Bezos said in the 2013 interview that it would take four or five years to have those drone deliveries. It turns out that using remote-controlled aerial gizmos to drop stuff at our homes is incredibly difficult, prone to risk and potentially more trouble than it's worth. Like driverless cars, drone technology in populated areas is more complicated than most people expected, and it has been -- mostly for good reason -- tightly controlled in the United States by government agencies worried about drones straying into the path of airplanes, dropping out of the sky onto our heads or unwittingly spying through people's windows. It wasn't until this week that the F.A.A. gave Amazon permission to do drone deliveries. And drones might never be practical for deliveries when someone in a vehicle could do the same thing in a fraction of the time and cost. Drones are a great public relations jolt for Amazon, but let's not put too much stock in them for awhile -- maybe ever.
The views expressed here are solely those of the author and do not necessarily represent the views of FreightWaves or its affiliates. Author's Disclosure: I am not an investor in Optimal Dynamics, either personally or through REFASHIOND Ventures. I have no other financial relationship with Optimal Dynamics. On July 7 I started a series on AI in Supply Chain (#AIinSupplyChain). The first article in the series profiled Optimal Dynamics, a startup that has launched a product to automatically optimize operations for large trucking fleets.
The coronavirus pandemic changed the way businesses of almost all types operate virtually overnight, hurting most and redefining which ones are truly essential in what quickly became the new normal for billions of people around the world. And it brought with it an unexpected kind of acceleration of trends, forcing the closure of businesses that would have struggled on for a few more years, while bringing a global spotlight to technologies that would have remained relatively obscure or experimental for years to come. Market trends that otherwise would have taken years to evolve transformed in a matter of weeks, it seemed, retiring outdated concepts while stretching emerging tech to its limits. One segment suddenly in the spotlight--and that seemingly saw years of demand and market interest explode in a matter of days--is delivery robots, which until the month of March had seen moderate interest from Silicon Valley and some skepticism from the general public. Suddenly, Amazon founder Jeff Bezos' comment in 2013 that the company was researching parcel delivery via flying drones went from a pie in the sky whimsy with seemingly few advantages to something that businesses large and small needed in 2020.
Artificial intelligence is one of the most promising technologies available to businesses today. A McKinsey study of more than 2,000 participants found that 58% of respondents use AI in at least one process. This impressive adoption rate isn't baseless, either, as AI can improve areas like supply chain management in several ways. Logistics is a fast-moving and essential industry, one that many other sectors rely on to function. As such, it stands to benefit from thorough and timely data analytics.
By 2020, people thought the autonomous car would whisk you to the office while you read the paper and tackle your emails, then taking you home from the bar on a Friday evening. That remains lodged somewhere in the pipeline for now. But another slice of science fiction is on the way – robots that deliver your food -- and it's already knocking at the door. Robotic food delivery (or, increasingly, the delivery of anything that fits into a robot) is being tackled by a wide range of companies, from garage startups to retail giants. Many use six-wheeled robots designed to drive themselves along the sidewalk and the pathways of business parks and college campuses.
A man who has been sleeping for twenty years and woke up in 2020 would find himself in a different transformational era. Along with numerous changes in ecology, politics, the way we live, and diseases we cure, he or she would be astonished by the consequences of the revolution that has redefined each of the aforementioned aspects of our lives. Digitalization has reached more people than any other revolution. Since Gutenberg's printing press, it has become the most outstanding event to mark a huge shift in the way we communicate. People have learned how to exhibit their intelligence by machines and systems that improve human thinking.
Part of the issue, according to Goldenberg, a retired Navy captain, is red tape. Medics and hospital corpsman in the Navy, despite having years of experience in the medical field, have to start from square one should they wish to pursue a career in medicine. Without having additional schooling, they're unable to come out of the military and become an EMT or paramedic. The same goes for truck drivers. While they're used to driving 18-wheelers in less-than-ideal conditions, they can't automatically qualify to drive tractor trailers on roads in the U.S. because they haven't been taught how to reverse the vehicle unassisted.
Hybrid variations of metaheuristics that include data mining strategies have been utilized to solve a variety of combinatorial optimization problems, with superior and encouraging results. Previous hybrid strategies applied mined patterns to guide the construction of initial solutions, leading to more effective exploration of the solution space. Solving a combinatorial optimization problem is usually a hard task because its solution space grows exponentially with its size. Therefore, problem size reduction is also a useful strategy in this context, especially in the case of large-scale problems. In this paper, we build upon these ideas by presenting an approach named MineReduce, which uses mined patterns to perform problem size reduction. We present an application of MineReduce to improve a heuristic for the heterogeneous fleet vehicle routing problem. The results obtained in computational experiments show that this proposed heuristic demonstrates superior performance compared to the original heuristic and other state-of-the-art heuristics, achieving better solution costs with shorter run times.