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This paper presents a column generation-based modelling and solution approach for a teaching assistant workload scheduling problem that arises at academic institutions. A typical weekly workload schedule involves teaching deficiency classes, instructing problem-solving tutorial sessions, and allocating help-hours for students. For this purpose, a mixed-integer programming model that selects valid combinations of weekly schedules from the set of all feasible schedules is formulated. Due to the overwhelming number of variables in this model, an effective column generation procedure is developed. To illustrate the proof-of-concept along with modelling and algorithmic constructs, a case study related to the Department of Mathematics at Kuwait University is addressed. Computational results based on real data indicate that the generated schedules using the proposed model and solution procedure yield improved weekly workloads for teaching assistants in terms of fairness, and achieve enhanced satisfaction levels among assistants, as compared to schedules obtained using ad-hoc manual approaches.

In this paper, a novel approach involving the concepts from mathematical programming and number theory is proposed to find the D-optimal designs. In specific, we will propose a mathematical formulation for the D-optimal design. In addition to that, we will present the use of cyclotomic cosets in the mathematical formulation, in order to reduce the total number of binary variables. We will illustrate the validity of our proposed method by solving a difficult known instance (N=126) of the D-optimal design.

The objective of expert systems is the use of Artificial Intelligence tools so as to solve problems within specific prefixed applications. Even when such systems are widely applied in diverse applications, as manufacturing or control systems, until now, there is an important gap in the development of a theory being applicable to a description of the involved problems in a unified way. This paper is an attempt in supplying a simple formal description of expert systems together with an application to a robot manipulator case.

The paper presents an intelligent GIS architecture that enables us to extend GIS functionality by using domain specific knowledge and inference engine. In this architecture, an intelligent agent monitors events, which occur in the GIS environment, and execute tasks depending on user's actions. The intelligent agent includes an expert system shell and knowledge base. A hybrid knowledge representation method is used that integrates rule-based, object-oriented, and procedural knowledge representations.

In this report an expert system AKU for diagnostics in acupuncture is described. The injured vital energy channels can be diagnosed using three independent methods: inquiring, Ryodoraku test and Akabane test. The inquiring is constructed as a set of trees whose internal vertices are questions while the leaves are the symptoms of diseases. The production rules describe the correspondence between the symptoms and the state of vital energy “qi” in the channels. AKU is realized by IBM PC computer and used for acupuncture treatment. The program is coded in Turbo Prolog.