Optimization models for manufacturing and personnel scheduling
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Personnel scheduling problems have been studied by many researchers over the last five decades but much of the literature has ignored the array of break types used in practice. We investigate the benefits that flexibility offers in daily shift scheduling, especially when demand is uncertain. The different forms of flexibility considered include shift start times, the number of breaks, break lengths, and break placement. Five related mixed-integer programming models are developed and used to compare break scheduling in advance and either sequentially or in real time for various shift and break profiles. In addition, we investigate the same problem under stochastic demand. We formulate a multi-stage stochastic programming model and then transform it into a two-stage model to ease the computational burden. For testing purpose, we consider 61 scenarios. Five metrics are used for evaluating performance. While the full range of shift and break options are rarely considered in personnel scheduling problems, many practical aspects of machine setups have been neglected in scheduling semiconductor assembly and test (AT) operations. We examine all sides of the problem in a multi-machine, multi-tooling environment to see the impact of using a hierarchical approach to setups on facility performance. The primary objectives of the problem investigated are to minimize the number of shortages of key devices and to maximize weighted throughput, in that order, over a planning horizon of up to five days. Secondary objectives include minimizing the number of machines used to meet output targets, and minimizing makespan. For the shift scheduling problems with flexible breaks the application studied involves airport ground handlers; for the hierarchical machine setup problem for semiconductor assembly and test facilities testing was done with data provided by Texas Instruments. In Chapter 2, we investigate the benefits of flexibility for shifts and breaks with both deterministic and randomized demand. A rolling horizon approach is proposed for real-time break scheduling as demand unfolds over the day. In Chapter 3, we extend the shift scheduling problem to more realistically accommodate stochasticity. We introduce a two-stage stochastic programming model and determine the value of stochastic solutions and the expected value of perfect information. In Chapter 4, we develop an optimization model for scheduling multi-pass lots under hierarchical machine setup rules at assembly and test facilities. We determine machine setups, lot assignments and sequences using a greedy randomized adaptive search procedure.