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In predictive modelling for high‐stake decision‐making, predictors must be not only accurate but also reliable. Conformal prediction (CP) is a promising approach for obtaining the coverage of prediction results with fewer theoretical assumptions. To obtain the prediction set by so‐called full‐CP, we need to refit the predictor for all possible values of prediction results, which is only possible for simple predictors. For complex predictors such as random forests (RFs) or neural networks (NNs), split‐CP is often employed where the data is split into two parts: one part for fitting and another for computing the prediction set. Unfortunately, because of the reduced sample size, split‐CP is inferior to full‐CP both in fitting as well as prediction set computation. In this paper, we develop a full‐CP of sparse high‐order interaction model (SHIM), which is sufficiently flexible as it can take into account high‐order interactions among variables. We resolve the computational challenge for full‐CP of SHIM by introducing a novel approach called homotopy mining. Through numerical experiments, we demonstrate that SHIM is as accurate as complex predictors such as RF and NN and enjoys the superior statistical power of full‐CP. [ABSTRACT FROM AUTHOR]

Image registration is a widely used tool for matching two images of the same scene with one another. In the literature, several image registration techniques are available to register rigid‐body and non‐rigid‐body transformations. One such important transformation is zooming. There are very few feature‐based methods that address this particular problem. These methods fail miserably when there are only a limited number of point features available in the image. This paper proposes a feature‐based approach that works with a feature that is readily available in almost all images, for registering two images of the same image object where one is a zoomed‐in version of the other. In the proposed method, we first detect the possible edge points which we consider as features in both the reference and the zoomed image. Then, we map these features of the reference and the zoomed image with one another and find the relationship between them using a mathematical model. Finally, we use the relationship to register the zoomed‐in image. This method outperforms some of the state‐of‐the‐art methods in many occasions. Several numerical examples and some statistical properties justify that this method works well in many applications. [ABSTRACT FROM AUTHOR]

Modelling multivariate time series of counts in a parsimonious way is a popular topic. In this paper, we consider an integer‐valued network autoregressive model with a non‐random neighbourhood structure, which uses negative binomial distribution as the conditional marginal distribution and the softplus function as the link function. The new model generalizes existing ones in the literature and has a great flexibility in modelling. Stationary conditions in cases of fixed dimension and increasing dimension are given. Parameters are estimated by maximizing the quasi‐likelihood function, and related asymptotic properties of the estimators are established. A simulation study is conducted to assess performances of the estimators, and a real data example is analysed to show superior performances of the proposed model compared with existing ones. [ABSTRACT FROM AUTHOR]

Data science consulting and collaboration units (DSUs) are core infrastructure for research at universities. Activities span data management, study design, data analysis, data visualization, predictive modelling, preparing reports, manuscript writing and advising on statistical methods and may include an experiential or teaching component. Partnerships are needed for a thriving DSU as an active part of the larger university network. Guidance for identifying, developing and managing successful partnerships for DSUs can be summarized in six rules: (1) align with institutional strategic plans, (2) cultivate partnerships that fit your mission, (3) ensure sustainability and prepare for growth, (4) define clear expectations in a partnership agreement, (5) communicate and (6) expect the unexpected. While these rules are not exhaustive, they are derived from experiences in a diverse set of DSUs, which vary by administrative home, mission, staffing and funding model. As examples in this paper illustrate, these rules can be adapted to different organizational models for DSUs. Clear expectations in partnership agreements are essential for high quality and consistent collaborations and address core activities, duration, staffing, cost and evaluation. A DSU is an organizational asset that should involve thoughtful investment if the institution is to gain real value. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic led to unprecedented changes in all levels of society, including the statistical consulting field. This paper focuses on the experiences of graduate student consultants and clients at our statistical consulting center (SCC) that operates all year independent of semesters. During the lockdown period, work continued without interruption and was conducted remotely, but there was a temporary reduction in utilization. Advice on statistical methods, help with data analysis and educational offerings are the main appeals to utilize SCC services. We describe our mentoring approach for graduate student research assistants (RAs) and how pandemic changes affected RAs and clients. Based on experiences during the pandemic, we offer practical suggestions for SCCs' approaches to research support, work characteristics and collaborations to improve the experiences of graduate students, both as consultants and clients. Most collaboration meetings are now virtual by request from clients. Telecommuting supports flexible personal schedules and needs. Online educational offerings provide easier access for participants and more opportunities for a wider range of topics and presenters. However, mentoring sessions for RAs are best conducted in‐person, and every effort should be made to encourage in‐person interactions and collaborations between staff members to advance the effectiveness of post‐pandemic SCCs. [ABSTRACT FROM AUTHOR]

Deep learning has achieved unprecedented success in recent years. This approach essentially uses the composition of nonlinear functions to model the complex relationship between input features and output labels. However, a comprehensive theoretical understanding of why the hierarchical layered structure can exhibit superior expressive power is still lacking. In this paper, we provide an explanation for this phenomenon by measuring the approximation efficiency of neural networks with respect to discontinuous target functions. We focus on deep neural networks with rectified linear unit (ReLU) activation functions. We find that to achieve the same degree of approximation accuracy, the number of neurons required by a single‐hidden‐layer (SHL) network is exponentially greater than that required by a multi‐hidden‐layer (MHL) network. In practice, discontinuous points tend to contain highly valuable information (i.e., edges in image classification). We argue that this may be a very important reason accounting for the impressive performance of deep neural networks. We validate our theory in extensive experiments. [ABSTRACT FROM AUTHOR]

