Hybrid method integrating agent-based modeling and heuristic tree search for scheduling of complex batch processes

Yunfei Chu, Fengqi You*, John M. Wassick

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

We propose a hybrid method integrating agent-based modeling and heuristic tree search to solve complex batch scheduling problems. Agent-based modeling describes the batch process and constructs a feasible schedule under various constraints. To overcome myopic decisions of agents, the agent-based simulation is embedded into a heuristic search algorithm. The heuristic algorithm partially explores the solution space generated by the agent-based simulation. Because global information of the objective function value is used in the search algorithm, the schedule performance is improved. The proposed method shares the advantages from both agent-based modeling and mixed integer programing, achieving a better balance between the solution efficiency and the schedule performance. As a polynomial-time algorithm, the hybrid method is applicable to large-scale complex industrial scheduling problems. Its performance is demonstrated by comparing with agent-based modeling and mixed integer programing in two case studies, including a complex one from The Dow Chemical Company.

Original languageEnglish (US)
Pages (from-to)277-296
Number of pages20
JournalComputers and Chemical Engineering
Volume60
DOIs
StatePublished - Jan 10 2014

Keywords

  • Agent-based modeling
  • Complex batch scheduling
  • Heuristic search
  • Hybrid method
  • Mixed integer programing

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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