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Tandem queueing networks are widely used to model systems where services are provided in sequential stages. In this study, we assume that each station in the tandem system operates under a general renewal process. Additionally, we assume that the arrival process for the first station is governed by a general renewal process, which implies that arrivals at subsequent stations will likely deviate from

This paper explores optimal insurance solutions based on the Lambda-Value-at-Risk (ΛVaR). Using the expected value premium principle, we first analyze a stop-loss indemnity and provide a closed-form expression for the deductible parameter. A necessary and sufficient condition for the existence of a positive and finite deductible is also established. We then generalize the stop-loss indemnity and show

In the present paper, we investigate the two-stage hybrid flowshop with uncertain processing time. The true probability distribution of the processing time is unknown, but the statistical features can be extracted from historical data, such as the mean, lower and upper bounds. To obtain the exact scheduling result, a distributionally robust optimization (DRO) model is built to minimize the worst-case

Bayes’ theorem incorporates distinct types of information through the likelihood and prior. Direct observations of state variables enter the likelihood and modify posterior probabilities through consistent updating. Information in terms of expected values of state variables modify posterior probabilities by constraining prior probabilities to be consistent with the information. Constraints on the prior

This work focuses on planning an intra-city express system in a practical environment. Various operation characteristics, such as vehicle capacity, hub capacity, time windows, and stochastic demands, have been considered. Therefore, we introduce a capacitated hub location routing problem with time windows and stochastic demand and formulate it using a multi-stage recourse model. In this model, long-term

Existing studies have not focused on the infeasibility of super-efficiency models under the weak disposability and null-jointness (WDJ) assumptions, despite the wide adoption of these two conditions in fields where undesirable outputs exist, like the evaluation of energy and environmental efficiency. This paper employs a directional distance function (DDF) approach to investigate super-efficiency feasibility

Chi-square tests are often employed to examine the association of categorical variables, the homogeneity of proportions between two or more samples, and the goodness-of-fit for a specified distribution. To account for the complex design of survey data, variants of chi-square tests as well as software packages that implement these tests have been developed. Nevertheless, from a survey practitioner’s

This paper delves into the concept of moral hazard in data envelopment analysis (DEA) benchmarking. The moral hazard issue emerges when decision-making units (DMUs) conceal their actions in the application of best practices, driven by the costs involved and the possibility of incomplete reimbursement. This issue remains unexplored in DEA benchmarking because previous studies assume that applying best

Motivated by semiconductor applications, where wafer lots are grouped into families and processed on batch machines, this paper addresses a generalized unrelated parallel-batch scheduling problem. The goal is to minimize total completion time (flow time) while considering family- and machine-dependent setup times. We propose a mixed-integer programming formulation, establish a necessary condition for

Graphon games have been introduced to study games with many players who interact through a weighted graph of interaction. By passing to the limit, a game with a continuum of players is obtained, in which the interactions are through a graphon. In this paper, we focus on a graphon game for optimal investment under relative performance criteria, and we propose a deep learning method. The method builds

Optimizing productivity in manufacturing is crucial for increasing output and reducing costs; however, it can also negatively impact product quality and accelerate system degradation. This study is the first to propose a method for dynamically adjusting productivity while considering both system degradation and product quality. We construct a dynamic programming model using optimal control theory to

This paper extends the two-echelon vehicle routing problem (2E-VRP) by considering multiple commodities, multiple depots, and mobile satellites (i.e., the so-called 3M-2E-VRP). This problem also accommodates flexible last-mile delivery strategies by allowing direct deliveries via first-echelon vehicles (mobile satellites) and indirect deliveries through goods exchanges at meeting points, such as parking

This paper explores the extension of a modelling framework that integrates data envelopment analysis (DEA) and markov systems, into a two-stage setting. In a recent paper in EJOR, a single-stage DEA-markov hybrid model was introduced, establishing a research direction blending these seemingly distinct approaches to address the attainability problem in workforce planning. Markov systems are widely used

In group decision-making (GDM), conflicts often arise, requiring decision-makers (DMs) to adjust their opinions. Variations in DMs’ backgrounds, expertise, and dynamic environmental interactions shape their psychological states, consequently affecting their information-processing strategies and potentially contributing to information cocoon effects. This study aims to develop a conflict-elimination