In recent years, multi‐response linear models have gained significant popularity in various statistical applications. However, the design aspects of multi‐response linear models with group‐wise considerations have received limited attention in the literature. This paper aims to thoroughly investigate D$$ D $$‐optimal designs for such models. Specifically, we focus on scenarios involving two groups, where the proportions of observations for each group can be arbitrarily selected or not. Two equivalence theorems are presented to elaborate the characterization of D$$ D $$‐optimal designs. Additionally, we delve into the admissibility of approximate designs and establish necessary conditions for a design to be deemed admissible. Several illustrative examples are addressed to demonstrate the application of the derived theoretical results. [ABSTRACT FROM AUTHOR]

Summary: Testing for equivalence, rather than testing for a difference, is an important component of some scientific studies. While the focus of the existing literature is on comparing two groups for equivalence, real‐world applications arise regularly that require testing across more than two groups. This paper reviews the existing approaches for testing across multiple groups and proposes a novel framework for multigroup equivalence testing under a Bayesian paradigm. This approach allows for a more scientifically meaningful definition of the equivalence margin and a more powerful test than the few existing alternatives. This approach also allows a new definition of equivalence based on future differences. [ABSTRACT FROM AUTHOR]

A key aspect where extreme values methods differ from standard statistical models is through having asymptotic theory to provide a theoretical justification for the nature of the models used for extrapolation. In multivariate extremes, many different asymptotic theories have been proposed, partly as a consequence of the lack of ordering property with vector random variables. One class of multivariate models, based on conditional limit theory as one variable becomes extreme has received wide practical usage. The underpinning value of this approach has been supported by further theoretical characterisations of the limiting relationships. However, the paper "Conditional extreme value models: fallacies and pitfalls" by Holger Drees and Anja Janßen provides a number of counterexamples to these results. This paper studies these counterexamples in a conditional extremes framework which involves marginal standardisation to a common exponentially decaying tailed marginal distribution. Our calculations show that some of the issues identified can be addressed in this way. [ABSTRACT FROM AUTHOR]

This paper introduces graph‐based mutually exciting processes (GB‐MEP) to model event times in network point processes, focusing on an application to docked bike‐sharing systems. GB‐MEP incorporates known relationships between nodes in a graph within the intensity function of a node‐based multivariate Hawkes process. This approach reduces the number of parameters to a quantity proportional to the number of nodes in the network, resulting in significant advantages for computational scalability when compared with traditional methods. The model is applied on event data observed on the Santander Cycles network in central London, demonstrating that exploiting network‐wide information related to geographical location of the stations is beneficial to improve the performance of node‐based models for applications in bike‐sharing systems. The proposed GB‐MEP framework is more generally applicable to any network point process where a distance function between nodes is available, demonstrating wider applicability. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to reducing statistical errors in generative models, with a specific focus on generative adversarial networks (GANs). Inspired by the error analysis of GANs, we find that statistical errors mainly arise from random sampling, leading to significant uncertainties in GANs. To address this issue, we propose a selective sampling mechanism called space‐filling sampling. Our method aims to increase the sampling probability in areas with insufficient data, thereby improving the learning performance of the generator. Theoretical analysis confirms the effectiveness of our approach in reducing statistical errors and accelerating convergence in GANs. This research represents a pioneering effort in targeting the reduction of statistical errors in GANs, and it demonstrates the potential for enhancing the training of other generative models. [ABSTRACT FROM AUTHOR]

The deterministic non‐autonomous multispecies Holling type II model and its stochastic version with a simple type of noise have been proposed to infer multispecies community structure. However, these models fail to account for complex types of noises, which may render the model overly simplistic. In this paper, a non‐autonomous multispecies Holling type II model with a complex type of noise has been proposed. We establish sufficient conditions for various mathematical properties of the solutions, including existence and uniqueness, stochastic permanence and extinction. Additionally, numerical simulation studies are provided to illustrate our theoretical findings. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the universally optimal designs for estimating total effects under crossover models with partial interactions. We provide necessary and sufficient conditions for a symmetric design to be universally optimal, based on which algorithms can be used to derive optimal symmetric designs under any form of the within‐block covariance matrix. To cope with the computational complexity of algorithms when the experimental scale is too large, we provide the analytical form of optimal designs under the type‐H covariance matrix. We find that for a fixed number of treatments, say t$$ t $$, the number of distinct treatments appearing in the support sequences increases with the increase of the number of periods, k$$ k $$, until k≥t2$$ k\ge {t}^2 $$, in which case all t$$ t $$ treatments appear. The optimal design can be constructed from up to two representative sequences, within which each treatment appears in consecutive periods with equal or almost equal numbers of replications. [ABSTRACT FROM AUTHOR]

In this paper, we present a heuristic for the non‐unicost set covering problem using local branching. Local branching eliminates the need to define a problem specific search neighbourhood for any particular (zero‐one) optimisation problem. It does this by incorporating a generalised Hamming distance neighbourhood into the problem, and this leads naturally to an appropriate neighbourhood search procedure. We apply our approach to the non‐unicost set covering problem. Computational results are presented for 65 test problems that have been widely considered in the literature. Our results indicate that our heuristic is better than six of the eight other heuristics we examined, slightly worse than that of one heuristic, but that there is a single heuristic that outperforms all others. We believe that the work described here illustrates that the potential for using local branching, operating as a stand‐alone matheuristic, has not been fully exploited in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we discuss discrete location problems where the objective is to locate a given number of facilities of different types in order to appropriately cover a given set of demand points. The coverage provided to each demand point is the result of the cooperation among the located facilities. The different types of facility refer to the coverage radius or quality of coverage that each type may provide. We present a non‐linear formulation of the problem where the objective is to maximize the percentage of demand points that are appropriately covered. We then show how the model can be linearized based on a representation of probabilities through a network structure. To address large instances of the problem, we introduce a genetic algorithm (GA) that is based on the representation of the solution by two chromosomes. Computational experiments over a wide range of randomly generated problems indicate the GA clearly outperforms CPLEX, especially in larger instances. [ABSTRACT FROM AUTHOR]

