Urban consolidation centers (UCCs) have a key role in many initiatives urban logistics, yet few of them are successful the long run. The high costs often prevent attracting sufficient number UCC users. In this paper, we study sustainable business models and supporting administrative policies. We perform an agent-based simulation applied to city Copenhagen collect data from variety sources model agents. Both case setup validated by means expert interviews. test 1,458 schemes that combine...

Effective humanitarian relief operations are challenging in the aftermath of disasters, as trucks often faced with considerable travel time uncertainties due to damaged transportation networks. Efficient deployment Unmanned Aerial Vehicles (UAVs) potentially mitigates this problem, supplementing truck fleets an impactful manner. To plan last-mile distribution setting, we introduce a multi-trip, split-delivery vehicle routing problem and UAVs, soft windows, stochastic times for distribution,...

Reliable availability to the internet and internet-based services is crucial in today’s world. DDoS attacks pose a severe threat of such online resources – especially owing booters virtually everyone can execute them nowadays. In order appropriately protect oneself against attacks, it essential have good insight into threats that exist. This paper proposes novel hybrid model combines postulates from various models on crime opportunity, analyzing targeted victim infrastructure conjunction. We...

Humanitarian logistics operations face increasing difficulties due to rising demands for aid in disaster areas. This paper investigates the dynamic allocation of scarce relief supplies across multiple affected districts over time. It introduces a novel stochastic postdisaster inventory problem (SDPDIAP) with trucks and unmanned aerial vehicles (UAVs) delivering goods under uncertain supply demand. The relevance this humanitarian lies importance considering intertemporal social impact...

The logistics industry faces an increasing shortage of truck parking spots. This results in illegal or fatigued driving with hazardous consequences for traffic safety, as drivers have no insight into future availability Accurate short-term predictions lot occupation are required to aid planning their routes and rest stops. To obtain such predictions, this research compares a variety machine learning algorithms, concluding that decision trees most suitable real-time application. model is...

Massive graphs are becoming increasingly common in a variety of domains such as social networks and web analytics. One approach to overcoming the challenges size is sample graph, perform analytics on smaller graph. However, be useful, must maintain properties interest original In this paper, we analyze quality five representative sampling algorithms how well they preserve graph structure, bisimulation structure particular. As part study, also develop new scalable algorithm for computing...

Large discrete action spaces (LDAS) remain a central challenge in reinforcement learning. Existing solution approaches can handle unstructured LDAS with up to few million actions. However, many real-world applications logistics, production, and transportation systems have combinatorial spaces, whose size grows well beyond millions of actions, even on small instances. Fortunately, such exhibit structure, e.g., equally spaced resource units. With this work, we focus handling structured (SLDAS)...

This paper addresses the planning of freight dispatch in flexible transport networks featuring multiple carriers. To deal with computational challenges problem, we develop an Approximate Dynamic Programming (ADP) algorithm that utilizes neural network techniques to learn policies. We test whether policies learned autonomously by carrier agents (based on local information) match quality a central planner full information). Numerical experiments show yield solutions comparable for small...

Real-world problems of operations research are typically high-dimensional and combinatorial. Linear programs generally used to formulate efficiently solve these large decision problems. However, in multi-period problems, we must often compute expected downstream values corresponding current decisions. When applying stochastic methods approximate values, linear become restrictive for designing value function approximations (VFAs). In particular, the manual design a polynomial VFA is...

Problems in operations research are typically combinatorial and high-dimensional. To a degree, linear programs may efficiently solve such large decision problems. For stochastic multi-period problems, decomposition into sequence of one-stage decisions with approximated downstream effects is often necessary, e.g., by deploying reinforcement learning to obtain value function approximations (VFAs). When embedding VFAs programs, VFA design restricted linearity. This paper presents an integrated...

Humanitarian logistics operations face increasing difficulties due to rising demands for aid in disaster areas. This paper investigates the dynamic allocation of scarce relief supplies across multiple affected districts over time. It introduces a novel stochastic post-disaster inventory problem with trucks and unmanned aerial vehicles delivering goods under uncertain supply demand. The relevance this humanitarian lies importance considering inter-temporal social impact deliveries. We achieve...

Smart modular freight containers -- as propagated in the Physical Internet paradigm are equipped with sensors, data storage capability and intelligence that enable them to route themselves from origin destination without manual intervention or central governance. In this self-organizing setting, can autonomously place bids on transport services a spot market setting. However, for individual it may be difficult learn good bidding policies due limited observations. By sharing information costs...

This paper presents a multi-agent reinforcement learning algorithm to represent strategic bidding behavior in freight transport markets. Using this algorithm, we investigate whether feasible market equilibriums arise without any central control or communication between agents. Studying such environments may serve as stepping stone towards self-organizing logistics systems like the Physical Internet. We model an agent-based environment which shipper and carrier actively learn strategies using...