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Dynamic Co-Operative Scheduling Based on HLA
Abstract
In an advanced factory, higher and higher flexibility is required to meet a great variety of customers requirements. In this environment, a dynamic management architecture is required for distributed production system. In this paper, a newly distributed simulation architecture called HLA (High Level Architecture) is utilized to achieve a distributed scheduling simulation for dynamic work assignment and flexible working group configuration in a distributed production system. It is verified that the distributed scheduling simulation based on HLA is effective to achieve higher flexibility for a distributed production system, by some case studies. Full Text at Springer, may require registration or fee
... understand production status and can respond to the status. These methods are suitable to adopt an automated lot production factory123456. However, most of such production systems work under the assumption that the production process in the factory is restricted to the result of process planning done beforehand while the distributed production system composed of some/many autonomous flexible machine cells does not need to fix the process beforehand. ...
... By using the network system, each production facility can understand production status and can respond to the status. These methods are suitable to adopt an automated lot production factory[1][2][3][4][5][6]. However, most of such production systems work under the assumption that the production process in the factory is restricted to the result of process planning done beforehand while the distributed production system composed of some/many autonomous flexible machine cells does not need to fix the process beforehand. ...
Automated factories have become complicated and need higher flexibility to satisfy various requirements today. In this environment, many concepts of autonomous and distributed production systems have been proposed for a dynamic production management recently. In this paper, the preparative operations are discussed, that is focused on the decision of production process order with consideration of set-up time, and a dynamic production management architecture considering such preparative operations is proposed. First, the decision rule of the processing order by using production process information and real-time production system information is introduced. Then, we also pay attention to the combination and timing of processing sequences on both machining cells and parts in order to propose the timing rule and the set-up time rule referring to the real status of that are applied to be dynamic scheduling. Finally, real-time production-scheduling system using the proposed system has the feasibility of the flexible correspondence against the disturbance. Full Text at Springer, may require registration or fee
... Technical issues include cell formation and cell design, whereas human issues involve such as worker assignment strategies, skill identification, training, communication, autonomy, reward system, conflict management and teamwork. Shirase et al. (2001) developed a system of distributed production system which consists of some cell groups. They developed a dynamic operator assignment method. ...
By noting the importance of flexible manufacturing system (FMS), variable market and reduction in product life cycle, this study presents a decision making model for optimization of operator allocation in a Cellular Manufacturing System (CMS) using computer simulation (CS) and genetic algorithm (GA). This study determines the number of cross trained operators and optimum operators' layout with respect to cellular condition in a CMS by an integrated approach. The main contribution of this paper is utilization of an integrated CS-GA approach for optimum allocation of operators in a CMS whereas previous studies only use computer simulation or other meta-heuristics. A typical U-shaped CMS is used as the case study to show superiority and advantage of the proposed approach.
Operators are assigned to operations in labor-intensive manufacturing cells using two assignment strategies: Max-Min and Max. The major concern is to see how these two approaches impact operators’ skill levels and makespan values in a multi-period environment. The impact is discussed under chaotic environment where sudden changes in product mix with different operation times are applied, and also under non-chaotic environment where same product mix is run period after period. In this chapter, operators’ skill levels are affected by learning and forgetting rates. The Max-Min strategy improved operators’ skill levels more significantly than Max in this multi-period study; particularly in chaotic environment. This eventually led to improved makespan values under Max-Min strategy.
Operators are assigned to operations in labor-intensive manufacturing cells using two assignment strategies: Max-Min and Max. The major concern is to see how these two approaches impact operators’ skill levels and makespan values in a multi-period environment. The impact is discussed under chaotic environment where sudden changes in product mix with different operation times are applied, and also under non-chaotic environment where same product mix is run period after period. In this chapter, operators’ skill levels are affected by learning and forgetting rates. The Max-Min strategy improved operators’ skill levels more significantly than Max in this multi-period study; particularly in chaotic environment. This eventually led to improved makespan values under Max-Min strategy.
This paper presents an integrated analytic hierarchy process (AHP) genetic algorithm (GA) and computer simulation (CS) for the optimisation of operator allocation in cellular manufacturing systems (CMS) with weighted variables. This is a challenging issue in manufacturing systems in general and in CMS in particular. A computer simulation model which considers various operators layout is developed. GA is utilised for optimisations of results of various operators layout and AHP is utilised to define the weight of variables of fitness function for GA. Previous studies only utilise multivariate analysis methods, fuzzy C-means and simulation, whereas this study uses an integrated GA, AHP and simulation for CMS with weighted variables. Also, previous CMS studies consider only one type of product whereas this study considers multi product CMS modelling through simulation. Moreover, more robust CMS assessment indicators are used in the proposed model. A practical case study illustrates the effectiveness and superiority of the proposed methodology.
The dynamic scheduling management system architecture that is based on the parts and information packets unified technology
is developed, and variable lot size production with the parts and packets unified technology is proposed. We assumed that
each part or unit has a data career, which handle such as identification number, position of the part, state of the part.
Using these data, progress of the project process is compared with the master schedule. If the difference arises between them,
scheduling system dynamically re-schedules with dynamic lot size to optimize the processes. A pilot system that realized our
proposal is developed.
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