Surrogate based derivative free optimization methodology for supply chain management

Nihar Sahay, Marianthi Ierapetritou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Simulation models are one of the most effective tools to study supply chains. Compared to analytical and mathematical programming techniques, they offer the capability to include a greater deal of fidelity. Different kinds of simulation models have been widely used to study various aspects of supply chain management. These models provide a very convenient approach to generate various "what-if" scenarios and find the optimal values of the discrete variables. However stand-alone simulation models cannot be used to optimize the continuous variables. It is necessary to couple the simulation model with an optimization approach in order to find the optimal values of the continuous variables. During the recent years, supply chains have evolved into global, highly complex networks and the overall supply chain operations are a result of numerous autonomous, adaptive and intelligent entities. A high fidelity simulation model is not only difficult to develop but also difficult to use in an optimization framework due to computational complexity involved in each function evaluation. In order to optimize the variables in these simulation models, deterministic optimization solvers cannot be used as the derivatives are unavailable. Also, since the simulations take long times to run, it is not possible to perform a large number of simulation runs in order to approximate the derivatives. Taking these factors into consideration, we propose a surrogate based derivative free optimization methodology to solve a supply chain planning problem.

Original languageEnglish (US)
Title of host publicationComputing and Systems Technology Division 2015 - Core Programming Area at the 2015 AIChE Annual Meeting
PublisherAIChE
Pages623-624
Number of pages2
ISBN (Electronic)9781510818569
StatePublished - 2015
EventComputing and Systems Technology Division 2015 - Core Programming Area at the 2015 AIChE Annual Meeting - Salt Lake City, United States
Duration: Nov 8 2015Nov 13 2015

Publication series

NameComputing and Systems Technology Division 2015 - Core Programming Area at the 2015 AIChE Annual Meeting
Volume2

Other

OtherComputing and Systems Technology Division 2015 - Core Programming Area at the 2015 AIChE Annual Meeting
Country/TerritoryUnited States
CitySalt Lake City
Period11/8/1511/13/15

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Engineering(all)
  • Computer Science(all)

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