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1 – 10 of over 34000Tarek Helmy and Zeehasham Rasheed
Grid computing is gaining more significance in the high‐performance computing world. This concept leads to the discovery of solutions for complicated problems regarding the…
Abstract
Purpose
Grid computing is gaining more significance in the high‐performance computing world. This concept leads to the discovery of solutions for complicated problems regarding the diversity of available resources among different jobs in the grid. However, the major problem is the optimal job scheduling for heterogeneous resources, in which each job needs to be allocated to a proper grid's node with the appropriate resources. An important challenge is to solve optimally the scheduling problem, because the capability and availability of resources vary dynamically and the complexity of scheduling increases with the size of the grid. The purpose of this paper is to present a framework which combines the fuzzy C‐mean (FCM) clustering with an ant colony optimization (ACO) algorithm to improve the scheduling decision when the grid is heterogeneous.
Design/methodology/approach
In the proposed model, the FCM algorithm classifies the jobs into appropriate classes, and the ACO algorithm maps the jobs to the appropriate resources. The ACO is characterized by ant‐like mobile agents that cooperate and stochastically explore a network, iteratively building solutions based on their own memory and on the traces (pheromone levels) left by other agents.
Findings
The simulation is done by using historical information on jobs in a grid. The experimental results show that the proposed algorithm can allocate jobs more efficiently and more effectively than the traditional algorithms for scheduling policies.
Originality/value
The paper provides a scheduling model based on FCM clustering and ACO algorithm for grid scheduling. The authors compared the performance of the proposed algorithm with the performance of various job‐scheduling algorithms in the grid computing environment. The comparison results show that the proposed algorithm outperforms other algorithms and gives optimal results.
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S. Rajakumar, V.P. Arunachalam and V. Selladurai
To propose a methodology based on genetic algorithm (GA) to solve the parallel machine scheduling problems with precedence constraints.
Abstract
Purpose
To propose a methodology based on genetic algorithm (GA) to solve the parallel machine scheduling problems with precedence constraints.
Design/methodology/approach
Workflow balancing helps to remove bottlenecks present in a shop floor yielding faster movements of components or jobs. Multiple machines are used in parallel for processing the jobs to meet the demand. In parallel machine scheduling with precedence constraints, there are m machines to which n jobs are assigned using suitable scheduling algorithms. Workflow of a machine is the sum of processing time of all jobs assigned. All the preceding jobs are allocated first to satisfy the constraints. GA is developed to solve parallel machine scheduling problems with precedence constraints based on the objective of workflow balancing. The GA was coded on IBM/PC compatible system in the C++ language for simulation to a standard manufacturing environment.
Findings
The relative percentage of imbalance (RPI) in workloads among the parallel machines is used to evaluate the performance of the GA developed. The proposed GA produces lesser RPI values against the RANDOM heuristic algorithm for a wider range of jobs and machines.
Research limitations/implications
The performance of GA can be compared with the performance of other meta‐heuristic algorithms to find out the robustness of the results obtained by this research.
Practical implications
The proposed GA also gives better solution for a case study of assembly scheduling.
Originality/value
The allocation of assembly operations to the operators is modeled into a parallel machine scheduling problem with precedence constraints using the objective of minimizing the workflow among the operators.
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Presents a microcomputer‐based finite scheduling approach to effective planning and execution of make‐to‐order production. Going beyond the traditional application of scheduling…
Abstract
Purpose
Presents a microcomputer‐based finite scheduling approach to effective planning and execution of make‐to‐order production. Going beyond the traditional application of scheduling algorithms to prioritize jobs through work centers, the finite scheduling approach can be used to establish “smart” promise dates, manage the jobs through the work centers and enable supervisors to meet these due dates in the dynamic MTO environment.
Design/methodology/approach
Using data from an operational jobshop and a simulation‐based finite scheduling algorithm linked to pre‐ and post‐processing capabilities developed in Access, this research provides specific examples of establishing smart due dates and managing resources to meet those dates. We provide some what‐ifs in order to more fully explore the benefits of a finite scheduling system.
Findings
Through use of actual jobshop data, the paper demonstrates that finite scheduling can be effectively performed on standard computing equipment. It also provides an understanding of finite scheduling and demonstrates that such a system can be of significant value in a MTO environment.
Research limitations/implications
Future research could review/compare various ERP packages and their scheduling components to provide guidance on selection and implementation.
Practical implications
The paper clearly indicates that managers, even of smaller companies, should be considering the use of finite scheduling.
Originality/value
The paper provides a new approach to finite scheduling using a combination of simulation and Microsoft Access on a personal computer. Additionally, it provides a very useful presentation for practitioners who want an understanding of finite scheduling and why they need to implement it.
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Vinod K.T., S. Prabagaran and O.A. Joseph
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Practical implications
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.
Originality/value
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.