This paper introduces a new flow‐based mathematical formulation for the multivehicle covering tour problem. This problem determines a set of vehicle routes for the available fleet; therefore, the total distance traveled is minimal. In the proposed mathematical formulation, each vertex represents a location of interest, and these vertices belong to three sets: vertices that can be visited; vertices that must be visited; and vertices that must be covered, in the sense that each vertex must be close to a visited vertex in a route. The proposed formulation aims to find solutions with balanced routes (all the routes with an equal or similar number of visited nodes) and extends the previous formulations proposed in the literature by considering a two‐index vehicle flow formulation with a decision variable to find the balanced routes. It constitutes an NP‐hard integer programming problem based on a two‐commodity flow model, which is solved by applying the branch‐and‐cut and biased random‐key genetic algorithms. This paper reports computational results for some adapted instances from TSPLIB to evaluate the efficiency of the proposed model. The computational results validate the branch‐and‐cut and genetic algorithms, whereby the latter performs better in large‐size instances with reduced computational runtime. Finally, the used approaches obtained competitive solutions compared to the literature. [ABSTRACT FROM AUTHOR]

In this paper, we address the biobjective versions of the perfect matching problem (PMP) and the Chinese postman problem (CPP). Both problems are solved by means of integer formulations or separating blossom inequalities, exploiting the PMP relationship with the CPP. In both cases, we first find the set of supported nondominated solutions and then we use them to obtain the nonsupported ones. The set of supported nondominated solutions are obtained solving scalarized integer formulations. To obtain the sets of nonsupported solutions, we resort to solving lexicographic problems based on adding additional linear constraints to the original problems. For this reason, we also characterize the combinatorial structure of the PMP vertices with one or two additional constraints. We also investigate when it is possible to use the PMP to solve CPP in the biobjective case. We report computational experiments comparing the different approaches and formulations based on different types of graphs with up to 700 nodes. [ABSTRACT FROM AUTHOR]

About 15 years ago, a paper proposed the first integer linear programming formulation for the constrained two‐dimensional guillotine cutting problem (with unlimited cutting stages). Since then, eight other formulations followed, seven of them in the last four years. This spike of interest gave no opportunity for a comprehensive comparison between the formulations. We review each formulation and compare their empirical results over instance datasets of the literature. We adapt most formulations to allow for piece rotation. The possibility of adaptation was already predicted but not realized by the prior work. The results show the dominance of pseudo‐polynomial formulations until the point instances become intractable by them, while more compact formulations keep achieving good primal solutions. Our study also reveals a mistake in the generation of the T instances, which should have the same optima with or without guillotine cuts. We also propose hybridising a recent formulation with a prior formulation for a restricted version of the problem. The hybridisations show a reduction of about 20% of the branch‐and‐bound time thanks to the symmetries broken by the hybridisation. [ABSTRACT FROM AUTHOR]

The team orienteering problem (TOP) is an NP‐hard optimization problem with an increasing number of potential applications in smart cities, humanitarian logistics, wildfire surveillance, etc. In the TOP, a fixed fleet of vehicles is employed to obtain rewards by visiting nodes in a network. All vehicles share common origin and destination locations. Since each vehicle has a limitation in time or traveling distance, not all nodes in the network can be visited. Hence, the goal is focused on the maximization of the collected reward, taking into account the aforementioned constraints. Most of the existing literature on the TOP focuses on its deterministic version, where rewards and travel times are assumed to be predefined values. This paper focuses on a more realistic TOP version, where travel times are modeled as random variables, which introduces reliability issues in the solutions due to the route‐length constraint. In order to deal with these complexities, we propose a simheuristic algorithm that hybridizes biased‐randomized heuristics with a variable neighborhood search and MCS. To test the quality of the solutions generated by the proposed simheuristic approach, we employ the well‐known sample average approximation (SAA) method, as well as a combination model that hybridizes the metaheuristic used in the simheuristic approach with the SAA algorithm. The results show that our proposed simheuristic outperforms the SAA and the hybrid model both on the objective function values and computational time. [ABSTRACT FROM AUTHOR]

Innovation is one of the driving forces of economic development and social progress, and the crowdsourcing contest is a well‐established mechanism for encouraging innovation. This paper examines two incentive schemes in two‐stage innovation contests: feedback and elimination. Feedback enhances the efforts by revealing the competitive status, and elimination intensifies the competition by removing less‐qualified participants. We build a game theoretical model to investigate how the organizer should design the feedback and elimination schemes and then analyze the equilibrium efforts and optimal contest design in four‐solver contests. The results suggest that the optimal design depends on the combined effects of the reward, effort sensitivity, and cost coefficiency. Elimination and nonelimination contests can be optimal under different conditions. Furthermore, we extend the equilibrium analysis to competitions with n>4$n>4$ contestants and investigate the optimal design with numerical studies. The most interesting result is that the elimination contest with feedback from the organizer is an ideal option for a budget‐constrained enterprise that seeks an innovative solution from the public for a complex innovation project. Also, the optimal number of contestants in the second stage is not always two when feedback is combined with elimination. [ABSTRACT FROM AUTHOR]

Chudnovsky and Seymour proposed the Three‐in‐a‐tree algorithm which solves the following problem in polynomial time: given three fixed vertices in a simple finite graph, check whether an induced tree containing these vertices exists. In this paper, we deal with a generalization of this problem, referred to henceforth as k$k$‐in‐a‐tree. The k$k$‐in‐a‐tree checks whether a graph contains an induced tree spanning k$k$ given vertices. When k$k$ is part of the input, the problem is known to be NP‐complete. If k≥4$k \ge 4$ is a fixed given number, its complexity is an open question, although there are efficient algorithms for restricted cases such as claw‐free graphs, graphs with a girth of at least k$k$ and chordal graphs. We present mixed‐integer programming formulations for this problem, and we show that instances with up to 25,000 vertices can be solved in reasonable computational time. [ABSTRACT FROM AUTHOR]

