A Comparison of Potential Applications of Expert Systems and Decision Support Systems

This paper describes ways in which decision support systems (DSS) and expert systems (ES) may be differentiated. It proposes a simplified systemic view which emphasises the functional differences of the environments within which each type of system is applied, thereby avoiding some of the problems often associated with attempting to define DSS and ES according to their constituent parts. This functional emphasis allows DSS and ES to be placed within a useful existing theoretical framework which takes account of contextual influences. The results of two independent surveys of DSS and ES applications in a sample domain-accountancy-are used to assess the view of their functionality held by users of the systems, particularly their functional differences. Some conclusions are drawn about the role of the user in the construction of such systems.     

An integrative evaluation framework for intelligent decision support systems

Evaluation of the overall effectiveness of decision support systems (DSS) has been a research topic since the early 1980s. As artificial intelligence methods have been incorporated into systems to create intelligent decision support systems (IDSS), researchers have attempted to quantify the value of the additional capabilities. Despite the useful and relevant insights generated by previous research, existing evaluation methodologies offer only a fragmented and incomplete view of IDSS value and the contribution of its technical infrastructure. This paper proposes an integrative, multiple criteria IDSS evaluation framework through a model that links the decision value of an IDSS to both the outcome from, and process of, decision making and down to specific components of the IDSS. The proposed methodology provides the designer and developer specific guidance on the intelligent tools most useful for a specific user with a particular decision problem. The proposed framework is illustrated by evaluating an actual IDSS that coordinates management of urban infrastructures.     

A two-stage fuzzy robust integer programming approach for capacity planning of environmental management systems

In this study, a two-stage fuzzy robust integer programming (TFRIP) method has been developed for planning environmental management systems under uncertainty. This approach integrates techniques of robust programming and two-stage stochastic programming within a mixed integer linear programming framework. It can facilitate dynamic analysis of capacity-expansion planning for waste management facilities within a multi-stage context. In the modeling formulation, uncertainties can be presented in terms of both possibilistic and probabilistic distributions, such that robustness of the optimization process could be enhanced. In its solution process, the fuzzy decision space is delimited into a more robust one by specifying the uncertainties through dimensional enlargement of the original fuzzy constraints. The TFRIP method is applied to a case study of long-term waste-management planning under uncertainty. The generated solutions for continuous and binary variables can provide desired waste-flow-allocation and capacity-expansion plans with a minimized system cost and a maximized system feasibility.     

Relating value-focused thinking and interactive planning

Value-focused thinking, using the dialogue decision process (DDP), and interactive planning appear to be two totally unrelated processes for making decisions. As this paper shows, new results on the interpretation of utility functions and new ways of thinking about downstream decisions allows us to reinterpret interactive planning as an ideal-focused decision process which is theoretically equivalent to DDP's value-focused decision process. But Ackoff's ideal-focused decision process may be more natural for certain organizational decision settings.     

A web-based decision support system for waste lube oils collection and recycling

This paper presents a web-based decision support system (DSS) that enables schedulers to tackle reverse supply chain management problems interactively. The focus is on the efficient and effective management of waste lube oils collection and recycling operations. The emphasis is given on the systemic dimensions and modular architecture of the proposed DSS. The latter incorporates intra- and inter-city vehicle routing with real-life operational constraints using shortest path and sophisticated hybrid metaheuristic algorithms. It is also integrated with an Enterprise Resource Planning system allowing the utilization of particular functional modules and the combination with other peripheral planning tools. Furthermore, the proposed DSS provides a framework for on-line monitoring and reporting to all stages of the waste collection processes. The system is developed using a web architecture that enables sharing of information and algorithms among multiple sites, along with wireless telecommunication facilities. The application to an industrial environment showed improved productivity and competitiveness, indicating its applicability on realistic reverse logistical planning problems.     

Integration of the human operator into responsive discrete production management systems

The need for production systems that can react or respond to dynamic changes is continuously increasing because of the reduction of product life cycle time and the rise of competition. To improve responsiveness, we show that integrating the intelligence of the human operator into the system helps to face complexity. However, little work has been done about the optimization of such integration considering production system constraints (real time decision making, observability, etc.) and human operator constraints (mental workload, trust in management system, self-confidence, etc.). This paper aims at discussing ways to take account of the cognitive abilities of the human operator and offers some advice on how to take accurately into account the integration of the human operator by the proposal of a set of global specifications. To illustrate how it is possible to contribute to the optimized design of a system based upon such specifications, we propose the concept of “distributed production management system”. We first address the specific interface issue. A classical example of “advanced display” designed for continuous systems, that is, the Ecological Interface Design (EID) approach, is applied to discrete production systems. We show that such an approach is coherent with parts of the introduced specifications but can be adapted to large complex and discrete systems with difficulty. To solve this issue, we propose the development of a distributed DSS where each local DSS integrates an advanced display and manages a set of production resources in cooperation with an operator, which reduces the global complexity.     

