Approximating the solution value of transportation problems has become more relevant in recent years, as these approximations can help to decrease the computational effort required for solving those routing problems. In this paper, we apply several regression methods to predict the total distance of the traveling salesman problem (TSP) and vehicle routing problem (VRP). We show that distance can be estimated fairly accurately using simple regression models and only a limited number of features. We use features found in scientific literature and introduce a new class of geographical features. The model is validated on a dynamic waste collection case in the city of Amsterdam, The Netherlands. We introduce a cost function that combines the travel distance and service level, and show that our model can reduce distances up to 17%, while maintaining the same service level, compared to a well-known heuristic approximation. Furthermore, we show the benefits of using approximations for combining offline learning with online or frequent optimization.
Estimating the solution value of transportation problems can be useful to assign customers to days for multi-period vehicle routing problems, or to make customer selection decisions very fast (e.g., within an online environment). In this paper, we apply several regression methods to predict the total distance of the traveling salesman problem (TSP) and vehicle routing problem (VRP). We show that distance can be estimated fairly accurate using simple regression models and only a limited number of features. Besides using features found in the scientific literature, we also introduce new classes of spatial features. The model is validated on a fictional case with different spatial instances considering both a backordering and lost sales configuration, and on a realistic case that involves dynamic waste collection in the city of Amsterdam, The Netherlands. For the fictional case, we show differences in performance per instance type and configuration, and we show that our model can save up to $$25.3\%$$ 25.3 % in distance compared with a heuristic approximation. For the waste collection case, we introduce a cost function that combines the travel distance and service level, and show that our model can reduce distances up to 17% compared to a well-known heuristic approximation while maintaining the same service level. Furthermore, we show the benefits of using approximations for combining offline learning with online or frequent optimization.
Cross-docking is a supply chain distribution and logistics strategy for which less-thantruckload shipments are consolidated into full-truckload shipments. Goods are stored up to a maximum of 24 hours in a cross-docking terminal. In this chapter, we build on the literature review by Ladier and Alpan (2016), who reviewed cross-docking research and conducted interviews with cross-docking managers to find research gaps and provide recommendations for future research. We conduct a systematic literature review, following the framework by Ladier and Alpan (2016), on cross-docking literature from 2015 up to 2020. We focus on papers that consider the intersection of research and industry, e.g., case studies or studies presenting real-world data. We investigate whether the research has changed according to the recommendations of Ladier and Alpan (2016). Additionally, we examine the adoption of Industry 4.0 practices in cross-docking research, e.g., related to features of the physical internet, the Internet of Things and cyber-physical systems in cross-docking methodologies or case studies. We conclude that only small adaptations have been done based on the recommendations of Ladier and Alpan ( 2016), but we see growing attention for Industry 4.0 concepts in cross-docking, especially for physical internet hubs.
Attended home delivery (AHD) is a popular type of home delivery for which companies typically offer delivery time slots. The costs for offering time slots are often double compared to standard home delivery services (Yrjölä, 2001). To influence customers to choose a time slot that results in fewer travel costs, companies often give incentives (discounts) or penalties (delivery charges) depending on the costs of a time slot. The main focus of this paper is on determining the costs of a time slot and adjusting time slot pricing accordingly, i.e., dynamic pricing. We compare two time slot cost approximation methods, a cheapest insertion formula and a method employing random forests with a limited set of features. Our results show that time slot incentives have added value for practice. In a hypothetical situation where customers are infinitely sensitive to incentives, we can plan 6% more customers and decrease the per-customer travel costs by 11%. Furthermore, we show that our method works especially well when customer locations are heavily clustered or when the area of operation is sparsely populated. For a realistic case of a European e-grocery retailer, we show that we can save approximately 6% in per-customer travel costs, and plan approximately 1% more customers when using our time slot incentive policy.
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