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Downtime of industrial assets such as wind turbines and medical imaging devices comes at a sharp cost. To avoid such downtime costs, companies seek to initiate maintenance just before failure. Unfortunately, this is challenging for the following two reasons: On the one hand, because asset failures are notoriously difficult to predict, even in the presence of real-time monitoring devices which signal early degradation. On the other hand, because the available resources to serve a network of geographically dispersed assets are typically limited. In this paper, we propose a novel model referred to as the dynamic traveling maintainer problem with alerts that incorporates these two challenges and we provide three solution approaches on how to dispatch the limited resources. Namely, we propose: (i) Greedy heuristic approaches that rank assets on urgency, proximity and economic risk; (ii) A novel traveling maintainer heuristic approach that optimizes short-term costs; and (iii) A deep reinforcement learning (DRL) approach that optimizes long-term costs. Each approach has different requirements concerning the available alert information. Experiments with small asset networks show that all methods can approximate the optimal policy when given access to complete condition information. For larger networks, the proposed methods yield competitive policies, with DRL consistently achieving the lowest costs.

We consider a system with a finite lifespan and a single critical component that is subject to random failures. An age-based replacement policy is applied to preventively replace the component before its failure. The components used for replacement come from either a weak population or a strong population, referred to as population heterogeneity. However, the true population type is unknown to the decision maker. By considering that the decision maker has a belief on the probability of having a weak population, we build a partially observable Markov decision process model with the objective of minimizing the total cost over the lifespan of the system. The resulting optimal policy updates the belief variable in a Bayesian fashion by using the data obtained over the course of the system lifespan, and it denotes when to execute preventive replacement. It optimally balances the trade-off between the cost of learning the true population type (via deliberately delaying the preventive replacement time to better learn the population type) and the cost of maintenance activities. By addressing this so-called exploration-exploitation trade-off, we generate insights on the optimal policy and compare its performance with existing heuristic approaches from the literature. We also characterize a lower bound to the optimal cost, allowing us to determine the value of resolving the uncertainty on the population type.

This paper considers the problem of scheduling automated guided vehicles (AGVs) with battery constraints. Each transport request involves a soft time window, and the AGV fleet used to service those requests is heterogeneous with a diverse set of capabilities and travel costs. In contrast to the existing literature, each transport request may require different AGV material handling capabilities (such as lift loads, tow loads, or handle loads with a mounted robot arm), and the AGV batteries can be recharged partially under consideration of a critical battery threshold. The problem is to assign the transport and charging requests to AGVs, sequence the requests, and determine their starting times and the recharging durations of the AGVs with the objective of minimizing a weighted sum of the tardiness costs of transport requests and travel costs of AGVs. A mixed-integer linear programming model is formulated. We also propose a new matheuristic that makes use of an adaptive large neighborhood search algorithm and a linear program to solve industry-size instances. We illustrate the efficacy of our approach with an industry case study using real-world data.

We study the inspection scheduling decisions for a production process that goes through a hidden defective state before its failure. The production process is equipped with a predictive model, generating alert and no-alert signals. An alert signal indicates that production process is in the defective state, while a no-alert signal indicates it is in the healthy state. The signals are imperfect, meaning that an alert signal can be generated for a healthy process and a no-alert signal can be generated for a defective process. Only a costly inspection can detect the true condition. We introduce a new inspection policy, which generalizes the age-based inspection policy that performs planned inspections at predetermined intervals, by considering that an inspection can also be triggered by a certain number of alerts from the predictive model. To optimize the proposed inspection policy, a stochastic dynamic programming model is formulated with the objective of minimizing the long-run expected cost rate. The performance improvement achieved by the optimal policy is quantified by comparing it to practically relevant benchmark policies. Numerical experiments with a set of realistic problem instances show that adding alert-triggered inspections to traditional age-based inspection scheduling brings up to 44% reduction in the expected cost rate when the predictive model is sufficiently accurate. Characterizing the performance of the optimal policy at a given level of imperfectness is especially useful in practice as it allows making an assessment on how much can be invested to justify a certain level of improvement in the predictive model.

We study how web publishers should set their floor prices in order to maximize expected revenues when they have access to two selling mechanisms, namely an ad exchange and header bidding, in order to sell impressions on the real-time bidding market. We consider the publisher’s problem under incomplete information, propose bandit-type algorithms, and show that their regret – the performance loss compared to the optimal algorithm – is sub-linear in the time horizon. Experiments illustrate the effectiveness of our algorithms.

