320,258 research outputs found
Guidelines for the deployment and implementation of manufacturing scheduling systems
It has frequently been stated that there exists a gap between production scheduling theory and practice. In order to put theoretical findings into practice, advances in scheduling models and solution procedures should be embedded into a piece of software - a scheduling system - in companies. This results in a process that entails (1) determining its functional features, and (2) adopting a successful strategy for its development and deployment. In this paper we address the latter question and review the related literature in order to identify descriptions and recommendations of the main aspects to be taken into account when developing such systems. These issues are then discussed and classified, resulting in a set of guidelines that can help practitioners during the process of developing and deploying a scheduling system. In addition, identification of these issues can provide some insights to drive theoretical scheduling research towards those topics more in demand by practitioners, and thus help to close the aforementioned gap.Framiñan Torres, JM.; Ruiz García, R. (2012). Guidelines for the deployment and implementation of manufacturing scheduling systems. International Journal of Production Research. 50(7):1799-1812. doi:10.1080/00207543.2011.564670S17991812507Baek, D. H. (1999). A visualized human-computer interactive approach to job shop scheduling. International Journal of Computer Integrated Manufacturing, 12(1), 75-83. doi:10.1080/095119299130489Comesaña Benavides, J. A., & Carlos Prado, J. (2002). Creating an expert system for detailed scheduling. International Journal of Operations & Production Management, 22(7), 806-819. doi:10.1108/01443570210433562Bensana, E. 1986. An expert-system approach to industrial job-shop scheduling. 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European Journal of Operational Research, 39(1), 17-31. doi:10.1016/0377-2217(89)90349-4Chen, J.-F. (2004). Unrelated parallel machine scheduling with secondary resource constraints. The International Journal of Advanced Manufacturing Technology, 26(3), 285-292. doi:10.1007/s00170-003-1622-1Collinot, A., Le Pape, C., & Pinoteau, G. (1988). SONIA: A knowledge-based scheduling system. Artificial Intelligence in Engineering, 3(2), 86-94. doi:10.1016/0954-1810(88)90024-6Cowling, P. (2003). A flexible decision support system for steel hot rolling mill scheduling. Computers & Industrial Engineering, 45(2), 307-321. doi:10.1016/s0360-8352(03)00038-xDudek, R. A., Panwalkar, S. S., & Smith, M. L. (1992). The Lessons of Flowshop Scheduling Research. Operations Research, 40(1), 7-13. doi:10.1287/opre.40.1.7Dumond, E. J. (2005). Understanding and using the capabilities of finite scheduling. Industrial Management & Data Systems, 105(4), 506-526. doi:10.1108/02635570510592398Fox, M. S., & Smith, S. F. (1984). ISIS?a knowledge-based system for factory scheduling. Expert Systems, 1(1), 25-49. doi:10.1111/j.1468-0394.1984.tb00424.xFraminan, J. M., & Ruiz, R. (2010). Architecture of manufacturing scheduling systems: Literature review and an integrated proposal. European Journal of Operational Research, 205(2), 237-246. doi:10.1016/j.ejor.2009.09.026Freed, T., Doerr, K. H., & Chang, T. (2007). In-house development of scheduling decision support systems: case study for scheduling semiconductor device test operations. International Journal of Production Research, 45(21), 5075-5093. doi:10.1080/00207540600818351Gao, C and Tang, L. 2008. A decision support system for color-coating line in steel industry. In: Proceedings of the IEEE international conference on automation and logistics, ICAL 2008. 2008. pp.1463–1468.Grant, T. J. (1986). Lessons for O.R. from A.I.: A Scheduling Case Study. Journal of the Operational Research Society, 37(1), 41-57. doi:10.1057/jors.1986.7Graves, S. C. (1981). A Review of Production Scheduling. Operations Research, 29(4), 646-675. doi:10.1287/opre.29.4.646HALSALL, D. N., MUHLEMANN, A. P., & PRICE, D. H. R. (1994). A review of production planning and scheduling in smaller manufacturing companies in the UK. Production Planning & Control, 5(5), 485-493. doi:10.1080/09537289408919520Higgins, P. G. (1996). Interaction in hybrid intelligent scheduling. International Journal of Human Factors in Manufacturing, 6(3), 185-203. doi:10.1002/(sici)1522-7111(199622)6:33.0.co;2-6Kanet, J. J., & Adelsberger, H. H. (1987). Expert systems in production scheduling. European Journal of Operational Research, 29(1), 51-59. doi:10.1016/0377-2217(87)90192-5Kathawala, Y., & Allen, W. R. (1993). Expert Systems and Job Shop Scheduling. International Journal of Operations & Production Management, 13(2), 23-35. doi:10.1108/01443579310025286Kerr, R. M. (1992). Expert systems in production scheduling: Lessons from a failed implementation. Journal of Systems and Software, 19(2), 123-130. doi:10.1016/0164-1212(92)90063-pKnolmayer, G., Mertens, P., & Zeier, A. (2002). Supply Chain Management Based on SAP Systems. doi:10.1007/978-3-540-24816-3Leachman, R. C., Benson, R. F., Liu, C., & Raar, D. J. (1996). IMPReSS: An Automated Production-Planning and Delivery-Quotation System at Harris Corporation—Semiconductor Sector. Interfaces, 26(1), 6-37. doi:10.1287/inte.26.1.6MACCARTHY, B. L., & LIU, J. (1993). Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. International Journal of Production Research, 31(1), 59-79. doi:10.1080/00207549308956713McKay, K. N., & Black, G. W. (2007). The evolution of a production planning system: A 10-year case study. Computers in Industry, 58(8-9), 756-771. doi:10.1016/j.compind.2007.02.002McKay, K. N., Safayeni, F. R., & Buzacott, J. A. (1988). Job-Shop Scheduling Theory: What Is Relevant? Interfaces, 18(4), 84-90. doi:10.1287/inte.18.4.84McKay, K. N., Morton, T. E., Ramnath, P., & Wang, J. (2000). ?Aversion dynamics? scheduling when the system changes. Journal of Scheduling, 3(2), 71-88. doi:10.1002/(sici)1099-1425(200003/04)3:23.0.co;2-0MCKAY, K., PINEDO, M., & WEBSTER, S. (2009). PRACTICE-FOCUSED RESEARCH ISSUES FOR SCHEDULING SYSTEMS*. Production and Operations Management, 11(2), 249-258. doi:10.1111/j.1937-5956.2002.tb00494.xMissbauer, H., Hauber, W., & Stadler, W. (2009). A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry. International Journal of Production Research, 47(15), 4147-4172. doi:10.1080/00207540801950136Numao, M and Morishita, S. 1989. A scheduling environment for steel-making processes. In: Proceedings of the 5th conference on artificial intelligence applications. 1989. pp.279–286.Olhager, J., & Rapp, B. (1995). Operations Research Techniques in Manufacturing Planning and Control Systems. International Transactions in Operational Research, 2(1), 29-43. doi:10.1111/j.1475-3995.1995.tb00003.xPerez-Gonzalez, P., & Framinan, J. M. (2009). Scheduling permutation flowshops with initial availability constraint: Analysis of solutions and constructive heuristics. Computers & Operations Research, 36(10), 2866-2876. doi:10.1016/j.cor.2008.12.018Pinedo, M., & Yen, B. P.-C. (1997). Annals of Operations Research, 70, 359-378. doi:10.1023/a:1018986524234Portougal, V., & Robb, D. J. (2000). Production Scheduling Theory: Just Where Is It Applicable? Interfaces, 30(6), 64-76. doi:10.1287/inte.30.6.64.11623Reisman, A., Kumar, A., & Motwani, J. (1997). Flowshop scheduling/sequencing research: a statistical review of the literature, 1952-1994. IEEE Transactions on Engineering Management, 44(3), 316-329. doi:10.1109/17.618173Steffen, MS. 1986. A survey of artificial intelligence-based scheduling systems. In: Proceedings of the fall industrial engineering conference. 1986.Storer, R. H., Wu, S. D., & Vaccari, R. (1992). New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling. Management Science, 38(10), 1495-1509. doi:10.1287/mnsc.38.10.1495Tang, L., & Wang, G. (2008). Decision support system for the batching problems of steelmaking and continuous-casting production. Omega, 36(6), 976-991. doi:10.1016/j.omega.2007.11.002T’kindt, V., Billaut, J.-C., Bouquard, J.-L., Lenté, C., Martineau, P., Néron, E., … Tacquard, C. (2005). The e-OCEA project: towards an Internet decision system for scheduling problems. Decision Support Systems, 40(2), 329-337. doi:10.1016/j.dss.2004.04.001Wiers, VCS. 1997. Human–computer interaction in production scheduling: Analysis and design of decision support systems for production scheduling tasks. Ph.D. Thesis, Technische Universiteit Eindhoven, NetherlandsWiers, V. C. S. (2002). A case study on the integration of APS and ERP in a steel processing plant. Production Planning & Control, 13(6), 552-560. doi:10.1080/09537280210160321Wiers, V. C. S., & Van Der Schaaf, T. W. (1997). A framework for decision support in production scheduling tasks. Production Planning & Control, 8(6), 533-544. doi:10.1080/095372897234876Zhang, L., Krishnamurthy, A., Malmborg, C. J., & Heragu, S. S. (2009). Variance-based approximations of transaction waiting times in autonomous vehicle storage and retrieval systems. European J. of Industrial Engineering, 3(2), 146. doi:10.1504/ejie.2009.02360
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
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Current practice and challenges towards handling uncertainty for effective outcomes in maintenance
The combination of viable heuristic attributes with statistical measurements presents significant challenges in industrial maintenance for complex assets under through-life service contracts. Techniques to obtain and process heuristic attributes raise numerous uncertainties which often go undefined and unmitigated. A holistic view of these uncertainties may improve decision-making capabilities and reduce maintenance costs and turnaround time. It is therefore necessary to identify and rank factors that influence uncertainties originating from challenges in the above context. This, along with an identification of who contributes to such challenges and current practice to handle them, sets the focus for this study.
