Optimisation exercises for the business world

The increasing amount and complexity of data we now have challenges the business world. Many questions emerge, among them “How we can use data to make better business decisions?”

In order to stay competitive you need to analyse data to get useful insight and adjust business processes accordingly. This is where typical business problems meet an academic solution. It gives business decision-making processes the opportunity to rely less on intuition, personal experience and chance. Instead, companies are able to use relevant information and to apply advanced analytical principals to define chains of future events and their possible outcomes.

Generally, the most crucial task of a business manager is finding a way to increase revenue and keep costs as low as possible. In academia this is what optimisation methods specialists do; using advanced maths, programming, and algorithms they search for minimums and maximums. Translating that to business needs, an analyst can find how to earn the maximum profits under certain conditions and what actions should be taken to cut expenditures to the minimum.

To get closer to this topic we can look at several real-life problems which are often used as theoretical framework for academic research.

One of the most popular exercises is the Travelling Salesman Problem. In this task a representative of a firm has to visit 50 cities, each only once, and go back to the office: you need to find the fastest or the cheapest route. The problem was first introduced as one common for salesmen and had a rather theoretical solution sketched on a map. Since the problem is complex, it started to serve as a model for the development of optimisation algorithms.

Another example is the Knapsack Problem. Here the goal is to pack as many of the most necessary (i.e. most valuable) things into your bag as possible while still keeping the bag under a total maximum weight. To adjust the problem to regular business challenges we can re-focus it as, for example, a situation in which a firm wants to release a new product while obtaining the maximum revenue with the lowest costs. Metaphorically the company wants to fill the bag with the most necessary things while keeping it as light as possible. Here we can treat the weight of the bag as the cost of the business processes which are required for the release of the new product. The maximum revenue would be the value of all of the necessary stuff you put to the bag. And the size of your bag is your own capital and investment capital.

A third exercise is the Nurse Scheduling Problem: the problem of finding the optimum working schedule for nurses (or any profession really). In this example you need to maximise the happiness of nurses: they prefer to work (or not to work) at specific times, varying between day shifts, night shifts, weekends and national holidays. Additionally, the optimal schedule must meet the hospital’s requirements regarding how many nurses need to be on duty at specific times. A solution to this problem is often used to create time-tables for call-centers, public services, and, of course, hospitals.

In a world full of complex information and complicated decisions, firms have started to co-operate closely with academic researchers. Those businesses which will be able to integrate that knowledge into their daily operations will always have more chances to prosper.

In our future market insights we will look at which algorithms are used in order to solve the problems listed above. Stay tuned!

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Author: Elli Vitchynova
Marketing Executive