This paper examines the service mode selection strategy of a ride‐hailing platform and analyzes the impact of the strategy on consumers and drivers. The platform has three service modes to select from, including Mode L (only low‐quality services), Mode H (only high‐quality services), and Mode HL (high‐ and low‐quality services). We find that each service mode does not affect the price and the wage of the low‐quality service but affects those of the high‐quality service. Additionally, the platform cannot optimally select Mode L. The platform is better off if the car quality difference between high‐ and low‐quality services (CQD) is small and is worse off otherwise, under Mode HL. Counter‐intuitively, it is conditional for Mode HL to bring the highest surpluses to both consumers and drivers, and the conditions are related to CQD, social status utility of consumers and cost ratio between the drivers providing the two types of services. [ABSTRACT FROM AUTHOR]

The e‐commerce platform has been adopting the agency mode to sell the products of the national brand manufacturer (NBM) while introducing its own store brand and obtaining the consumer's preference information (CPI) to reinforce the competitiveness of the store‐brand products. This paper investigates the platform's decisions on whether to share the CPI with the NBM and its impact on the production timing decisions of the platform and the NBM. The results show that the platform may benefit from CPI sharing which may also change the equilibrium production timing of the platform and the NBM from sequential decision to simultaneous decision. In addition, the equilibria of the joint decision between the CPI sharing and the production timing are obtained, which is related to the market acceptance uncertainties of the products and the competitiveness of the national brands. [ABSTRACT FROM AUTHOR]

The paper deals with assortment optimization when customers purchase products using a nonparametric choice model. Each customer is presented with a preference list and will buy an available highest‐ranked offered product on the preference list. The paper presents a novel bi‐objective integer programming model for assortment optimization following a ranking‐based consumer choice model. The objectives are maximizing the expected revenue and maximizing customer satisfaction. This problem is recognized as an NP‐hard problem. After developing the bi‐objective model, an improved version of the augmented ε$\varepsilon $‐constraint method is proposed to solve the problem and retrieve the Pareto‐optimal solutions. In each iteration, this algorithm solves a single‐objective linear integer programming sub‐problem. Our approach aims to obtain a complete set of Pareto‐optimal solutions. Finally, we present computational experiments on synthetic data. Several numerical illustrations are provided at the end to demonstrate the application of the proposed methodology. The computational results provide the decision‐makers with valuable information regarding the factors that mainly affect the solutions. [ABSTRACT FROM AUTHOR]

This paper examines the interplay between the information strategy of an e‐commerce platform and the selling mode strategy of a manufacturer within a co‐opetitive supply chain, as well as the identification of the optimal supply chain strategy. We develop a supply chain model where a platform outsources production of its private label product to a manufacturer, who also sells its national brand product through the platform. The platform must decide whether to acquire consumer quality preference information at a cost and share it with the manufacturer, while the manufacturer needs to choose between the reselling mode or the agency selling mode for its national brand product. The two driving effects (competition‐intensification effect and mode differentiation effect) are identified. Our findings show that the platform will acquire and share information when the acquisition cost is sufficiently low, leading to the "competition‐intensification effect." Additionally, the manufacturer prefers the agency selling mode when cost‐quality efficiency is low, and the reselling mode otherwise, driven by the "mode differentiation effect." In cases where information sharing is absent, the manufacturer is more likely to choose the agency selling mode. Interestingly, when the cost‐quality efficiency of the manufacturer's product is moderate and the information acquisition cost is low, the "competition‐intensification effect" and the "mode differentiation effect" offset each other, resulting in the expansion of the region where the manufacturer chooses the reselling mode due to the platform's information‐sharing strategy. As a result, this enhances a cooperative relationship between the manufacturer and the platform. We also derive the optimal supply chain strategy, providing insights into both the manufacturer's selling mode and the platform's information strategies in online retailing. [ABSTRACT FROM AUTHOR]

This paper develops a game‐theoretic model that examines the entry decisions of two competing platform owners with differentiated research and development (R&D) efficiency and the response strategy of an original equipment manufacturer (OEM) in selecting which platform to cooperate with. The OEM and platform owners compete on quality and price in the face of consumers with heterogeneous preferences across the dimensions of the device and platform. We analyze the interaction between the entry decisions of platform owners and the OEM's response strategy within two cooperation models. In the charging model, we find that in the absence of entry, the OEM prefers the high‐efficiency platform. When only one platform owner enters, the OEM prefers the low‐efficiency platform. If the differentiated R&D efficiency and the device differentiation cost are large, both platform owners will choose to enter and the OEM will cooperate with the low‐efficiency platform. In the bundling model, when only one platform owner enters, only at a large cost of device differentiation will the OEM cooperate with the platform that enters the market to weaken market competition. In particular, when only the high‐efficiency platform enters, it is interesting that there exists a large cost of device differentiation that makes the high‐efficiency platform more favorable in the case when the OEM cooperates with the low‐efficiency platform. Our study provides practical insights into platform entry and OEM response. [ABSTRACT FROM AUTHOR]

