Journal of the Operational Research Society

doi: 10.1057/jors.1990.62pmid: N/A

Doukidis, Georgios I.; Paul, Ray J.

doi: 10.1057/jors.1990.63pmid: N/A

AbstractThis paper presents the findings of a survey of the application of artificial intelligence techniques amongst OR Society members. The survey involved sending a questionnaire to one-third of the UK-based members of the OR Society and generated a 26% response rate. The survey investigated general characteristics of the respondents to enable comparison with previous surveys; determined the current and growing use of AI techniques, and in which areas they are applied; and characterized the benefits and constraints of using such techniques.

Duchessi, Peter; O'Keefe, Robert M.

doi: 10.1057/jors.1990.64pmid: N/A

AbstractThis paper discusses a knowledge-based approach to production planning, the process of establishing production rates, work force levels and on-hand inventories for product families as a possible alternative to traditional operational research methods. It describes one company's experienced-based approach to production planning and how this was incorporated into a knowledge-based system. The production planning state-space, use of common planning constraints and heuristic procedures, and a prototype of a knowledge-based system that develops production plans for one product family are also described.

Zhang, Guangming; Lu, Stephen C.-Y.

doi: 10.1057/jors.1990.65pmid: N/A

AbstractRecognising the importance of combining manufacturing and management systems for machining operation planning, this paper presents a new methodology for the evaluation of economic aspects in an operation plan. To ensure that the quality of machined parts satisfies the required specifications, the manufacturing system acts as an alternative generator that provides meaningful and practical plans. Through cost analysis, the variable, fixed, and total costs associated with the machining operation are quantitatively determined. The management system, which functions as an evaluation mechanism, then selects the optimal plan based on the defined goal. The proposed methodology has been applied in the framework design of an expert system. The program establishes a sequence of machining operation planning and searches for the optimal plan. This optimal plan integrates considerations from both managers and production engineers, and balances their needs for efficient machining of a quality product.

Pierre, Samuel; Hoang, Hai-Hoc

doi: 10.1057/jors.1990.66pmid: N/A

AbstractThis paper presents an artificial intelligence approach to solving the network design problem. The major issue under consideration is the various topological aspects of a design, especially in the case of large computer networks. The combinatorial explosion can only be dealt with, hopefully, by means of heuristics, which drastically reduce the search space of candidate topologies. The proposed approach is essentially a general framework for integrating conventional approximate methods and knowledge-based systems. An inference engine is then a means suited to extending the realm of local transformations used in conventional heuristics. Sophisticated goal-directed searches can be achieved by using knowledge bases, which are large sets of detailed design rules.

Balestra, Gabriella; Tsoukiàs, Alexis

doi: 10.1057/jors.1990.67pmid: N/A

AbstractThis paper presents a possible representation of multicriteria analysis by means of artificial intelligence techniques. The decision process activities characterized by the existence of formal and technical knowledge were identified and attention was focused on the area of multicriteria outranking methods. The knowledge characteristics suggested the use of artificial intelligence techniques, based on a conceptualization in which the domain of discourse is the set of the multicriteria methodology concepts used in the analysed area of activities, and the relational set is the union of the admissible relations among the concepts and the relations elicited from experience. The suitable AI techniques were tested by implementing a knowledge-based interface between the outranking methods and a user who was not very familiar with this approach.

Connell, N. A. D.; Powell, P. L.

doi: 10.1057/jors.1990.68pmid: N/A

AbstractThis 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.

Kiountouzis, Evangelos; Papatheodorou, Christos

doi: 10.1057/jors.1990.69pmid: N/A

AbstractDistributed artificial intelligence systems consist of multiple physically separated processing nodes, each having exact and complete knowledge sources, to solve a problem. The behaviour of these systems is studied by the open systems approach. This paper presents the findings of a comparison between the open systems approach and soft systems methodology proposed by Peter Checkland.1

Neale, I. M.

doi: 10.1057/jors.1990.70pmid: N/A

AbstractThe benefits of a qualitative modelling approach to building knowledge-based systems include better project control, communication between participants, more accurate validation and easier maintenance. Knowledge acquisition from experts will be more successful if a conceptual model is developed from a knowledge level description, to act as a publicly examinable basis for system design. Analytical tools are available to structure both content and process knowledge on paper, before any commitment is made to implementation. The role of abstraction in developing models of generic tasks as a set of templates for conceptual model development is reviewed, with particular regard to knowledge acquisition tools. Modelling problem-solving methods, especially for constructive tasks such as planning, is more difficult than modelling content knowledge. Epistemological support for conceptual abstraction, particularly at middle levels, remains inadequate.