Single-machine scheduling

About: Single-machine scheduling is a research topic. Over the lifetime, 2473 publications have been published within this topic receiving 56288 citations.

Minimizing Mean Squared Deviation of Completion Times About a Common Due Date

Abstract: This paper addresses a nonpreemptive single machine scheduling problem where all jobs have a common due date and have zero ready time. The scheduling objective is to minimize mean squared deviation MSD of job completion times about the due date. This nonregular measure of performance is appropriate when earliness and tardiness are both penalized, and when large deviations of completion time from the due date are undesirable. A special case of the MSD problem, referred to as the unconstrained MSD problem, is shown to be equivalent to the completion time variance problem CTV studied by Merten and Muller Merten, A. G., M. E. Muller. 1972. Variance minimization in single machine sequencing problems. Management Sci.18September 518-528. and Schrage Schrage, L. 1975. Minimizing the time-in-system variance for a finite jobset. Management Sci.21May 540-543.. Strong results for this latter problem are combined with several new propositions to develop a reasonably efficient procedure for solving the unconstrained MSD problem. This enables us to improve the existing procedures for the CTV problem. We also propose a branching procedure for the constrained MSD problem and present computational results.

Multicluster tools scheduling: an integrated event graph and network model approach

Abstract: Steady-state throughput and scheduling of a multicluster tool become complex as the number of modules and clusters grows. We propose a new methodology integrating event graph and network models to study the scheduling and throughput of multicluster tools. A symbolic decision-move-done graph modeling is developed to simplify discrete-event dynamics for the multicluster tool. This event graph is further used for searching feasible action sequences of the cluster tool. By representing sequences with networks, an extended critical path method is applied to calculate the corresponding cycle time. Grouping methods that are based on network are also introduced to reduce the searching complexity. Compared with optimization-based scheduling approaches, the proposed methodology can directly capture the cyclic characteristic of cluster tool schedules and be applied to analyze the impact of process and wafer flow variations on cycle time and robot schedules. We have successfully applied this new methodology to dozens of cluster tools at Intel Corporation. A chemical-mechanical planarization polisher is employed as an example to illustrate and validate the proposed methodology

Common due window size and location determination in a single machine scheduling problem

Abstract: We consider a single machine static and deterministic scheduling problem in which jobs have a common due window. Jobs completed within the window incur no penalties, other jobs incur either earliness or tardiness penalties. The objective is to find the optimal size and location of the window as well as an optimal sequence to minimise a cost function based on earliness, tardiness, window size, and window location. We propose an O(n log n) algorithm to solve the problem.

Single machine scheduling to minimize total late work

Abstract: In the problem of scheduling a single machine to minimize total late work, there are n jobs to be processed for which each has an integer processing time and a due date. The objective is to minimize the total late work, where the late work for a job is the amount of processing of this job that is performed after its due date. For the preemptive total late work problem, an O(n log n) algorithm is derived. The nonpreemptive total late work problem is shown to be NP-hard, although efficient algorithms are derived for the special cases in which all processing times are equal and all due dates are equal. A pseudopolynomial dynamic programming algorithm is presented for the general nonpreemptive total late work problem; it requires O(nUB) time, where UB is any upper bound on the total late work. Computational results for problems with up to 10,000 jobs are given.