Abstract: In this paper, we introduce an approximate approach to public service system design making use of a universal IP-solver. The solved problem consists in minimization of the total discomfort of system users, which is usually proportional to the sum of demand-weighted distances between users and the nearest source of provided service. Presented approach is based on radial formulation. The disutility values are estimated by some upper and lower bounds given by so-called dividing points. Deployment of dividing points in uences the solution accuracy. The process of the dividing point deployment is based on the idea that some disutility values can be considered relevant and are expected to obtain in the optimal solution. Hereby, we study various approaches to the relevance with their impact on the accuracy and computational time.

Abstract: Despite being computationally more efficient than vector based approaches, the use of raster-based techniques for simulating wildfire spread has been limited by the distortions that affect the fire shapes. This work presents a Cellular Automata (CA) approach that is able to mitigate this problem with a redefinition of the spread velocity, where the equations generally used in vector-based approaches are modified by mean of a number of correction factors. A numerical optimization approach is used to find the optimal values for the correction factors. The results are compared to the ones given by two well-known Cellular Automata simulators. According to this work, the proposed approach provides better results, in terms of accuracy, at a comparable computational cost.

Abstract: This paper presents an Iterative process based on Distributed Constraint Optimization (I-DCOP), to solve train classification problems. The input of the I-DCOP is the train classification problem modelled as a DCOP, named Optimization Model for Train Classification (OMTC). The OMTC generates a feasible schedule for a train classification problem defined by the inbound trains, the total of outbound trains and the cars assigned to them. The expected result, named feasible schedule, leads to the correct formation of the outbound trains, based on the order criteria defined. The OMTC minimizes the schedule execution time and the total number of roll-ins (operation executed on cars, sometimes charged by the yards). I-DCOP extends the OMTC including the constraints of limited amount of classification tracks ant their capacity. However, these constraints are included iteratively by adding domain restrictions on the OMTC. Both OMTC and I-DCOP have been measured using scenarios based on real yard data. OMTC has generated optimal and feasible schedules to the scenarios, optimizing the total number of roll-ins. I-DCOP solved more complex scenarios, providing sub-optimal solutions. The experiments have shown that distributed constraint optimization problems can include additional constraints based on interactively defined domain.

Abstract: This paper presents a generalised parametrisation as well as an approach to computational optimisation for small, planar antennas. A history of previous, more limited antenna optimisation techniques is discussed and a new parametrisation introduced in this context. Validation of this new approach against previously developed structures is provided and preliminary results of the optimisation are demonstrated and discussed. For the optimisation, a binary Multi-Objective Particle Swarm Optimisation (MOPSO) is used and several methods for generating a viable initial population are introduced and discussed in the context of practical limitations computational simulations.