On multi-stage production/inventory systems under stochastic demand

This paper was presented at the 1992 Conference of the International Society of Inventory Research in Budapest, as a tribute to professor Andrew C. Clark for his inspiring work on multi-echelon inventory models both in theory and practice. It reviews and extends the work of the authors on periodic review serial and convergent multi-echelon systems under stochastic stationary demand. In particular, we highlight the structure of echelon cost functions which play a central role in the derivation of the decomposition results and the optimality of base stock policies. The resulting optimal base stock policy is then compared with an MRP system in terms of cost effectiveness, given a predefined target customer service level. Another extension concerns an at first glance rather different problem; it is shown that the problem of setting safety leadtimes in a multi-stage production-to-order system with stochastic lead times leads to similar decomposition structures as those derived for multi-stage inventory systems. Finally, a discussion on possible extensions to capacitated models, models with uncertainty in both demand and production lead time as well as models with an aborescent structure concludes the paper

Joint Inventory and Scheduling Control in a Repair Facility

We study inventory and repair scheduling decisions of a maintenance service provider for repairable capital goods. Due to high downtime costs, the service provider keeps spare parts on stock to replace broken parts quickly. The service provider should determine the inventory level of spare parts for each component and the repair scheduling policy. Furthermore, in case of a stock-out, the service provider should decide whether to backorder the demand or execute an emergency repair, which is an urgent but expensive repair operation for abroken part followed by a fast form of installation. The objective is to minimize the long-run average inventory holding, backorder, and emergency repair costs. We formulate the repairable network as a closed queueing system and consider an asymptotic regime in which the repair facility is in the conventional heavy-traffic regime. Then, we formulate and solve a Brownian control problem (BCP). From the optimal BCP solution, we derive a simple and intuitive decision rule stating if the emergency repairs are necessary to achieve a close-to-optimal system performance. Moreover, we propose a simple, intuitive, and easy-to-implement heuristic control policy and demonstrate its close-to-optimal performance via numerical experiments

Optimal data pooling for shared learning in maintenance operations

This paper addresses the benefits of pooling data for shared learning in maintenance operations. We consider a set of systems subject to Poisson degradation that are coupled through an a-priori unknown rate. Decision problems involving these systems are high-dimensional Markov decision processes (MDPs). We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. We leverage this decomposition to demonstrate that pooling data can lead to significant cost reductions compared to not pooling

Improving Ambulance Dispatching with Machine Learning and Simulation

As an industry where performance improvements can save lives, but resources are often scarce, emergency medical services (EMS) providers continuously look for ways to deploy available resources more efficiently. In this paper, we report a case study executed at a Dutch EMS region to improve ambulance dispatching. We first capture the way in which dispatch human agents currently make decisions on which ambulance to dispatch to a request. We build a decision tree based on historical data to learn human agents’ dispatch decisions. Then, insights from the fitted decision tree are used to enrich the commonly assumed closest-idle dispatch policy. Subsequently, we use the captured dispatch policy as input to a discrete event simulation to investigate two enhancements to current practices and evaluate their performance relative to the current policy. Our results show that complementing the current dispatch policy with redispatching and reevaluation policies yields an improvement of the on-time performance of highly urgent ambulance requests of 0.77% points. The performance gain is significant, which is equivalent to adding additional seven weekly ambulance shifts.</p

Optimal data pooling for shared learning in maintenance operations

We study optimal data pooling for shared learning in two common maintenance operations: condition-based maintenance and spare parts management. We consider systems subject to Poisson input – the degradation or demand process – that are coupled through an unknown rate. Decision problems for these systems are high-dimensional Markov decision processes (MDPs) and are thus notoriously difficult to solve. We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. Leveraging this decomposition, we (i) show that pooling data can lead to significant cost reductions compared to not pooling, and (ii) prove that the optimal policy for the condition-based maintenance problem is a control limit policy, while for the spare parts management problem, it is an order-up-to level policy, both dependent on the pooled data

Optimal data pooling for shared learning in maintenance operations

We study optimal data pooling for shared learning in two common maintenance operations: condition-based maintenance and spare parts management. We consider systems subject to Poisson input – the degradation or demand process – that are coupled through an unknown rate. Decision problems for these systems are high-dimensional Markov decision processes (MDPs) and are thus notoriously difficult to solve. We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. Leveraging this decomposition, we (i) show that pooling data can lead to significant cost reductions compared to not pooling, and (ii) prove that the optimal policy for the condition-based maintenance problem is a control limit policy, while for the spare parts management problem, it is an order-up-to level policy, both dependent on the pooled data

