Journal ArticleDOI
Multi-objective scheduling of dynamic job shop using variable neighborhood search
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TLDR
The proposed method is compared with some common dispatching rules that have widely used in the literature for dynamic job shop scheduling problem and shows the high effectiveness and efficiency of the proposed method in a variety of shop floor conditions.Abstract:
Dynamic job shop scheduling that considers random job arrivals and machine breakdowns is studied in this paper. Considering an event driven policy rescheduling, is triggered in response to dynamic events by variable neighborhood search (VNS). A trained artificial neural network (ANN) updates parameters of VNS at any rescheduling point. Also, a multi-objective performance measure is applied as objective function that consists of makespan and tardiness. The proposed method is compared with some common dispatching rules that have widely used in the literature for dynamic job shop scheduling problem. Results illustrate the high effectiveness and efficiency of the proposed method in a variety of shop floor conditions.read more
Citations
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Journal ArticleDOI
Review of job shop scheduling research and its new perspectives under Industry 4.0
TL;DR: This paper explores the future research direction in SDS and discusses the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.
Journal ArticleDOI
A reinforcement learning approach to parameter estimation in dynamic job shop scheduling
TL;DR: Reinforcement learning with a Q-factor algorithm is used to enhance performance of the scheduling method proposed for dynamic job shop scheduling (DJSS) problem which considers random job arrivals and machine breakdowns.
Journal ArticleDOI
Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems
Xiaoning Shen,Xin Yao +1 more
TL;DR: The novelty of this multi-objective evolutionary algorithm (MOEA)-based proactive-reactive method is that it is able to handle multiple objectives including efficiency and stability simultaneously, adapt to the new environment quickly by incorporating heuristic dynamic optimization strategies, and deal with two scheduling policies of machine assignment and operation sequencing together.
Journal ArticleDOI
A review of machine learning for the optimization of production processes
TL;DR: This study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry and shows that there is hardly any correlation between the used data, the amount ofData, the machine learning algorithms, the used optimizers, and the respective problem from the production.
Journal ArticleDOI
Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem
Nilsen Kundakci,Osman Kulak +1 more
TL;DR: Efficient hybrid Genetic Algorithm methodologies for minimizing makespan in dynamic job shop scheduling problem are introduced and detailed numerical experiments are carried out to evaluate the performance of proposed methodologies.
References
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Journal ArticleDOI
Variable neighborhood search
Nenad Mladenović,Pierre Hansen +1 more
TL;DR: This chapter presents the basic schemes of VNS and some of its extensions, and presents five families of applications in which VNS has proven to be very successful.
Book
Neural Network Fundamentals with Graphs, Algorithms and Applications
Nirmal K. Bose,Ping Liang +1 more
TL;DR: This chapter explains the basics of neuroscience and artificial neuron models graphs algorithms and applications of neural networks approach to solving hard problems.
Journal ArticleDOI
Dynamic job-shop scheduling using reinforcement learning agents
TL;DR: An intelligent agent based dynamic scheduling system that selects the most appropriate priority rule according to the shop conditions in real time, while simulated environment performs scheduling activities using the rule selected by the agent.
Journal ArticleDOI
Dynamic rescheduling that simultaneously considers efficiency and stability
TL;DR: A rescheduling methodology is proposed that uses a multiobjective performance measures that contain both efficiency and stability measures and is tested on a simulated job shop to determine the impact of the key parameters on the performance measures.