In Finland, colorectal cancer (CRC) incidence rates have steadily increased over the last decades and as of 2020, CRC is the second most common cancer in both males and females. CRC is a crucial concern for the public health of Finland, highlighted by the recent implementation of a national population screening program. In this paper, we optimize the screening test positivity cut-off levels and the

The resource-constrained project scheduling problem is one of the most investigated problems in the project scheduling literature, and has a rich history. This article provides a perspective on this challenging scheduling problem, without having the ambition to provide a complete overview. Instead, the article does aim to summarize a number of reasons why this problem has been so intensely investigated

This paper derives the optimal capital structure of a firm whose earnings follow a geometric Brownian motion with a lower reflecting barrier. The barrier can be interpreted as a market intervention threshold (e.g., a price floor) by the government or an exit threshold of weak competitors in the market. Unlike in the standard model with no barrier, the firm is able to issue riskless debt to a certain

This study addresses the NP-hard reliable path problem, which seeks the path with minimum travel cost in correlated road networks, formulated as mean-variance (m-v) and mean-standard deviation (m-s) shortest path problems. This study proposes a novel approach that transforms these nonlinear binary integer programming models into standard linear programming (LP) problems using structure-preserving linearization

The growing prevalence of electric vehicles (EVs) requires efficient charging management strategies to tackle the challenges associated with their integration into the power grid. This requirement is particularly true for Charging-as-a-Service (CaaS) providers, who manage charging services for fleet operators in exchange for a fixed service fee. Incorporating uncertainty into optimization models for

In this work, we introduce a two-dimensional cutting stock problem with flexible length and usable leftovers, in which multiple objectives, including minimizing the waste area of material, the exceeding area of orders and the number of slitter adjustments, are considered simultaneously. This problem is inspired by a real-world made-to-order manufacturer of special steel plates. We propose a non-linear

Simulation-based ranking and selection (R&S) is a popular technique for optimizing discrete-event systems (DESs). It evaluates the mean performance of system designs by simulation outputs and aims to identify the best system design from a set of alternatives by intelligently allocating a limited simulation budget. In R&S, the optimal computing budget allocation (OCBA) is an efficient budget allocation

This study explores the integration of a representative large language model, ChatGPT, into lending decision-making with a focus on credit default prediction. Specifically, we use ChatGPT to analyse and interpret loan assessments written by loan officers and generate refined versions of these texts. Our comparative analysis reveals significant differences between generative artificial intelligence

This work first presents a stochastic shelter location and evacuation planning problem with considering road capacity improvement strategies, in which the fixed setup cost of shelters and the improvement cost of road capacity are subject to a budget limit. To explicitly capture the impact of traffic volumes and road capacity improvement decisions on evacuation time, the Bureau of Public Roads function

This study introduces a hub network design problem that considers three key factors: congestion, demand uncertainty, and multi-periodicity. Unlike classical models, which tend to address these factors separately, our model considers them simultaneously, providing a more realistic representation of hub network design challenges. Our model also incorporates service level considerations of network users

This paper investigates a scheduling problem characterized by uncertain task durations and maximum time lags, a combination that has received little attention in the literature. The problem involves a set of jobs, each comprising a sequence of tasks where the penultimate task has uncertain duration, known only within a given range, and the final tasks are identical across all jobs. There must be no

In this paper, we study different solution methods for two two-stage robust, multi-machine scheduling problems, namely permutation flow-shop and job-shop scheduling problems under uncertainty budget. Compact formulations of the problems are proposed and two decomposition approaches are presented: a Benders decomposition approach and a column and constraint generation approach. Computational experiments

The standard mathematical approach to fourth-down decision-making in American football is to make the decision that maximizes estimated win probability. Win probability estimates arise from machine learning models fit from historical data. These models attempt to capture a nuanced relationship between a noisy binary outcome variable and game-state variables replete with interactions and non-linearities

This work presents a rescue network design problem involving uncertainty and deprivation cost, in which decisions on pumping station setup and drainage truck location and allocation are considered simultaneously. We formulate the problem as a two-stage nonlinear stochastic programming model that is difficult to solve directly because the objective function contains a nonlinear convex deprivation cost

