{'fraction_non_alphanumeric': 0.02225519287833828, 'fraction_numerical': 0.000741839762611276, 'mean_word_length': 5.303738317757009, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Genetic algorithms as a promising tool for optimisation of the MSW collection routes

Important advantages, including reductions in fuel consumption and labour cost, arise from the optimal design of solid waste (SW) collection routes. Further, optimal design can reduce vehicle maintenance expenditures and improve traffic conditions in urban areas. To date, optimal routes have been developed according to intuitive methodologies and field experience. However, increasing attention is being devoted to innovative approaches, such as those able to simulate complex collection systems. To analyse these complexities, operational research applications are used. They are typically based on the implementation of heuristic procedures allowing for high quality solutions to the problem at hand. From a computational point of view, however, heuristic procedures have a complexity which is o(n3), where n is the number of points which have to be visited during each route. This is a limit for an accurate representation of urban areas and for the quality of the calculated solutions. An alternative methodology, which is the subject of this paper, is based on a genetic algorithm. Also, an ad hoc algorithm, developed in the framework of a wider research, is illustrated. Results of a preliminary field test conducted for verification are also presented.