Milton L. Smith

Common Due Date Assignment to Minimize Total Penalty for the One Machine Scheduling Problem

We consider an n job, one machine scheduling problem in which all jobs have a common due date. The objective is to determine the optimal value of this due date and an optimal sequence to minimize a total penalty function. This penalty function is based on the due date value and on the earliness or the lateness of each job in the selected sequence. We present a polynomial bound scheduling algorithm for the solution of this problem along with the proof of optimality, a numerical example and discuss some extensions.

Optimal assignment of due-dates for a single processor scheduling problem

Given processing times of n jobs on a single machine with penalties for earliness and tardiness and penalties associated with assignment of due-dates, the objective is to select optimal due-dates and optimal sequence. Scheduling procedure for the solution of this problem is presented along with proof of optimality and illustrative numerical examples.

Sequencing Research and the Industrial Scheduling Problem

Types of industrial scheduling problems were investigated by personal visits to plants and by questionnaires mailed to scheduling departments. Information on problem sizes, job flow, optimization criteria and job similarity was obtained. Results indicate that most of the present procedures in theoretical research cannot handle average industrial problems. Also most commonly used objective criteria differ from industrial goals. There is a definite need for better communication between sequencing researchers and scheduling practioners.

A heuristic for the single machine tardiness problem

Abstract A heuristic (P-S-K) is presented in this paper for minimizing the mean tardiness for the single machine sequencing problem. This heuristic is compared with other available construction heuristics from the literature like the Wilkerson-Irwin (W-I), Holsenback-Russel (H-R), and API heuristics. It is shown that P-S-K yields better results than the other methods on a wide range of problems. Furthermore, as due dates become tight, P-S-K is substantially better than the other methods with respect to computational time.

Simulation studies in job shop scheduling—II: Performance of priority rules

Abstract Major simulation studies of dynamic job shop scheduling problem and approaches taken to model dynamic job shops have been considered in Part I[25] of this paper. In Part II we focus our attention on basic results on relative effectiveness of priority rules in job shop simulation literature. Information on surveyed articles also is provided in the Appendix.

Due date selection procedures for job-shop simulation

Abstract This paper introduces a comprehensive classification of due date selection procedures from which three major categories are derived. Emphasis is centered on providing a summary of selection procedures in a form that can be readily used by both researcher and practitioners. The paper concludes with a review and critique of recent discrete digital computer simulation studies concerning the impact of due date assignment procedures on shop performance and priority scheduling rules.

Simulation studies in job shop sheduling—I a survey

Abstract The dynamic job shop scheduling problem has been studied extensively during the last two decades. Because of the complexity of the dynamic job shop scheduling problem, numerous simulation studies have been conducted and published in the area. These studies fall into one of the following categories: the studies comparing and/or developing scheduling rules which will give good shop performance under a given set of job and shop parameters, and the studies investigating sensitivity of shop performance to job and shop parameters under a given set of scheduling rules. In the literature, shop performance has been evaluated in terms of (1) criteria based on job completion times, (2) criteria based on due dates, (3) criteria based on costs. This paper discusses approaches taken in major simulation studies of dynamic job shop scheduling problem according to the above classification.

Modified Tukey's Control Chart

Phase I of control analysis requires large amount of data to fit a distribution and estimate the corresponding parameters of the process under study. However, when only individual observations are available, and no a priori knowledge exists, the presence of outliers can bias the analysis. A relatively recent and successful approach to address this situation is Tukeys Control Chart (TCC), a charting method that applies the Box Plot technique to estimate the control limits. This procedure has proven to be effective for symmetric distributions. However, when skewness is present the average run length performance diminishes significantly. This article proposes a modified version of TCC to consider skewness with minimum assumptions on the underlying distribution of observations. Using theoretical results and Monte Carlo simulation, the modified TCC is tested over several distributions proving a better representation of skewed populations, even in cases when only a limited number of observations are available.

Robot and machine scheduling with state-dependent part input sequencing in flexible manufacturing systems

In competitive global markets, it is important to meet customer demands on multiple priorities such as price, quality, customisation and quick delivery. This paper investigates the problems of part input sequencing and scheduling in flexible manufacturing systems in a mass customisation/mass personalisation (MC/MP) environment. Both robot and machine scheduling rules using a state-dependent part input sequencing algorithm are investigated. Simulation experiments and statistical analyses are carried out. Effective rules are identified. The results show interactions between robot scheduling and machine scheduling in the MC/MP environment. Further research suggestions are provided.

A canned food scheduling problem with batch due date

This article considers a canned food scheduling problem where jobs are grouped into several batches. Jobs can be sent to the next operation only when all the jobs in the same batch have finished their processing, i.e. jobs in a batch, have a common due date. This batch due date problem is quite common in canned food factories, but there is no efficient heuristic to solve the problem. The problem can be formulated as an identical parallel machine problem with batch due date to minimize the total tardiness. Since the problem is NP hard, two heuristics are proposed to find the near-optimal solution. Computational results comparing the effectiveness and efficiency of the two proposed heuristics with an existing heuristic are reported and discussed.