First histological observations on the incorporation of a novel nanocrystalline hydroxyapatite paste OSTIM(® )in human cancellous bone

BACKGROUND: A commercially available nanocrystalline hydroxyapatite paste Ostim(® )has been reported in few recent studies to surpass other synthetic bone substitutes with respect to the observed clinical results. However, the integration of this implantable material has been histologically evaluated only in animal experimental models up to now. This study aimed to evaluate the tissue incorporation of Ostim(® )in human cancellous bone after reconstructive bone surgery for trauma. METHODS: Biopsy specimens from 6 adult patients with a total of 7 tibial, calcaneal or distal radial fractures were obtained at the time of osteosynthesis removal. The median interval from initial operation to tissue sampling was 13 (range 3–15) months. Samples were stained with Masson-Goldner, von Kossa, and toluidine blue. Osteoid volume, trabecular width and bone volume, and cortical porosity were analyzed. Samples were immunolabeled with antibodies against CD68, CD56 and human prolyl 4-hydroxylase to detect macrophages, osteoblasts, and fibroblasts, respectively. TRAP stainings were used to identify osteoclasts. RESULTS: Histomorphometric data indicated good regeneration with normal bone turnover: mean osteoid volume was 1.93% of the trabecular bone mass, trabecular bone volume – 28.4%, trabecular width – 225.12 μm, and porosity index – 2.6%. Cortical and spongious bone tissue were well structured. Neither inflammatory reaction, nor osteofibrosis or osteonecrosis were observed. The implanted material was widely absorbed. CONCLUSION: The studied nanocrystalline hydroxyapatite paste showed good tissue incorporation. It is highly biocompatible and appears to be a suitable bone substitute for juxtaarticular comminuted fractures in combination with a stable screw-plate osteosynthesis

Spare parts stocking and expediting in a fluctuating demand environment

Capital goods, such as trains and railway infrastructure that facilitate our public transport, are an important part of our daily lives. Maintenance operations are necessary to ensure safety and prevent disruptive failures. To make these operations run smoothly, it is crucial to have the right amount of spare parts available. Eindhoven University of Technology and NedTrain collaborate to optimize the spare parts supply chain using stochastic modelling and optimization

Single-item models with minimal repair for multi-item maintenance optimization

Capital assets, such as wind turbines and ships, require maintenance throughout their long life times. Assets usually need to go down to perform maintenance and such downs can be either scheduled or unscheduled. Since different components in an asset have different maintenance policies, it is key to have a maintenance program in place that coordinates the maintenance policies of all components, so as to minimize costs associated with maintenance and downtime. Single component maintenance policies have been developed for decades, but such policies do not usually allow coordination between different components within an asset. We study a periodic maintenance policy and a condition based maintenance policy in which the scheduled downs can be coordinated between components. In both policies, we assume that at unscheduled downs, a minimal repair is performed to keep unscheduled downtime as short as possible. Both policies can be evaluated exactly using renewal theory, and we show how these policies can be used as building blocks to design and optimize maintenance programs for multi-component assets

Economies of scale in recoverable robust maintenance location routing for rolling stock

\u3cp\u3eWe consider the problem of locating maintenance facilities in a railway setting. Different facility sizes can be chosen for each candidate location and for each size there is an associated annual facility costs that can capture economies of scale in facility size. Because of the strategic nature of facility location, the opened facilities should be able to handle the current maintenance demand, but also the demand for any of the scenarios that can occur in the future. These scenarios capture changes such as changes to the line plan and the introduction of new rolling stock types. We allow recovery in the form of opening additional facilities, closing facilities, and increasing the facility size for each scenario. We provide a two-stage robust programming formulation. In the first-stage, we decide where to open what size of facility. In the second-stage, we solve a NP-hard maintenance location routing problem. We reformulate the problem as a mixed integer program that can be used to make an efficient column-and-constraint generation algorithm. To show that our algorithm works on practical sized instances, and to gain managerial insights, we perform a case study with instances from the Netherlands Railways. A counter intuitive insight is that economies of scale only play a limited role and that it is more important to reduce the transportation cost by building many small facilities, rather than a few large ones to profit from economies of scale.\u3c/p\u3

