If I were to point out one core functionality of any software that deals with transports, it would be the Basic Optimizer. This functionality pretty much exists to solve the vehicle routing challenge, which is the challenge of visiting customers by vehicles.
Generally speaking, the goal of this Basic Optimizer is to calculate low cost delivery or collection routes, usually from a depot or driver’s house to customers that have constraints. This can be anything from time windows of delivery, truck type, driver preference to side of the road, quantity to load offload, and innumerous others. This optimizer is applied in many situations, such as FTL, Drop, Distribution, Collection, inspection… you name it, it can be optimized.
The first algorithms started in the 1950’s. About ten years later, Clarke and Wright proposed a VRP algorithm known as the Savings Method. This well-known algorithm is based on the calculation of savings by serving two customers in the same route instead of two separate ones. It is the first of many techniques used in the basic algorithms. As mentioned in the first article of the series, with the rise of the desktop these algorithms went from being used by consultants to improve businesses once a year to every day operational use.
Over time, besides distribution, other algorithms were developed with this as basis to include collection, drop, multi-trip, multi-depot and transfer scenarios. Nowadays, the solution methods consider all kinds of real-life aspects like time windows, traffic congestion, compartments and characteristics that change dynamically over time, like demand information in gas distribution. This introduces additional complexity; therefore, it will get its own specific article in this series.
Coping with this resulted in advanced algorithms, using so-called metaheuristics, which are the next level of techniques used to solve the challenge and will be discussed in a further article. Today, every VRS/TMS should have the basic algorithm well used, defined and implemented. This needs specific attention, because there are many levels of quality and diversity of restrictions that make certain optimizers better for your business than others.
The first challenge of finding the perfect optimizer is restrictions. Can it deal with compartments, time windows per product type, ullage, axle restrictions, pick sequence, road restrictions due to safety, and other restrictions?
The next concern is algorithm speed, if that fits within your business process and highly important if it can optimize for your KPI, $/ton or total cost or CO2, or even service level or reliability. All of these are important to check before deciding.
In the last decades, there have been a lot of innovations such as GPS systems, parallel and cloud computing. They enable taking into account real-time information as well as new highly scalable techniques for solving the VRP.
These innovations also grant the ability to solve large-scale problems and will also result in more accurate solutions. They have already and will continue improving solutions already door to door instead of nodes and in street returns instead of having to go to the next street to turn around are being taken into account. Soon algorithms will solve in real time with real time data again for the relevant industries, like ambulances, and collection of cash-in-transit, for example. It also makes the move of having generic mega algorithms with constant improvement in the cloud, with customer specific implementations accessing this cloud, a reality in the next few years.
The methods for efficiently solving VRPs have enormously developed in the last fifty years, taking into account real-life restrictions and dealing with a continuously growing scale of the problems to solve. They are an integral part of any Logistics solution and need sufficient understanding of your business on the vendor side to implement. With Cloud computing real time GPS, many solutions will become more generic and more customer specific at the same time, giving more businesses access to the best solution for them.