René Haijema

Blood platelet production : optimization by dynamic programming and simulation

Abstract Blood platelets are precious, as voluntarily supplied by donors, and highly perishable, with limited lifetimes of 5–7 days. Demand is highly variable and uncertain. A practical production and inventory rule is strived for that minimizes shortages and spill. The demand and production are periodic, as varying over the seven days of the week. Demand for ‘young’ platelets (oncology and hematology) and demand for platelets of ‘any’ age up to the maximal shelf life (traumatology and general surgery) are distinguished. A combined Markov dynamic programming (MDP) and simulation approach is presented and applied to a real life case of a Dutch blood bank. By down-sizing the dimension and applying this combined approach it is shown that order-up-to type replenishment rules that perform quite well can be found. Particularly, a double-level order-up-to rule, so-called 2D rule, is derived, with one level corresponding to ‘young’ platelets and one to the total inventory. This rule is easy to implement and is shown to be ‘nearly optimal’. This approach and the double order-up-to rule seem to be new. The results are most suitable for sensitivity analyses such as with respect to shortages and production costs for blood platelet inventory management.

An mdp decomposition approach for traffic control at isolated signalized intersections

This article presents a novel approach for the dynamic control of a signalized intersection. At the intersection, there is a number of arrival flows of cars, each having a single queue (lane). The set of all flows is partitioned into disjoint combinations of nonconflicting flows that will receive green together. The dynamic control of the traffic lights is based on the numbers of cars waiting in the queues. The problem concerning when to switch (and which combination to serve next) is modeled as a Markovian decision process in discrete time. For large intersections (i.e., intersections with a large number of flows), the number of states becomes tremendously large, prohibiting straightforward optimization using value iteration or policy iteration. Starting from an optimal (or nearly optimal) fixed-cycle strategy, a one-step policy improvement is proposed that is easy to compute and is shown to give a close to optimal strategy for the dynamic problem.

Simulation and or (operations research) in combination for practical optimization

Should we pool capacities or not? This is a question that one can regularly be confronted with in operations and service management. It is a question that necessarily requires a combination of queueing (as OR discipline) and simulation (as evaluative tool) and further steps for optimization. It was illustrated that a combined approach (SimOR) of simulation (techniques and tools) and classical operations research (queueing, linear programming and scheduling) can be most beneficial. First, an instructive example of parallel queues was provided which shows the necessary and fruitful combination of queueing and simulation. Next, the combined approach was also illustrated for the optimization of: call centers, checking-in at airports, blood platelet production, and train scheduling for railways. Whether we should pool or not is thus just one simple question for which this SimOR approach can be most fruitful if not necessary for practical optimization.

OR and simulation in combination for optimization

This chapter aims to promote and illustrate the fruitful combination of classical Operations Research (OR) and Computer Simulation. First, a highly instructive example of parallel queues will be studied. This simple example already shows the necessary combination of OR (queueing) and simulation that appears to be of practical interest such as for call center optimization. Next, two more ’real life’ applications are regarded: - blood platelet production and inventory management at blood banks, and - train conflict resolution for railway junctions. Both applications show the useful combination of Simulation and optimization methods from OR, in particular Stochastic Dynamic Programming (SDP) and Markov decision theory (MDP), to obtain simple rules that are nearly optimal. The results are based on real life Dutch case studies and show that this combined OR-Simulation approach can be most useful for ’practical optimization’ and that it is still wide open for further application.