An optimization-based decision support system for strategic planning in a process industry: the case of aluminium company in India

We describe how a generic multi-period optimization-based decision support system (DSS) can be used for strategic planning in process industries. The DSS is built on five fundamental elements—materials, facilities, activities, storage areas, and time periods. It requires little direct knowledge of optimization techniques to be used effectively. Results based on real data from an aluminium company in India demonstrate significant potential for improvement in profits. We conclude with a comparison of similar studies in two other process industries.     

Selecting preferred solutions in the minimax approach to dynamic programming problems under flexible constraints

Dynamic Programming is a powerful approach to the optimization of sequential or multistage decision processes, e.g., in planning or in system control. In this paper, we consider both theoretical and algorithmic issues in sequential decision processes under flexible constraints. Such processes must attain a given goal within some tolerance. Tolerances or preferences also apply to the values the decision variables may take or on the action chosen at each step. Such problems boil down to maximin optimization. Unfortunately, this approach suffers from the so-called “drowning effect” (lack of discrimination) and the optimality principle of dynamic programming is not always verified. In this context, we introduce a general framework for refined minimax optimization procedures in order to compare and select preferred alternatives. This framework encompasses already introduced methods such as LexiMin and DiscriMin, but it allows their extension to the comparison of vectors of unequal lengths. We show that these refined comparisons restore compatibility with the optimality principle, and that classical algorithms can be adapted to compute such preferred solutions, by exploiting existing results on idempotent semirings.     

Evaluating environmentally conscious business practices

Corporate environmental management is becoming more strategically oriented. With increased emphasis on the natural environmental by organizational stakeholders, including governments, stockholders, customers, employees and communities, the need for explicit consideration and incorporation of environmental strategy within corporate strategy has never been more critical to the organization. With such programs as design for the environment, total quality environmental management, life cycle analysis, green supply chain management, and ISO 14000 standards gaining notoriety, the operational and strategic decisions for environmental managers and businesses is becoming more complex. This paper integrates these elements and their attributes into a strategic assessment and decision tool using the systems with feedback or analytical network process (ANP) technique first introduced by Saaty. The ANP technique, which has been sparingly investigated by researchers or applied by practitioners is useful for modeling dynamic strategies systemic influences on managerial decisions.     

Optimal planning of network structures within an exchange area

The cost-optimal planning and capacity extension of a given network structure is described by a mixed-integer program. The decision variables in this model are first discrete digital systems (PCM) set up on existing cable lines and secondly new cable links required in the future. Furthermore, this formulation takes account of the circuit capacity of the system and path diversification required for reliability reasons. This planning model describes a real-world problem that can be formulated not only mathematically as a true reproduction of reality but can also be solved in a closed form within reasonable computer time. Opposite to the operational planning methods, this model allows a global cost-optimal network to be obtained, which is subject to a certain set of constraints.     

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.

     

Optimal decisions for an insurance contract with experience rating

In this paper we consider the insurance of assets with experience rating in the framework of a discrete-time dynamic decision model. The goal of the risk-averse agent (insurance buyer) is to maximize the expected utility of wealth at the finite planning horizon. First, it has to be decided whether a contract should be bought or not. The contract gives the possibility to choose in each period between three alternatives: to buy insurance and to file a claim, to buy insurance and not to file a claim, to suspend insurance. For this case structural properties of the optimal strategy are obtained by means of dynamic programming. Especially, we present a condition for the experience rating scheme such that the decision to suspend insurance is irreversible during the planning period (stopping rule). In the final section we present some numerical experiences. Among others it will be shown that the optimal decision functions generally are not monotone with respect to the agent's claim history.     

A transdisciplinary approach for formalized integrated planning and decision-making in complex systems

Multi-sectoral systems, such as cities or regional economies, face strategic challenges of optimal development due to the complexity of interacting perspectives, interests, and preferences of decision-makers and stakeholders. In coping with these challenges, integrated approaches in strategic planning and decision support promise to generate more efficient and effective results than sectoral approaches. In this article, we follow the concept of operational research (OR) by applying a formalized approach to cross-sectoral integrated planning and decision-making in complex, i.e., multi-sectoral systems. The presented Transdisciplinary Integrated Planning and Synthesis (TIPS) approach relies on (soft) OR methods combined in a multi-methodological framework that is adapted to the cognitive skills and habits of the stakeholders and experts involved in mutual and joint learning processes (transdisciplinarity). This methodological contribution structures, formalizes, and empirically illustrates cross-sectoral integrated strategic planning step by step, and evaluates the TIPS methodology against requirements for integrated approaches derived from literature.     

