- Paviljoen E1
P.O. BOX 513
5600 MB Eindhoven
The Netherlands - 0031402474983
- Supply Chain Management, Operations Research, Stochastic Process, Inventory Control, Queueing theory, Applied Operations Research, and 18 moreDual Sourcing, Spare parts management, Inventory Management, Global Sourcing, Inventory Management System, Inventory Planning and Control, INVENTORY POLICIES, Johann Joachim Winckelmann Eighteenth century art history philosophy Enlightenment, Computer Science, Operations research and Optimization, Inventory systems, Maintenance Engineering and Management, Markov Decision Process, Repair and Maintenance, Stochastic processes, Dynamic programming, Multicriteria Decision Analysis, and Stochastic Optimizationedit
- I am professor at the university of Luxembourg within the Luxembourg Center for Logistics and Supply Chain Management... moreI am professor at the university of Luxembourg within the Luxembourg Center for Logistics and Supply Chain Management which is part of the MIT Scale network. I also hold a visiting appointments at the MIT Centre for Transportation and Logistics. Previously, I was a visiting scholar at MIT Sloan school of management and an assistant professor at Eindhoven University of Technology, where I also received my PhD. My research interests are operations research defined broadly, especially applications in supply chain, logistics, maintenance and inventory. My PhD research received the EDDA award from the European federation of Operations Research societies, and it was funded by NedTrain, a division of the Dutch railways that maintains rolling stock. In current research, I work with NedTrain, ASML, and Philips. I am a veni laureate, recipient of the VvS+OR master thesis award and a Prins Bernhard Cultuurfonds Fellow.edit
We consider the canonical periodic review lost sales inventory system with positive lead times and stochastic i.i.d. demand under the average cost criterion. We introduce a new policy that places orders such that the expected inventory... more
We consider the canonical periodic review lost sales inventory system with positive lead times and stochastic i.i.d. demand under the average cost criterion. We introduce a new policy that places orders such that the expected inventory level at the time of arrival of an order is at a fixed level and call it the projected inventory-level policy. We prove that this policy has a cost rate superior to the equivalent system where excess demand is backordered instead of lost and therefore, is asymptotically optimal as the cost of losing a sale approaches infinity under mild distributional assumptions. We further show that this policy dominates the constant-order policy for any finite lead time and therefore, is asymptotically optimal as the lead time approaches infinity for the case of exponentially distributed demand per period. Numerical results show that this policy also performs superior relative to other policies.
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The industry and transportation sectors account for more than 35% of global CO 2 emissions and there is increasing pressure on industry to reduce emissions. To remain competitive in their markets while reducing their emissions, companies... more
The industry and transportation sectors account for more than 35% of global CO 2 emissions and there is increasing pressure on industry to reduce emissions. To remain competitive in their markets while reducing their emissions, companies need to re-optimise their entire value chain focusing not only on traditional costs, related to manufacturing and transport, but also on emission reduction targets. In this work, we propose a linear program to optimise a deterministic multi-objective value-chain problem aimed at minimising CO 2 emissions and maximising a company's total contribution margin. We test the model on a real-world dataset, provided by a multinational chemical company, to determine the main sources of emissions and their geographical distribution. Moreover, we analyse how much emissions can be reduced at a negligible impact on the total contribution margin and describe what the best strategies are to achieve the targeted emission reduction. We find that it is beneficial to move production to less polluting production sites, even when that increases the transportation in our setting. It is therefore advisable to jointly address the reduction of production-and transportation-related emissions, rather than separately.
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A line replaceable unit (LRU) is a collection of connected parts in a system that is replaced when any part of the LRU fails. Companies use LRUs as a mechanism to reduce downtime of systems following a failure. The design of LRUs... more
A line replaceable unit (LRU) is a collection of connected parts in a system that is replaced when any part of the LRU fails. Companies use LRUs as a mechanism to reduce downtime of systems following a failure. The design of LRUs determines how fast a replacement is performed, so a smart design reduces replacement and downtime cost. A firm must purchase/repair a LRU upon failure, and large LRUs are more expensive to purchase/repair. Hence, a firm seeks to design LRUs such that the average costs per time unit are minimized. We formalize this problem in a new model that captures how parts in a system are connected, and how they are disassembled from the system. Our model optimizes the design of LRUs such that the replacement (and downtime) costs and LRU purchase/repair costs are minimized. We present a set partitioning formulation for which we prove a rare result: the optimal solution is integer, despite a nonintegral feasible polyhedron. Second, we formulate our problem as a binary l...