Approximation of the optimal tour length in a Euclidean traveling salesman problem (TSP) has been studied by many researchers. In a previous study, we used the standard deviation in random tour lengths to approximate the optimal tour length in both Euclidean and non‐Euclidean TSPs and we obtained good estimates. In this paper, we show that the strong power‐law relationship between the standard deviation in random feasible solution values and the optimal solution value also holds for other Euclidean and near‐Euclidean combinatorial optimization problems like the minimum spanning tree (MST) and maximum weight matching (MWM) problems. We then enhance the estimation ability of the model by considering a second predictor: the mean in random feasible solution values. Experimental results show that by using the mean, standard deviation, and randomization, we can accurately predict the optimal solution values for the TSP, MST, MWM, and the capacitated vehicle routing problem (VRP). [ABSTRACT FROM AUTHOR]

Pharmaceutical safety has received increasing attention from governments and corporations, and building a safe and effective pharmaceutical cold supply chain network has become an important issue. This paper proposes a two‐stage pharmaceutical cold supply chain network design problem considering drug safety, in which the drug demand, transportation costs, and drug safety risk costs are assumed to be random variables. Due to the presence of uncertainty and the fact that information about the distribution of uncertain parameters is often only partially known, a distributionally robust optimization method is used to handle the uncertainty. A two‐stage distributionally robust optimization model is constructed, in which the reliability of the estimation of the demand necessary to satisfy the entire cold chain network is ensured to be greater than a certain predetermined level by introducing an ambiguous joint chance constraint. The decision process for the problem can be divided into strategic and operational decisions with the goal of minimizing the total costs related to facility construction, the purchase of raw materials, drug production, transportation, and safety risks. By introducing an ambiguity set with mean and covariance information to describe the uncertain parameters, the two‐stage model is eventually reformulated as a standard second‐order cone program, thus making it computationally tractable. Finally, numerical experiments are presented to demonstrate the effectiveness of the proposed models and optimization methods. [ABSTRACT FROM AUTHOR]

This paper considers an e‐tailing supply chain for fresh produce (E‐FSC) composed of a supplier investing in freshness‐keeping effort and an e‐retailer providing value‐added service. We study the e‐retailer's information sharing strategy with supplier encroachment. First, we discuss the supplier's encroachment strategy without or with information sharing and indicate that the e‐retailer may profit more from encroachment. Second, we explore the e‐retailer's information sharing strategy under two scenarios. Under the independent scenario, the e‐retailer may share information when the freshness sensitivity is comparatively high, which cannot affect the supplier's encroachment strategy. Under the interactive scenario, the e‐retailer influences the supplier's encroachment strategy through an information sharing strategy. The e‐retailer may share (withhold) information to induce (deter) encroachment when the competition intensity is comparatively low or high (moderate). Finally, we discuss the influence of the entry cost on E‐FSC members' profits, which finds that increasing entry cost may improve the supplier's profit. [ABSTRACT FROM AUTHOR]

The article discusses the 2030 Agenda for Sustainable Development and the 17 sustainable development goals (SDGs) approved by the United Nations in 2015. It highlights the importance of Multiple Criteria Decision Making (MCDM) in modeling complex decision-making problems related to the SDGs. The article announces a special issue of the International Transactions in Operational Research (ITOR) dedicated to the latest developments and applications of MCDM to the SDGs. Researchers and practitioners are invited to contribute original research papers on topics such as MCDM methodologies, applications of MCDM to SDGs, decision support systems, uncertainty in MCDM, and artificial intelligence and machine learning in MCDM. The deadline for submissions is February 28, 2025. [Extracted from the article]

In this paper we study critical sets of solutions uε$u_\varepsilon$ of second‐order elliptic equations in divergence form with rapidly oscillating and periodic coefficients. Under some condition on the first‐order correctors, we show that the (d−2)$(d-2)$‐dimensional Hausdorff measures of the critical sets are bounded uniformly with respect to the period ε$\varepsilon$, provided that doubling indices for solutions are bounded. The key step is an estimate of "turning" of an approximate tangent map, the projection of a non‐constant solution uε$u_\varepsilon$ onto the subspace of spherical harmonics of order ℓ$\ell$, when the doubling index for uε$u_\varepsilon$ on a sphere ∂B(0,r)$\partial B(0, r)$ is trapped between ℓ−δ$\ell -\delta$ and ℓ+δ$\ell +\delta$, for r$r$ between 1 and a minimal radius r∗≥C0ε$r^*\ge C_0\varepsilon$. This estimate is proved by using harmonic approximation successively. With a suitable L2$L^2$ renormalization as well as rescaling we are able to control the accumulated errors introduced by homogenization and projection. Our proof also gives uniform bounds for Minkowski contents of the critical sets. [ABSTRACT FROM AUTHOR]

In the prize‐collecting geometric enclosure problem (PCGEP), a set S$S$ of points in the plane is given, each with an associated benefit. The goal is to find a simple polygon P$\mathcal {P}$ with vertices in S$S$ that maximizes the sum of the benefits of the points of S$S$ enclosed by P$\mathcal {P}$ minus the perimeter of P$\mathcal {P}$ multiplied by a given nonnegative cost. The PCGEP is NP‐complete and has applications to land surveying for exploration or preservation of natural resources. In this paper, we develop the first heuristic, called PCGEP‐GR, for the PCGEP and revisit a previously proposed integer linear programming (ILP) model to solve it to optimality. We conducted a comprehensive experimental study of that heuristic and an exact algorithm based on the ILP model. We show that a new set of constraints, together with the previous set, is necessary to guarantee the correctness of the ILP model and introduce preprocessing strategies that allow us to prove optimality 40% faster on average. The proposed heuristic is able to reach the optimum in 32% of our benchmark instances and, for those with unknown optima, PCGEP‐GR was found better than or at least as good solutions as the ones obtained by the cplex ILP solver in 54% of the cases. Notwithstanding these positive results, the design of effective heuristics for the PCGEP proved to be very challenging, which also led us to obtain a result that provides the theoretical foundation for future advances in the study of this problem. [ABSTRACT FROM AUTHOR]

