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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.

As the manufacturing activities in today's industries are getting more and more complex, it is required for the manufacturing firm to have a good shop floor production scheduling to plan and schedule their production orders. An accurate scheduling is essential to any manufacturing firm in order to be competitive in global market. The paper aims to discuss these issues.

Two types of shop floors, job shop and cellular layout, were developed by using WITNESS simulation package. Consequently, the performance of forward scheduling and backward scheduling in both job shop and cellular layout was compared using simulation method, and the results were analyzed by using analysis of variance (ANOVA). Through analysis, the best scheduling approach and layout to be used by manufacturing firm in order to achieve the make-to-order (MTO) production and inventory strategy were reported.

The results from simulation show that backward scheduling in job shop layout has the lowest average throughput time, lowest lateness, and highest labour productivity than forward scheduling. While in cellular layout, forward scheduling has the lowest average throughput time, lowest lateness, and highest labour productivity than backward scheduling in all conditions. It shows that the performance of scheduling approach is different in each production layout.

Suitable scheduling approach is needed in manufacturing industry as to maximize production rate and optimize machine and process capability. This paper presents an empirical study about the assembly process of radio cassette player of one manufacturing industry in order to investigate the impact of variety of orders and different number of two workers on the performance of production scheduling approach. Forward scheduling and backward scheduling are used to schedule the production orders.

Influenced by the constantly changing manufacturing environment, no single dispatching rule (SDR) can consistently obtain better scheduling results than other rules for the dynamic job-shop scheduling problem (DJSP). Although the dynamic SDR selection classifier (DSSC) mined by traditional data-mining-based scheduling method has shown some improvement in comparison to an SDR, the enhancement is not significant since the rule selected by DSSC is still an SDR.

This paper presents a novel data-mining-based scheduling method for the DJSP with machine failure aiming at minimizing the makespan. Firstly, a scheduling priority relation model (SPRM) is constructed to determine the appropriate priority relation between two operations based on the production system state and the difference between their priority values calculated using multiple SDRs. Subsequently, a training sample acquisition mechanism based on the optimal scheduling schemes is proposed to acquire training samples for the SPRM. Furthermore, feature selection and machine learning are conducted using the genetic algorithm and extreme learning machine to mine the SPRM.

Results from numerical experiments demonstrate that the SPRM, mined by the proposed method, not only achieves better scheduling results in most manufacturing environments but also maintains a higher level of stability in diverse manufacturing environments than an SDR and the DSSC.

This paper constructs a SPRM and mines it based on data mining technologies to obtain better results than an SDR and the DSSC in various manufacturing environments.

The current study aims to propose a new analytical approach by considering energy consumption (EC), maximum tardiness and completion time as the primary objective functions to assess the performance of parallel, non-bottleneck and multitasking machines operating in dynamic job shops.

An analytical and iterative method is presented to optimize a novel dynamic job shop under technical constraints. The machine’s performance is analyzed by considering the setup energy. An optimization model from initial processing until scheduling and planning is proposed, and data sets consisting of design parameters are fed into the model.

Significant variations of EC and tardiness are observed. The minimum EC was calculated to be 141.5 hp.s when the defined decision variables were constantly increasing. Analysis of the optimum completion time has shown that among all studied methods, first come first served (FCFS), earliest due date (EDD) and shortest processing time (SPT) have resulted in the least completion time with a value of 20 s.

Considerable amount of energy can be dissipated when parallel, non-bottleneck and multitasking machines operate in lower-power modes. Additionally, in a dynamic job shop, adjusting the trend and arrangement of decision variables plays a crucial role in enhancing the system’s reliability. Such issues have never caught the attention of scientists for addressing the aforementioned problems. Therefore, with these underlying goals, this paper presents a new approach for evaluating and optimizing the system’s performance, considering different objective functions and technical constraints.

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.

Deals with the dynamic scheduling problems and solutions of job shop comprising six work centres and n components. Concerns the behaviour of the system, with the arrival of urgent orders and normal orders. In a typical manufacturing system, urgent orders are scheduled for processing based on their urgency and given priority over normal orders. Describes an analysis of urgent order processing on the basis of non pre‐emptive priority and pre‐emptive resume priority over normal orders. Enumerates manufacturing system performances which had been analysed for the two most popular scheduling rules – first in first out (FIFO) and shortest processing time (SPT) – through a system simulation program. Concludes by asserting that the simulation program can be used to schedule the manufacturing system dynamically by choosing the appropriate scheduling rule to measure optimal system performance leading to higher productivity.

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.

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.

In the current competitive environment, each company faces a number of challenges: quick response to customers’ demands, high quality of products or services, customers’ satisfaction, reliable delivery dates, high efficiency, and others. As a result, during the last five years many firms have proceeded to the adoption of enterprise resource planning (ERP) solutions. ERP is a packaged software system, which enables the integration of operations, business processes and functions, through common data‐processing and communications protocols. However, the majority, if not all, of these systems do not support the production scheduling process that is of crucial importance in today’s manufacturing and service industries. In this paper, the authors propose a knowledge‐based system for production‐scheduling that could be incorporated as a custom module in an ERP system. This system uses the prevailing conditions in the industrial environment in order to select dynamically and propose the most appropriate scheduling algorithm from a library of many candidate algorithms.

In any real time control system, its scheduling and control policy should be reassessed every time the state of the system changes. In large and complex systems, this could be a self‐defeating goal. Implementing real time control in such systems would require an enormous amount of computation time. Determination of discrete time interval (simulation window length) is the main objective of this study. To implement and demonstrate this methodology, we selected a Flexible Manufacturing System (FMS) which approximates a dynamic job shop. The Expert Control System (ECS) developed in this study integrated programmes for different functions and employed multi‐pass simulation to determine the best scheduling strategy in the system. The simulation output is then subjected to Analysis of Variance (ANOVA) and Newman‐Keuls′ range tests to obtain a “good” simulation window length for different performance criteria of optimisation.