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With the emergence of digital technologies, we have witnessed a rapid growth of the sharing economy in recent years. In fashion industry, sharing economy concept is also widely applied. Instances can be found all over the world, like Rent the Runway, Golden Tote, Stitch Fix, and Le Tote in the US, Air Closet in Japan, Y Closet, Ms Paris, and Dora's Dream in China, etc. In this paper, motivated by the industrial practices of fashion sharing platforms, we first conduct a literature review on business operations of sharing platforms, including the sharing platform operations, digital and information technologies employed in sharing economy, business models, and sustainability and circularity in business operations. Afterwards, we analyze the challenges faced by fashion sharing platforms by exploring the two leading fashion sharing platforms in the US and in China, respectively. At last, we propose future research directions on fashion sharing platform operations and conclude the paper. [ABSTRACT FROM AUTHOR]

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively. [ABSTRACT FROM AUTHOR]

Digital Contact Tracing (DCT) has been proved to be an effective tool to counteract the new SARS-CoV-2 or Covid-19. Despite this widespread effort to adopt the DCT, less attention has been paid to the organisation of the health logistics system that should support the tracing activities. Actually, the DCT poses a challenge to the logistics of the local health system in terms of number of daily tests to be collected and evaluated, especially when the spreading of the virus is soaring. In this paper we introduce a new optimisation problem called the Daily Swab Test Collection (DSTC) problem, that is the daily problem of collecting swab tests at home in such a way to guarantee a timely testing to people notified by the app to be in contact with a positive case. The problem is formulated as a variant of the team orienteering problem. The contributions of this paper are the following: (i) the new optimisation problem DSTC that complements and improves the DCT approach proposed by Ferretti et al. (Science https://doi.org/10.1126/science.abb6936, 2020), (ii) the DSCT formulation as a variant of the TOP and a literature review highlighting that this variant can have useful application in healthcare management, (iii) new realistic benchmark instances for the DSTC based on the city of Turin, (iv) two new efficient and effective hybrid algorithms capable to deal with realistic instances, (v) the managerial insights of our approach with a special regard on the fairness of the solutions. The main finding is that it possible to optimise the underlying logistics system in such a way to guarantee a timely testing to people recognised by the DCT. [ABSTRACT FROM AUTHOR]

The anti-epidemic supply chain plays an important role in the prevention and control of the COVID-19 pandemic. Prior research has focused on studying the facility location, inventory management, and route optimization of the supply chain by using certain parameters and models. Nevertheless, uncertainty, as a vital influence factor, greatly affects the supply chain. As such, the uncertainty that comes with technological innovation has a heightened influence on the supply chain. Few studies have explicitly investigated the influence of technological innovation on the anti-epidemic supply chain under the COVID-19 pandemic. Hence, the current research aims to investigate the influences of the uncertainty caused by technological innovation on the supply chain from demand and supply, shortage penalty, and budget. This paper presents a three-level model of the anti-epidemic supply chain under technological innovation and employs an interval data robust optimization to tackle the uncertainties of the model. The findings are obtained as follows. Firstly, the shortage penalty will increase the costs of the objective function but effectively improve demand satisfaction. Secondly, if the shortage penalty is sufficiently large, the minimum demand satisfaction rate can ensure a fair distribution of materials among the affected areas. Thirdly, technological innovation can reduce costs. The technological innovation related to the transportation costs of the anti-epidemic material distribution center has a greater influence on the optimal value. Meanwhile, the technological innovation related to the transportation costs of the supplier has the least influence. Fourthly, both supply and demand uncertainty can influence costs, but demand uncertainty has a greater influence. Fifthly, the multi-scenario budgeting approach can decrease the calculation complexity. These findings provide theoretical support for anti-epidemic dispatchers to adjust the conservativeness of uncertain parameters under the influence of technological innovation. [ABSTRACT FROM AUTHOR]

Industry 4.0 represents the most advanced stage of organization of industrial companies, allowing them to respond to an uncertain and changing environment, particularly as accentuated by the recent crisis resulting from COVID-19. Management innovation (MI) contributes to this process of permanent adaptation. The MI implementation phase is a critical step in MI generation that can generate many potential obstacles. This study focuses on these obstacles while considering the different activities (or subprocesses) embedded in this phase and the different actors involved in this complex process. We conducted a longitudinal case study in real time to investigate the implementation of MI internally generated by a multinational industrial company. Our results show that the obstacles encountered during the MI implementation phase may differ depending on the different activities and actors of this phase, thus leading us to question current implementation frameworks. This paper contributes by refining the theoretical model of MI generation and providing a better understanding of the obstacles that occur during the MI implementation phase. From a managerial perspective, this paper highlights key management principles to overcome the obstacles identified. [ABSTRACT FROM AUTHOR]

