{'fraction_non_alphanumeric': 0.014961101137043686, 'fraction_numerical': 0.002992220227408737, 'mean_word_length': 5.556862745098039, '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}}
Dual-mode round-robin greedy search with fair factor algorithm for relief logistics scheduling

Humanitarian support management is one of the areas of interest after any disaster cases. All the disaster affected regions need relief logistics to improve the suffered people's life. Disaster regions more often have an asymmetric level of relief logistics requirements. Because of this, even distribution of relief logistics to all disaster-affected regions becomes more challenging, particularly in the limited resources scenario. A fair distribution strategy that distributes relief logistics evenly to all disaster regions based on the demand and available resources is vital. This paper applied two basic round-robin based greedy search algorithms and hence proposed an optimized algorithm for fair distribution of relief logistics. The algorithms iteratively regulate distribution schedule in a round-robin fashion with the greedy search from various supply points to the demand regions based on demand, supply, and distance. The optimized algorithm is aimed for fair relief distribution strategy after each round of distribution. The distribution fairness is measured considering minimization of the absolute standard deviation between demand and supply at the demand regions and also considering the number of disaster regions receiving the relief logistics. A benchmark case study, school29 based on the 921 (ChiChi) earthquake in Taiwan, has been considered to evaluate the performance of the algorithm. The simulation results show that the proposed algorithm has even relief logistics distribution to all disaster regions in the limited vehicle resource scenario.