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Much of the research literature in job shop scheduling deals with pure job shop environments. However, currently most processes involve a hybrid of both the job shop and a flow shop with a combination of flexible and conventional machine tools. Presents a study of such a job shop under varying conditions and performance criteria. Argues that for scheduling in this environment, certain combinations of scheduling rules should be utilized under different arrival rates and for different job types. A simulation model is developed using a hypothetical hybrid job shop to study the performance of rule combinations with variations in arrival rates and processing times. The performance criteria used are flowtime as a measure of work‐in‐process inventory, tardiness for JIT, and throughput for completed items inventory. It was found that rule combination performance varied with the performance criteria. Furthermore, it was found that the combinations were sensitive to arrival rates and processing times. Concludes, from the insights gained in the study, that the rule combination to be implemented should depend on the performance objective and the arrival rate/processing time condition of the hybrid job shop.

Job‐shop operations, the emerging norm of industry, require reform of the management methods employed to achieve cost control and efficiency. Maximisation of capacity utilisation, the standard of efficiency for more than a century, does not work in the job shop. Queueless work flow and the flexible labour policies that support it offer potential for reform which can substantially enhance customer service and quality. Simulation of work flow and capacity to identify bottlenecks along with scheduling of set‐ups on a leadtime basis are potentially important supporting methodologies.

Examines the planning and scheduling systems in an integrative framework for a dual resource constrained (DRC) job shop. Models a bottleneck shop environment which represents a common situation in practice. Research on workload smoothing mechanisms which integrate the planning and scheduling systems is sparse. Simulates 13 smoothing rules (utilizing aggregate workload and bottleneck information), three despatching rules and two order review/release rules. The results of this study indicate that workload smoothing by the planning system has a significant effect on the performance of the DRC job shop. Pulling work ahead in valley periods improved the tardy measures of performance, while pushing jobs back in peak periods usually deteriorated shop performance. Suggests that combining a planning system effectively with order review/release can improve mean tardiness, percentage tardy and mean flowtime measures of performance.

Simulation experiment was employed to investigate the schemes for coordinating JIT practices to promote performance upgrade in a job shop environment with the pull system.

The four related essential JIT practices (job shop JIT practices) investigated include: cellular manufacturing (CM), operations overlapping (OPOVR), reduction of set‐up/processing time variability (variability reduction) and set‐up time reduction (STR).

Experiment findings suggest that coordination of CM and STR should be given the priority. While the extent of STR effected by CM substantially influences the efficacy of adopting a cellular layout, the choice of adopting a functional layout (FL) is more likely to be affected by the STR resulted from improvement of set‐up operations (set‐up improvement). Variability reduction tends to be more effective for a cellular layout. For a cellular layout without OPOVR, the effectiveness of reducing set‐up time variability is prominent and almost impervious to the extent of set‐up improvement. For a FL, the effect of variability reduction is minor; reduction of set‐up time variability is effective in this case only for a set‐up to processing time ratio of 20 or larger. The findings of this study do not justify the implementation of OPOVR in the shop environment, even with the support of the other three job shop JIT practices.

This study is notable in integrating STR into the job shop JIT practices to achieve overall performance improvement. In addition, the resulting strategies for variability reduction are essential for adapting the pull system to job shop manufacturing. Therefore, the findings of this study form systematic guidelines enabling exercise of the job shop JIT practices coherently to promote reform of job shop manufacturing.

Reviews a critical aspect of job shop scheduling research, namely the decision regarding release timing of orders to the manufacturing shopfloor. Covers articles specifically addressing the order release problem; the information should prove helpful for researchers employing order release policies in their research. The release decision literature is classified into three areas: descriptive studies including case and survey research, analytical or optimization‐based research, and simulations of theoretical and empirically derived job shops. Presents tables describing the release rules and summarizing the characteristics of the simulation research. Identifies a number of topics within the order release research area in need of further investigation, including the comparison of larger sets of release rules, the use of more realistic simulation models such as the dual resource constrained job shop model, the need for further empirical identification of release policies, and the use of release policies that consider dynamic shop conditions.

The purpose of this paper is to demonstrate the use of Petri nets in a job shop setup for the improvement in the utilization of machines.

