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the current status of applied clinical prediction modeling is poor. many models are developed with suboptimal methods and are not evaluated, and hence have little impact on clinical care. we review 12 challenges—provocatively labeled enemies—that jeopardize the creation of prediction models that make it to clinical practice to improve treatment decisions and clinical outcomes for individual patients

we focus on a behavioral model that has been recently proposed in the literature, whose rationale can be traced back to the half-full/half-empty glass metaphor. more precisely, we generalize the half-full/half-empty approach to the context of positive and negative lotteries and give financial and behavioral interpretations of the half-full/half-empty parameters. we develop a portfolio selection model

the solution algorithms of constrained markov decision process (cmdp), a widely adopted model for sequential decision-making, have been intensively studied in the literature. despite increasing effort, the linear programming (lp) formulation of cmdp remains the dominant exact method that leads to the optimal solution without constraint violations. however, the lp formulation is computationally inefficient

with increased data collection, the need to fuse data sources has emerged as an important and rapidly growing research activity in the statistical community. in considering spatial and spatio-temporal datasets to examine complex environmental and ecological processes of interest, we often have multiple sources that are jointly informative about features of interest of the processes. model-based data

this paper considers contextual ranking and selection problems where the objective is to identify the best design under every possible context. we assume the mean performance of each alternative design to be a quadratic function across a continuous context space. by judiciously pre-selecting a finite set of contexts for sampling and leveraging this quadratic model structure, we develop an efficient

disasters, such as the covid-19 pandemic, cause critical supply shortages and require the immediate injection of additional resources. governments often set aside capacity for use during crises or opt to rely on third parties, such as the private sector. both options, however, may not be viable, as the former is expensive and the latter is not reliable. to overcome this dilemma, we introduce a strategic

as industrial activities contribute significantly to global co2 emissions, firms face increasing pressure to adopt low-emission clean technologies. this paper investigates optimal strategies for firms to partially retrofit existing technologies and adjust production under various carbon policies, including scenarios without emission regulation, carbon taxation, and cap-and-trade systems. we propose

given that manufacturers increasingly enter the sharing economy market via equity investment in third-party sharing platforms, this paper establishes a game-theoretical model to characterize such partial vertical ownership (pvo) cooperation. we consider a manufacturer (m) interacting with a retailer (r) and sharing economy platform (sep), where m first negotiates a transfer payment to sep for their

computing cliques in an undirected graph g=(vg,eg) is a fundamental problem in social network analysis. however, in some cases, the strict definition of a clique (a subset of vertices pairwise adjacent in g) often limits its applicability in real-world settings. to address this issue, we study the s-club: a clique relaxation that induces a subgraph of diameter at most s. note that a clique is simply

we establish and investigate a theoretical framework for controlling covariance matrices in the method of antithetic variates through control variates to further reduce estimator variance. instead of preemptively and carefully designing an estimator vector with negatively correlated components, the proposed framework starts with a predefined estimator vector that incorporates specified control variates

a single-period stochastic insurance duopoly is formulated to examine the pre-assignment of roles to the insurance game’s players. this paper considers two information structures. in the first structure, one insurer assumes the role of the stackelberg leader by setting the premium first, while the competitor, acting as the stackelberg follower, responds after observing the leader’s premium. in the

scattered storage is a common operational approach in business-to-consumer online retailing, where each stock keeping unit (sku) is stored at multiple locations within the warehouse. this approach aims to increase the likelihood of having items per sku nearby consistently, thereby reducing unproductive walking time during order picking. however, reaping these benefits necessitates additional decision-making

we study the joint collection and distribution of less-than-truckload freight in a multi-depot setting. linehauls between the depots allow for an increased freight consolidation, saving cost and greenhouse gas emissions. however, to realize these benefits, the linehauls must be coordinated with daily pickup-and-delivery routes from the depots to collect and distribute products. the routes are subject

the electrification of the transportation sector is a key component of the global transition to green energy. as european countries increasingly adopt electric vehicles (evs), they are in the process of rapidly deploying ev charging stations (css) to meet ever-increasing demand. however, the current approach to cs deployment is highly reactive and typically requires a manual case-by-case response from

consumer returns are common in live streaming selling, and metaverse technology is gradually advocated to solve the issue of consumer returns since this technology can significantly enhance the live streaming shopping experience. we consider a supply chain composed of a manufacturer and a retailer with two live streaming scenarios: ml and rl. in scenario ml (rl), the manufacturer (retailer) invites

