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Delivery route optimization is a well-known NPcomplete problem based on the Traveling Salesman Problem (TSP) involving 20-2000 cities though human oriented factors make the problem more complex. Despite of NP-completeness, the scheduling should be solved every time within interactive response time and below expert level error or local optimality, considering human oriented factors including personal, social, and cultural factors. To cope with this, Cases and NI (Nearest Insertion) are introduced into a Genetic Algorithm (GA), based on the insight that real problems are similar to previous ones. A solution can be derived from former solutions, considering human oriented factors as follows: (1) retrieving the most similar cases, (2) modifying them by removing and adding locations by NI, and (3) further optimizing them by a GA using only NI operations. This cannot only diminish the costs to compute new solutions from scratch but also inherit many parts of previous routes to respect human factors. Experimental evaluation revealed remarkable results. Though the most effective TSP solving method LKH needed more than 3 seconds, the proposed method yielded results within 3% of the worst error rate and in less than 3 seconds. Furthermore, the proposed method is able to inherit most of the delivery routes, while LKH leads to significant changes.

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