{'fraction_non_alphanumeric': 0.030201342281879196, 'fraction_numerical': 0.011409395973154362, 'mean_word_length': 5.291139240506329, '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': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An adaptive and diversified vehicle routing approach to reducing the security risk of cash‐in‐transit operations

We consider the route optimization problem of transporting valuables in cash‐in‐transit (CIT) operations. The problem arises as a rich variant of the capacitated vehicle routing problem (CVRP) with time windows and pickup and deliveries. Due to the high‐risk nature of this operation (e.g., robberies) we consider a bi‐objective function where we attempt to minimize the total transportation cost and the security risk of transporting valuables along the designed routes. For risk minimization, we propose a composite risk measure that is a weighted sum of two risk components: (i) following the same or very similar routes, and (ii) visiting neighborhoods with low socio‐economic status along the routes. We also consider vehicle capacities in terms of monetary value carried as per insurance regulations. We develop an adaptive randomized bi‐objective path selection algorithm that uses the composite risk measure in choosing alternative paths between origin‐destination pairs over a sequence of days. We solve the rich CVRP approximately for each day with updated costs. We test our solution approach on a data set from a CIT delivery service provider and provide insights on how the routes diversify daily. Our approach generates a spectrum of solutions with cost‐risk trade‐off to support decision making. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 69(3), 256–269 2017