reliability demonstration testing (rdt) has been extensively employed to verify whether a product meets specific reliability requirements at a desired confidence level. driven by intense market competition and constrained test resources, manufacturers are motivated to seek effective strategies to reduce the testing efforts required for rdt. in this paper, we propose a method that utilizes existing

governments increasingly rely on industry capability to assess the feasibility of implementing stricter environmental regulations. rather than passively awaiting their imposition, this trend incentivizes firms to shape future regulations through green activities. to investigate how firms with different cost structures and market power strategically adopt and invest in green technology under uncertain

with increasing freight demands for inner-city transport, shifting freight from road to scheduled line services such as buses, metros, trams, and barges is a sustainable solution. public authorities typically impose economic policies, including road taxes and subsidies for scheduled line services, to achieve this modal shift. this study models such a policy using a bilevel approach: at the upper level

with the rapid development of technology, big data-driven information services are gradually becoming an important tool for firms on e-commerce platforms. firms can achieve more accurate demand forecasting and optimize the decision-making process through information. this paper examines how big-data-driven information services affect supply chain decisions by constructing stackelberg models involving

prospect stochastic dominance conditions can be used to compare pairs of uncertain decision alternatives when the decision makers’ choice behavior is characterized by cumulative prospect theory, but their preferences are not precisely specified. this paper extends the use of prospect stochastic dominance conditions to decision settings in which the use of pairwise comparisons is not possible due to

we address the challenge of multiple criteria ranking in dynamic decision contexts, where a decision maker’s (dm’s) preferences evolve in response to changing environments. traditional ranking methods assume static preferences, but real-world scenarios often involve fluctuating decision factors, necessitating a more flexible approach. we propose a novel probabilistic preference learning framework using

time-to-build of an investment project induces a difference between the timing of investment and that of revenue generation. jeon (2024b) showed that uncertainty in the time-to-build always accelerates investment and enhances pre-investment firm value, regardless of its distribution. this study examines the extent to which the uncertainty advances the timing of investment and improves firm value. specifically

congestions caused by insufficient ship lock capacity is a significant challenge for inland waterway transportation. this research addresses the lock and port resources collaborative scheduling problem (lprcsp), aiming to minimize the overall stay time of ships within the lock-port system. the lprcsp is mapped to a variant of flexible job shop scheduling problem (fjsp), considering various ship routes

this paper investigates the equilibrium risk mitigation strategies involving both self-protection efforts and market insurance within a mean–variance framework. we formulate a time-inconsistent optimal control problem and solve it using extended hamilton–jacobi-bellman (hjb) equations. we theoretically identify a substitution effect between self-protection and insurance demand in response to changes

this paper investigates the periodic capacitated arc routing problem with irregular services, a challenging combinatorial optimization problem that arises in various real-world scenarios, such as waste collection and road maintenance. this problem is defined over a mixed graph and asks for scheduling a fleet of capacitated vehicles to meet the demands associated with a set of required links, while

this study develops an integrated analytical framework to examine how crowdfunding, market structure, and government policy interact to shape environmentally sustainable product development (spd). focusing on profit-sharing and securities-based crowdfunding, we model how investor opportunity costs, risk preferences, platform fees, and regulatory schemes (voluntary vs. mandatory under fiscal vs. non-fiscal

this study addresses the blocking flow shop scheduling problem with makespan minimization. in this problem, n jobs need to be scheduled in a serially ordered environment of m machines, where all jobs must follow the same processing order. additionally, there is no intermediate buffer between these machines. to solve it, we devise a hybrid population-based heuristic, combining a ruin-and-recreate operator

the multi-agent pathfinding problem (mapf) is the problem of finding conflict-free paths for agents moving on a graph. recently, as mapf applications have been growing, time-spacing constraints have been introduced to address more realistic scenarios. these constraints prohibit multiple agents from occupying the same vertex within a given time window, thereby addressing timing deviations and allowing

multistatic (active) sonar networks (msns) consist of a set of active sonar systems distributed over an area of interest (aoi) in a monostatic and/or bistatic configuration. the former refers to the case where the source and receiver are co-located, and the latter to the case where they are separated, sometimes several kilometers apart. furthermore, here we focus on a specific type of sonar, namely

we consider maximum likelihood estimation of a distribution over a general measurable space where realizations of the uncertainty are not directly observable but instead are known to lie within observable sets. we show that maximum likelihood estimates concentrate on a collection of maximal intersections (cmi) and can be found by solving a convex optimization problem whose size is linear in the size

