Learning-based scheduling of flexible manufacturing systems using ensemble methods

Dispatching rules are commonly applied to schedule jobs in Flexible Manufacturing Systems (FMSs). However, the suitability of these rules relies heavily on the state of the system; hence, there is no single rule that always outperforms the others. In this scenario, machine learning techniques, such as support vector machines (SVMs), inductive learning-based decision trees (DTs), backpropagation neural networks (BPNs), and case based-reasoning (CBR), offer a powerful approach for dynamic scheduling, as they help managers identify the most appropriate rule in each moment. Nonetheless, different machine learning algorithms may provide different recommendations. In this research, we take the analysis one step further by employing ensemble methods, which are designed to select the most reliable recommendations over time. Specifically, we compare the behaviour of the bagging, boosting, and stacking methods. Building on the aforementioned machine learning algorithms, our results reveal that ensemble methods enhance the dynamic performance of the FMS. Through a simulation study, we show that this new approach results in an improvement of key performance metrics (namely, mean tardiness and mean flow time) over existing dispatching rules and the individual use of each machine learning algorithm

A Knowledge Enriched Computational Model to Support Lifecycle Activities of Computational Models in Smart Manufacturing

Due to the needs in supporting lifecycle activities of computational models in Smart Manufacturing (SM), a Knowledge Enriched Computational Model (KECM) is proposed in this dissertation to capture and integrate domain knowledge with standardized computational models. The KECM captures domain knowledge into information model(s), physics-based model(s), and rationales. To support model development in a distributed environment, the KECM can be used as the medium for formal information sharing between model developers. A case study has been developed to demonstrate the utilization of the KECM in supporting the construction of a Bayesian Network model. To support the deployment of computational models in SM systems, the KECM can be used for data integration between computational models and SM systems. A case study has been developed to show the deployment of a Constraint Programming optimization model into a Business To Manufacturing Markup Language (B2MML) -based system. In another situation where multiple computational models need to be deployed, the KECM can be used to support the combination of computational models. A case study has been developed to show the combination of an Agent-based model and a Decision Tree model using the KECM. To support model retrieval, a semantics-based method is suggested in this dissertation. As an example, a dispatching rule model retrieval problem has been addressed with a semantics-based approach. The semantics-based approach has been verified and it demonstrates good capability in using the KECM to retrieve computational models

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

Dynamic adjustment of dispatching rule parameters in flow shops with sequence dependent setup times

Decentralized scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence dependent setup times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs

Comparative simulation study of production scheduling in the hybrid and the parallel flow

Scheduling is one of the most important decisions in production control. An approach is proposed for supporting users to solve scheduling problems, by choosing the combination of physical manufacturing system configuration and the material handling system settings. The approach considers two alternative manufacturing scheduling configurations in a two stage product oriented manufacturing system, exploring the hybrid flow shop (HFS) and the parallel flow shop (PFS) environments. For illustrating the application of the proposed approach an industrial case from the automotive components industry is studied. The main aim of this research to compare results of study of production scheduling in the hybrid and the parallel flow, taking into account the makespan minimization criterion. Thus the HFS and the PFS performance is compared and analyzed, mainly in terms of the makespan, as the transportation times vary. The study shows that the performance HFS is clearly better when the work stations' processing times are unbalanced, either in nature or as a consequence of the addition of transport times just to one of the work station processing time but loses advantage, becoming worse than the performance of the PFS configuration when the work stations' processing times are balanced, either in nature or as a consequence of the addition of transport times added on the work stations' processing times. This means that physical layout configurations along with the way transport time are including the work stations' processing times should be carefully taken into consideration due to its influence on the performance reached by both HFS and PFS configurations.This work was supported by National Funds through FCT "Fundacao para a Ciencia e a Tecnologia" under the program: PEst2015-2020, ref. UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

A Novel Fuzzy-Neural Slack-Diversifying Rule Based on Soft Computing Applications for Job Dispatching in a Wafer Fabrication Factory

This study proposes a slack-diversifying fuzzy-neural rule to improve job dispatching in a wafer fabrication factory. Several soft computing techniques, including fuzzy classification and artificial neural network prediction, have been applied in the proposed methodology. A highly effective fuzzy-neural approach is applied to estimate the remaining cycle time of a job. This research presents empirical evidence of the relationship between the estimation accuracy and the scheduling performance. Because dynamic maximization of the standard deviation of schedule slack has been shown to improve performance, this work applies such maximization to a slack-diversifying fuzzy-neural rule derived from a two-factor tailored nonlinear fluctuation smoothing rule for mean cycle time (2f-TNFSMCT). The effectiveness of the proposed rule was checked with a simulated case, which provided evidence of the rule’s effectiveness. The findings in this research point to several directions that can be exploited in the future

Design of a Reference Architecture for Production Scheduling Applications based on a Problem Representation including Practical Constraints

Changing customer demands increase the complexity and importance of production scheduling, requiring better scheduling algorithms, e.g., machine learning algorithms. At the same time, current research often neglects practical constraints, e.g., changeovers or transportation. To address this issue, we derive a representation of the scheduling problem and develop a reference architecture for future scheduling applications to increase the impact of future research. To achieve this goal, we apply a design science research approach and, first, rigorously identify the problem and derive requirements for a scheduling application based on a structured literature review. Then, we develop the problem representation and reference architecture as design science artifacts. Finally, we demonstrate the artifacts in an application scenario and publish the resulting prototypical scheduling application, enabling machine learning-based scheduling algorithms, for usage in future development projects. Our results guide future research into including practical constraints and provide practitioners with a framework for developing scheduling applications

Scheduling Algorithms: Challenges Towards Smart Manufacturing

Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms\u27 characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario

Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach

Flexible flow shop scheduling problems are NP-hard and tend to become more complex when stochastic uncertainties are taken into consideration. Although some methods have been developed to address such problems, they remain inherently difficult to solve by any single approach. This paper presents a novel decomposition-based approach (DBA), which combines both the shortest processing time (SPT) and the genetic algorithm (GA), to minimizing the makespan of a flexible flow shop (FFS) with stochastic processing times. In the proposed DBA, a neighbouring K-means clustering algorithm is developed to firstly group the machines of an FFS into an appropriate number of machine clusters, based on their stochastic nature. Two optimal back propagation networks (BPN), corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either SPT or GA to each machine cluster for sub-schedule generation. Finally, an overall schedule is generated by integrating the sub-schedules of machine clusters. Computation results show that the DBA outperforms SPT and GA alone for FFS scheduling with stochastic processing times. © 2012 Elsevier Ltd. All rights reserved.postprin

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM