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• W2739669902 abstract "Over the last decade, manufacturing companies have identified renewable energy as a promising means to cope with time-varying energy prices and to reduce energy-related greenhouse gas emissions. As a result of this development, global installed capacity of wind power has expanded significantly. To make efficient use of onsite wind power generation facilities in manufacturing, production scheduling tools need to consider the uncertainty attached to wind power generation along with changes in the energy procurement cost and in the products’ environmental footprints. To this end, we propose a solution procedure that first generates a large number of wind power scenarios that characterise the variability in wind power over time. Subsequently, a two-stage stochastic optimisation procedure computes a production schedule and energy supply decisions for a flow shop system. In the first stage, a bi-objective mixed integer linear programme simultaneously minimises the total weighted flow time and the expected energy cost, based on the generated wind power scenarios. In the second stage, energy supply decisions are adjusted based on real-time wind power data. A numerical example is used to illustrate the ability of the developed decision support tool to handle the uncertainty attached to wind power generation and its effectiveness in realising energy-related objectives in manufacturing." @default.
• W2739669902 created "2017-08-08" @default.
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• W2739669902 date "2017-07-27" @default.
• W2739669902 modified "2023-09-30" @default.
• W2739669902 title "Flow shop scheduling with grid-integrated onsite wind power using stochastic MILP" @default.
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• W2739669902 doi "https://doi.org/10.1080/00207543.2017.1351638" @default.
• W2739669902 hasPublicationYear "2017" @default.
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