The study discusses concepts such as reachable state, token and matrix equations set, and demonstrates the improvements in machines’ utilization in a job shop. It makes use of algorithms to generate reachable markings to obtain utilization. The study not only describes the application of theory, but also extends the body of knowledge on Petri nets and job shops.

In this study, machines’ utilization has been studied in a job shop with six machines and eight products. The study finds that substantial utilization improvement in job shop set up can be obtained through the application of Petri nets. The study also exposes that Petri nets are mostly used for machines, jobs and tools scheduling problems, but its use in machines’ utilization is neglected. The framework and application presented here along with generalizable findings, is the first to report about machine utilization improvement in job shop manufacturing environment.

Job shops are characterized by high unit production cost, low investments, low volume and high variety, complex flows, flexible and skilled work force, general purpose machines, high material handling; resulting in poor utilization of machines. Therefore, the findings of this study can help in reducing such costs through better machine utilization. This can help in increasing the competitiveness of the companies.

The contribution of study lies in investigating and improving stage wise utilization in a job shop setup. It has never been reported before.

The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system in which setup times are sequence dependent. Two due date assignment methods and six scheduling rules are considered for detailed investigation. The scheduling rules include two new rules which are modifications of the existing rules. The performance of the job shop system is evaluated using various measures related to flow time and tardiness.

A discrete-event simulation model is developed to describe the operation of the job shop. The simulation results are subjected to statistical analysis based on the method of analysis of variance. Regression-based analytical models have been developed using the simulation results. Since the due date assignment methods and the scheduling rules are qualitative in nature, they are modeled using dummy variables. The validation of the regression models involves comparing the predictions of the performance measures of the system with the results obtained through simulation.

The proposed scheduling rules provide better performance for the mean tardiness measure under both the due date assignment methods. The regression models yield a good prediction of the performance of the job shop.

Other methods of due date assignment can also be considered. There is a need for further research to investigate the performance of due date assignment methods and scheduling rules for the experimental conditions that involve system disruptions, namely, breakdowns of machines.

The explicit consideration of sequence-dependent setup time (SDST) certainly enhances the performance of the system. With appropriate combination of due date assignment methods and scheduling rules, better performance of the system can be obtained under different shop floor conditions characterized by setup time and arrival rate of jobs. With reductions in mean flow time and mean tardiness, customers are benefitted in terms of timely delivery promises, thus leading to improved service level of the firm. Reductions in manufacturing lead time can generate numerous other benefits, including lower inventory levels, improved quality, lower costs, and lesser forecasting error.

Two modified scheduling rules for scheduling a dynamic job shop with SDST are proposed. The analysis of the dynamic due date assignment methods in a dynamic job shop with SDST is a significant contribution of the present study. The development of regression-based analytical models for a dynamic job shop operating in an SDST environment is a novelty of the present study.

The development of a rule‐based expert system (ES), driven by a discrete event simulation model, that performs dynamic shop scheduling is described. Based on a flowtime prediction heuristic that has been developed and base‐line runs to establish the efficacy of scheduling strategies such as shortest processing time (SPT), critical ratio, total work, etc., a rescheduling‐based dispatching strategy is investigated in a dynamic job shop environment. The results are discussed and analyzed.

Using data from a retail chain of 193 bakery shops that underwent downsizing, we study the effects of two types of downsizing announcements – closure or sale to another operator – on sales in the affected shops, and how these effects are moderated by job security perceptions. On average, sales in the affected shops go down by 26% after a closure announcement and by 7% after a sale announcement. Sales decline more sharply in shops where employees had higher job security perceptions before the announcement. Our findings are consistent with psychological contract theory: a breach of an implicit contract promising job security in exchange for work effort results in a reciprocal effort withdrawal. We rule out several alternative explanations to our findings.

Describes the scheduling problem and JOB, then presents an extensive job shop scheduling session in which a variety of scheduling problems are encountered and overcome using JOB′s interactive scheduling option. The example shows how work orders may be created and scheduled, and the schedules evaluated, all within the framework of the JOB system. By working with typical job shop scheduling opportunities in a realistic though simulated environment, users will better understand the problems job shop schedulers actually face and will be better able to solve them.