The influence of 32 categorised factors on uncertainty is assessed through a questionnaire completed by nine experienced maintenance managers from a leading defence company. The pedigree approach is applied to score validity of respondents’ answers according to their experience and job role to normalise scores. Results are discussed in interviews with respondents along with current practice in and ways to improve uncertainty assessment. Scores are weighted through the Analytical Hierarchy Process (AHP) in order to identify the most influential factors on uncertainty in maintenance. The analysis revealed that these include: intellectual property rights (IPR), maintainer performance, quality of information, resistance to change, stakeholder communication and technology integration. These are verified with 40 practitioners from various industrial backgrounds. From the interviews, it is deemed that a holistic view of heuristic and statistical attributes ultimately allows for more accomplished decision-making but requires trade-offs between quality and cost over the asset’s life cycle
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
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
A framework for the selection of the right nuclear power plant
Civil nuclear reactors are used for the production of electrical energy. In the nuclear industry vendors propose several nuclear reactor designs with a size from 35–45 MWe up to 1600–1700 MWe. The choice of the right design is a multidimensional problem since a utility has to include not only financial factors as levelised cost of electricity (LCOE) and internal rate of return (IRR), but also the so called “external factors” like the required spinning reserve, the impact on local industry and the social acceptability. Therefore it is necessary to balance advantages and disadvantages of each design during the entire life cycle of the plant, usually 40–60 years. In the scientific literature there are several techniques for solving this multidimensional problem. Unfortunately it does not seem possible to apply these methodologies as they are, since the problem is too complex and it is difficult to provide consistent and trustworthy expert judgments. This paper fills the gap, proposing a two-step framework to choosing the best nuclear reactor at the pre-feasibility study phase. The paper shows in detail how to use the methodology, comparing the choice of a small-medium reactor (SMR) with a large reactor (LR), characterised, according to the International Atomic Energy Agency (2006), by an electrical output respectively lower and higher than 700 MWe
Collection and integration of local knowledge and experience through a collective spatial analysis
This article discusses the convenience of adopting an approach of Collective Spatial Analysis in the P/PGIS processes, with the aim of improving the collection and integration of knowledge and local expertise in decision-making, mainly in the fields of planning and adopting territorial policies. Based on empirical evidence, as a result of the review of scientific articles from the Web of Science database, in which it is displayed how the knowledge and experience of people involved in decision-making supported by P/PGIS are collected and used, a prototype of a WEB-GSDSS application has been developed. This prototype allows a group of people to participate anonymously, in an asynchronous and distributed way, in a decision-making process to locate goods, services, or events through the convergence of their views. Via this application, two case studies for planning services in districts of Ecuador and Italy were carried out. Early results suggest that in P/PGIS local and external actors contribute their knowledge and experience to generate information that afterwards is integrated and analysed in the decision-making process. On the other hand, in a Collective Spatial Analysis, these actors analyse and generate information in conjunction with their knowledge and experience during the process of decision-making. We conclude that, although the Collective Spatial Analysis approach presented is in a subjective and initial stage, it does drive improvements in the collection and integration of knowledge and local experience, foremost among them is an interdisciplinary geo-consensusPeer ReviewedPostprint (published version
Measuring Expert Performance at Manually Classifying Domain Entities under Upper Ontology Classes
Classifying entities in domain ontologies under upper ontology classes is a
recommended task in ontology engineering to facilitate semantic
interoperability and modelling consistency. Integrating upper ontologies this
way is difficult and, despite emerging automated methods, remains a largely
manual task.
Little is known about how well experts perform at upper ontology integration.
To develop methodological and tool support, we first need to understand how
well experts do this task. We designed a study to measure the performance of
human experts at manually classifying classes in a general knowledge domain
ontology with entities in the Basic Formal Ontology (BFO), an upper ontology
used widely in the biomedical domain.
We conclude that manually classifying domain entities under upper ontology
classes is indeed very difficult to do correctly. Given the importance of the
task and the high degree of inconsistent classifications we encountered, we
further conclude that it is necessary to improve the methodological framework
surrounding the manual integration of domain and upper ontologies
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