In this paper, we utilize the Chicago Board Option Exchange (CBOE) implied volatility indices to estimate the time-frequency information transmission among financial markets from 01.08.2008 to 31.10.2019. In doing so, we utilize the rolling window wavelet correlation (RWWC), Diebold & Yilmaz (The Economic Journal 119: 158–171, 2012), and Barunik & Krehlik (Journal of Financial Econometrics 16: 271–296, 2018). Our empirical findings suggest short-term and long-term dynamic connectedness between implied volatility indices of alternative assets. The long-term analysis findings suggest potential hedging and diversification opportunities that can be exploited by taking offsetting positions across volatility indices. The findings confirm heterogeneity between short-term and long-term connectedness results. Our findings also show superior out of sample hedging effectiveness of GVZ. The implications of the findings are further discussed in the paper. [ABSTRACT FROM AUTHOR]

This paper aims to understand the impact of big data analytics on the retail supply chain. For doing so, we set our context to select the best big data practices amongst the available alternatives based on retail supply chain performance. We have applied TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) for the selection of the best big data analytics tools among the identified nine practices (data science, neural networks, enterprise resource planning, cloud computing, machine learning, data mining, RFID, Blockchain and IoT and Business intelligence) based on seven supply chain performance criteria (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value). One of the intriguing understandings from this paper is that most of the retail firms are in a dilemma between customer loyalty and cost while implementing the big data practices in their organization. This study analyses the dominance of the big data practices at the retail supply chain level. This helps the newly emerging retail firms in evaluating the best big data practice based on the importance and dominance of supply chain performance measures. [ABSTRACT FROM AUTHOR]

Operations in areas of importance to society are frequently modeled as mixed-integer linear programming (MILP) problems. While MILP problems suffer from combinatorial complexity, Lagrangian Relaxation has been a beacon of hope to resolve the associated difficulties through decomposition. Due to the non-smooth nature of Lagrangian dual functions, the coordination aspect of the method has posed serious challenges. This paper presents several significant historical milestones (beginning with Polyak's pioneering work in 1967) toward improving Lagrangian Relaxation coordination through improved optimization of non-smooth functionals. Finally, this paper presents the most recent developments in Lagrangian Relaxation for fast resolution of MILP problems. The paper also briefly discusses the opportunities that Lagrangian Relaxation can provide at this point in time. [ABSTRACT FROM AUTHOR]

Availability contracting is a prominent procurement strategy among many defense and other capital equipment suppliers and their customers. Under availability contracting, the revenue generated for a service provider is a function of equipment/system availability over the predetermined contract period. A major challenge in implementing availability contracting is predicting the availability of equipment, defining the target availability, and determining the best contract duration based on the target availability. In this paper, we extend the classical operational availability model for estimating equipment availability under hierarchical maintenance, which is frequently used for complex equipment such as aircraft. Under an availability contract, bounds have been proposed on system-level operational availability. The current study also develops optimization models for finding the optimal availability contract duration under different scenarios: stochastic and non-stochastic contract durations; contract written at parts level, system level; minimizing total maintenance costs with minimum level of target operational availability as a constraint etc. The study also shows that the optimal contract duration is a non-decreasing function of spares on hand, and the inherent availability of system part. The results from the study have been validated using numerical study with both simulated and real data. [ABSTRACT FROM AUTHOR]

Providing accurate travel time prediction plays an important role in Intelligent Transportation System. It is critical in urban travel decision making and significant for traffic control. The main limitation of existing studies is that they do not fully consider the spatiotemporal dependence, exogenous dependence and dynamics of travel time prediction. In this paper, we propose a deep learning model, called DLSF-GR, based on graph neural networks and recurrent neural networks for travel time prediction, which combines multiple learning components to improve learning efficiency. We evaluate the proposed model on the real-world trip dataset in China by comparing with several state-of-the-art methods. The results demonstrate that the developed model performs the best in terms of all considered indicators compared to several state-of-the-art methods, and that the developed specified cross-validation method can enhance the performance of the comparison methods against to the random cross-validation method. [ABSTRACT FROM AUTHOR]

This paper presents a generalized software reliability modeling framework that predominantly encompasses randomness of field environment, fault reduction factor (FRF), and change point to study their simultaneous effect on software bug removal process. To address and capture unpredicted randomness of the field environment in testing phase it is assumed that the environment before and after a change point may not be the same and therefore there is a need to consider distinct environment factors before and after the change point in the modeling of bug removal action. Based on the general approach, three new change point-based software reliability growth models (SRGMs) are proposed incorporating time-dependent exponentiated Weibull (EW) FRF and distinct random environment distributions. To elaborate, in SRGM-I and SRGM-II, a steady environment is supposed before the change point whereas one-parameter exponential environment is assumed after the change point in SRGM-I and a two-parameter Gamma environment is considered after the change point in SRGM-II while in SRGM-III, exponential environment before change point and Gamma environment after change point are taken into consideration. In addition, a few established FRF-based models are deduced from the proposed general approach. Estimation of parameters of proposed SRGMs is carried out in two phases using three real test data sets and their validation is evaluated through several performance measures including coefficient of determination, mean square error, bias, variation, root mean square prediction error and Akaike information criteria. Fitting of the models to the datasets is also examined in related goodness of fit curves. Further, numerical illustration is worked out to conduct a cost-reliability assessment and suggest optimum release time. [ABSTRACT FROM AUTHOR]

