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

Narain Gupta*, Goutam Dutta, Robert Fourer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)43-56
Number of pages14
JournalDecision Support Systems
Volume64
DOIs
StatePublished - Jan 1 2014

Keywords

  • Database structure
  • Decision support system
  • Management science
  • Optimization
  • Process industries
  • Stochastic programming (SLP)

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

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