Heuristic optimization of the p-median problem and population re-distribution

This thesis contributes to the heuristic optimization of the p-median problem and Swedish population redistribution.   The p-median model is the most representative model in the location analysis. When facilities are located to a population geographically distributed in Q demand points, the p-median model systematically considers all the demand points such that each demand point will have an effect on the decision of the location. However, a series of questions arise. How do we measure the distances? Does the number of facilities to be located have a strong impact on the result? What scale of the network is suitable? How good is our solution? We have scrutinized a lot of issues like those. The reason why we are interested in those questions is that there are a lot of uncertainties in the solutions. We cannot guarantee our solution is good enough for making decisions. The technique of heuristic optimization is formulated in the thesis.   Swedish population redistribution is examined by a spatio-temporal covariance model. A descriptive analysis is not always enough to describe the moving effects from the neighbouring population. A correlation or a covariance analysis is more explicit to show the tendencies. Similarly, the optimization technique of the parameter estimation is required and is executed in the frame of statistical modeling. 

Refraction in spacetime

Refraction, interference, and diffraction are distinguishing features of wavelike phenomena. Although they are usually associated only with a purely spatial wave-propagation pattern, analogs to interference and diffraction involving the spatio-temporal dynamics of waves in one dimension have been discussed. We complete the triplet of analogies by discussing how spatio-temporal analogs to refraction are exhibited by a quantum particle in one dimension that is scattering off a step barrier. Similarly, birefringence in spacetime occurs for a spin-1/2 particle in a magnetic field.