“…= 1, the event (13,4,4,2) is inserted, (14, 3, 3, 3, 1) is removed. = 0, (13,4,4,2) is removed, (12, 3, 3, 1) is inserted. At the end of the iteration = −2, thus (13, 4, 3, 2) is inserted.…”
Section: Convex Piecewise Linear Costsmentioning
confidence: 99%
“…In our presentation, we are going to use the elegant reduction of the isotonic regression problem to our problem, which was proposed by Pan and Shi [8]. Finally, we mention another, also recent, algorithm by Colin and Quinino [13], which is useful when cost functions are not piecewise linear. The complexity of their algorithm depends on the time horizon.…”
“…= 1, the event (13,4,4,2) is inserted, (14, 3, 3, 3, 1) is removed. = 0, (13,4,4,2) is removed, (12, 3, 3, 1) is inserted. At the end of the iteration = −2, thus (13, 4, 3, 2) is inserted.…”
Section: Convex Piecewise Linear Costsmentioning
confidence: 99%
“…In our presentation, we are going to use the elegant reduction of the isotonic regression problem to our problem, which was proposed by Pan and Shi [8]. Finally, we mention another, also recent, algorithm by Colin and Quinino [13], which is useful when cost functions are not piecewise linear. The complexity of their algorithm depends on the time horizon.…”
“…If the sequences of the jobs are predetermined, then other timing issues may be considered. When the sequences are fixed and the cost of each job is a convex function of its completion time, the idle time insertion problem to minimize the total cost can be considered; a pseudopolynomial time algorithm for finding a solution within some tolerance of the optimality space has been proposed [8]. A single machine scheduling problem with a fixed sequence of n jobs to minimize the makespan subject to time-lag restrictions, that is,…”
Abstract:We consider a parallel-machine scheduling problem with jobs that require setups. The duration of a setup does not depend only on the job just completed but on a number of preceding jobs. These setup times are referred to as history-dependent. Such a scheduling problem is often encountered in the food processing industry as well as in other process industries. In our model, we consider two types of setup times-a regular setup time and a major setup time that becomes necessary after several "hard-toclean" jobs have been processed on the same machine. We consider multiple objectives, including facility utilization, flexibility, number of major setups, and tardiness. We solve several special cases assuming predetermined job sequences and propose strongly polynomial time algorithms to determine the optimal timing of the major setups for given job sequences. We also extend our analysis to develop pseudopolynomial time algorithms for cases with additional objectives, including the total weighted completion time, the total weighted tardiness, and the weighted number of tardy jobs.
“…Unrelated parallel machine scheduling is widely applied in manufacturing environments [1]. In addition, idle time insertion arises in JIT environments where costs associated with early completion time of jobs are relevant [2]. As a result, it has drawn attention of numerous researchers.…”
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