Design of an IT-driven decision support system for vehicle routing and scheduling

This paper presents the development of a decision support system used by an oil downstream company for routing and scheduling purposes. The studied problem refers to a complex delivery process of oil products from a number of distribution centers to all customers. The latest rapid advance of operations research (OR) applications, in the form of advanced planning and scheduling (APS) systems, has shown that OR algorithms can be applied in practice if (a) they are embodied in packaged information technology (IT) solutions, (b) the interface problems to mainstream ERP software applications are solved. In this study the utilisation of advanced IT systems supports effectively the planning and management of distribution operations. The combination of a supply chain management (SCM) application with a geographical information system (GIS) integrated with an enterprise resource planning (ERP) software resulted to this innovative decision support tool. The objectives of this new tool are: optimum use of the distribution network resources, transportation cost reduction and customer service improvement. The paper concludes with the benefits of the new system, emphasising at how new technologies can support transportation processes with the help of operations research algorithms embedded in software applications.     

Integrated decision support for aviation safety inspectors

This paper presents a systems viewpoint for developing an advanced decision support system for aircraft safety inspectors. Research results from a Federal Aviation Administration (FAA) sponsored project to use neural network and expert systems technology to analyze aircraft maintenance databases are summarized. One of the main objectives of this research is to define more refined “alert” indicators for national comparison purposes that can signal potential problem areas by aircraft type for safety inspector consideration.

Integration aspects are addressed on two levels: (1) integration of the various technical components of the decision support system, and (2) integration of the decision support system with individual behavior, management systems and organizational structure, as well as corporate culture across both formal and informal dimensions. The paper summarizes the creation of strategic “inspection profiles” for aging aircraft and reliability curve fitting for structural components both based upon using neural network technology. Also, the potential use of a model-based expert system to facilitate field inspection diagnostics is presented. Finally, a framework for developing an intelligent decision system to support aircraft safety inspections is proposed that links expert systems, neural networks, as well as a paradigm of the decision making process typically used in unstructured situations.     

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.     

Operational risk: Emerging markets,sectors and measurement

The role of decision support systems in mitigating operational risks in firms is well established. However, there is a lack of investment in decision support systems in emerging markets, even though inadequate operational risk management is a key cause of discouraging external investment. This has also been exacerbated by insufficient understanding of operational risk in emerging markets, which can be attributed to past operational risk measurement techniques, limited studies on emerging markets and inadequate data.     

Perspectives on operations research in data and knowledge management

A number of problems in the design and management of database systems and knowledge base systems (KBSs) can be addressed using techniques from operations research (OR). This article provides a perspective on these problems and the types of models that have been applied to them, and identifies some areas that pose interesting modeling and analysis questions for researchers working in areas such as mathematical programming, stochastic modeling, dynamic programming and simulation.     

Scheduling of production and logistics operations of steam production systems in food industries: a case study of the tomato processing industry

This study presents an optimization approach by mathematical modelling to support some of the main operational decisions in steam production systems with multiple industrial boilers. Decisions are related to boiler operations scheduling (start-up, warm-up and shutdown time), fuel replenishment (transportation and inventory management) and fuel composition consumed by each piece of equipment. These decisions are often taken based on practical experience of people involved, instead of any decision support tool using optimization techniques; as a consequence, unnecessary costs are likely to be incurred. The optimization approach is based on mixed integer programming and parameters experimental adjustment procedures. A case study of a large tomato processing plant in Brazil was carried out along 1 year using a 3-year database. Owing to the reasonably good outcomes achieved (annually potential savings around 10%), we consider the proposed approach as a suitable tool to support some of the key decisions in boiler scheduling and fuel logistics in steam production systems for tomato processing and other similar industries.     

Dynamic construction of statistical models in managerial DSS

The fields of operations research (OR) and artificial intelligence (AI) provide complementary methods that may be combined into managerial decision support systems (DSS). However, the management domain is substantially different from domains in which prior expert systems have been developed. Consequently, successful application of OR/AI techniques in managerial DSS requires careful analysis and additional development. Ongoing research concerning design and implementation of managerial DSS is discussed. A prototype system capable of constructing linear statistical models of direct and indirect relationships from a knowledge base of relationships is described and evaluated.     

Organizational decision support systems: Centralized support for decentralized organizations

Organizations are becoming increasingly decentralized in their operations and decision making. Advanced information technologies provide the glue that holds such organizations together and facilitates their operations. Organizational decision support systems (ODSSs) provide mechanisms for ensuring that the decisions being made throughout such organizations are consistent with each other and with the overall organization goals. Through means of an ODSS, information and guidance is automatically passed from higher levels to lower levels for use in decision-making models.     

A review of expert systems

Expert systems have recently become popular and are attracting more and more attention. The high quality performance achieved by some systems in areas previously not considered practical for computational solutions has lead to great interest from many different disciplines. Most expert systems use a subset of techniques from the general area of computer science research known as artificial intelligence. However, some expert systems have been developed that incorporate more traditional mathematical modeling techniques. The combination of artificial intelligence techniques and more traditional mathematical techniques has been shown to be quite effective in developing several high quality performance computer software systems. The techniques used in expert systems may be what is needed to bridge the gap between classical operational research modeling and human decision making processes. This paper addresses how expert systems techniques are being used in problem solving and why someone in operational research might want to use them.     

Artificial intelligence for decision support systems in the field of operations research: review and future scope of research

Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions.