Design of multi-component periodic maintenance programs with single-component models

\u3cp\u3eCapital assets, such as wind turbines and ships, require maintenance throughout their long lifetimes. Assets usually need to go offline to perform maintenance, and such downs can be either scheduled or unscheduled. Since different components in an asset have different maintenance policies, it is key to have a maintenance program in place that coordinates the maintenance policies of all components, to minimize costs associated with maintenance and downtime. Single-component maintenance policies have been developed for decades, but such policies do not usually allow coordination between different components within an asset. We study a periodic maintenance policy and a condition-based maintenance policy in which the scheduled downs can be coordinated between components. In both policies, we assume that at unscheduled downs, a minimal repair is performed to keep the unscheduled downtime as short as possible. Both policies can be evaluated exactly using renewal theory, and we show how these policies can be used as building blocks to design and optimize maintenance programs for multi-component assets.\u3c/p\u3

Aggregate overhaul and supply chain planning for rotables

We consider the problem of planning preventive maintenance and overhaul for modules that are used in a fleet of assets such as trains or airplanes. Each type of module, or rotable, has its own maintenance program in which a maximum amount of time/usage between overhauls of a module is stipulated. Overhauls are performed in an overhaul workshop with limited capacity. The problem we study is to determine aggregate workforce levels, turn-around stock levels of modules, and overhaul and replacement quantities per period so as to minimize the sum of labor costs, material costs of overhaul, and turn-around stock investments over the entire life-cycle of the maintained asset. We prove that this planning problem is strongly NP -hard, but we also provide computational evidence that the mixed integer programming formulation can be solved within reasonable time for real-life instances. Furthermore, we show that the linear programming relaxation can be used to aid decision making. We apply the model in a case study and provide computational results for randomly generated instances

Maintenance location routing for rolling stock under line and fleet planning uncertainty

Rolling stock needs regular maintenance in a maintenance facility. Rolling stock from different fleets are routed to maintenance facilities by interchanging the destinations of trains at common stations and by using empty drives. We consider the problem of locating maintenance facilities in a railway network under uncertain or changing line planning, fleet planning, and other uncertain factors. These uncertainties and changes are modeled by a discrete set of scenarios. We show that this new problem is NP-hard and provide a two-stage stochastic programming and a two-stage robust optimization formulation. The second-stage decision is a maintenance routing problem with similarity to a minimum cost-flow problem. We prove that the facility location decisions remain unchanged under a simplified routing problem, and this gives rise to an efficient mixed-integer programming (MIP) formulation. This result also allows us to find an efficient decomposition algorithm for the robust formulation based on scenario addition (SA). Computational work shows that our improved MIP formulation can efficiently solve instances of industrial size. SA improves the computational time for the robust formulation even further and can handle larger instances due to more efficient memory usage. Finally, we apply our algorithms on practical instances of the Netherlands Railways and give managerial insights

Repairable stocking and expediting in a fluctuating demand environment: optimal policy and heuristics

We consider a single stock-point for a repairable item facing Markov modulated Poisson demand. Repair of failed parts may be expedited at an additional cost to receive a shorter lead time. Demand that cannot be filled immediately is backordered and penalized. The manager decides on the number of spare repairables to purchase and on the expediting policy. We characterize the optimal expediting policy using a Markov decision process formulation and provide closed-form necessary and sufficient conditions that determine whether the optimal policy is a type of threshold policy or a no-expediting policy. We derive further asymptotic results as demand fluctuates arbitrarily slowly. In this regime, the cost of this system can be written as a weighted average of costs for systems facing Poisson demand. These asymptotics are leveraged to show that approximating Markov modulated Poisson demand by stationary Poisson demand can lead to arbitrarily poor results. We propose two heuristics based on our analytical results, and numerical tests show good performance with average optimality gaps of 0.11% and 0.33% respectively. Naive heuristics that ignore demand fluctuations have average optimality gaps of more than 11%. This shows that there is great value in leveraging knowledge about demand fluctuations in making repairable expediting and stocking decisions