India [17,19], a steel company in North America [12,13], to a pharmaceutical company in Western India [15] and even further to an aluminum company [16] in Eastern India. The DSS custom-ized for the integrated steel plant in North-America, demonstrated a potential impact of 16–17% increase in the bottom line of the company [13]. In the first part of the paper, we discuss the design and development of a multiple period, multiple scenario DSS. While several researchers [4,22,27,28] have done work in the application of stochastic optimization and a set of researchers [11,18,20] has worked on the need for user friendly DSS, this is probably the first attempt that tries to integrate these two concepts. Here, we attempt to address the following seven questions in detail: 1. How is the database structure of a SLP model …