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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.

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.

Job shop scheduling has been the focus of much research. Heuristic rules to assist in this endeavour abound in the literature. However, rules leading to the optimum schedule have been elusive. The predominant scheduling methods now used are specifically tailored to the type of job shop. Generally, various rules are tried and those giving the best result are used as a starting point. The human expert sifts the schedule through his experience filter, negotiates with affected parties, and finalizes a schedule. Expert systems are beginning to impact in this area. By assuming some of the filtering and negotiating roles of the human expert, they can allow schedulers to look at more alternatives and/or produce more timely schedules. Contains an extensive review of the literature pertaining to expert systems and job shop scheduling. Conclusions are drawn in terms of the advantages and disadvantages of these applications. Finally, recommendations for future directions are given.

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.

In order to achieve excellence in manufacturing, goals like lean, economic and quality production with enhanced productivity play a crucial role in this competitive environment. It also necessitates major improvements in generally three primary technical areas: variation reduction, equipment reliability, and production scheduling. Complexity of the real world scheduling problems also increases with interactive multiple decision‐making criteria. This paper aims to deal with multi‐objective flow shop scheduling problems, including sequence dependent set up time (SDST). The paper also aims to consider the objective of minimizing the weighted sum of total weighted tardiness, total weighted earliness and makespan simultaneously. It proposes a new heuristic‐based hybrid simulated annealing (HSA) for near optimal solutions in a reasonable time.

Six modified NEH's based HSA algorithms are proposed for efficient scheduling of jobs in a multi‐objective SDST flow shop. Problems of up to 200 jobs and 20 machines are tested by the proposed HSA and a defined relative percentage improvement index is used for analysis and comparison of different MNEH's based hybrid simulated annealing algorithms.

From the results, it has been derived that performance of SA_EWDD (NEH) up to ten machines' problems, and SA_EPWDD (NEH) up to 20 machines' problems, were better over others especially for large sized SDST flow shop scheduling problems for the considered multi‐objective fitness function.

HSA and multi‐objective decision making proposed in the present work is a modified approach in the area of SDST flow shop scheduling.

Scheduling is defined by Baker as, “the allocation of resources over time to perform a collection of tasks”. The term facilities is often used instead of resources and the tasks to be performed may involve a variety of different operations.

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.

purpose of this paper is providing a solution for flexible flow shop scheduling problem with uncertain processing time in aeronautical composite lay-up workshop.

A flexible flow scheduling model and algorithm with interval grey processing time is established. First, according to actual needs of composite laminate shop scheduling process, interval grey number is used to represent uncertain processing time, and interval grey processing time measurement method, grey number calculation and comparison rules, grey Gantt chart, and other methods are further applied. Then a flexible flow shop scheduling model with interval grey processing time (G-FFSP) is established, and an artificial bee colony algorithm based on an adaptive neighbourhood search strategy is designed to solve the model. Finally, six examples are generated for simulation scheduling, and the efficiency and performance of the model and algorithm are evaluated by comparing the results.

Results show that flexible flow shop scheduling model and algorithm with interval grey processing time can provide an optimal solution for composite lay-up shop scheduling problems and other similar flow shop scheduling problems.

Uncertain processing time is common in flexible workshop manufacturing, and manual scheduling greatly restricts the production efficiency of workshop. In this paper, combined with grey system theory, an intelligent algorithm is used to solve flexible flow shop scheduling problem to promote intelligent and efficient production of enterprises.

This paper applies and perfects interval grey processing time measurement method, grey number calculation and comparison rules, grey Gantt chart and other methods. A flexible flow shop scheduling model with interval grey processing time is established, and an artificial bee colony algorithm with an adaptive domain search strategy is designed. It provides a comprehensive solution for flexible flow shop scheduling with uncertain processing time.

In today's very competitive, dynamic and unpredictable manufacturing environments it is critical to improve manufacturing performance in order to be able to compete. Responsiveness and agility become important characteristics of manufacturing systems and organizations. Manufacturing systems must be designed optimally by taking into account responsiveness and agility related measures in order to improve effectiveness and performance. One of the important enablers of performance improvement is flexibility. It is a known fact that flexibility has a positive effect on the manufacturing system performance if it is properly utilized by the control system (usually scheduling). However, the relationship between flexibility and manufacturing system performance through scheduling is not entirely explored in the previous literature. The purpose of this paper is to investigate the effects of process plan and machine flexibilities on the scheduling performance of manufacturing job‐shops.

Effects of process plan and machine flexibilities on the scheduling performance of manufacturing job‐shops are analyzed at different flexibility levels by using the grammar‐based flexible job shop scheduling system that is developed by Baykasoğlu et al.. Three different flexibility levels are defined for process plans and machines. Four different problem sizes are evaluated according to “makespan” “machine load balance” and “mean waiting times of jobs”. Performance differences among “process plan” and “machine flexibility” levels are determined and statistically analyzed through Taguchi experimental design methodology.

It is found out after detailed analysis that the effect of machine flexibility on job shop performance is higher than the process plan flexibility. It is also figured out that after a certain level of machine flexibility, the speed of scheduling performance improvement decreases considerably.

The paper presents the interaction between flexibility and scheduling performance of manufacturing job‐shops. The findings should be taken into account while designing scheduling systems for job shops that have flexible processing capabilities.

In many manufacturing systems, machines are subject to preventive maintenance. This paper aims to schedule the operations of jobs and preventive maintenance tasks in such a way that the completion time of jobs and preventive maintenance tasks is minimized.

An heuristic approach based on artificial immune algorithm is proposed for solving the multiple‐route job shop‐scheduling problem subject to fixed periodic and age‐dependent preventive maintenance tasks. Under fixed periodic assumption, the time between two consecutive preventive maintenance tasks is assumed constant. Under age‐dependent assumption, a preventive maintenance task is triggered if the machine operates for a certain amount of time. The goal is to schedule the jobs and preventive maintenance task subject to makespan minimization.

In addition to presenting mathematical formulation for the multiple‐route job shop‐scheduling problem, this paper proposes a novel approach by which one can tackle the complexity that is raised in scheduling and sequencing the jobs and the preventive maintenance simultaneously and obtain the required schedule in reasonable time.

Integrating preventive maintenance tasks into the scheduling procedure is vital in many manufacturing systems. Using the proposed approach, one can obtain a schedule that defines the production route through which each part is processed, the time each operation must be started, and when preventive maintenance must be carried out on each machine. This, in turn, results in overall manufacturing cost reduction.

Using the approach proposed in this paper, good solutions, if not optimal, can be obtained for scheduling jobs and preventive maintenance task in one of the most complicated job shop configurations, namely, multiple‐route job shop. Thus, the approach can dominate all other simpler configurations.