Dynamic scheduling of manufacturing job shops using extreme value theory

Chryssolouris, George; Subramaniam, Velusamy

doi:10.1080/095372800232324

Cited by 30 publications

(33 citation statements)

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“…Finally, the proposed methodology is applied in a real manufacturing environment utilising data from a mould-making SME and the scheduling algorithm is compared with others dispatching rules in order to benchmark its performance. This work extends the research presented in [36][37][38] by enhancing the scheduling algorithm with knowledge reuse capabilities and by verifying the method in a case coming from the domain of ETO products.…”

Section: State Of the Artmentioning

confidence: 83%

A mobile application for knowledge-enriched short-term scheduling of complex products

2016

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The ever-increasing product complexity, especially for the case of engineer-to-order products, highly affects the performance of manufacturing systems. Therefore, a high degree of flexibility is needed during daily decision-making activities, such as production scheduling. For addressing this challenge, this research work proposes a knowledge-enriched short-term job-shop scheduling mechanism, which is implemented into a mobile application. More precisely, it focuses on the short-term scheduling of the resources of the machine shop, through an intelligent algorithm that generates and evaluates alternative assignments of resources to tasks. Based on the requirements of a new order, a similarity mechanism retrieves successfully executed past orders together with a dataset that includes the processing times, the job and task sequence, and the suitable resources. In addition to that, the similarity mechanism is used to calculate the due-date assignments of the orders based on the knowledge stored in past cases. Afterwards, it adapts these parameters to the requirements of the new order so as to evaluate the alternative schedules and identify a good alternative in a timely manner. The deriving schedule can be presented on mobile devices, and it can be manipulated by the planner on-thefly respecting tasks precedence constraints and machine availability. A case study from the mould-making industry is used for validating the proposed method and application.

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Section: State Of the Artmentioning

confidence: 83%

A mobile application for knowledge-enriched short-term scheduling of complex products

2016

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“…The algorithm noted that the test data were too bad to be dealt with manually. In an earlier work, we have tried to complete this optimization using a genetic algorithm [10]. But GA's are relatively inefficient, as the design and calculation is very complex due to a lot of invalid solutions in the crossover process.…”

Section: Methodsmentioning

confidence: 99%

“…(7,9,10) is the first aluminum ladle, (14,15,16) is the second aluminum ladle, etc. The 6th aluminum ladle only contains one electrolytic cell (8).…”

Section: The Gene Code Rule and The Initializationmentioning

confidence: 99%

A New Approach to Blending and Loading Problem of Molten Aluminum

Wei²

2014

j.eng.technol.sci.

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Abstract. The problems of blending electrolyzer and multi-constraint optimization of electrolytic aluminum scheduling in the electrolytic aluminum production process were addressed. Based on a mathematical model analysis, a novel hybrid optimization algorithm is proposed for optimization of blending together the molten aluminum in different electrolytic cells. An affinity degree function was designed to represent the path of aluminum scheduling. The mutation operators were designed to implement the transformation of electrolyzer combination and change the route of loading. A typical optimization example from an aluminum plant in northwest China is given in this paper, the results of which demonstrate the effectiveness of the proposed method.

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“…The most energyefficient selection will emerge through the appropriate production planning and scheduling. Several tools have already been developed for this purpose [51,52]; nevertheless, they need to be upgraded with the inclusion of energy efficiency in their decision-making arguments [53].…”

Section: Energy Efficiency At the Factory Levelmentioning

confidence: 99%

On a generalized approach to manufacturing energy efficiency

Fysikopoulos

Pastras

Alexopoulos

et al. 2014

Int J Adv Manuf Technol

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Energy efficiency has become a very significant factor, requiring its inclusion in the manufacturing decision-making attributes. This paper proposes a generalized approach to manufacturing energy efficiency. The basic element of the approach is the division of energy efficiency definition and study into four manufacturing levels, namely process, machine, production line, and factory. Process-level definitions are provided for the majority of manufacturing processes. A machine-level study indicates and solves difficulties, generated by the workpiece geometry, and points out the interaction with the process level through factors, such as the process time. Moreover, machine tool peripherals are responsible for a significant portion of the consumed energy, and classification based on the dependence of their consumption on process variables is required. Studies made on the production line and factory levels show that energy efficiency, at these levels, is heavily dependent on production planning and scheduling and can be improved through the appropriate utilization of machines, with the inclusion of shutdown and eco-modes. Finally, a case study is presented, showing that many of the difficulties towards the optimization of energy efficiency can be dealt with successfully, using the proposed generalized approach.

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Dynamic scheduling of manufacturing job shops using extreme value theory

Cited by 30 publications

References 0 publications

A mobile application for knowledge-enriched short-term scheduling of complex products

A mobile application for knowledge-enriched short-term scheduling of complex products

A New Approach to Blending and Loading Problem of Molten Aluminum

On a generalized approach to manufacturing energy efficiency

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