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A study of the effect of [BPy]PF6 as a flame retardant property
Published in Dawei Zheng, Industrial Engineering and Manufacturing Technology, 2015
Job shop scheduling problem is a branch of production scheduling in the production management of manufacturing processes, and exists almost ubiquitously in the industrial engineering world. In the market, job shop scheduling has become a key to reflect the competitiveness of manufacturing enterprise. It is a set of jobs for each operation and to define the sequence of the constrained operations on a set of machines to minimize the makespan. The purpose is to improve the production efficiency and reduce the processing duration so as to gain profits as high as possible. But JSSP is one of the hardest combinatorial optimization problems, and is a NP-hard problem which is to find the best schedule can be very difficult, depending on the shop environment and the constraint indicators [1]. Thereof, JSSP has aroused growing concerns and researches interest over the past decades.
Production Scheduling
Published in Katsundo Hitomi, Manufacturing Systems Engineering, 2017
(3) Job-shop scheduling— scheduling for a job shop, where the sequence of machines differs for each job. This shop is typical for the case of varied production of most jobbing types and some batch types.
Digital-twin-based job shop multi-objective scheduling model and strategy
Published in International Journal of Computer Integrated Manufacturing, 2023
Zhuo Zhou, Liyun Xu, Xufeng Ling, Beikun Zhang
Efficient job shop scheduling is important for improving enterprise competitiveness and reducing production energy consumption. The job shop production mode is characterised by multiple varieties, small batches, different resource requirements, complex process routes, and multi-source disturbances (Liu et al. 2019; Baykasoglu, Madenoglu, and Hamzadayi 2020). Recently, with the rapid changes in the market environment, manufacturing enterprises have been encountering considerable development pressure. Enterprises must improve their production efficiency and implement product diversification strategies to enhance their competitiveness. As an important management component, efficient job shop scheduling can reduce production costs and enhance competitiveness (Delgado-Gomes, Oliveira-Lima, and Martins 2017). In addition, statistics show that the energy consumption of manufacturing industry accounts for approximately one-third of the global energy consumption. In large and developing industrialised countries, the manufacturing industry has a high proportion of energy consumption (International Energy Agency 2018). Recently, stringent constraints on energy consumption have been proposed for the manufacturing industry (Chou, Yang, and Wu 2020). Some countries will charge carbon tariffs on products causing large carbon emissions. Therefore, energy consumption must be accurately evaluated and optimised during production. Moreover, efficient job shop scheduling is an effective method for reducing energy consumption through reasonable production arrangements.
Dynamic scheduling of manufacturing systems: a product-driven approach using hyper-heuristics
Published in International Journal of Computer Integrated Manufacturing, 2021
Wassim Bouazza, Yves Sallez, Damien Trentesaux
It is also interesting to note that methods combining scheduling issues with Game Theory have also attracted attention from researchers, especially in a cloud manufacturing context. Although it does not deal with the sequencing aspect of operations, it proposes an interesting approach for the allocation of tasks to resources. For example, (Zhang et al. 2017) propose a real-time allocation strategy using the Nash equilibrium. The proposed algorithm improves efficiency while reducing the processing cost for a flexible job shop scheduling problem. Another example is provided by Carlucci et al. (2020). In the context of Cloud Manufacturing, the authors propose a noncooperation model based on the Minority Game Theory, especially when few information is provided by competitors (i.e. resources). The simulation results show how the proposed model generates a better solution than a classic formulation taking into account merely experience. The performance obtained is as close to the centralized benchmark as when information is freely available among the participants.
Integrated scheduling algorithm based on an operation relationship matrix table for tree-structured products
Published in International Journal of Production Research, 2018
Qi Lei, Weifei Guo, Yuchuan Song
The difficulty of the job-shop scheduling problem is due mainly to the high number of constraints (e.g. capacity constraints, precedence constraints such as technological constraints, etc.). In particular, the precedence constraints are the ones that make the job-shop scheduling problem difficult to treat by GAs (Falkenauer and Bouffouix 1991). Compared with the job-shop scheduling problem, the integrated scheduling problem considers the constraint between jobs and the two problems of processing and assembly at the same time. This arouses unique coordination and pacing problems and has more complex precedence constraints. The most attractive feature of GA which is one of the evolutionary search methods that can provide optimal or near optimal solutions for the combinatorial optimisation problems is the flexibility of handling various kinds of objective functions with fewer requirements on fine mathematical properties (Gen and Cheng 1997). It has been implemented for a wide variety of optimisation problems. The key issues in developing a GA-based approach are the encoding scheme of the solution, the initialisation of the population, genetic operators and selection strategy. In this section, these issues are described in detail to present a GA-based approach for the integrated scheduling problem.