Robust Data-Driven Optimization for Production Planning with Onsite Power

Detta är en Master-uppsats från KTH/Industriell produktion

Författare: Ziliang Jin; [2020]

Nyckelord: ;

Sammanfattning: Recently on-site power generation is considered as an effective mean to attain lowcarbonmanufacturing operations. However, there is a lack of literatures studying theproblem integrating productions and onsite power generations together. Motivated bythis, in this study I consider problems in multi-machines and multi-products manufacturingsystem incorporated with onsite generators including conventional ones andrenewable ones. To tackle this complex optimization problem, I construct five modelsamong which the first model is a basic one while remaining four models are extensionsof their previous one. In the proposed models, with the objective of minimizingthe total cost, I determine production related decisions including production scheduling,inventory levels and backorder in each time period and power generation relateddecisions such as unit commitments and power generation scheduling. And to efficientlyaddress uncertainties resulted by random products’ demands and renewablegeneration, I propose a data-driven robust optimization approach. Finally the Bender’sdecomposition algorithm is employed to solve the proposed stochastic model.And numerical results suggest contributions of onsite generators, and potentials ofproposed approach are also justified.

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