[证券从业资格]物流两点之间哪条路线最短？看UPS如何用大数据优化送货路线

快递司机一天中简直有很多条路途可供挑选。对UPS这样的巨子来说，假如每位司机每天少开一英里，公司便能省下5,000万美元。因而它运用大数据剖析打造了一个名为Orion的导航体系，能够在约3秒内找出最佳路途。

在任何一天中，UPS的司机都有许多条快递路途能够挑选。

或许换个说法——UPS的司机在任何一天中，能够挑选的快递路途的数目都是令人不可思议的。这绝不是夸大。

这家快递公司的司机一般每天要送120至175次货。在任何两个目的地之间，都能够挑选多条路途。明显，司机和UPS想要找到其间最有功率的那条。不过如此一来，工作就变得复杂了。

UPS运用组合数学的算法得出，以上所述的情形中所有或许的线路的总数，是一个199位的数字。这一数字乃至大过了换算成纳秒单位的地球年纪。

UPS的流程办理高档总监杰克?里维斯表明：“这数字太大了，令人不可思议。你只能从剖析学上得出一个概念。”

对UPS而言，这是一项巨大的应战。不过他们有激烈的动力去完成路途最优化：假如每位司机每天少开一英里，公司便能省下5,000万美元。

这家坐落亚特兰大的公司是怎么做的？他们研发了一个名为Orion的体系，这是路途优化与导航集成体系（On-Road Integrated Optimization and Navigation）的缩写，也是希腊神话中猎户座的姓名。假如说现在有什么大数据剖析学上的成果的话，那便是它了。Orion的算法诞生于21世纪初，并于2009年开端试运行。该体系的代码长达1,000页，能够剖析每种实时路途的20万种或许性，并能在大约3秒内找出最佳路途。

里维斯表明：“起先，数学家们以为或许需求15分钟才干算出成果。所以他们很快乐。”

UPS正在公司悉数的5.5万条北美快递线路上安装这一体系。到2013年末，Orion已经在大约1万条线路上得到运用，这让公司节约了150万吨燃料，少排放了1.4万立方公吨的二氧化碳。公司方案在2017年完全完成该方案。

依据高德纳研讨公司（Gartner）的剖析师斯维特拉娜?西库勒的说法，有两个“很不起眼的”职业正在遭到大数据的冲击，一个是运输业，其间包含UPS这类物流公司，另一个是农业。

西库勒信任这一冲击会涉及很大规模。西库勒表明，能够看看互易商货航运业的比如：澳大利亚海事安全局（Australian Maritime Safety Authority）供给了实时的港口活动信息，船舶能够据此改动航速，节约燃料，让港口服务费降到最低。海事局还运用了地舆围栏（一种动态的数字定位区域）来触发和主动核算这些费用。她说：“经过揭露数据，这一切都是通明的。”

西库勒表明，导致这种改变的不仅仅是大数据技能，移动设备和云核算在其间也扮演了重要人物。

她解说说：“在搜集信息、给司机实时供给数据上，移动性起到了重要作用。这不仅是指移动设备，还包含货车、飞机和轮船上的感应器。”

在进步运营功率的压力下，UPS在20世纪90年代为司机引入了手持设备。里维斯表明：“咱们必须在智能手机和网络通信呈现前就创造它们。”2008年，公司在运货货车上安装了GPS追寻体系，而Orion则建立在这一基础上。

虽然想要替代在大陆上络绎不绝的轿车快递不是件简单的事——总归，在亚马逊（Amazon）的无人机快递正式得到运用前是这样——但云核算的鼓起让草创公司更简单接触到之前只要大型企业才干具有的尖端技能。UPS理应走在前面。

里维斯表明：“对我来说，这便是剖析技能和大数据的未来——不仅仅是告知你产生了什么，还能告知你即将产生什么，怎么实时纠正它们。假如有个体系能够智能到猜测你的问题，并在它产生前予以处理，那它就像福尔摩斯相同了。看起来像千里眼，但实际上不是。”

“把它变成实际还需求一段时间。不过这便是未来的现象。”

The number of possible routes that a UPS driver could take on any given day is enormous.

Strike that—the number of possible routes that a UPS driver could take on any given day defies comprehension. That’s not an exaggeration.

A driver for the delivery company typically makes between 120 and 175 “drops” per day. Between any two of those, there are a number of available paths to take. It is, of course, in the best interest of the driver and UPS to find the most efficient route. And that is where things get complicated.

According to UPS, the number to describe the complete set of possibilities in the scenario outlined above, as calculated using combinatorial mathematics, would have 199 digits. The number of possible options would exceed the number of nanoseconds that the Earth has existed.

“It’s huge—unimaginably large,” said Jack Levis, the company’s senior director of process management. “This is as high as you can get in analytics.”

For UPS, it was nothing if not a daunting optimization challenge. But the motivation was powerful: a reduction of just one mile a day per driver would save the company as much as $50 million.

The Atlanta-based company’s answer? A system called Orion, short for On-Road Integrated Optimization and Navigation, named after the hunter in Greek mythology, and a big data analytics effort if there ever was one. Orion—whose algorithm was developed in the early 2000s and piloted through 2009—uses 1,000 pages of code to analyze 200,000 possibilities for each route in real time to deliver the optimal route in about three seconds’ time.

“At first, the mathematicians thought it was going to take about 15 minutes to run,” Levis said. “They were pleased.”

UPS is working to deploy the system to all of its 55,000 North American delivery routes. By the end of 2013—after being applied to just 10,000 routes so far—Orion had already saved 1.5 million gallons of fuel and 14,000 metric tonnes of CO2 emissions. The company expects to complete the rollout by 2017.

There are two “rather inconspicuous” industries that are being disrupted by big data, according to Svetlana Sicular: transportation—which includes a logistics company like UPS—and agriculture.

Sicular, a Gartner analyst, believes that disruption is happening on a broad scale. Consider the commercial shipping industry: the Australian Maritime Safety Authority provides information about port activity in real time so that ships can vary their speed to save fuel and minimize port fees, Sicular said. The authority also uses geofencing—dynamic, location-based digital zones—to trigger and automate those fees, “and this is all transparent through open data,” she said.

It’s not just big data technologies that are causing this transformation, Sicular said. The convergence of mobile devices and cloud computing also play a major role.

“Mobility is important in collecting information and providing live connection to the drivers,” she explained. “Mobility is not just about mobile devices but also about sensors in trucks, planes and ships.”

Feeling pressure to make its operations more efficient, UPS introduced handheld devices for its drivers in the 1990s. “We had to create smartphones and communications online before they existed,” Levis said. In 2008, the company installed GPS tracking equipment on its delivery trucks. Orion is layered on top of this foundation.

Though there’s no easy replacement for a continent-spanning fleet of vehicles—until Amazon’s delivery drones materialize, anyway—the rise of cloud computing has made it easier for startup companies to access the sophisticated technology that only the largest enterprises previously enjoyed. It behooves UPS to stay ahead.

“To me, that’s the future of analytics and big data—not just telling you what has happened, but telling you what should happen and how to correct it in real time,” Levis said. “If a system is so smart that it predicts you’ll have a problem and solves it before it happens, it’s like Sherlock Holmes. It looks clairvoyant, but it’s not.

“It will take a while. But that’s the vision.”
金融工程, 数学算法