With the impending launch of Biarri’s workbench and our ongoing close relationship with Schweppes for the daily routing of soft drink deliveries (an application of perhaps the most well known operations research problem: the vehicle routing problem), I thought that the following excerpt from a journal article submitted to the Asia Pacific Journal of Operations Research would be a very timely blog post.
The journal article is entitled “Real-Life Vehicle Routing with Time Windows for Visual Attractiveness and Operational Robustness” and it describes the vehicle routing algorithm we have implemented for Schweppes.
The excerpt details a specific example encompassing two things we are very passionate about at Biarri. First “Commercial Mathematics” – that is making OR (well not strictly just OR) work in the real world. And second, the revolutionary capabilities that the advent of cloud computing has for the delivery of software.
“Vehicle routing problems manifest in a remarkably wide range of commercial and non-commercial enterprises. From: industrial waste collection to grocery delivery; underground mining crew replenishment to postal and courier collection and delivery; inbound manufacturing component transportation to finished car distribution; in-home primary health care delivery to pathology specimen clearances from surgeries for analysis; and from coal seam gas field equipment maintenance to beverage distribution, to name but a few.
Automated planning systems used by industry at present are predominantly client-server or desktop based applications. Such systems are often: expensive, requiring a large upfront capital investment; accompanied by a large software deployment project requiring initial and ongoing IT department cooperation; customisable to a particular organisations requirements, however commonly retain a large amount of exposed functionality due to the breadth of the existing client base; and require substantial user training as the workflow is usually not restricted in a linear fashion …. Each of these characteristics constitutes a barrier to adoption of automated planning systems, and for most small to medium enterprises these barriers prove insurmountable.
With the advent of cloud computing and software as a service (SaaS) these barriers are being removed. SaaS: embodies a different commercial model; has essentially no IT footprint; mandates (as vendors may never directly interact with potential clients) simple intuitive linear workflows; and involves almost no end user training beyond perhaps an optional demonstration video.
The emergence of this new avenue for the delivery of optimisation based planning systems heralds, a heretofore, unparalleled opportunity for operations research practitioners to engage with a wider potential consumer base than ever before. However, the nature of the delivery mechanism requires the algorithms developed: to be robust and flexible (within their domain of application they must be capable to dealing with a wide range of input data); to have very short run times (the user base is more likely to be under time pressure than ever before); to produce high quality solutions (noting the inherent trade off between run time and solution quality); to be wrapped in a simple linear workflow (meaning it is always obvious what the next step in the planning process is); but above all, be able to produce real-life, practically implementable solutions, without the need for user training and/or experience.
For pure delivery, or pure pick up vehicle routing applications, real-life, practically implementable solutions are often synonymous with geographically compact, non-overlapping routes with little or no intra-route cross over. There are numerous reasons why such solutions are preferred …. If a customer cannot be serviced at the preferred time (e.g. the vehicle cannot get access, the customer is closed, another delivery is taking place, the customer is too busy), because the route stays in the same geographical area, it is easy to return to the customer at a later time. During busy traffic periods drivers are loathe to exit and re-enter a motorway to service individual customers. Even though such customers may be enroute to the
bulk of the customers the route services, thus incurring a minimum of additional kilometres, they may nevertheless be far from the majority of the customers the route services. If there is severe traffic disruption, it is easier to use local alternate routes between customers in a route that is geographically compact to ensure that pick-ups or deliveries can still be made. Third party transport providers, which prefer routes to be as simple as possible, may exert some influence over the planning process. Finally … it is easier to maintain customer relationships by assigning drivers to routes that routinely service a similar geographical area. In summary, solutions which are more visually attractive are more robust, and thus more likely to actually deliver the full extent of the cost savings that should flow from the use of automated planning systems.
This paper describes an algorithm for the vehicle routing problem with time windows, …. The algorithm is: robust and flexible; fast; wrapped in a user interface utilising a simple linear workflow and so requires no user training or experience; and produces high quality, visually attractive and practically implementable solutions.”