Posts

3 Elements of a successful fleet management system

/0 Comments/in , /by

MiningIQ recently published an article looking at how to make mining operations more efficient. As a more competitive market opens up and there is an increase in compliance and external forces (exchange rate, supply, demand, labour costs, etc.) these businesses are required to maximise productivity at a minimum operating cost all in order to increase profitability.

They found that one of the key business processes that large mining operations can deliver on is their fleet management. MiningIQ discovered that companies such as Glencore, BHP and ADG Mining are all rethinking their approach to fleet management and looking at the smarter way to improve their efficiency and bottom line.

Effective Maintenance Management

MiningiQ stated that Glencore is at the forefront of implementing optimisation into their maintenance management system. Their fleet is made up of millions of dollars’ worth of equipment and their loader rebuilds cost upwards of 1 million dollars.

Through the use of maintenance management optimisation they were able to save; in one circumstance 390,000 dollars.

Truck Haulage Continuous Improvement

They went on to say that Peter Knights, Chair of BHP Billiton Mitsubishi Alliance has even gone beyond using optimisation within the maintenance of his vehicle fleet. He has done this by looking at the most economical way to run his vehicles; through energy and fuel they were able to improve efficiency.

Streamlining The Communications system

MiningIQ’s report found that power, communications, GPS and general infrastructure were key barriers to business efficiencies.

Adam Gray, specialist consultant, mining systems at ADGMining said

“The improvements in truck utilisation were astounding –we’re talking in the realms of more than three per cent, which is a massive figure at the end of an NPV,”

In a lot of cases streamlining the communication systems is as simple as moving off basic excel based programs that don’t allow you to perform specific tasks.

How can I achieve these sort of savings with my fleet?

Biarri is an Australian owned and operated company that was established in 2008. We have worked in many industries but have proven capabilities within Rail, Mining and Oil and Gas. Working with companies such as Rio Tinto, QLD Cotton, Arrow Energy, Origin Energy and Boral (to name a few) and have been able to work across a range of not only Fleet Management problems but; FIFO scheduling, Manpower Planning, Vehicle Routing, Rolling Stock Operational Management, Vessel Load optimisation, and many more.

Biarri builds custom software for you, with you over the cloud. Using the power of mathematics we can provide you with accessible optimisation that anyone in your business can use.

If you want to find out more about how we can help you, Contact us!

Schweppes Vehicle Routing Optimisation

Helping students deliver Vehicle Routing Optimisation

/0 Comments/in /by

When Griffith University’s Business School asked for support developing a network optimisation case for the Applied Business Modelling course, Biarri was happy to assist.

An opportunity to teach and demonstrate state-of-the-art thinking

Robert Ogulin Lecturer and Program Director of Griffith’s Master of Supply Network Management (MSNM) identified an opportunity to “teach and demonstrate state of the art thinking” to his logistics and supply chain students. The Masters program provides students with the opportunity to develop an understanding of effective and sustainable management of global sourcing and international business, and Ogulin recognised the importance of ensuring that students get practical experience with advanced supply chain and optimisation tools.

“It is becoming increasingly clear to me that logistics and supply chain students (and business students in general) require more exposure to related IT” Ogulin said.

The course required access to advanced logistics and supply chain software that had been proven in the rigours of a commercial setting. Biarri’s expertise in the development of easy to use, powerful optimisation solutions across a range of commercial applications and industry sectors proved a good fit with Griffith University’s high performance expectations. Biarri’s Vehicle Routing Optimisation (VRO) tool could accommodate a wide variety of constraints and scenarios, from basic functionality for a homogeneous fleet and fixed load/unload times, to more complex issues such as multi-day workload balancing or managing multiple depots and tours.

Biarri’s VRO helped Schweppes Australia achieve great results with the development of a comprehensive vehicle routing solution, customised to Schweppes’ unique requirements. This application was designed to optimise routes based on their customers’ limited delivery windows while still meeting rigorous customer service level agreements. The VRO tool helped Schweppes achieve a 10% reduction in total fleet kilometres, saving the business thousands of unnecessary kilometers and reduced overall environmental impact via reduced fuel and truck usage.