This paper introduces the three‐dimensional case picking problem (3D‐CPP) and proposes a multistart biased‐randomized algorithm (BRA) to solve it. The 3D‐CPP combines two important topics in modern warehouse logistics: the pallet loading problem and the routing of pickers in manual warehouses. The proposed optimization procedure aims at minimizing the overall distance traveled by the pickers, and is achieved by combining a routing problem (i.e., the order in which picking positions are visited) with a loading problem (i.e., the way in which cases are placed onto the pallet). We also consider additional constraints regarding the weight, vertical support, and strength of the cases. In order to solve this problem, we first propose a constructive heuristic which combines routing and packing procedures. This initial heuristic is then extended into a multistart BRA by employing a skewed probability distribution to introduce a certain degree of randomness during the solution‐construction process. A series of computational experiments allow us to assess the quality of the proposed approach, through a comparison with other algorithms as well as using real‐life data provided by an industrial partner. [ABSTRACT FROM AUTHOR]

Waste collection is an important logistic operation that is often inefficient due to the high uncertainty associated with bin fill levels, resulting either in routes that visit empty bins or in bins overflowing due to lack of routes. To reduce such uncertainty, sensors installed in the bins can provide real‐time information on waste levels. However, this is not enough, and the management of this information needs to be combined with dynamic optimization approaches to effectively design smart collection routes. This paper investigates this challenge and uses real‐time information on waste levels, treated through machine learning techniques, to feed a dynamic reverse inventory routing optimization model. The resulting data‐driven optimization approach allows the definition of a medium‐term collection route plan that can be updated daily as new information becomes available. To demonstrate the applicability of such an approach, a real‐world case is solved, and the results show significant improvements in operational efficiency and service levels. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of finding the optimal configuration of a network of land parcels connecting and protecting core areas through corridors and buffer areas within budget. We focus on polynomial‐sized integer linear programming formulations, building on previous research and borrowing some ideas from other authors. We establish the relationships between the proposed and past models in linear programming relaxation terms and test them on synthetic instances with up to 400 land parcels. [ABSTRACT FROM AUTHOR]

The optimization of Multidimensional Knapsacks with Family‐Split Penalties has been introduced in the literature as a variant of the more classical Multidimensional Knapsack and Multi‐Knapsack problems. This problem deals with a set of items partitioned in families, and when a single item is picked to maximize the utility, then all items in its family must be picked. Items from the same family can be assigned to different knapsacks, and in this situation split penalties are paid. This problem arises in real applications in various fields. This paper proposes a new exact and fast algorithm based on a specific Combinatorial Benders Cuts scheme. An extensive experimental campaign computationally shows the validity of the proposed method and its superior performance compared to both commercial solvers and state‐of‐the‐art approaches. The paper also addresses algorithmic flexibility and scalability issues, investigates challenging cases, and analyzes the impact of problem parameters on the algorithm behavior. Moreover, it shows the applicability of the proposed approach to a wider class of realistic problems, including fixed costs related to each knapsack utilization. Finally, further possible research directions are considered. [ABSTRACT FROM AUTHOR]

This paper studies a pickup and delivery routing problem for fourth‐party logistics providers. The problem aims to schedule routes of vehicles to pick up orders from suppliers and deliver them to factory locations considering multiple time windows at suppliers and factory locations, a non‐conventional cost structure, and certain factory dock constraints. We formulate the problem as a mathematical model and develop an efficient algorithm based on the adaptive large neighborhood search to solve the problem. The algorithm incorporates several heuristics to efficiently explore the search space for optimal solutions. The algorithm is refined through extensive statistical experiments to optimize the performances of the heuristics and to tune the parameters of the algorithm. The mathematical model and algorithm are evaluated on several problem instances based on a real case study in Europe. The numerical results demonstrate that the solution algorithm consistently obtains near‐optimal solutions to real‐sized problem instances. [ABSTRACT FROM AUTHOR]

This research addresses a flexible vehicle scheduling problem considering precedence constraints (FVS‐P problem), which is prevalent in many scenic areas worldwide. Each group of tourists in the FVS‐P problem comprises a set of visit requests that must be served by shuttle vehicles in a predefined order. A three‐indexed integer linear programming model is introduced. Furthermore, an index‐reduction strategy is proposed to strengthen the model. A math‐heuristic route‐segment generation algorithm embedded in a mathematical model is designed to solve the FVS‐P problem, particularly in the case of large‐sized instances. Experiments on different sizes of near‐practical instances validate the mathematical models and the algorithm. The proposed math‐heuristic can provide better solutions for large‐sized instances in a much shorter time than the models. The trade‐off between tourists' feelings and the cost of the area is investigated to provide further insights for managers. [ABSTRACT FROM AUTHOR]

As the prefabrication construction method plays an increasingly important role in the construction of cross‐sea bridges, coordinating the schedule between off‐site prefabrication and on‐site assembly becomes crucial and challenging. This paper studies a just‐in‐time single‐machine scheduling problem with due windows and release dates, which originates from the production and supply process of large segments of precast steel box girders in bridge construction. To solve this problem, we equivalently decompose it into the independent and same type of subproblems and formulate a mixed‐integer linear mathematical programming model. Furthermore, we propose a branch‐and‐bound algorithm with the initialization of a novel linear early and tardy dispatching rule with due windows to solve the problem exactly. The dispatching rule, based on the local dominance criteria with due windows, is designed and used to improve the upper bound. Moreover, a lower bound is obtained by further decomposing and relaxing the subproblems. Finally, the parameter of the dispatching rule is calibrated, and the best search strategy of the branch‐and‐bound algorithm is determined. The comprehensive computational experiments carried out show the efficiency and effectiveness of the proposed branch‐and‐bound algorithm. [ABSTRACT FROM AUTHOR]

