Companies often face various challenges when trying to implement optimization techniques. These challenges include problem complexity, limited data, and dynamic changes in the business environment.
To address these challenges, Prof. Daniel Gartner, a prominent expert in operations research at the School of Mathematics, Cardiff University, England, highlighted the importance of using appropriate mathematical models, gathering accurate data, and being adaptable to change. He stated that companies must operate efficiently and effectively in an era of globalization and heightened business competition. Optimization is crucial in achieving these objectives, as businesses aim to maximize profits, minimize costs, and enhance productivity.
Gartner delivered a presentation at the MSM DSM Lounge seminar titled “Optimization Problems in Business: Challenges, Models, and Strategies,” organized by the SBM ITB, on February 21. He explained that optimization problems are prevalent in various aspects of business, including supply chain management, resource allocation, and production scheduling. Interestingly, even everyday situations, like planning a family vacation, involve optimization challenges. For example, a family must decide which items to pack in a car trunk with limited capacity, considering both the volume and value of the items they want to bring.
Assessing whether to bring an item involves asking several key questions, such as: What is the item’s purpose? Is it essential? Does it need to be packed? What are the disadvantages of not bringing it? We can quantify the information by considering these questions to determine if the item should be included.
In the service industry, optimization can be used to arrange flight schedules, manage queues at customer service centers, and determine delivery routes. In the healthcare sector, optimization can assist in scheduling surgeries, allocating hospital beds, and planning drug procurement.
Gartner explained how optimization techniques can be effectively applied in the service and healthcare industries. For instance, when managing a waiting list for patients, understanding the capacity requirements, including the duration of services, allows for more efficient scheduling. This approach can help to reduce patient waiting times and optimize the scheduling of visits.
Gartner also introduced various mathematical methods that can be used to solve optimization problems, such as linear programming, integer programming, and heuristic algorithms. These methods enable companies to make better decisions based on accurate data and analysis.