Integrated Planning of Usage-Based Maintenance and Load Sharing Under Resource Dependence

In many systems, functionally interchangeable units are used together to meet a common demand or production target. Such examples include parallel machines in production facilities, engines of a vessel, and fleets of ships, airplanes, or trucks. These units typically receive large-scale maintenance dependent on their usage (such as overhauls) and therefore, the timing of their maintenance is directly affected by the policy that determines how the total demand is allocated to the units. We assume that there is a limit on how many units can get maintenance simultaneously because of the limited resources that are involved (e.g., a dry-dock, hangar, or specialized workforce) and/or because the demand needs to be met at all times. In this study, the problem of integrated planning of usage-based maintenance and load sharing (i.e., the allocation of total demand to different units) for multi-unit systems is mathematically analyzed. Also, a mathematical model is built to minimize the total maintenance costs during thefinite lifetime of the units (which is generally 10 to 40 years). An asymptotically near-optimal policy is proposed, and its performance is compared with the performance of the optimal policy

Simulation Supported Bayesian Network Approach for Performance Assessment of Infrastructure Systems

We present a simulation supported Bayesian Network modeling approach to evaluate the performance of bridge networks with respect to both infrastructure owner's cost and users' travel time based on bridge level maintenance decisions. By combining system decomposition, simulation and Bayesian Networkm (BN) modelling, our approach enables the construction of a BN model of bridge networks where probabilistic information resulting from simulation are used to populate the conditional probability tables. Our approach is therefore useful when access to actual conditions of bridges and their monitoring is difficult, and the conditional dependencies accross different networks elements are not easily quantifiable. Once built, the BN can be used by infrastructure managers as a scenario analysis tool to assess how maintenance decisions on individual bridges affect maintenance costs and travel time for the whole network. The approach is presented on a small-scale bridge network for demonstration purposes

Integrated Planning of Usage-Based Maintenance and Load Sharing Under Resource Dependence

In many systems, functionally interchangeable units are used together to meet a common demand or production target. Such examples include parallel machines in production facilities, engines of a vessel, and fleets of ships, airplanes, or trucks. These units typically receive large-scale maintenance dependent on their usage (such as overhauls) and therefore, the timing of their maintenance is directly affected by the policy that determines how the total demand is allocated to the units. We assume that there is a limit on how many units can get maintenance simultaneously because of the limited resources that are involved (e.g., a dry-dock, hangar, or specialized workforce) and/or because the demand needs to be met at all times. In this study, the problem of integrated planning of usage-based maintenance and load sharing (i.e., the allocation of total demand to different units) for multi-unit systems is mathematically analyzed. Also, a mathematical model is built to minimize the total maintenance costs during thefinite lifetime of the units (which is generally 10 to 40 years). An asymptotically near-optimal policy is proposed, and its performance is compared with the performance of the optimal policy

Integrated planning of asset-use and dry-docking for a fleet of maritime assets

In maritime industry, moving assets (e.g., naval ships, dredgers, pilot vessels) are subject to obligatory inspections based on calendar time. These inspections consist of exhaustive operations that need the assets to be towed into specialized facilities referred to as dry-docks. In addition, there are maintenance operations needed as a result of usage-related deterioration of the assets, also requiring the assets to be dry-docked. In practice, a common approach for a fleet of assets is to synchronize these inspection and maintenance operations to avoid unnecessary dry-dockings. However, when and how these operations, some of which are calendar-based and some of which are usage-based, should be synchronized, and whether synchronizing them is always optimal remain as important questions. Since how an asset is used influences when it requires maintenance, answering these questions requires solving an integrated planning problem that combines the planning of asset-use and the planning of dry-docking. Operational constraints such as the locations of assets, limited dry-docking capacity, and the requirement to meet the demand for asset-use in each location make the problem even more challenging. This real-life problem is formulated as a mixed integer linear programming model which minimizes the total discounted cost for a finite time horizon and ensures the full satisfaction of the demand in every time period. The resulting optimal policy is compared with a sequential planning approach to quantify the economic benefit of integrated planning for asset-use and dry-docking. Additionally, two alternative planning approaches are presented for large problem instances. Results of the numerical analysis show that integrated planning can save up to 28.5% of the total cost