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This paper deals with the optimization of the operational costs of service systems. The cost function consists of service costs and waiting costs. Service cost is associated with the employment of service-providing personnel, while the waiting cost is associated with the customers having to wait for the service. The cost function is minimized subject to the server utilization as well as to the customer satisfaction constraints, using the Particle Swarm Optimization (PSO) algorithm. PSO is a fairly recent swarm intelligence meta-heuristic algorithm known for its simplicity in programming and its rapid convergence. The optimization procedure is illustrated with the example of a practical service system. A series of experiments show optimum results for the operation of the service systems.

The Software Development Project Scheduling Problem is similar to the well-known Resource-Constrained Multi-Project Scheduling Problem (RCMPSP). It consists in determining a schedule of tasks taking into consideration resource availabilities and precedence constraints, while optimizing an objective. Like RCMPSP, it is an NP-hard problem. In this paper, a task segmentation scheme to schedule a software development project is proposed and the average duration of the multiple concurrent projects is minimized using the Particle Swarm Optimization (PSO) meta-heuristic. PSO is a recent meta-heuristic algorithm, known for its simplicity in programming and its rapid convergence. A series of experiments show optimum results for several software development schedule scenarios. © 2008 IEEE.

In the conventional system development life cycle (SDLC), the system performance evaluation phase comes after the implementation phase. Our strategy is to project system performance estimate at the requirement analysis and design phase itself much before the implementation phase. To achieve this objective, we propose a technology-neutral integrated environment for the Core life cycle of system development. This core life cycle consists of three phases: system modelling, performance evaluation and performance improvement.

Single and parallel structure servers, although powerful mathematical models in queueing theory, do not address a wide range of real-life systems. In this paper we propose a composite-server model and make use of the knowledge of the intrinsic composition of its service providing units (personnel or equipment) to arrive at Qualitative Rules needed to evaluate its performance. The composite server model has wider scope in its applications and can be used to represent a variety of system classes. We use this novel concept in the performance design and improvement of collaborative engineering systems. A frequent source of bottleneck in collaborative systems is the lack or surplus of service-providing units, known as "Perspectives" in the Multi-Context Map (MCM) modelling terminology. Bottlenecks due to inappropriate Perspective allocation are resolved by the Qualitative Reasoning approach.