Stochastic simulation is a commonly used tool by practitioners for evaluating the performance of inventory policies. A typical inventory simulation starts with the determination of the best-fit input models (e.g. probability distribution function of the demand random variable) and then obtains a performance measure estimate under these input models. However, this sequential approach ignores the uncertainty around the input models, leading to inaccurate performance measures, especially when there is limited historical input data. In this paper, we take an alternative approach and propose a simulation replication algorithm that jointly estimates the input models and the performance measure, leading to a credible interval for the performance measure under input-model uncertainty. Our approach builds on a nonparametric Bayesian input model and frees the inventory manager from making any restrictive assumptions on the functional form of the input models. Focusing on a single-product inventory simulation, we show that the proposed method improves the estimation of the service levels when compared to the traditional practice of using the best-fit or the empirical distribution as the unknown demand distribution.

This paper provides a risk assessment framework to select shipping lanes for pharmaceutical products. The main categories of risks are determined through an algorithm based on yes/no decisions. Then, according to the risk categories, a Failure Mode and Effects Analysis (FMEA) table is proposed for risk assessment of pharmaceutical product shipments and logistics. The evaluations are based on Intuitionistic Fuzzy Numbers (IFNs) to be able to account for the uncertainty in the experts’ judgments. By using an intuitionistic fuzzy hybrid TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach, the evaluated risks of each shipment lane can be scored and prioritized. The proposed TOPSIS-based FMEA approach in the intuitionistic fuzzy environment provides an opportunity to aggregate the risk assessments of different experts in a practically efficient way. Different from the earlier literature, we address risk identification and risk assessment under uncertainty as the two key challenges in group decision making. Our method further provides a framework that integrates the categorization and evaluation of risks with subsequent decision making. A case study of shipping lane selection in the context of air cargo distribution of pharmaceutical products demonstrates a potential implementation of the proposed approach.

Stochastic simulation is an invaluable tool for operations-research practitioners for the performance evaluation of systems with random behavior and mathematically intractable performance measures. An important step in the development of a simulation model is input modeling, which is the selection of appropriate probability models that characterize the stochastic behavior of the system inputs. For example, in a queueing-system simulation, input modeling includes choosing the probability distributions for stochastic interarrival and service times. The lack of knowledge about the true input models is an important practical challenge. The impact of the lack of information about the true input model on the simulation output is referred to as ‘input uncertainty’ in the simulation literature. Ignoring input uncertainty often leads to poor estimates of the system performance, especially when there is limited amount of historical data to make inference on the input models. Therefore, it is critically important to assess the impact of input uncertainty on the estimated performance measures in a statistically valid and computationally efficient way. The goal of this paper is to present input uncertainty research in stochastic simulations by providing a classification of major research streams and focusing on the new developments in recent years. We also review application papers that investigate the value of representing input uncertainty in the simulation of real-world stochastic systems in various industries. We provide a self-contained presentation of the major research streams with a special attention on the new developments in the last couple of years.

We provide a new method for solving a very general model of an assemble-to-order system: multiple products, multiple components that may be demanded in different quantities by different products, batch production, random lead times, and lost sales, modeled as a Markov decision process under the discounted cost criterion. A control policy specifies when a batch of components should be produced and whether an arriving demand for each product should be satisfied. As optimal solutions for our model are computationally intractable for even moderately sized systems, we approximate the optimal cost function by reformulating it on an aggregate state space and restricting each aggregate state to be represented by its extreme original states. Our aggregation drastically reduces the value iteration computational burden. We derive an upper bound on the distance between aggregate and optimal solutions. This guarantees that the value iteration algorithm for the original problem initialized with the aggregate solution converges to the optimal solution. We also establish the optimality of a lattice-dependent base-stock and rationing policy in the aggregate problem when certain product and component characteristics are incorporated into the aggregation/disaggregation schemes. This enables us to further alleviate the value iteration computational burden in the aggregate problem by eliminating suboptimal actions. Leveraging all of our results, we can solve the aggregate problem for systems of up to 22 components, with an average distance of 11.09% from the optimal cost in systems of up to 4 components (for which we could solve the original problem to optimality). The e-companion is available at https://doi.org/10.1287/opre.2017.1710 .

Most of the literature on inventory management assumes that the demand distribution and the values of its parameters are known with certainty. In this paper, we consider a repeated newsvendor setting where this is not the case and study the problem of setting inventory targets when there is a limited amount of historical demand data. Consequently, we achieve the following objectives: (1) to quantify the inaccuracy in the inventory-target estimation as a function of the length of the historical demand data, the critical fractile, and the shape parameters of the demand distribution; and (2) to determine the inventory target that minimizes the expected cost and accounts for the uncertainty around the demand parameters estimated from limited historical data. We achieve these objectives by using the concept of expected total operating cost and representing the demand distribution with the highly flexible Johnson translation system. Our procedures require no restrictive assumptions about the first four moments of the demand random variables, and they can be easily implemented in practical settings with reduced expected total operating costs.