Load optimization with xTrack TMS

Vehicle load optimization is a feature of xTrack TMS that is primarily aimed at delivering the right products to customers in a timely manner, safely and efficiently, with as few vehicles as possible and on the shortest transport routes. Basically, the load optimization algorithm aims to maximize the utilization of the available space in the delivery vehicles or trucks and offers a number of benefits that we will discuss in the second part of this article.

How does the load optimization algorithm work?

The efficiency of the loading process of goods into vehicles is achieved by considering several aspects.

To start with, existing volumetric and weight restrictions are obviously respected. Next, items need to be distributed evenly to avoid their movement during the trip. Placing them according to the order in which deliveries are made also helps drivers to distribute goods quickly and with maximum accuracy.

The most important aspects that were taken into consideration when developing the algorithm to achieve optimal truck loading are the following:

• the center of gravity of the vehicle;
• the volume available in the vehicles (payload capacity) and the volume of the ‘boxes’ associated with the orders  (‘box’ means any package, parcel or pallet in which the products specified in the order are packed);
• the maximum weight supported by the drive axle or axles of the vehicle.

On the whole, the algorithm is based on a step-by-step process involving three main operations:

• calculation of the space required to load the boxes according to the dimensions of the boxes and the dimensions of the available space in the transport vehicle;
• gradual creation of the block of boxes (structure of rows and layers) to be placed in the space in the vehicle;

and last, but not least,

• structure updates according to the data of the boxes that are added, one by one, to the previously built block. This updating process involves, for example, recalculating the center of gravity in such a way as to finally arrive at an optimum loading option for each vehicle, i.e., a high space utilization rate without exceeding the maximum permissible weight.

To launch the algorithm, it is necessary to first import some data sets stored in other applications (e.g., WMS, TMS, ERP): list of items, their number and estimated delivery times that have been communicated to customers. On the basis of this data, the weight and dimensions of the boxes are calculated and the priority of order deliveries is determined.

Each box is thus defined by its weight, size and repositioning possibilities (possible rotations), data according to which the algorithm “decides” where to place it on the truck. The number of layers formed by the boxes in which the items are transported is limited, and their arrangement on several levels depends on the fragility and/or weight of the products: the heaviest and strongest products are placed in the boxes on the bottom layer, and the lightest and/or most fragile on the upper layers. This avoids damage to the products during loading and transport.

Since the dimensions, load capacity and maximum weight supported are known for each truck, the algorithm distributes the boxes evenly and always places the center of gravity between the drive axles. The boxes are places starting from the sides towards the center of the vehicle in compact layers and rows so that they cannot change position during transport.

The load optimization algorithm allows for flexibility and speed in establishing the layout pattern of the goods during transport, as another pattern suitable for the new data is automatically generated when other parameters are entered. This enables users to react quickly and efficiently to any unforeseen changes before the actual loading.

The results of combining route optimization with truck loading planning are also taken into account when optimizing loading. After centralizing the data (order number, delivery deadline, box weight and size, delivery addresses), the loading of boxes is planned depending on the distances to the locations where the products are to be delivered: the order for the first destination/customer is positioned closest to the unloading door of the vehicle and so on.

Benefits of an efficient vehicle loading process

Reduced loading and unloading times

The automatic determination, via the application, of how to utilize the available space leads to a reduction in the time it takes to load products at the warehouse. In addition, since loading is done according to the LIFO (Last In, First Out) principle – the last order loaded is first unloaded, second last order loaded is the next to be unloaded, etc. – the algorithm helps to reduce the time allocated to each unloading at the destinations along the route.

Maximum utilization of transport capacity

The loading optimization algorithm ensures that the unused space in the vehicles transporting the goods is minimized. This is also due to the possibility of grouping orders, which eliminates the need for separate trips with a low space utilization rate.

Optimal conditions for the transport of goods

The automatic calculation of the volume of goods that can be transported by a vehicle and the positioning of the boxes in the transport vehicles according to weight and size eliminates the possibility of the items being damaged on the way to their destination.

Minimization of transport and maintenance costs

Optimizing loading also means reducing the number of trips made by vehicles whose capacity is not fully utilized. This reduces fuel consumption and related costs.  And maintenance costs become lower. Since the vehicles no longer make frequent partly loaded trips and the restrictions on the maximum weight allowed for each vehicle are respected when loading, accelerated wear and tear of the vehicles as well as unwanted technical breakdowns are avoided.

Positive impact on the environment

Reducing the number of journeys and optimizing the routes results in lower fuel consumption and therefore in less pollution through lower CO2 emissions. Digitalization in logistics has environmental benefits and it is one of the methods that contribute to sustainability.

Cloud integration of xTrack TMS solution

The xTrack TMS system, which includes the load optimization algorithm, is fully integrated on a cloud platform and the benefits are similar to those of the xTrack WMS cloud integration.

The development of the load optimization algorithm is part of a series of activities aimed at extending existing functionalities or creating new applications for logistics included in the xTrack LMS (Logistics Management Suite). These activities are part of the Axes Software project, called “Innovative platform for artificial intelligence management of work processes in factories and warehouses“, which is co-financed by the European Social Fund through the Competitiveness Operational Programme 2014-2020.

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