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Chao‐Lin Chang, Nicholas A.J. Hastings and Chris White
A fast production scheduling system, the very fast scheduler (VFS), hasbeen developed by the authors. It creates a capacity constrainedproduction schedule within one minute of…
Abstract
A fast production scheduling system, the very fast scheduler (VFS), has been developed by the authors. It creates a capacity constrained production schedule within one minute of elapsed time for problems of a size encountered in industry. The quality of the schedules is comparable with the best alternative heuristic scheduling techniques. The speed of the scheduler is such that it can be used on a real‐time basis to plan capacity, adjust priorities and other parameters and derive new schedules.
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James M. Pruett and Andreas Schartner
Describes the scheduling problem and JOB, then presents anextensive job shop scheduling session in which a variety of schedulingproblems are encountered and overcome using JOB′s…
Abstract
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.
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Machine scheduling plays an important role in most manufacturing industries and has received a great amount of attention from operation researchers. Production scheduling is…
Abstract
Purpose
Machine scheduling plays an important role in most manufacturing industries and has received a great amount of attention from operation researchers. Production scheduling is concerned with the allocation of resources and the sequencing of tasks to produce goods and services. Dispatching rules help in the identification of efficient or optimized scheduling sequences. The purpose of this paper is to consider a data mining‐based approach to discover previously unknown priority dispatching rules for the single machine scheduling problem.
Design/methodology/approach
In this work, the supervised statistical data mining algorithm, namely Bayesian, is implemented for the single machine scheduling problem. Data mining techniques are used to find hidden patterns and rules through large amounts of structured or unstructured data. The constructed training set is analyzed using Bayesian method and an efficient production schedule is proposed for machine scheduling.
Findings
After integration of naive Bayesian classification, the proposed methodology suggests an optimized scheduling sequence.
Originality/value
This paper analyzes the progressive results of a supervised learning algorithm tested with the production data along with a few of the system attributes. The data are collected from the literature and converted into the training data set suitable for implementation. The supervised data mining algorithm has not previously been explored in production scheduling.
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S.O. Duffuaa and K.S. Al‐Sultan
Addresses the problem of maintenance planning and scheduling and reviews pertinent literature. Discusses the characteristics and the complexity of the problem. Advocates…
Abstract
Addresses the problem of maintenance planning and scheduling and reviews pertinent literature. Discusses the characteristics and the complexity of the problem. Advocates mathematical programming approaches for addressing the maintenance scheduling problem. Gives examples to demonstrate the utility of these approaches. Proposes expansion of the state‐of‐the‐art maintenance management information system to utilize the mathematical programming approaches and to have better control over the maintenance scheduling problem.
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This paper aims to investigate the grey scheduling, which is the combination of grey system theory and scheduling problems with uncertain processing time. Based on the interval…
Abstract
Purpose
This paper aims to investigate the grey scheduling, which is the combination of grey system theory and scheduling problems with uncertain processing time. Based on the interval grey number and its related definitions, properties, and theorems, the single machine scheduling with uncertain processing time and its general forms are studied as the research object. Then several single machine scheduling models are reconstructed, and an actual production case is developed to illustrate the rationality of the research.
Design/methodology/approach
In this paper, the authors first summarize the definitions and properties related to interval grey numbers, especially the transitivity of the partial order of interval grey numbers, and give an example to illustrate that the transitivity has a positive effect on the computational time complexity of multiple interval grey number comparisons. Second, the authors redefine the general form of the single machine scheduling problem with uncertain processing time according to the definitions and theorems of interval grey numbers. The authors then reconstruct three single machine scheduling models with uncertain processing time, give the corresponding heuristic algorithms based on the interval grey numbers and prove them. Finally, the authors develop a case study based on the engine test shop of K Company, the results show that the proposed single machine scheduling models and algorithms with uncertain processing time can provide effective guidance for actual production in an uncertain environment.
Findings
The main findings of this paper are as follows: (1) summarize the definitions and theorems related to interval grey numbers and prove the transitivity of the partial order of interval grey numbers; (2) define the general form of the single machine scheduling problem with interval grey processing time; (3) reconstruct three single machine scheduling models with uncertain processing time and give the corresponding heuristic algorithms; (4) develop a case study to illustrate the rationality of the research.
Research limitations/implications
In the further research, the authors will continue to summarize more advanced general forms of grey scheduling, improve the theory of grey scheduling and prove it, and further explore the application of grey scheduling in the real world. In general, grey scheduling needs to be further combined with grey system theory to form a complete theoretical system.
Originality/value
It is a fundamental work to define the general form of single machine scheduling with uncertain processing time used the interval grey number. However, it can be seen as an important theoretical basis for the grey scheduling, and it is also beneficial to expand the application of grey system theory in real world.
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John R. King and Alexander S. Spachis
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…
Abstract
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.