Scheduling parallel machines with major and minor setup times

Wittrock, Robert J.

doi:10.1007/bf00186472

Cited by 34 publications

(9 citation statements)

References 6 publications

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“…The proposed process has been tested using the data of an IBM test line described in [11]. It covers a wide range of testers which are classified into tester families.…”

Section: Application and Evaluation Resultsmentioning

confidence: 99%

Extending an agent-based FMS scheduling approach with parallel genetic algorithms

Abaza

Badr

Goehner

et al. 2010

IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society

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Flexible manufacturing systems (FMS) aim at efficiently reacting to changing market needs to stand the increasing competitiveness. This imposes efficiency and flexibility requirements on FMS scheduling. Manufacturing scheduling is the process of allocating available manufacturing resources to the set of planned jobs over time. It is an optimization process by which limited manufacturing resources are to be allocated to several jobs of different products efficiently. The agent-based scheduling approach has shown the ability to fulfill the flexibility requirement. Although this approach emphasizes flexibility, it lacks the optimization support. In this paper, an agent-based scheduling approach is extended with parallel genetic algorithms (PGA) to provide the required optimization support. Test results have shown a remarkable enhancement to the optimality of the generated schedules with respect to the predefined set of manufacturing objectives. The extended approach fulfils both flexibility and efficiency requirements on manufacturing scheduling.

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“…The proposed process has been tested using the data of an IBM test line described in [11]. It covers a wide range of testers which are classified into tester families.…”

Section: Application and Evaluation Resultsmentioning

confidence: 99%

Extending an agent-based FMS scheduling approach with parallel genetic algorithms

Abaza

Badr

Goehner

et al. 2010

IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society

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“…This processing stage is characterized by major and minor family sequencedependent setup times. The first investigations of family sequence-dependent setup time can be found in [20,38]. The surface mounting process of PCB is highly automated.…”

Section: Problem Statement 21 System Descriptionmentioning

confidence: 99%

“…The surface mounting process of PCB is highly automated. Therefore, the setup process of machines significantly impacts the level of system efficiency [38]. Accordingly, considering the number of major setup times is crucial to enhance the overall system utilization.…”

Section: Problem Statement 21 System Descriptionmentioning

confidence: 99%

An adaptive scheduling framework for solving multi-objective hybrid flow shop scheduling problems

Nahhas

Krist²,

Turowski

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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The proposed new technologies in the context of industry 4.0 challenge the current practices of scheduling in industry and their associated research in academia. The conventional optimization techniques that are employed for solving scheduling problems are either computationally expensive or lack the required quality. Therefore, in this paper, we propose an adaptive scheduling framework to address scheduling problems taking into account multi-objective optimality measures. The framework is motivated by a hybrid design to combine the use of heuristic and metaheuristic approaches. The main idea behind the presented concept is to achieve an acceptable tradeoff between the quality of the suggested solutions for a problem and the required computational effort to obtain them. The perused narrative in such implementation is combining some advantages of heuristic and metaheuristic approaches such as: the light execution time of heuristics and the robustness as well as the quality of metaheuristic approaches. The framework is evaluated for solving hybrid flow shop scheduling problems that are derived from a real use case.

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“…Wittrock [23] and So [24] dealt with the scheduling problem with two types of setup, minor and major. Also, Xing and Zhang [25] developed a heuristic algorithm in which a group of job can be split and processed on more than two parallel machines by using results of Monma and Potts [26].…”

Section: Literature Reviewsmentioning

confidence: 99%

Technology for Production Scheduling of Jobs for Open Innovation and Sustainability with Fixed Processing Property on Parallel Machines

Shim

Park

2016

Sustainability

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Abstract:In this paper, a technology for production scheduling is addressed for the sustainability and open innovation in a manufacturing business. Methodologies for scheduling jobs on parallel machines with the fixed processing property are devised. The fixed processing property, in which a group of specific jobs can be processed on the predetermined machine, can be found in most manufacturing systems due to the quality issues. Usually, even though parallel machines can process various types of jobs, the fixed processing is preferred as to not deteriorate products' quality in real manufacturing systems. To minimize makespan of jobs, which is defined as the final completion time of all jobs, technology for production scheduling is developed. Several heuristic algorithms are devised for solving the problem and to evaluate performance of the suggested algorithms, a series of computational experiments is performed. Results show that better solutions are obtained by the suggested algorithms in a reasonable amount of computation time. That is, if the proposed technology is applied to the production scheduling system of a real manufacturing business, it can be expected that quantity and quality of the product will be enhanced since they are influenced by the production scheduling.

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Scheduling parallel machines with major and minor setup times

Cited by 34 publications

References 6 publications

Extending an agent-based FMS scheduling approach with parallel genetic algorithms

Extending an agent-based FMS scheduling approach with parallel genetic algorithms

An adaptive scheduling framework for solving multi-objective hybrid flow shop scheduling problems

Technology for Production Scheduling of Jobs for Open Innovation and Sustainability with Fixed Processing Property on Parallel Machines

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