{'fraction_non_alphanumeric': 0.016, 'fraction_numerical': 0.0, 'mean_word_length': 5.217391304347826, '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}}
Various Approaches to the Application of Answer Set Programming in Order-picking Systems with Intelligent Vehicles

Intelligent, self-driving vehicles on public roads are widely noted in the media nowadays. In warehouse and production systems, such vehicles have been common for a number of years, even though their intelligence has only come to awareness in the recent years. Those systems are especially tailored to work in volatile environments which change (to some degree) every few weeks or months. Thus, programming is required to be as flexible as possible while still providing high efficiency. Answer set programming is a well known paradigm which has received remarkable attention in the recent years. In this paper several approaches are presented and evaluated to apply answer set programming to an order-picking system with intelligent vehicles. The interconnected planning tasks are the dispatching of vehicles to driving jobs and the assignment of customer orders to picking stations.