Although our developments are primarily focused on logistics problems in accordance with market needs, we are also interested in optimization problems in other areas.
The development of custom optimization algorithms must of course be preceded by a comprehensive problem specification with the client, after which we can make a concrete proposal for the implementation of a suitable algorithm.
In the course of doing business, we often try to make a decision or a series of decisions that aim to reach the minimum value of a cost factor or the maximum value of a benefit factor, taking into account certain constraints. For example, the production programme of a plant's production lines may be created to minimise the time needed to change the machines' technology while fulfilling received orders on time. Or we want to plan the daily route of the fleet's vehicles in order to minimise the number of kilometres travelled while at the same time ensuring that the drivers' working hours and the opening hours of the destinations are respected. Perhaps the cutting of standard-sized production raw materials should be solved with minimal material loss.
Problems like these are called optimization problems. These examples mainly concern lower level operational decision situations. Of course, optimisation problems do not only arise at the operational decision-making level, but also at the tactical or strategic level. For example: optimal choice of geographical location of sites, planning of optimal telecommunication networks, etc.
In practice, in addition to the cited classical examples, we can find a wide variety of tasks. In the specialized literature, there are numerous references to optimization problems in the transport, production management, agriculture, telecommunications, architecture and chemical industries. The strictness of the constraints is relaxed in some problems, in other cases it is necessary top optimize several target factor together according to some hierarch or weighting.
Dealing with real-life problems manually is usually a hopeless task. The number of variables, rules, constraints, i.e. the complexity of the problem, is usually so great that the time and flexibility of methods based on the "common sense judgement" of experienced professionals, and the quality of the solutions produced, are unacceptable in a highly competitive market.
Of course, the use of modern information technology alone is not sufficient to deal with optimization problems. Unfortunately, even for the majority of simplified problems, we do not have a mathematically correct procedure that produces an optimal solution with an acceptable amount of time. In addition, in practice, it is usually necessary to take into account many more factors than in the "academic" models that are the subject of extensive research. In recent decades, these difficulties have led to an increasing focus on techniques that do not necessarily aim at producing an optimum, but are satisfied with very high quality solutions. At the same time, these methodologies are general and can be applied not only to a narrower range of simplified problems. Despite the difficulties that encountered in practical optimization, we can say that the vast majority of business optimization problems that arise can be handled with the methodological apparatus available today.