The Use of Persistent Explorer Artificial Ants to Solve the Car Sequencing Problem

Ant Colony Optimisation is a widely researched meta-heuristic which uses the behaviour and pheromone laying activities of foraging ants to find paths through graphs. Since the early 1990’s this approach has been applied to problems such as the Travelling Salesman Problem, Quadratic Assignment Problem and Car Sequencing Problem to name a few. The ACO is not without its problems it tends to find good local optima and not good global optima. To solve this problem modifications have been made to the original ACO such as the Max Min ant system. Other solutions involve combining it with Evolutionary Algorithms to improve results. These improvements focused on the pheromone structures. Inspired by other swarm intelligence algorithms this work attempts to develop a new type of ant to explore different problem paths and thus improve the algorithm. The exploring ant would persist throughout the running time of the algorithm and explore unused paths. The Car Sequencing problem was chosen as a method to test the Exploring Ants. An existing algorithm was modified to implement the explorers. The results show that for the car sequencing problem the exploring ants did not have any positive impact, as the paths they chose were always sub-optimal

Geophysical modeling for groundwater and soil contamination risk assessment

This PhD thesis is focused on the study of environmental problems linked to contaminant detection and transport in soil and groundwater. The research has two main objectives: development, testing and application of geophysical data inversion methods for identifying and characterizing possible anomaly sources of contamination and development and application of numerical models for simulating contaminant propagation in saturated and unsaturated conditions. Initially, three different approaches for self-potential (SP) data inversion, based on spectral, tomographical and global optimization methods, respectively, are proposed to characterize the SP anomalous sources and to study their time evolution. The developed approaches are first tested on synthetic SP data generated by simple polarized structures, (like sphere, vertical cylinder, horizontal cylinder and inclined sheet) and, then, applied to SP field data taken from literature. In particular, the comparison of the results with those coming from other numerical approaches strengthens their usefulness. As it concerns the modelling of groundwater flow and contaminant transport, two cellular automata (CA) models have been developed to simulate diffusion-dispersion processes in unsaturated and saturated conditions, respectively, and to delineate the most dangerous scenarios in terms of maximum distances travelled by the contaminant. The developed CA models have been applied to two study areas affected by a different phenomenon of contamination. The first area is located in the western basin of the Crete island (Greece), which is affected by organic contaminant due to olive oil mills wastes (OOMWs). The numerical simulations provided by the CA model predict contaminant infiltration in the saturated zone and such results are in very good agreement with the high phenol concentrations provided by geochemical analyses on soil samples collected in the survey area at different depths and times. The second case study refers to an area located in the western basin of Solofrana river valley (southern Italy), which is often affected by heavy flooding and contamination from agricultural and industrial activities in the surroundings. The application of a multidisciplinary approach, which integrates geophysical data with hydrogeological and geochemical studies, and the development of a CA model for contaminant propagation in saturated conditions, have permitted to identify a possible phenomenon of contamination and the delineation of the most dangerous scenarios in terms of infiltration rates are currently in progress