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Problem definition: Unexpected failures of equipment can have severe consequences and costs. Such unexpected failures can be prevented by performing preventive replacement based on real-time degradation data. We study a component that... more
Problem definition: Unexpected failures of equipment can have severe consequences and costs. Such unexpected failures can be prevented by performing preventive replacement based on real-time degradation data. We study a component that degrades according to a compound Poisson process and fails when the degradation exceeds the failure threshold. An online sensor measures the degradation in real time, but interventions are only possible during planned downtime. Academic/practical relevance: We characterize the optimal replacement policy that integrates real-time learning from the online sensor. We demonstrate the effectiveness in practice with a case study on interventional x-ray machines. The data set of this case study is available in the online companion. As such, it can serve as a benchmark data set for future studies on stochastically deteriorating systems. Methodology: The degradation parameters vary from one component to the next but cannot be observed directly; the component po...
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Competitive Original equipment manufacturers (OEMs) do not only sell equipment, but also service contracts that ensure proper functioning and uptime of equipment after the sale. It then becomes the concern of OEMs to minimize equipment... more
Competitive Original equipment manufacturers (OEMs) do not only sell equipment, but also service contracts that ensure proper functioning and uptime of equipment after the sale. It then becomes the concern of OEMs to minimize equipment downtime by providing after-sales services such as repairs and spare parts over the lifetime of equipment. OEMs will therefore aim to minimize the total Life Cycle Costs (LCC) of their equipment by deciding for each component (1) whether to use a common component (one-for-all-systems) or a dedicated component (one-for-each-system), (2) the reliability and (3) the spare parts stock levels. We present life cycle cost functions in case of both dedicated and common components. These original life cycle cost functions can only be analyzed numerically. Therefore, we prove that there is a simpler expression that is asymptotically equivalent to the LCC in the regime where downtime of equipment is expensive. These asymptotics allow us to show that commonality ...
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Research Interests: Applied Mathematics, Operations Management, Operations Research, Queueing theory, Markov Decision Process, and 10 moreRepair and Maintenance, Dual Sourcing, Spare parts management, Inventory Management and Control, Business and Management, Maintenance, Inventory Management, Production and Operations Management, Operations, and Expedition
Research Interests: Supply Chain Management, Operations Management, Operations Research, Inventory Control, Markov Decision Process, and 8 moreIndustrial Engineering, Operations research and Optimization, Markov chains, Production and Inventory planning and control systems, Maintenance Engineering and Management, Column Generation, Inventory Management, and Inventory systems
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We consider the canonical periodic review lost sales inventory system with positive lead-times and stochastic i.i.d. demand under the average cost criterion. We introduce a new policy that places orders such that the expected inventory... more
We consider the canonical periodic review lost sales inventory system with positive lead-times and stochastic i.i.d. demand under the average cost criterion. We introduce a new policy that places orders such that the expected inventory level at the time of arrival of an order is at a fixed level and call it the Projected Inventory Level (PIL) policy. We prove that this policy has a cost-rate superior to the equivalent system where excess demand is back-ordered instead of lost and is therefore asymptotically optimal as the cost of losing a sale approaches infinity under mild distributional assumptions. We further show that this policy dominates the constant order policy for any finite lead-time and is therefore asymptotically optimal as the lead-time approaches infinity for the case of exponentially distributed demand per period. Numerical results show this policy also performs superior relative to other policies.
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We consider the inventory control of a single product in one location with two supply sources facing stochastic demand. A premium is paid for each product ordered from the faster ‘emergency ’ supply source. Unsatisfied demand is... more
We consider the inventory control of a single product in one location with two supply sources facing stochastic demand. A premium is paid for each product ordered from the faster ‘emergency ’ supply source. Unsatisfied demand is backordered and ordering decisions are made periodically. The optimal control policy for this system is known to be complex. For this reason we study a type of base-stock policy known as the dual-index policy (DIP) as control mechanism for this inventory system. Under this policy ordering decisions are based on a regular and an emergency inventory position and their corresponding order-up-to-levels. Previous work on this policy assumes deterministic lead times and uses simulation to find the optimal order-up-to levels. We provide an alternate proof for the result that separates the optimization of the DIP in two one-dimensional problems. An insight from this proof allows us to generalize the model to accommodate stochastic regular lead times and provide an a...
Research Interests: Engineering, Stochastic Process, Economics, Supply Chain Management, Optimal Control, and 14 moreOperations Research, Inventory Control, Queueing theory, Global Sourcing, Inventory, Dual Sourcing, Applied Operations Research, Spare parts management, Mathematical Sciences, Inventory Management, Approximation, Markov chain, Indexation, and lead times
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We consider the inventory control of a single product in one location with two supply sources facing stochastic demand. A premium is paid for each product ordered from the faster ‘emergency’ supply source. Unsatisfied demand is... more
We consider the inventory control of a single product in one location with two supply sources facing stochastic demand. A premium is paid for each product ordered from the faster ‘emergency’ supply source. Unsatisfied demand is backordered and ordering decisions are made periodically. The optimal control policy for this type of system is known to be complex. For this reason we study a type of base-stock policy known as the Dual-Index policy as control mechanism for this inventory system. Under this policy ordering decisions are based on a regular and an emergency inventory position and their order-up-to-levels. In each period first an order is placed with the emergency supplier so that the emergency inventory position (onhand stock + outstanding orders arriving within the emergency lead time backorders) meets its order-up-to-level. Next a regular order is placed to raise the regular inventory position (on-hand stock + all outstanding orders backorders) to its order-up-to-level. Prev...
