Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/153711
Title: DATA-DRIVEN MODELS FOR SCHEDULING OPTIMIZATION UNDER UNCERTAINTY
Authors: WANG ZHIGUO
Keywords: scheduling under uncertainty, robust optimization, risk modelling and analysis, energy efficiency, linear program, Petri net
Issue Date: 28-Dec-2018
Citation: WANG ZHIGUO (2018-12-28). DATA-DRIVEN MODELS FOR SCHEDULING OPTIMIZATION UNDER UNCERTAINTY. ScholarBank@NUS Repository.
Abstract: As a critical decision-making tool across a wide range of industries, scheduling aims to allocate limited resources to jobs over time optimally. During execution, however, the scheduling system is subject to considerable uncertainty which may cause infeasibilities and disturbances. This thesis proposes three different scheduling models which respectively cater for three well-known sources of uncertainties in practice. First, a data-driven scheduling optimization model using Renyi mean-entropy-skewness information criterion is developed to deal with resource cost uncertainties. Second, based on the concept of activity duration tolerance levels, a due-date achievement model using a proposed performance measure termed the activity exposure level is formulated for resource-constrained activity scheduling under uncertain activity durations. Third, a scheduling model is formulated to tackle uncertainties in resource disruptions by optimizing the threshold scenario, bounded by which the planned due-dates can be achieved.
URI: https://scholarbank.nus.edu.sg/handle/10635/153711
Appears in Collections:Ph.D Theses (Open)

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