we study the time window assignment traveling salesperson problem with stochastic travel times, a two-stage stochastic problem where the first-stage decisions involve the routing aspects and the customer time window definition. second-stage decisions follow, which integrate travel time uncertainties into the optimization process. the objective is to minimize the combined routing and time window cost

inventory management of perishable products has seen extensive study over the years; the perishable nature capturing the real-world phenomena of expiration after a limited shelf life. such problems are challenging as they involve balancing demand fulfillment with minimal wastage. an added dimension to such problems, given the rise of machine learning, is that demand predictions are often available

in elastic optical networks (eons), the frequency spectrum is divided into narrow frequency slots, and sequences of contiguous slots form channels. given an eon and a set of traffic demands, the np-hard routing and spectrum assignment (rsa) problem consists of establishing for each traffic demand a lightpath, composed of a source-to-destination path and a channel, such that no two traffic demands share

a key problem in decision-making is selecting a winning candidate or establishing a global ranking for a set of candidates when individuals’ preferences are expressed through linear orders. scoring rules are a specific case of positional voting systems (pvss) that are widely used in sports competitions. likewise, some scoring rules, such as the borda rule and plurality, have also been extensively analyzed

governments and consumers increasingly hold companies responsible for sustainability violations across their supply chain. however, companies often have only limited information on sustainability practices in their upstream supply chain. to still achieve sustainable behavior of lower-tier suppliers, they have to choose adequate incentives or delegate this responsibility to their direct suppliers. we

the wind farm cable routing problem is an important element of offshore wind power plant design optimization. the prohibition of cable crossings places it in a category of its own compared to related combinatorial optimization problems. a novel framework that systematizes implementing this constraint with benefits to solution quality and computational cost is presented. it reframes the problem with

this paper presents a novel approach to anomaly-based portfolio construction, integrating multiple market anomalies into a unified selection framework. we adapt the majority judgment (mj) method from social choice theory to financial decision-making, ranking stocks based on multiple anomalies, each treated as an independent evaluation criterion.

establishing a reliable closed-loop supply chain is crucial for companies aiming to reduce costs and enhance sustainability. two main structures are prevalent: one diversifies risk and logistics through a specific recycling distribution center, while the other pools risk and bidirectional logistics into the co-located distribution center. in this work, we propose a hybrid structure that integrates

this work investigates to which extent the known substantial differences between technical efficiencies on convex and nonconvex technologies translate into different learning possibilities. we also study whether radial and nonradial efficiency measures lead to a different learning experience. to our knowledge, these questions have never been investigated. our empirical research is guided by three working

the expected regret and target semi-variance are two of the most important risk measures for downside risk. when the distribution of a loss is uncertain, and only partial information of the loss is known, their worst-case values play important roles in robust risk management for finance, insurance, and many other fields. jagannathan (1977) derived the worst-case expected regrets when only the mean

in the context of centrally managed systems (cmss), accurately determining the influence of units is crucial for effective incentive and compensation plans. previous attempts to measure the impact of a specific dmu involved evaluating overall system efficiency after removing that dmu. however, challenges arise in accurately assessing post-removal system efficiency primarily because the aggregate unit

this paper addresses profit maximization and pricing in capacitated and uncapacitated single allocation hub location problems taking into account the uncertain and price-elastic demand. we formulate the problem as a two-stage stochastic program in which a finite set of discrete price values are used to derive the demand. the models aim to determine simultaneously the location of hubs, the allocation

the malmquist productivity index is one of the best known and most widely used measures in the economic literature to quantify and decompose changes in productivity of multi-input multi-output production processes over time. two main approaches are used to calculate this index: the adjacent malmquist index and the base period malmquist index. no base period is required to calculate the adjacent malmquist

identifying anchor points and determining their input and output adjustment bounds are significant topics in data envelopment analysis (dea). an anchor point (ap) is an extremely efficient decision-making unit (dmu) positioned at the intersection of the strongly and weakly efficient frontiers of the technology. this implies that increasing at least one input or decreasing at least one output of an

in credit risk analysis, survival models with fixed and time-varying covariates are commonly used to predict a borrower’s time-to-event. when time-varying covariates are endogenous, jointly modeling their evolution with the event time — known as the joint model for longitudinal and time-to-event data — provides a principled approach. in addition to temporal dynamics, incorporating borrowers’ geographical

in this article we address the problem of partitioning a carsharing business area into pricing zones. we formalize the problem mathematically showing that it forms a special set partitioning problem. for this problem we propose a binary integer programming problem. tighter reformulations can then be obtained by reformulating certain constraints emerged in the literature as closed assignment constraints