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We consider the canonical periodic review lost sales inventory system with positive lead-times and stochastic i.i.d. demand under the average cost criterion. We introduce a new policy that places orders such that the expected inventory... more
We consider the canonical periodic review lost sales inventory system with positive lead-times and stochastic i.i.d. demand under the average cost criterion. We introduce a new policy that places orders such that the expected inventory level at the time of arrival of an order is at a fixed level and call it the Projected Inventory Level (PIL) policy. We prove that this policy has a cost-rate superior to the equivalent system where excess demand is back-ordered instead of lost and is therefore asymptotically optimal as the cost of losing a sale approaches infinity under mild distributional assumptions. We further show that this policy dominates the constant order policy for any finite lead-time and is therefore asymptotically optimal as the lead-time approaches infinity for the case of exponentially distributed demand per period. Numerical results show this policy also performs superior relative to other policies.
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textabstractThis paper considers the optimization of the base-stock level for the classical periodic review lost-sales inventory system. The optimal policy for this system is not fully understood and computationally expensive to obtain.... more
textabstractThis paper considers the optimization of the base-stock level for the classical periodic review lost-sales inventory system. The optimal policy for this system is not fully understood and computationally expensive to obtain. Base-stock policies for this system are asymptotically optimal as lost-sales costs approach infinity, easy to implement and prevalent in practice. Unfortunately, the state space needed to evaluate a base-stock policy exactly grows exponentially in both the lead time and the base-stock level. We show that the dynamics of this system can be aggregated into a one-dimensional state space description that grows linearly in the base-stock level only by taking a non-traditional view of the dynamics. We provide asymptotics for the transition probabilities within this single dimensional state space and show that these asymptotics have good convergence properties that are independent of the lead time under mild conditions on the demand distribution. Furthermor...
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Rolling stock needs regular maintenance in a maintenance facility. Rolling stock from different fleets needs to be routed to maintenance facilities using interchanges between train lines and possible empty drives. We consider the problem... more
Rolling stock needs regular maintenance in a maintenance facility. Rolling stock from different fleets needs to be routed to maintenance facilities using interchanges between train lines and possible empty drives. We consider the problem of locating maintenance facilities in a railway network under uncertain or changing line planning, fleet planning and other 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 programming 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. We also provide an accelerated Benders decomposition algorithm that uses these insights and bounds obtained from this MIP formul...
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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... more
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.
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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... more
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 the...
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Problem definition: We consider dual sourcing in a distribution network for spare parts consisting of one central warehouse and multiple local warehouses. Each warehouse keeps multiple types of repairable parts to maintain several types... more
Problem definition: We consider dual sourcing in a distribution network for spare parts consisting of one central warehouse and multiple local warehouses. Each warehouse keeps multiple types of repairable parts to maintain several types of capital goods. The repair shop at the central warehouse has two repair options for each repairable part: a regular repair option and an expedited repair option. Irrespective of the repair option, each repairable part uses a certain resource for its repair. In the design of these inventory systems, companies need to decide on stocking levels and expedite thresholds such that total stock investments are minimized while satisfying asset availability and expediting constraints. Academic/practical relevance: Although most companies have the possibility to expedite the repair of parts in short supply, no contributions have been made that incorporate such dynamic expediting policies in repairable investment decisions. Anticipating expediting decisions th...
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Problem definition: We consider dual sourcing in a distribution network for spare parts consisting of one central warehouse and multiple local warehouses. Each warehouse keeps multiple types of repairable parts to maintain several types... more
Problem definition: We consider dual sourcing in a distribution network for spare parts consisting of one central warehouse and multiple local warehouses. Each warehouse keeps multiple types of repairable parts to maintain several types of capital goods. The repair shop at the central warehouse has two repair options for each repairable part: a regular repair option and an expedited repair option. Irrespective of the repair option, each repairable part uses a certain resource for its repair. In the design of these inventory systems, companies need to decide on stocking levels and expedite thresholds such that total stock investments are minimized while satisfying asset availability and expediting constraints. Academic/practical relevance: Although most companies have the possibility to expedite the repair of parts in short supply, no contributions have been made that incorporate such dynamic expediting policies in repairable investment decisions. Anticipating expediting decisions that will be made later leads to substantial reductions in repairable investments. Methodology: We use queueing theory to determine the performance of the central warehouse and subsequently find the performance of all local warehouses using binomial disaggregation. For the optimization problem, we develop a greedy heuristic and a decomposition and column generation based algorithm. Results: Both solution approaches perform very well with average optimality gaps of 2.38 and 0.27%, respectively, across a large test bed of industrial size. The possibility to expedite the repair of failed parts is effective in reducing stock investments with average reductions of 7.94% and even reductions up to 19.61% relative to the state of the art. Managerial implications: Based on a case study at Netherlands Railways, we show how managers can significantly reduce the investment in repairable spare parts when dynamic repair policies are leveraged to prioritize repair of parts whose inventory is critically low.
