Distance approximation to support customer selection in vehicle routing problems

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 % 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

A Comparative Analysis of Neural Networks in Anticipatory Transportation Planning

Anticipatory transportation planning (ATP) problems can be formalized as sequential decision-making problems under uncertainty, where expected future consequences are included in current decision-making. Approximate dynamic programming (ADP) and reinforcement learning (RL) are popular machine learning techniques used for sequential decision-making problems, often used in conjuction with a parametric value function approximation (VFA). The most widely used form of such a VFA is linear regression (LVFA), although we may use more complex, non-linear predictors to represent a VFA, e.g., neural network VFAs (NNVFA). Alternatively, we may represent the policy directly, using a neural network policy function approximation (NNPFA). The use of (deep) neural networks (NNVFA or NNPFA) seems promising but is underexposed in ATP research. The possible advantages of NNVFAs or NNPFAs over LVFAs are poorly understood, as are the disadvantages. Therefore, we compare three different policies: (i) LVFA, (ii) NNVFA, and (iii) NNPFA and explore whether neural network based policies can deliver value beyond LVFAs, and if so, what the accompanying disadvantages are. This paper presents a structured comparison in terms of performance, robustness, and computational efficiency. We compare policies on several stylized variants of the dynamic vehicle routing problem with stochastic customer requests, which is a much studied and typical ATP problem. We also compare using a realistic case that involves the routing of robots in an automated storage an retrieval system (AS/RS) warehouse of a Norwegian electronic equipment retailer. We show that (i) whether neural network policy based approaches or linear based VFAs are better depends subtly on problem characteristics, (ii) the potential ability of neural networks to improve upon LVFAs is drawn from their ability to include interaction effects between features, but (iii) comes at the expense of longer computational times. Furthermore, we show that (iv) in most cases, neural network PFAs (NNPFAs) outperform neural network VFAs (NNVFAs), but NNVFAs are significantly faster and more robust to problem changes

Handling Large Discrete Action Spaces via Dynamic Neighborhood Construction

Large discrete action spaces remain a central challenge for reinforcement learning methods. Such spaces are encountered in many real-world applications, e.g., recommender systems, multi-step planning, and inventory replenishment. The mapping of continuous proxies to discrete actions is a promising paradigm for handling large discrete action spaces. Existing continuous-to-discrete mapping approaches involve searching for discrete neighboring actions in a static pre-defined neighborhood, which requires discrete neighbor lookups across the entire action space. Hence, scalability issues persist. To mitigate this drawback, we propose a novel Dynamic Neighborhood Construction (DNC) method, which dynamically constructs a discrete neighborhood to map the continuous proxy, thus efficiently exploiting the underlying action space. We demonstrate the robustness of our method by benchmarking it against three state-of-the-art approaches designed for large discrete action spaces across three different environments. Our results show that DNC matches or outperforms state-of-the-art approaches while being more computationally efficient. Furthermore, our method scales to action spaces that so far remained computationally intractable for existing methodologies

Cross-Docking: Current Research Versus Industry Practice and Industry 4.0 Adoption

Cross-docking is a supply chain distribution and logistics strategy for which less-than-truckload 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