Multi-horizon stochastic programming

Infrastructure-planning models are challenging because of their combination of different time scales: while planning and building the infrastructure involves strategic decisions with time horizons of many years, one needs an operational time scale to get a proper picture of the infrastructure’s performance and profitability. In addition, both the strategic and operational levels are typically subject to significant uncertainty, which has to be taken into account. This combination of uncertainties on two different time scales creates problems for the traditional multistage stochastic-programming formulation of the problem due to the exponential growth in model size. In this paper, we present an alternative formulation of the problem that combines the two time scales, using what we call a multi-horizon approach, and illustrate it on a stylized optimization model. We show that the new approach drastically reduces the model size compared to the traditional formulation and present two real-life applications from energy planning.     

Dynamic project selection and funding under risk: A decision tree based MILP approach

This paper presents a mixed integer linear programming (MILP) model for a new class of dynamic project selection and funding problems under risk given multiple scarce resources of different qualifications. The underlying stochastic decision tree concept extends classical approaches mainly in that it adds a novel node type that allows for the continuous control of discrete branching probability distributions. The control functions are piecewise linear and are convex for the costs and concave for the benefits. The MILP-model has been embedded in a prototype Decision Support System (DSS). With respect to the proposed solution the DSS provides complete probability distributions for both costs and benefits.     

Issues Mapping: A problem structuring method for addressing science and technology conflicts

There are new opportunities for the application of problem structuring methods to address science and technology risk conflicts through stakeholder dialogue. Most previous approaches to addressing risk conflicts have been developed from a traditional risk communication perspective, which tends to construct engagement between stakeholders based on the assumption that scientists evaluate technologies using facts, and lay participants do so based on their values. ‘Understanding the facts’ is generally privileged, so the value framings of experts often remain unexposed, and the perspectives of lay participants are marginalized. When this happens, risk communication methodologies fail to achieve authentic dialogue and can exacerbate conflict. This paper introduces ‘Issues Mapping’, a problem structuring method that enables dialogue by using visual modelling techniques to clarify issues and develop mutual understanding between stakeholders. A case study of the first application of Issues Mapping is presented, which engaged science and community protagonists in the genetic engineering debate in New Zealand. Participant and researcher evaluations suggest that Issues Mapping helped to break down stereotypes of both scientists and environmental activists; increased mutual understanding; reduced conflict; identified common ground; started building trust; and supported the emergence of policy options that all stakeholders in the room could live with. The paper ends with some reflections and priorities for further research.     

An integrated framework for inventory management and transportation of refined petroleum products: Pipeline or marine?

We study the integrated problem of managing inventory of refined petroleum products, and their multi-modal (ships and pipeline) transportation between a refinery and the served distribution centers. It is important that the transportation decisions are driven not just by the inventory levels and customer demand, but also the environmental risks associated with different refined products. A bi-objective mixed integer linear programming optimization model (MILP) is proposed, where constituent components were independently developed and then interfaced to capture the complexity of the resulting integrated model. A time-based decomposition heuristic is also employed to solve the integrated problem. The proposed framework was used to study a number of problem instances generated using a realistic infrastructure in the United States, and the resulting analyses lead to the following inferences: pipeline is the preferred mode of transportation only when cost is the sole consideration; on the other hand, when environmental risks are considered marine is the preferred mode for most of the refined petroleum products, except for heavier oils; and, the proportion of traffic on the two modes is a function of the type and volume of products, and the number of vessels available at the start of the planning horizon.     

A theory of rolling horizon decision making

In this paper, we develop a theoretical framework for the common business practice of rolling horizon decision making. The main idea of our approach is that the usefulness of rolling horizon methods is, to a great extent, implied by the fact that forecasting the future is a costly activity. We, therefore, consider a general, discrete-time, stochastic dynamic optimization problem in which the decision maker has the possibility to obtain information on the uncertain future at given cost. For this non-standard optimization problem with optimal stopping decisions, we develop a dynamic programming formulation. We treat both finite and infinite horizon cases. We also provide a careful interpretation of the dynamic programming equations and illustrate our results by a simple numerical example. Various generalizations are shown to be captured by straightforward modifications of our model.This research is supported in part by NSERC Grant A4619, SSHRC Grant 410-87-0524, and Manufacturing Research Corporation of Ontario. Comments and suggestions from Qing Zhang and the referees are gratefully acknowledged.     

Multiobjective strategies in power systems planning

The rapidly changing decision environment in electrical systems planning calls for a new approach in planning procedures and mathematical tools. The method proposed in this study decomposes the decision making into two strata, one at the strategy setting, the other at the project selection level. For the strategy setting level, a multiobjective dynamic linear programming model is used in generating system expansion decision alternatives. The suitability of the modelling approach is considered from the various aspects such as congruence, flexibility, transparency, and accuracy.An application of the model to the Turkish electrical system is also presented, where expansion alternatives to the year 2000 are generated. Three criteria were observed: cost, environmental impact and risk.