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This paper presents a column-and-constraint generation algorithm for two-stage stochastic programming problems. A distinctive feature of the algorithm is that it does not assume fixed recourse and as a consequence the values and... more
This paper presents a column-and-constraint generation algorithm for two-stage stochastic programming problems. A distinctive feature of the algorithm is that it does not assume fixed recourse and as a consequence the values and dimensions of the recourse matrix can be uncertain. The proposed algorithm contains multi-cut (partial) Benders decomposition and the deterministic equivalent model as special cases and can be used to trade-off computational speed and memory requirements. The algorithm outperforms multi-cut (partial) Benders decomposition in computational time and the deterministic equivalent model in memory requirements for a maintenance location routing problem. In addition, for instances with a large number of scenarios, the algorithm outperforms the deterministic equivalent model in both computational time and memory requirements. Furthermore, we present an adaptive relative tolerance for instances for which the solution time of the master problem is the bottleneck and t...
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We consider the inventory control of a single product in one location with two supply sources facing stochastic demand. A premium is paid for each product ordered from the faster 'emergency' supply source. Unsatised demand is... more
We consider the inventory control of a single product in one location with two supply sources facing stochastic demand. A premium is paid for each product ordered from the faster 'emergency' supply source. Unsatised demand is backordered and ordering decisions are made periodically. The optimal control policy for this system is known to be complex. For this reason we study a type of base-stock policy known as the dual-index policy (DIP) as control mechanism for this inventory system. Under this policy ordering decisions are based on a regular and an emergency inventory position and their corresponding order-up-to-levels. Previous work on this policy assumes deterministic lead times and uses simulation to nd the optimal order-up-to levels. We provide an alternate proof for the result that separates the optimization of the DIP in two one-dimensional problems. An insight from this proof allows us to generalize the model to accommodate stochastic regular lead times and provide...
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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... more
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.
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We show that the asymptotic hazard rate of the sum of discrete random variables is dominated by the smallest asymptotic failure rate of the summands.
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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... more
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 an average optimality gap of 0.11% and 0.33% respectively. Naive heuristics that ignore demand fluctuations have an 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.
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We consider a single inventory location where multiple types of repairable spare parts are kept to service several different fleets of assets. Demand for each part is modeled by a Markov modulated Poisson process (MMPP). Each fleet has a... more
We consider a single inventory location where multiple types of repairable spare parts are kept to service several different fleets of assets. Demand for each part is modeled by a Markov modulated Poisson process (MMPP). Each fleet has a target for the maximum expected mean number of assets down for lack of a spare part. The inventory manager can meet this target by stocking repairables and by expediting the repair of parts. Expedited repairs have a shorter lead time. There are multiple repair shops (or departments) that handle the repair of parts and the mean number of expedited repairs that can be requested per time unit is constrained per repair shop. A dual-index policy makes stocking and expediting decisions that depend on demand fluctuations for each spare part type. We formulate the above problem as a non-linear non-convex integer programming problem and provide an algorithm based on column generation to compute feasible solutions and tight lower bounds. We show how to use the MMPP to model demand fluctuations in this and other settings, including a moment fitting algorithm. We quantify the value of lead time
flexibility and show that effective use of this flexibility can yield cost reductions of around 25%.
flexibility and show that effective use of this flexibility can yield cost reductions of around 25%.
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We consider the problem of planning preventive maintenance and overhaul for modules that occur 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... more
We consider the problem of planning preventive maintenance and overhaul for modules that occur 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 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 to sum of labor costs, material costs of overhaul, and turn-around-stock investments over the entire life-cycle of the system to be maintained. 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 also be used to aid decision making. We apply the model in a case study.
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Research Interests: Supply Chain Management, Operations Management, Operations Research, Inventory Control, Markov Decision Process, and 8 moreIndustrial Engineering, Operations research and Optimization, Markov chains, Production and Inventory planning and control systems, Maintenance Engineering and Management, Column Generation, Inventory Management, and Inventory systems
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... more
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.
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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... more
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.