This made Biarri’s VRO a perfect choice for the program, allowing students to test challenging scenarios and deliver optimised results within real-world constraints. A total of 40 students from the Undergraduate BBUS (Logistics and Supply Network Management) and Master of Supply Network Management courses were offered free on-line access to the VRO software across a two week period and by all accounts the project proved to be a success.

“The tool provided an easy to use but powerful learning environment. The scenarios that (Biarri) provided were perfect to get the students to think through the implications of changing constraints or cost parameters”

Optimisation: Striking the Right Balance

/0 Comments/in , , /by

One of the guiding principles we use in commercial mathematics is to “Model Conservatively, but Optimise Aggressively”. This means that the problem domain should be modeled with sufficient “fat” in the data to ensure that the results are both legal and robust; but given this, we should then seek to apply the best (fastest and highest quality) solution approach that we can get our hands on.

Optimising aggressively can sometimes have it’s downfalls, though, if taken too literally. I’ve been doing a few experiments with numerical weightings of the objective function in a Vehicle Routing problem, where this issue is readily apparent. (Actually it is a Vehicle Routing problem with time windows, heterogeneous fleet, travel times with peak hours, both volume and weight capacities, and various other side constraints).

Our Vehicle Routing uses travel times (based on shortest paths through the street network) that are characterised by distance and duration. Durations can vary due to different road speeds on different types of streets (highways vs suburban roads for example). This leads to the question of how (on what basis) to optimise the vehicle routes – given that the optimisation has already to some extent minimised the number of vehicles and created well-clustered routes – what is the most desirable outcome for KPIs in terms of duration and distance?

In one experiment I’ve tried three different weightings for the duration (cost per hour) while keeping the cost per distance constant. I’ve run three values for this cost per hour – low, medium, and high weightings – on real-life delivery problems across two different Australian metropolitan regions.

Region 1
Total Duration Driving Duration Distance
Cost/hour
Low 74:47 24:38 708
Medium 72:45 23:55 712
High 72:58 23:42 768
Region 2
Total Duration Driving Duration Distance
Cost/hour
Low 113:54 46:44 1465
Medium 107:51 41:36 1479
High 108:51 43:49 1518

From these results, there is a (more-or-less) general correspondence between distance and the driver cost per hour as you would expect. However, if you push one weighting too far (ie. optimise too aggressively or naively), it will sometimes be to the detriment of all the KPIs as the optimisation will be pushing too strongly in one direction (perhaps it is outside the parameter space for which it was originally tuned, or perhaps it pushes the metaheuristic into search-space regions which are more difficult to escape from). This is most acutely seen in Region 2 when using the high cost per hour value. Conversely if you drop the cost per hour to a low value, the (very modest) reduction you get in distance is very badly paid for in terms of much longer durations. What is most likely happening in this case is that the routes are including much more waiting time (waiting at delivery points for the time windows to “open”), in order to avoid even a short trip (incurring distance) to a nearby delivery point that could be done instead of waiting.

The problem of striking the right balance is most acute with metaheuristics which can only really be tuned and investigated by being run many times across multiple data sets, in order to get a feel for how the solution “cost curve” looks in response to different input weightings. In our example, an in-between value for cost per hour seems to strike the best balance to produce the overall most desirable KPI outcome.

The Launch of Biarri’s WorkBench

/0 Comments/in , , , /by

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.”

The Trajectory of a Vehicle Routing

/1 Comment/in , , /by

I recently wanted to view an animation of a vehicle routing optimisation algorithm I have been working on.

The algorithm uses a two phase optimisation approach. The first phase is a construction heuristic. The second phase is guided local search meta heuristic. The algorithms primary focus is to produce a visually attractive solution. Visually attractive solutions are usually synonymous with compact, non-overlapping routes with little or no intra-route cross over.

A colleague suggested I have a look at pygame. I hunted around to try and find an existing script that did something similar to what I wanted and found the fracture simulator. It is always easier to modify something than to start from scratch!

I was pleasantly surprised at how easy it turned out to be. I am very much a python novice, and had never heard of pygame before, but have been programming for a number of years. I was able to get the basics of my animation up and running in one evening! This far exceeded my expectations!

The python script can be viewed here

The input file used to create the uploaded animation can be viewed here

Portfolio Items