Multi‐criteria methods and systems have been developed over many years to support decision‐making with a process of eliciting preferences. The emphasis has been on those that incorporate compensatory rationality, the characteristics of which make the process more complex. Neuroscience has offered the opportunity to capture aspects of cognition, generating insights. This paper investigates the preference elicitation process with Flexible and Interactive Tradeoff (FITradeoff), using electroencephalography and eye‐tracking applied to a sample of undergraduate and graduate students. While FITradeoff offers greater flexibility when evaluating recent advances in the area, the experiment carried out incorporates paradigms present in other methods developed for compensatory rationality. The results indicate the effect the type of predominant criterion has on cognitive effort. Moreover, a non‐linear relationship between the number of criteria and questions was found, which suggests that decision‐makers use a strategy of minimizing cognitive effort. Such findings help to direct efforts to enhance systems and methods with compensatory rationality. [ABSTRACT FROM AUTHOR]

Cap‐and‐trade regulation is increasingly adopted by countries and governments to curb carbon emissions. This regulation can be implemented based on two rules, that is, the traditional cap allocation rule and the linear cap allocation rule. Considering the impact of online channel structure on the manufacturer's production and low‐carbon investment strategy, this paper aims to explore the economic and environmental performances of the two rules. The results show that the linear rule encourages the government to allocate less cap to each unit product as the emission abatement level increases, preventing the manufacturer from gaining a windfall profit from the free carbon credits. Besides, the government could use the traditional cap allocation rule to better control the total carbon emissions but use the linear cap allocation rule to obtain more social welfare as well as economic benefit. No matter which cap allocation rule is implemented, the manufacturer always performs better in reseller mode, and the e‐tailer might perform better in reseller (marketplace) mode given a high (low) low‐carbon investment efficiency. This study enriches the literature on the interplay between the cap allocation rule and selling mode and provides insights into decision‐making for governments and firms. [ABSTRACT FROM AUTHOR]

For plenty of existing maintenance policies in literature, it is always assumed that the spare unit for replacement is available at any time if needed, while this perquisite may be unrealistic or even impossible. In this paper, we investigate an ordering and replacement modeling framework for a deteriorating repairable system with two failure types, involving a repairable failure (Type I failure) and a nonrepairable failure (Type II failure). A regular order and an expedited order are both incorporated into the spare unit ordering policy while a preventive replacement and a corrective replacement are both considered into the replacement policy, of which the ordering and replacement policy is scheduled in regard to the number of repairable failures. The expected profit per unit of time over an infinite time span is maximized to seek the optimal solutions in terms of the renewal reward theorem with a recursive algorithm procedure. An illustrative example is designed to validate the applicability and effectiveness of the developed ordering and replacement policy. In addition, the sensitivity of the optimal ordering‐replacement policy is studied and the effect of some key characteristics on the optimal decisions is discussed. [ABSTRACT FROM AUTHOR]

This paper aims to help financial institutions identify credit risk and reduce default losses more effectively by improving the accuracy and efficiency of the credit evaluation process using two methods. The paper first mines indicators that comprehensively describe the supply chain aspects of credit risk to obtain a precise credit risk portrait of the enterprise. The study then innovatively designs a random forest‐weighted naive Bayes (RF‐WNB) model that offers good interpretability and generalization qualities. The model forms an effective two‐stage process of enterprise credit risk evaluation that selects key features, quantifies the importance of each, and then evaluates credit risk. By applying empirical analysis to 1363 listed enterprises and performing one typical case analysis, we verified the effectiveness of the indicators and the two‐stage RF‐WNB model in evaluating enterprise credit risk. [ABSTRACT FROM AUTHOR]

Ytterbium disilicate (Yb2Si2O7) is a potential environmental barrier coating (EBC) material. In this paper, the microstructure and phase composition of plasma‐sprayed Yb2Si2O7 coatings with different spraying process were systematically investigated to optimize the spraying power and spraying distance. Furthermore, in order to obtain Yb2Si2O7 coating with single phase, a certain proportion of SiO2 was added to the Yb2Si2O7 powder to compensate for the volatilization of SiO2 during plasma‐spraying process, and the influence of spraying powder composition on the microstructure and phase structure (composition and content) of Yb2Si2O7 coatings were also characterized in detail. The results showed that the Yb2Si2O7 coating was dense with fewer pores and cracks at the spraying power of 40 kW and the spraying distance of 100 mm. Yb2Si2O7 coating with the "sandwich" structure was composed of Yb2Si2O7, Yb2Si2O7 + Yb2SiO5, and Yb2SiO5 three microstructures, which was mainly caused by the volatilization of SiO2 in Yb2Si2O7 spraying powder. The Yb2Si2O7 coating contained 90.68 mol.% Yb2Si2O7 phase and 9.32 mol.% Yb2SiO5 phase after crystallization at 1300°C for 20 h. The coating was composed of Yb2Si2O7 phase and amorphous phase after SiO2 addition. Yb2SiO5 phase precipitated from the coating after heat treatment at 1300°C, and its content decreased with the increase of SiO2 content. The content of the Yb2Si2O7 phase was as high as 99.41 mol.% when SiO2 content was 10 mol.%, which would provide a scientific basis for further research on stoichiometric Yb2Si2O7 EBCs with excellent properties. [ABSTRACT FROM AUTHOR]

