Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/13539
Title: | Learning effect, Time-dependent Processing Time and Bicriteria Scheduling Problems in a Supply Chain |
Authors: | Qian, Jianbo |
Advisor: | Steiner, George |
Department: | Business |
Keywords: | Single-machine scheduling; learning effect; time-dependent;deteriorating effect; due date assignment; positional penalties; polynomial-time algorithm;Management Sciences and Quantitative Methods;Management Sciences and Quantitative Methods |
Publication Date: | Oct-2013 |
Abstract: | <p>This thesis contains two parts. In the first part, which contains Chapter 2 and Chapter 3, we consider scheduling problems with learning effect and time-dependent processing time on a single machine. In Chapter 2, we investigate the earliness-tardiness objective, as well as the objective without due date assignment consideration. By reducing them to a special linear assignment problem, we solve them in near-linear time. As a consequence, we improve the time complexity for some previous algorithms for scheduling problems with learning effect and/or time-dependent processing time. In Chapter 3, we investigate the total number of tardy jobs objective. By reducing them to a linear assignment problem, we solve them in polynomial time. For some important special cases, where there is only learning effect OR time-dependent processing time, we reduce the time complexity to quadratic time. In the second part, which contains Chapter 4 and Chapter 5, we investigate the bicriteria scheduling problems in a supply chain. We separate the objectives in two parts, where the delivery cost is one of them. We present efficient algorithms to identify all the Pareto-optimal solutions for various scenarios. In Chapter 4, we study the cases without due date assignment; while in Chapter 5 we study the cases with due date assignment consideration.</p> |
URI: | http://hdl.handle.net/11375/13539 |
Identifier: | opendissertations/8375 9271 4589133 |
Appears in Collections: | Open Access Dissertations and Theses |
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fulltext.pdf | 494.4 kB | Adobe PDF | View/Open |
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