Optimization of Logistics and Production Processes – Using the Example of Dynamic Route Optimization in Warehouses

The dynamic optimization of logistics and production processes in real time is at the top of the wish list of many small and large companies in order to gain a significant reduction of costs while improving machine utilization, workload distribution and product delivery times. Using the example of a fictitious warehouse, it can be shown which optimization measures interlock with each other and how complex processes become manageable even for small companies.

The management of a modern warehouse is a highly complex business. While in the past it was often sufficient to structure the warehouse well, to clearly define static process flows and to employ enough warehouse workers, today the challenges are much more complex. The markets are more global, large mail order companies are constantly expanding their network of logistics centers – and customers expect a wide range of products and short delivery times.

In order to remain successful and competitive, it is necessary to design all processes efficiently and to be able to react dynamically to unforeseen events. This requires a precise analysis and optimization of all work steps in the warehouse. The minimization of delivery times affects all processes that accelerate the picking of incoming orders. On an operational level, this includes above all the possible combination of several orders (batching) and route optimization (routing).

In addition, decisions in preceding processes – such as dividing the warehouse into different product areas (zoning) or the strategy for sorting new goods (storage allocation) – can have a decisive influence on picking efficiency.

Additional complexity arises from the fact that the individual optimization problems should not be considered separately, because the combination of different strategies for different problems also holds enormous optimization potential (van Gils, Ramaekers, Braekers, Depaire, & Caris, 2018).

The intuitive approach of determining the positions of the ordered goods in the warehouse for each received order and calculating the shortest route from there is not advisable for various reasons. On the one hand, depending on the layout of the warehouse, as in the case of the so-called Traveling Salesman Problem (TSP), an NP-complete problem may arise whose optimal solution cannot be calculated efficiently. Even if a calculation of this solution is possible and an optimal route can be determined, this calculation can take a lot of computing time for larger warehouses. Therefore it makes sense to use heuristic approaches, which are more efficient in the calculation and also provide very good solutions.

In addition, significantly shorter overall routes can usually be achieved by combining several orders into one route. Since both routing and batching occur at the operational level, it is obvious and promising to solve both problems together. For the simultaneous calculation of batches and routes, some efficient combined heuristics exist (Cheng, Chen, Chen, & Jung-Woon Yoo, 2015) (Kulak, Sahin, & Taner, 2012) (Li, Huang, & Dai, 2016) (Lin, Kang, Hou, & Cheng, 2016).

As mentioned in the introduction, not only routing and batching, but also storage allocation and routing must be considered together. This is because much better solutions can be achieved if coordinated heuristics are used to decide where goods are stored and to determine the shortest possible routes. In the following section, we want to give an impression of which heuristics can be used here, how the different heuristics interact and what quality the calculated solutions have.

Although there are some standard software packages in the field of warehouse optimization, scientific research shows that the calculated results are only good if they are tailored to the individual processes and layout of the respective warehouse.

In addition, there are a number of previously unmentioned aspects that can play an equally important role in optimization. For example, the strategy of zoning in larger warehouses in combination with routing can also have immense effects. The number of loading and unloading gates and the transport equipment used must also be taken into account, as must the personnel. The operational decisions in routing and batching depend heavily on the number of warehouse workers employed. If several order pickers are on the road in the warehouse at the same time, optimal results are only achieved if possible »traffic jams« are already avoided in the planning stage. In this case, it can make sense to modify the original strategy. Shorter waiting times not only reduce picking time, they also increase worker satisfaction.

If all these aspects are taken into account, the result is an individual optimization solution that not only saves time and money, but also conserves resources and increases customer and employee satisfaction.

Fraunhofer SCAI is working with partners on an integrated solution for warehouses in the food sector, for example in the ERTRaWOS project funded by the German Federal Ministry of Economics. The above-mentioned and many additional and non-intuitively detectable constraints are incorporated into the optimization solution.

This solution is generated by using a newly created optimization platform. With this platform, Fraunhofer SCAI offers the basis for largely customized real-time-capable optimization solutions in many areas of logistics (up to 3PL and 4PL), production planning (e.g. scheduling of machine occupancy) and supply chain management. All these solutions can often be integrated into an existing infrastructure such as a WMS and other tools.