Elicitable functionals and (strictly) consistent scoring functions are of interest due to their utility of determining (uniquely) optimal forecasts, and thus the ability to effectively backtest predictions. However, in practice, assuming that a distribution is correctly specified is too strong a belief to reliably hold. To remediate this, we incorporate a notion of statistical robustness into the framework

We consider the periodic interaction between a leader and a follower in the context of network interdiction where, in each period, the leader first blocks (momentarily) passage through a subset of arcs in a network, and then the follower traverses the shortest path in the interdicted network. We assume that arc costs are stochastic and that while their underlying distribution is known to the follower

We consider financial networks where agents are linked to each other via mutual liabilities. In case of bankruptcy, one needs to distribute the assets of bankrupt agents over the other agents. One common approach is to first apply pairwise netting of mutual liabilities and next use the proportional rule to determine the payments based on the net liabilities. We refer to this as the pairwise netting

Robust portfolio optimization models are crucial for mitigating the impact of significant forecasting errors on expected asset returns. However, despite their significance, existing approaches often overlook a fundamental characteristic of financial markets: the absence of arbitrage opportunities. This paper presents a novel portfolio optimization model that integrates the classical mean–variance approach

This paper presents work towards a systematic mapping of the consequences of an incident at a public-use gas station, informing a subsequent decision regarding which gas stations to close in a given geographical zone in times of heightened risk. A criteria hierarchy of economic, environmental and social sustainability impacts is proposed. A scoring method over the set of sustainability criteria is

Decision making is the process of resolving conflict between different options that vary in discriminability. We study how conflict between the goals of profit maximisation and customer satisfaction determines decision making in the newsvendor problem. The paper consists of three studies which explore conflict from different positions. In Study 1 we show that stockouts cause newsvendor subjects to

Building sustainability composite indicators is a complex process that has been addressed according to different strategies. One interesting approach is based on the compromise between the maximum aggregate solution and the most balanced solution, by considering the most displaced indicator regarding the ideal. However, some shortcomings were identified in this approach. First, several decision-making

Delivery couriers increasingly demand working in a flexible arrangement. Flexible contracts can be cost-effective from the perspective of a delivery company, but may also cripple its ability to serve all customers in time as couriers are not always available. To alleviate the complexities caused by courier-related constraints, delivery companies usually operate with a work schedule consisting of multiple

With the rapid development of information technology, there is an increasing amount of available data that can reflect a borrower’s creditworthiness, providing new avenues for credit scoring innovations. However, such data is commonly distributed across companies in various industries, and how to take advantage of multi-party collaboration while protecting customer data privacy is a major challenge

We propose the REORIENT (REnewable resOuRce Investment for the ENergy Transition) model for energy systems planning with the following novelties: (1) integrating capacity expansion, retrofit and abandonment planning, and (2) using multi-horizon stochastic mixed-integer linear programming with multi-timescale uncertainty. We apply the model to the European energy system considering: (a) investment in

In future power systems dominated by intermittent renewable generation, nuclear power plants will increasingly need to follow uncertain loads. However, regulatory and technical constraints limit the frequency of load-following operations, making their efficient allocation crucial. This paper explores the economic value of nuclear flexibility by modeling it as a stock constraint within a stochastic

The problem of scheduling conflicting jobs on parallel machines consists in assigning a set of jobs to a set of machines so that no two conflicting jobs are allocated to the same machine, and the maximum processing time among all machines is minimized. We propose a new compact mixed integer linear formulation based on the representatives model for the vertex coloring problem, which overcomes a number

Motivated by the application of large models in artificial intelligence (AI), this paper proposes a new business model for AI-driven data product transactions in the freight market. We develop a game-theoretic model for the logistics data supply chain comprising a logistics data provider and a logistics data integrator. Observing the opportunity for the logistics data provider to directly sell AI-driven

In many applications, risk-averse decision-making is crucial. In this context, the mean–variance (MV) criterion is widely accepted and often used to find the right balance between maximizing expected rewards and avoiding poor performances. In dynamic settings, however, it is challenging to efficiently compute policies under the MV objective and hence, surrogates like the exponential utility model are

This paper introduces a volleyball point-by-point win probability model that updates the probability of winning a set after each play in the set. The covariate informed product partition model (PPMx) is well suited to flexibly include in-set team performance information when making predictions. However, making predictions in real time would be too expensive computationally as it would require refitting

