Operational research techniques applied throughout cancer care services: a review.

Cancer is a disease affecting increasing numbers of people. In the UK, the proportion of people affected by cancer is projected to increase from 1 in 3 in 1992, to nearly 1 in 2 by 2020. Health services to tackle cancer can be grouped broadly into prevention, diagnosis, staging, and treatment. We review examples of Operational Research (OR) papers addressing decisions encountered in each of these areas. In conclusion, we find many examples of OR research on screening strategies, as well as on treatment planning and scheduling. On the other hand, our search strategy uncovered comparatively few examples of OR models applied to reducing cancer risks, optimising diagnostic procedures, and staging. Improvements to cancer care services have been made as a result of successful OR modelling. There is potential for closer working with clinicians to enable the impact of other OR studies to be of greater benefit to cancer sufferers.

Designing the blood supply chain: how much, how and where?

BACKGROUND: The topology of the blood supply chain network can take different forms in different settings, depending on geography, politics, costs, etc. Many developed countries are moving towards centralized networks. The goal for all blood distribution networks, regardless of topology, remains the same: to satisfy demand at minimal cost and minimal wastage. STUDY DESIGN AND METHODS: Mathematically, the blood supply system design can be viewed as a location-allocation problem, where the aim is to find the optimal location of collection and production facilities and to assign hospitals to them to minimize total system cost. However, most location-allocation models in the blood supply chain literature omit several important aspects of the problem, such as selecting amongst differing methods of collection and production. In this paper, we present a location-allocation model that takes these factors into account to support strategic decision-making at different levels of centralization. RESULTS: Our approach is illustrated by a case study (Colombia) to redesign the national blood supply chain under a range of realistic travel time limitations. For each scenario, an optimal supply chain configuration is obtained, together with optimal collection and production strategies. We show that the total costs for the most centralized scenario are around 40% of the costs for the least centralized scenario. CONCLUSION: Centralized systems are more efficient than decentralized systems. However, the latter may be preferred for political or geographical reasons. Our model allows decision-makers to redesign the supply network per local circumstances and determine optimal collection and production strategies that minimize total costs.

Simulation-optimization model for production planning in the blood supply chain.

Production planning in the blood supply chain is a challenging task. Many complex factors such as uncertain supply and demand, blood group proportions, shelf life constraints and different collection and production methods have to be taken into account, and thus advanced methodologies are required for decision making. This paper presents an integrated simulation-optimization model to support both strategic and operational decisions in production planning. Discrete-event simulation is used to represent the flows through the supply chain, incorporating collection, production, storing and distribution. On the other hand, an integer linear optimization model running over a rolling planning horizon is used to support daily decisions, such as the required number of donors, collection methods and production planning. This approach is evaluated using real data from a blood center in Colombia. The results show that, using the proposed model, key indicators such as shortages, outdated units, donors required and cost are improved.