MS. POOJA BISHT
ASSISTANT PROFESSOR
The transportation problem is a classic optimization tool in operations research used to decide the lowest-cost way to move resources from several supply locations to multiple demand points. In the sensitive domain of medical waste management, this framework supports the planning of routes and schedules for collecting hazardous healthcare waste from hospitals and clinics and transporting it to treatment or disposal facilities. By focusing on cost, distance, and risk, it helps design systems that are both economically efficient and protective of human health and the environment.
Medical waste is generated every day in healthcare centers and includes infectious and hazardous materials that require strict handling and disposal procedures. In this setting, hospitals and clinics act as sources of waste, while incinerators, treatment plants, or secure landfills serve as the destinations that must receive that waste.
Real-world medical waste logistics are shaped by several constraints. Vehicles have finite capacity, there are acceptable risk thresholds, and each facility can store waste only for a limited time before it must be removed. These practical restrictions extend the basic transportation model into more sophisticated forms such as the Periodic Vehicle Routing Problem (PVRP) and the Load-Capacitated Vehicle Routing Problem (PLCVRP).
In many developing regions, healthcare centres have very limited space for storing medical waste and must clear it within specified time windows—often around two weeks—to avoid environmental and occupational hazards. Applying the transportation problem in this context involves designing collection schedules and vehicle routes that ensure every facility is serviced on time, without breaching storage limits. Because these are multi-period and often large-scale problems, linear programming formulations are combined with heuristic methods to generate solutions that are both feasible and efficient.
To mirror actual conditions, models frequently include inventory-related components so that waste volumes at each facility remain within safe bounds over time. Another important aspect is the minimization of overall transportation risk, which generally increases with the amount of waste carried at any point in the route, due to potential spills or contamination. Some systems employ multi-compartment vehicles to separate different types of waste during transport, embedding both operational and safety requirements directly into the optimization framework.
A number of applications in different countries demonstrate the usefulness of these methods. In Dolj, Romania, for instance, researchers used a PLCVRP-based approach to design weekly routes that reduced both transportation cost and the risks linked to on-site storage. In Surabaya, Indonesia, a PVRP with time windows helped optimize vendor routes and vehicle loads while meeting strict rules on maximum storage times, yielding lower overall collection costs and improved reliability of service.
Recent smart city initiatives extend these concepts by incorporating digital and communication technologies into medical waste logistics. IoT-equipped vehicles, GPS tracking, and real-time data exchange allow route plans to be adjusted dynamically when conditions change, such as sudden surges in waste volume or traffic disruptions. These systems are often combined with genetic algorithms and explainable AI tools, which help optimize routing decisions while maintaining transparency and compliance with environmental and public health standards.
Solving transportation problems in medical waste networks usually requires advanced optimization techniques because of the large number of facilities involved and the complexity of the constraints. Methods such as decomposition heuristics, genetic algorithms, and variable neighbourhood search are commonly used to provide high-quality solutions within reasonable computational time. At the same time, the perspective of reverse logistics is central: instead of moving products from producers to consumers, waste flows from many scattered healthcare sites back to centralized treatment centres.
To manage this reverse flow effectively, decision-making tools like the Best–Worst Method (BWM) can be applied to identify, rank, and control risks at different stages of the chain, thereby supporting more sustainable and safer healthcare waste systems. Emerging technologies—such as sensor networks, advanced analytics, and dynamic routing platforms—continue to increase the responsiveness and safety of these systems. They enable health authorities to react rapidly to changes in waste generation, especially during emergencies like epidemics, ensuring ongoing protection of public health and environmentally responsible disposal.
For Management Colleges, B-Schools, and students in PGDM, BBA, and BCOM programs, the application of the transportation problem in medical waste management offers a powerful real-world learning context. It integrates core curriculum areas including logistics and supply chain management, quantitative techniques, risk assessment, and operations research. Through case studies and projects that focus on route design, capacity constraints, regulatory compliance, and sustainability, students sharpen their analytical and decision-making skills. This exposure prepares them for careers in healthcare administration, environmental management, logistics, and consulting, while also cultivating a sense of social responsibility. In this way, the topic forms a valuable bridge between theoretical management concepts and impactful practical solutions in the healthcare sector.