It is common for multiple firms—such as manufacturers, retailers, and third-party insurers—to coexist and compete in the aftermarket for durable products. In this paper, we study price competition in a partially concentrated aftermarket where one firm offers multiple extended warranty (EW) contracts while the others offer a single one. The demand for EWs is described by the multinomial logit model

Tom Alby. Boca Raton, FL: Chapman & Hall/CRC Press, 2024, xvi + 301 pp., $200.00(H), ISBN: 978-1-032-50524-4.This book is a comprehensive introduction to data science, with a focus on how it is use...

Pedro J. Aphalo. Boca Raton, FL: Chapman & Hall/CRC Press, 2024, xvii + 447 pp., $220.00(H), ISBN: 978-1-032-51843-5.R programming has become an essential tool for data analysis and statistical com...

Inspired by the classical cumulative prospect theory (CPT), we propose a CPT-like functional characterized by the modeling of uncertainty on gains and losses through two epsilon-contaminations of a reference probability measure. Such functional is used to perform a dynamic portfolio selection in a finite horizon binomial market model, reducing it to an iterative search problem over the set of optimal

Traditional assembly lines have become less efficient due to increasing customization and changing demand (e.g., the trend in e-vehicles). Matrix assembly systems, in which automated guided vehicles move products between the workstations laid out on a grid, are gaining popularity. One advantage of such systems is that they are easier to reconfigure compared to traditional assembly lines. In this work

In this paper, we propose a Support Vector Machine (SVM) method for multi-class classification. It follows multi-objective multi-class SVM (MMSVM), which maximizes class-pair margins on a multi-class linear classifier. The proposed method, called reciprocal-geometric-margin-norm SVM (RGMNSVM) is derived by applying the ?p-norm scalarization and convex approximation to MMSVM. Additionally, we develop

In human–machine workplaces, employee skills change over time due to learning and forgetting effects, which greatly affects efficiency. We model these effects within a simple production system. When a job is processed, due to learning the next job of the same type requires less processing time. Meanwhile, jobs of other types are forgotten, hence their future processing times increase. In our work,

Recently, the International Conference on Harmonisation finalized an estimand framework for randomized trials that was adopted by regulatory bodies worldwide. The framework introduced five strategies for handling post-randomization events; namely the treatment policy, composite variable, while on treatment, hypothetical and principal stratum estimands. We describe an illustrative example to elucidate

A control group versus treatment group design is considered where the responses in the treatment group are modeled as a two-component mixture model that accounts for the possibility that only a fraction of the patients in the treated group will respond to the treatment. In this setting, the treatment effect is generalized to include both the fraction of treated patients that respond to the treatment

Consider a study whose primary results are “not statistically significant”. How often does it lead to the following published conclusion that “there is no effect of the treatment/exposure on the outcome”? We believe too often and that the requirement to report counternull values could help to avoid this! In statistical parlance, the null value of an estimand is a value that is distinguished in some

Foundations of Data Science with Python, by John M. Shea, provides a comprehensive and modern introduction of data science. The book illustrates different aspects of working with data computational...

This book is available in hardcover and as downloadable chapters on the internet. The author states he wants “to provide you with the tools to both select and create graphs that present data as cle...

Machine learning has become an important tool in scientific research and industry, driven in part by the availability of user-friendly software for model development. While most of the notable soft...

In this paper, we develop a new general class of discrete bivariate distributions that can model the effect of the so-called ‘load-sharing configuration’. Under such load-sharing configuration, after the failure of one component, the surviving component has to shoulder extra load, which eventually results in its failure at an earlier time than what is expected under the case of independence. To model

Probability surveys are a major source of population representative data for policy research and program evaluation. However, the data come with the added complications of being observational and selected with unequal probabilities. Propensity score adjustments have become increasingly popular for inferring causal relationships in non-randomized studies, but when using survey data, estimates of the

Accurate model performance assessment in survival analysis is imperative for robust predictions and informed decision-making. Traditional residual diagnostic tools like martingale and deviance residuals lack a well-characterized reference distribution for censored regression, making numerical statistical tests based on these residuals challenging. Recently, the introduction of Z-residuals for diagnosing