Middle-mile logistics describes the problem of routing parcels through a network of hubs, which are linked by a fixed set of trucks. The main challenge comes from the finite capacity of the trucks. The decision to allocate a parcel to a specific truck might block another parcel from using the same truck. It is thus necessary to solve for all parcel routes simultaneously. Exact solution methods scale poorly with the problem size and real-world instances are intractable.

The demand for resilient logistics networks has increased because of recent disasters. When we consider optimization problems, entropy regularization is a powerful tool for the diversification of a solution. In this study, we proposed a method for designing a resilient logistics network based on entropy regularization. Moreover, we proposed a method for analytical resilience criteria to reduce the ambiguity of resilience. First, we modeled the logistics network, including factories, distribution bases, and sales outlets in an efficient framework using entropy regularization. Next, we formulated a resilience criterion based on probabilistic cost and Kullback--Leibler divergence. Finally, our method was performed using a simple logistics network, and the resilience of the three logistics plans designed by entropy regularization was demonstrated.

The company will add D. Cloostermans–Huwaert NV to the Amazon Robotics division it launched in 2012 with the acquisition of Kiva Systems Inc. Terms of the deal were not disclosed. Cloostermans, which has about 200 employees, was founded in 1884 as a manufacturer of machinery for textiles manufacturers. It has been refashioned as a specialist in engineering and automation. The company designs and makes warehouse technology Amazon said it already uses to move and stack heavy pallets and totes and to package products for delivery.

Resource balancing within complex transportation networks is one of the most important problems in real logistics domain. Traditional solutions on these problems leverage combinatorial optimization with demand and supply forecasting. However, the high complexity of transportation routes, severe uncertainty of future demand and supply, together with non-convex business constraints make it extremely challenging in the traditional resource management field. In this paper, we propose a novel sophisticated multi-agent reinforcement learning approach to address these challenges. In particular, inspired by the externalities especially the interactions among resource agents, we introduce an innovative cooperative mechanism for state and reward design resulting in more effective and efficient transportation. Extensive experiments on a simulated ocean transportation service demonstrate that our new approach can stimulate cooperation among agents and lead to much better performance. Compared with traditional solutions based on combinatorial optimization, our approach can give rise to a significant improvement in terms of both performance and stability.

In a business where shaving off a mile per day per driver can result in savings of up to $50 million per year, UPS has plenty of incentive to incorporate technology to drive efficiencies in every area of its operations. Here are just a few of the ways UPS uses big data and artificial intelligence (AI) to prepare for the 4th Industrial Revolution. UPS was founded in 1907 and has a history of embracing change and evolving as new technologies arise. It's the use of big data and artificial intelligence that allows the company to operate its global logistics network in more than 220 countries and territories. On an average day, there are typically 96,000 UPS vehicles on the road handling 19 million packages.

In a business where shaving off a mile per day per driver can result in savings of up to $50 million per year, UPS has plenty of incentive to incorporate technology to drive efficiencies in every area of its operations. Here are just a few of the ways UPS uses big data and artificial intelligence (AI) to prepare for the 4th Industrial Revolution. UPS was founded in 1907 and has a history of embracing change and evolving as new technologies arise. It's the use of big data and artificial intelligence that allows the company to operate its global logistics network in more than 220 countries and territories. On an average day, there are typically 96,000 UPS vehicles on the road handling 19 million packages.

Two of the world's most influential research and advisory firms predict major acceleration in artificial intelligence (AI) use as more businesses look to make the digital transformation leap. Forrester projects AI investment tripled in 2017, and Gartner expects AI to be pervasive in almost every new software product and service by 2020. With its cognitive interfaces, in-depth analytics, and machine-learning technology, AI provides business users operating within complex systems and dynamic networks with powerful, actionable intelligence that drives faster decision-making. While the technology is clearly applicable to companies across a range of industries, global logistics stands to benefit substantially. The global logistics industry struggles with the ability to "make sense" of Big Data―and for some, even not-so-big data.

Amazon is the exception to nearly every rule in business. Rising from humble beginnings as a Seattle-based internet bookstore, Amazon has grown into a propulsive force in at least five different giant industries: retail, logistics, consumer technology, cloud computing, and most recently, media and entertainment. The company has had its share of missteps -- the expensive Fire phone flop comes to mind -- but is also rightly known for strokes of strategic genius that have put it ahead of competitors in promising new industries. This was the case with the launch of cloud business AWS in the mid-2000s, and more recently the surprising consumer hit in the Echo device and its Alexa AI assistant. Today's Amazon is far more than just an "everything store," it's a leader in consumer-facing AI and enterprise cloud services. And its insatiable appetite for new markets mean competitors must always be on guard against its next moves.