AI and OR in management of operations: history and trends

Kobbacy, Khairy A. H.; Vadera, Sunil; Rasmy, M.E.

doi:10.1057/palgrave.jors.2602132

Cited by 57 publications

(25 citation statements)

References 308 publications

(303 reference statements)

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“…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 10 year period 1995 to 2004(Kobbacy et al 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source.…”

mentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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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 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.

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confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…There are various typologies of decision-making tools in the management and production logistics. Kobbacy et al [9] divide them into two major classes: Operational Research (OR) and Artificial Intelligence (AI). OR techniques include mathematical programming, network analysis, regression, queuing theory, simulation, and maintenance models.…”

Section: Solution Techniquesmentioning

confidence: 99%

Models and Algorithms for Production Planning and Scheduling in Foundries - Current State and Development Perspectives

Stawowy¹,

Duda²

2012

Archives of Foundry Engineering

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Mathematical programming, constraint programming and computational intelligence techniques, presented in the literature in the field of operations research and production management, are generally inadequate for planning real-life production process. These methods are in fact dedicated to solving the standard problems such as shop floor scheduling or lot-sizing, or their simple combinations such as scheduling with batching. Whereas many real-world production planning problems require the simultaneous solution of several problems (in addition to task scheduling and lot-sizing, the problems such as cutting, workforce scheduling, packing and transport issues), including the problems that are difficult to structure. The article presents examples and classification of production planning and scheduling systems in the foundry industry described in the literature, and also outlines the possible development directions of models and algorithms used in such systems.

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“…In the last decade, many ideas of Artificial Intelligence (AI) have been proposed to solve operation management problems [7]. Multi-agent systems are a distributed IA paradigm precisely designed to deal with problems of these characteristics.…”

Section: Multi-agent Systems For Project Planning and Controlmentioning

confidence: 99%

An Agent Based Information System for Project Portfolio Management

Araúzo

Paredes

Pajares

2010

Balanced Automation Systems for Future Manufacturing Networks

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Abstract. With the increase in globalization, integration of world markets, and rise in the concept of transnational corporations, the importance of project management has increased many folds. An efficient project management helps new enterprises to achieve competitiveness, speed of response, and closeness to customer demands by improving their flexibility and agility, while maintaining their productivity and quality. But for project management to be successful in the new competitive and dynamic environment management, systems should incorporate new characteristics. In these circumstances, the current challenge is to develop information and control systems for project management that exhibit intelligence, robustness and adaptation to the environment changes and disturbances. The introduction of multi-agent systems paradigms addresses these requirements, bringing the advantages of distribution, autonomy, scalability and re-usability. This paper proposes a distributed information and control system for project portfolio management that integrates strategic issues, planning and control into a community of software agents.

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AI and OR in management of operations: history and trends

Cited by 57 publications

References 308 publications

A survey of AI in operations management from 2005 to 2009

A survey of AI in operations management from 2005 to 2009

Models and Algorithms for Production Planning and Scheduling in Foundries - Current State and Development Perspectives

An Agent Based Information System for Project Portfolio Management

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