In ceramic injection molding, the conventional solvent‐debinding process encounters limitations such as solvent cost and environmental pollution. In this paper, a suitable thermal‐debinding process from room temperature to 250°C using carbon/alumina embedding powders with nano and submicron size was developed to remove the soluble binders replacing the conventional solvent debinding of injection molding ZrO2 ceramic. The effects of the embedding powders on microstructure and debinding ratio were studied in detail. The results showed that the main factor influencing debinding ratio was associated with the sufficient dispersing of mixed particles, which was characterized by the relative change ratio of tap volume. A debinding mechanism was proposed to explain the effects of carbon/alumina mixed powders on the debinding ratio and surface cracks. Based on the influencing mechanism, the debinded green part with the elimination of soluble binders and optimal surface was achieved using the mixed powders with C/A = 8/2 on the top and C/A = 4/6 on the bottom. [ABSTRACT FROM AUTHOR]

Powder synthesis is crucial for achieving the densification and facilitating widespread applications of high entropy carbide (HEC). Specifically, the production of fine particle‐size powders is essential to enhance the densification of HECs. Consequently, this paper proposes the molten salt method for synthesizing HEC powders. The synthesis process successfully yielded (Ta Nb Ti V) C powders with a particle size of only 0.44 µm at 1400°C, exhibiting a uniform distribution of each element. Subsequent densification of (Ta Nb Ti V) C within the 1700–1900°C temperature range was carried out using spark plasma sintering. It was observed that as the densification temperature increased, the densification and mechanical properties of HEC significantly improved. This study presents innovative ideas for the preparation of HEC at lower temperatures. [ABSTRACT FROM AUTHOR]

A large adiabatic temperature change along with a wide operating temperature region is usually required for the electrocaloric materials to fulfill their refrigeration function. In general, doping foreign ions into BaTiO3 is the frequently utilized strategy in order to obtain excellent electrocaloric materials for the practical application. In the current paper, BaTiO3 lead‐free ferroelectric ceramics were doped by Ca2+ and Sn4+ simultaneously in order to generate a coexistence state at room temperature, where ferroelectric domains and nanodomains exit. As a result, a large polarization and a wide operating temperature range were finally induced. It is found that Ba0.9Ca0.1Ti0.85Sn0.15O3 has a maximum adiabatic temperature change of 0.85 K and an excellent working temperature region (65 K, ∆T > 90% Tmax) was achieved under an electric field of 50 kV/cm. The above phenomenon indicates that the modified BaTiO3 ceramics can be a good potential electrocaloric material in the state of cross‐existence of ferroelectric domains and nanodomains. [ABSTRACT FROM AUTHOR]

The full matrix parameters of a piezoelectric material are crucial for designing and evaluating piezoelectric devices. However, due to their inverse problematic nature, commonly used single‐sample characterization methods heavily depend on the accuracy of initial values and mode matching. In this paper, we propose a quasi‐31‐vibrator method to directly evaluate these parameters by designing the sample geometry and establishing the relationship equation between the parameters. This approach completely bypasses the need for characteristic spectrum simulation and mode identification processes. Subsequently, we tested the full matrix parameters of PZT‐5H ceramics and compared the simulated impedance spectra with the measured ones. The presence of well‐fitting spectra validates the feasibility of the method. This method will contribute to characterize the parameters of new piezoelectric materials and advance the intelligent implementation of measuring full matrix parameters. [ABSTRACT FROM AUTHOR]

Proton resonance frequency shift (PRFS) MR thermometry is the most common method used in clinical thermal treatments because of its fast acquisition and high sensitivity to temperature. However, motion is the biggest obstacle in PRFS MR thermometry for monitoring thermal treatment in moving organs. This challenge arises because of the introduction of phase errors into the PRFS calculation through multiple methods, such as image misregistration, susceptibility changes in the magnetic field, and intraframe motion during MRI acquisition. Various approaches for motion correction have been developed for real‐time, motion‐robust, and volumetric MR thermometry. However, current technologies have inherent trade‐offs among volume coverage, processing time, and temperature accuracy. These tradeoffs should be considered and chosen according to the thermal treatment application. In hyperthermia treatment, precise temperature measurements are of increased importance rather than the requirement for exceedingly high temporal resolution. In contrast, ablation procedures require robust temporal resolution to accurately capture a rapid temperature rise. This paper presents a comprehensive review of current cutting‐edge MRI techniques for motion‐robust MR thermometry, and recommends which techniques are better suited for each thermal treatment. We expect that this study will help discern the selection of motion‐robust MR thermometry strategies and inspire the development of motion‐robust volumetric MR thermometry for practical use in clinics. [ABSTRACT FROM AUTHOR]

Purpose: In MRI, motion artifacts can significantly degrade image quality. Motion artifact correction methods using deep neural networks usually required extensive training on large datasets, making them time‐consuming and resource‐intensive. In this paper, an unsupervised deep learning‐based motion artifact correction method for turbo‐spin echo MRI is proposed using the deep image prior framework. Theory and Methods: The proposed approach takes advantage of the high impedance to motion artifacts offered by the neural network parameterization to remove motion artifacts in MR images. The framework consists of parameterization of MR image, automatic spatial transformation, and motion simulation model. The proposed method synthesizes motion‐corrupted images from the motion‐corrected images generated by the convolutional neural network, where an optimization process minimizes the objective function between the synthesized images and the acquired images. Results: In the simulation study of 280 slices from 14 subjects, the proposed method showed a significant increase in the averaged structural similarity index measure by 0.2737 in individual coil images and by 0.4550 in the root‐sum‐of‐square images. In addition, the ablation study demonstrated the effectiveness of each proposed component in correcting motion artifacts compared to the corrected images produced by the baseline method. The experiments on real motion dataset has shown its clinical potential. Conclusion: The proposed method exhibited significant quantitative and qualitative improvements in correcting rigid and in‐plane motion artifacts in MR images acquired using turbo spin‐echo sequence. [ABSTRACT FROM AUTHOR]