     

Modeling recreational systems using optimization techniques and information technologies

Due to intrinsic complexity and sophistication of decision problems in tourism and recreation, respective decision making processes can not be implemented without making use of modern computer technologies and operations research approaches. In this paper, we review research works on modeling recreational systems.     

Developments in linear and integer programming

In this review we describe recent developments in linear and integer (linear) programming. For over 50 years Operational Research practitioners have made use of linear optimisation models to aid decision making and over this period the size of problems that can be solved has increased dramatically, the time required to solve problems has decreased substantially and the flexibility of modelling and solving systems has increased steadily. Large models are no longer confined to large computers, and the flexibility of optimisation systems embedded in other decision support tools has made on-line decision making using linear programming a reality (and using integer programming a possibility). The review focuses on recent developments in algorithms, software and applications and investigates some connections between linear optimisation and other technologies.     

Computer aided decision support system for water strategic planning in Jordan

The Nominal Group Technique and a multi-criteria decision aid software are utilised to develop a decision support system for strategic planning of water resources in Jordan. The system described is novel in that it integrates the various decision analytical management techniques in order to increase the flexibility and efficiency of the decision making process.     

Decision Analysis Using an Enumerative Approach

A computer based, enumerative approach to decision making in a practical environment is described for circumstances where many feasible alternative plans can be generated within given constraints. The method lies between simple techniques for evaluating a few alternative plans and very general techniques such as integer programming, and has advantages over both of these approaches in certain cases. Its main use is as a decision support system, providing management with a range of ranked alternatives. The technique is embodied in a computer program which is used to give an example of how the method may be applied in practice.     

Managing risk in the real world

Exploitation by corporate management of what we know about decision analysis and simulation techniques is not limited by an understanding of the relevant analytical frameworks or the availability of the computer programs necessary for data manipulations. Rather, effectively utilizing the power of these concepts is inhibited by our inability to weave them into the fabric of the real management systems and decision-making processes of the firm.Texts on decision analysis often belabor the unwillingness on the part of management to employ these sophisticated tools. Management, on the other hand, is neither enchanted nor convinced by the ‘black boxes’ (which are perceived as black magic) of the decision analyst. This paper will not argue the merits of where the responsibility lies. Rather, it will present a method for effectively integrating decision and simulation analysis into the real strategic decision-making process that actually exists within the firm. At least, the capability of employing such a method is now under our control; the author argues that if it is effected, then exploitation of these powerful analytical approaches by management can follow.Specifically, we will deal here with the application of decision analysis to the management of risk and return in petroleum exploration. This is in itself an important application of great practical significance. However, the principles illustrated through this example are widely applicable to many other areas. It is especially applicable to areas where risks are great and uncertainties are large. Such areas would include research, the marketing of new products, and proposed large capital projects. Therefore, careful attention will be given to the underlying principles involved, and to the implications of applying these principles in other areas.     

An intelligent decision support system for assisting industrial wastewater management

Sustainable economic development requires the inclusion of environmental factors in the decision making procedure. The generic objective of the Environmentally Sensitive Investment System (ESIS) Project is to provide industry and governmental departments or agencies with a tool to assess the technical and economic implications of capital-intensive projects, in response to stated environmental policies. More specifically, the ESIS prototype helps to find wastewater management alternatives that meet given environmental regulatory standards in a technologically sound and cost-efficient manner. The use of this decision support system will enhance the ability of managers and planners to explore the quantitative implications of a wide range of options. ESIS incorporates a combination of artificial intelligence and operations research techniques, database management and visualization tools, integrated under a graphical user interface. The ESIS prototype runs on top-of-the-line personal computers.     

Formulating dynamic strategies using decision calculus

This paper presents an applied methodology to assist managers in strategically setting prices and allocating resources over the product, brand, or adoption (diffusion) life cycle. While substantial theoretical work has been achieved in this area in the management science and operations research disciplines, approaches which can be implemented as managerial tools are generally lacking. Our methodology, which has been applied as a PC-based decision support tool in the telecommunications industry, marries the dynamic optimization literature with decision calculus approaches offering sufficient flexibility for applied contexts. We discuss the approach and describe its use to derive optimal price and advertising policies. Strategies not commonly suggested using traditional formulations are also presented.     

可拓数据挖掘在高校教学质量评价中的应用

高校在教学和管理工作中积累了大量的数据,但这些数据没有得到有效利用.将可拓数据挖掘技术引入教学领域,从教学评价数据中提取出隐藏在数据之中的有用信息,为教学管理者提供决策支持.首先通过可拓分析,寻找质量达到要求、可进行有效挖掘的教学评价数据,然后对这些数据进行两方面的挖掘:影响教学质量的关键因素挖掘、教学质量与教师特征之间的关联规则挖掘.     

The development,validation and implementation of knowledge-based systems

It is now ten years since Operational Research, and other groups that support management decision making, seized upon Knowledge-based Systems (KBS) as an alternative set of techniques and methods for building systems that support, augment or automate decision making. Success, as with most information technologies, has been mixed. This paper briefly reviews where KBS have been successful, and what benefits have been derived. It notes that interest in KBS has been rekindled by the present interest in business process re-engineering. Given the mature state of development tools, most KBS developers are now focusing on methodological and life-cycle issues. The paper reviews progress in understanding and using development methodologies, validation methods (now generally considered as crucial to technical success) and implementation. Some ideas about where KBS ‘fits in’ to the array of tools, techniques and methodologies available to the modern analyst and system developer are presented.