scholarly journals Job-shop Scheduling Over a Heterogeneous Platform

2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Hector Benitez-Perez ◽  
Jose A. Hermosillo-Gomez
Keyword(s):  
Embedded System ◽  
Manufacturing Systems ◽  
Production Efficiency ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Human Interaction ◽  
Periodic Tasks ◽  
Real Time Scheduling ◽  
Shop Scheduling ◽  
Heterogeneous Platform

Real-time scheduling involves determining the allocation of platform resources in such a way tasks can meet their temporal restrictions. This work focuses on job-shop tasks model in which a task have a finite number of nonpreemptive different instances (jobs) that share a unique hard deadline and their time requirements are known until task arrival. Non-preemptive scheduling is considered because this characteristic is widely used in industry. Besides job-shop scheduling has direct impacts on the production efficiency and costs of manufacturing systems. So that the development of analysis for tasks with these characteristics is necessary. The aim of this work is to propose an online scheduling test able to guarantee the execution of a new arriving task, which is generated by human interaction with an embedded system, otherwise to discart it. An extension of the schedulability test proposed by Baruah in 2006 for non-preemptive periodic tasks over an identical platform is presented in this paper. Such extension is applied to non-preemptive tasks that have hard deadlines over a heterogeneous platform. To do that, some virtual changes over both the task set and the platform are effectuated.

scholarly journals Bi-local search based variable neighborhood search for job-shop scheduling problem with transport constraints

2020 ◽  
Author(s):  
Moussa Abderrahim ◽  
Abdelghani Bekrar ◽  
Damien Trentesaux ◽  
Nassima Aissani ◽  
Karim Bouamrane
Keyword(s):  
Manufacturing Systems ◽  
Variable Neighborhood Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Efficient Production ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

AbstractIn job-shop manufacturing systems, an efficient production schedule acts to reduce unnecessary costs and better manage resources. For the same purposes, modern manufacturing cells, in compliance with industry 4.0 concepts, use material handling systems in order to allow more control on the transport tasks. In this paper, a job-shop scheduling problem in vehicle based manufacturing facility that is mainly related to job assignment to resources is addressed. The considered job-shop production cell has two types of resources: processing resources that accomplish fabrication tasks for specific products, and transporting resources that assure parts’ transport to the processing area. A Variable Neighborhood Search algorithm is used to schedule product manufacturing and handling tasks in the aim to minimize the maximum completion time of a job set and an improved lower bound with new calculation method is presented. Experimental tests are conducted to evaluate the efficiency of the proposed approach.

scholarly journals Research on Construction and Application for the Model of Multistage Job Shop Scheduling Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jianzhong Xu ◽  
Song Zhang ◽  
Yuzhen Hu
Keyword(s):  
Production Efficiency ◽  
Parallel Machines ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Second Stage ◽  
Sequencing Algorithm ◽  
Two Stages

Based on the practical application of an enterprise, we address the multistage job shop scheduling problem with several parallel machines in the first stage (production), a few parallel machines in the second stage (processing and assembly), and one machine in the following stages (including joint debugging, testing, inspection, and packaging). First, we establish the optimization objective model for the first two stages. Then, based on the design of the sequencing algorithm in the first two stages, a correction algorithm is designed between the first stage and the second stage to solve this problem systematically. Finally, we propose two benchmark approaches to verify the performance of our proposed algorithm. Verification of numerical experiments shows that the model and algorithm constructed in this paper effectively improve the production efficiency of the enterprise.

scholarly journals A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem

2020 ◽  
Author(s):  
S Nguyen ◽  
Mengjie Zhang ◽  
M Johnston ◽  
K Chen Tan
Keyword(s):  
Genetic Programming ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Computational Study ◽  
Dispatching Rules ◽  
Scheduling Problem ◽  
Learning Approaches ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Designing effective dispatching rules is an important factor for many manufacturing systems. However, this time-consuming process has been performed manually for a very long time. Recently, some machine learning approaches have been proposed to support this task. In this paper, we investigate the use of genetic programming for automatically discovering new dispatching rules for the single objective job shop scheduling problem (JSP). Different representations of the dispatching rules in the literature are newly proposed in this paper and are compared and analysed. Experimental results show that the representation that integrates system and machine attributes can improve the quality of the evolved rules. Analysis of the evolved rules also provides useful knowledge about how these rules can effectively solve JSP. © 1997-2012 IEEE.

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