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 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

A Unified Model for the Two-stage Offline-then-Online Resource Allocation

With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various real-world matching markets ranging from ridesharing to crowdsourcing. There are some emerging applications such as rebalancing in bike sharing and trip-vehicle dispatching in ridesharing, which involve a two-stage resource allocation process. The process consists of an offline phase and another sequential online phase, and both phases compete for the same set of resources. In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework. Our model assumes non-uniform and known arrival distributions for online agents in the second online phase, which can be learned from historical data. We propose a parameterized linear programming (LP)-based algorithm, which is shown to be at most a constant factor of $1/4$ from the optimal. Experimental results on the real dataset show that our LP-based approaches outperform the LP-agnostic heuristics in terms of robustness and effectiveness.Comment: Accepted by IJCAI 2020 (http://static.ijcai.org/2020-accepted_papers.html) and SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserve

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Autonomic computing for scheduling in manufacturing systems

We describe a novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Autonomic Computing has emerged as paradigm aiming at embedding applications with a management structure similar to a central nervous system. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. In this paper we envisage the use of Multi-Agent Systems paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with Autonomic properties, in order to reduce the complexity of managing systems and human interference. Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems

Distribution network optimisation for an active network management system

The connection of Distributed Generators (DGs) to a distribution network causes technical concerns for Distribution Network Operators (DNOs) which include power flow management, loss increase and voltage management problems. An Active Network Management System can provide monitoring and control of the distribution network as well as providing the infrastructure and technology for full integration of DGs into the distribution network. The Optimal Power Flow (OPF) method is a valuable tool in providing optimal control solutions for active network management system applications. The research presented here has concentrated on the development of a multi-objective OPF to provide power flow management, voltage control solutions and network optimisation strategies. The OPF has been shown to provide accurate solutions for variety of network topologies. It is possible to apply time-series of load and generation data to the OPF in a loop, generating optimal network solutions to maintain the network within thermal and voltage limits. The OPF incorporates not only the DG real power output maximisation, but also network loss minimisation as well as minimising the dispatch of DG reactive power. This investigation uses a direct Interior Point (IP) method as the solution methodology which is speed efficient and converges in polynomial time. Each objective function has been assigned a weighting factor, making it possible to favour one objective function and ignore the others. Contributions to enhance the performance of the IP OPF algorithm include a new generic barrier parameter formulation and a new swing bus formulation to model energy export/import in the main optimisation routine. A Terminal Voltage Regulator Mode (TVRM) and Power Factor Regulation Mode (PFRM) for DG were incorporated in the main optimisation routine. The main motivation is to compare these two decentralised DG control methods in terms of the achieving the maximum DG real power generation. The DG operation methods of TVRM and PFRM are compared with the optimisation results obtained from centralised dispatch in terms of the DG capacity achieved as it produces the optimum overall network solution. A suitable value of the droop and local voltage regulator dead-bands were determined for particular DGs. Furthermore, the effect of these decentralised DG control methods on distribution network losses are considered in a measure to assess the financial implications from a DNO's perspective

A distance ecological model for individual and collaborative-learning support

With the rapid development of information technology (IT) and the Internet spread, it is widely accepted that computer and information communication literacy has become extremely important, and will play a major part in everyone’s lives in the future. Under the umbrella of life-long education, many people from many parts of the social environment will need to be trained and learn about IT. We have started building a framework for such people, as a distance ecological model for self/ collaborative learning support. We call our system RAPSODY: Remote and AdaPtive educational System Offering a DYnamic communicative environment. To show the functionality of our general framework, we instantiated it in the form of a distance learning environment for teacher training. The purpose of this study direction is to propose and develop a distance educational model, as a school-based curriculum development and training-system. In this environment, a teacher can learn via an Internet-based self-training system about subject contents, modern teaching know-how, and students’ learning activities evaluation methods, about the new subject called "Information". This paper describes the structure, functions and mechanism of our distance educational model, in order to realize the above-mentioned goal, and then discuss the educational meaning of this model in consideration of the new learning ecology, which is based on multi-modality and new learning situations and forms, and we perform some tests and evaluation. Moreover, we show an extension of our RAPSODY framework, RAPSODY-EXT, which also embraces collaboration in remote learning environments

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

Simultaneous data rate and transmission power adaptation in V2V communications: A deep reinforcement learning approach

In Vehicle-to-Vehicle (V2V) communications, channel load is key to ensuring the appropriate operation of safety applications as well as driver-assistance systems. As the number of vehicles increases, so do their communication messages. Therefore, channel congestion may arise, negatively impacting channel performance. Through suitable adjustment of the data rate, this problem would be mitigated. However, this usually involves using different modulation schemes, which can jeopardize the robustness of the solution due to unfavorable channel conditions. To date, little effort has been made to adjust the data rate, alone or together with other parameters, and its effects on the aforementioned sensitive safety applications remain to be investigated. In this paper, we employ an analytical model which balances the data rate and transmission power in a non-cooperative scheme. In particular, we train a Deep Neural Network (DNN) to precisely optimize both parameters for each vehicle without using additional information from neighbors, and without requiring any additional infrastructure to be deployed on the road. The results obtained reveal that our approach, called NNDP, not only alleviates congestion, leaving a certain fraction of the channel available for emergency-related messages, but also provides enough transmission power to fulfill the application layer requirements at a given coverage distance. Finally, NNDP is thoroughly tested and evaluated in three realistic scenarios and under different channel conditions, demonstrating its robustness and excellent performance in comparison with other solutions found in the scientific literature.This work was supported in part by the AEI/FEDER/UE [Agencia Estatal de Investigación (AEI), Fondo Europeo de Desarrollo Regional (FEDER), and Unión Europea (UE)] under Grant PID2020-116329GB-C22 [ARISE2: Future IoT Networks and Nano-networks (FINe)] and Grant PID2020-112675RB-C41 (ONOFRE-3), in part by the Fundación Séneca, Región de Murcia, under Grant 20889/PI/18 (ATENTO), and in part by the LIFE project (Fondo SUPERA COVID-19 through the Agencia Estatal Consejo Superior de Investigaciones Científicas CSIC, Universidades Españolas, and Banco Santander). The work of Juan Aznar-Poveda was supported by the Spanish Ministerio de Educación, Cultura y Deporte (MECD) through the Formación de Personal Investigador (FPI) Predoctoral Scholarship under Grant BES-2017-08106