In this paper, a new repairable k-out-of-n system model is proposed, in which the deviation cost of the system is taken into account. Firstly, the standard k-out-of-n system is replaced at planned time or at the failure time. Next, replacement policies are studied at planned time or periodic time when k is a constant. Thirdly, replacement policies are studied at planned time or periodic time when k is a random number, respectively. Finally, replacement policies are planned at the completion of random working time. The expected cost rate functions and its optimum replacement policies are derived analytically for each model. Meanwhile, optimum number of units is designed. The analytical solution of replacement policies for each model is discussed. Numerical examples are given to demonstrate our results. [ABSTRACT FROM AUTHOR]

The paper presents the model resilience measurement based on the complex network theory and analyzes the resilience of project portfolio network considering risk propagation. The model can be used to evaluate the resilience and improve its success probability of projects in the portfolio network. Firstly, the research analyzes the dynamic changes of the portfolio network derived from the construction of the project portfolio matrix, as well as the main factors to be taken into consideration in measuring the resilience of the project portfolio. Further, the research measures the resilience of the project portfolio network according to the node attributes (project) and the relationship attributes (the relationship that one project will impact another in the portfolio network) respectively. Then, to integrate the resilience of the projects and influence relationship between projects, the research proposes the dynamic PageRank algorithm to analyze the resilience of the project portfolio based on the analysis of traditional PageRank algorithm. In addition, resilience is not only affected by the projects and its influence relationship between them, but is also affected by risk propagation. Therefore, the research presents a model for analyzing the portfolio network resilience considering multiple risk propagation. Finally, a research and development project portfolio are taken as an example to demonstrate the effectiveness of the method presented in this research. Our approach can be used by managers to identify the scores of project resilience capacity in portfolio network. Our method explicitly allows to uncover the most resilient projects considering the resilience of project (node) and their influence relationship (network structure), and the risk propagation. Utilizing the outcomes of this research can enhance the capacity of the whole project portfolio to manage risks and improve the success probability of the whole project portfolio by enhancing network resilience. [ABSTRACT FROM AUTHOR]

Power distribution networks should strive for reliable delivery of energy. In this paper, we support this endeavor by addressing the Maintenance Resources Allocation Problem (MRAP). This problem consists of scheduling preventive maintenance plans on the equipment of distribution networks for a planning horizon, seeking the best trade-offs between system reliability and maintenance budgets. We propose a novel integer linear programming (ILP) formulation to effectively model and solve the MRAP for a single distribution network. The formulation also enables flexibility to suit new developments, such as different reliability metrics and smart-grid innovations. Then we develop a straightforward ILP formulation to address the MRAP for several distribution networks which takes the advantages of exchanging maintenance information between local agents and upper management. Using a general-purpose ILP solver, we performed computational experiments to assess the performance of the proposed approaches. Optimal maintenance trade-offs were achieved with the new formulations for real-scale distribution networks within short running times. To the best of our knowledge, this is the first time that the MRAP is optimally solved using ILP, for single or multiple distribution networks. [ABSTRACT FROM AUTHOR]

Aiming to improve the robustness and adaptiveness of the recently investigated capped norm linear discriminant analysis (CLDA), this paper proposes a regularized linear discriminant analysis based on the generalized capped l 2 , q -norm (GCLDA). Compared to CLDA, there are two improvements in GCLDA. Firstly, GCLDA uses the capped l 2 , q -norm rather than the capped l 2 , 1 -norm to measure the within-class and between-class distances for arbitrary q > 0 . By selecting an appropriate q, GCLDA is adaptive to different data, and also removes extreme outliers and suppresses the effect of noise more effectively. Secondly, by taking into account a regularization term, GCLDA not only improves its generalization ability but also avoids singularity. GCLDA is solved through a series of generalized eigenvalue problems. Experiments on an artificial dataset, some real world datasets and a high-dimensional dataset demonstrate the effectiveness of GCLDA. [ABSTRACT FROM AUTHOR]

As one of the most popular techniques for performance evaluation, Data Envelopment Analysis (DEA) has been widely applied in many areas. However, the self-evaluation used in DEA leaves it open to much criticism. Moreover, most researchers have ignored the fact that reality abounds with uncertainty and have assumed that the data used for evaluation is deterministic and accurate. Both assumptions make it difficult to evaluate the efficiency of real-world production processes correctly and reasonably. In this paper, we propose a series of robust cross-efficiency (RCE) models based on robust optimization theory and cross-efficiency to deal with these problems. First of all, the proposed RCE models allow the conservatism level to be adjusted easily to suit the attitude of the decision-maker towards uncertainty. In addition, the RCE models have better discrimination power than the existing robust CCR models. We present two applications to demonstrate the effectiveness and stability of our models. [ABSTRACT FROM AUTHOR]

Multiple stakeholders involved in the decision-making process have inherent interests that are sought to be maximized along with the collective goals specified by the organization as a whole. Due to the nature of these interests being diverse and often conflicting, an apparent dispute emerges which results in an even greater chaotic situation among stakeholders. Despite the introduction of several analytical and optimization tools to put the perspective of stakeholders into balance, there appears to be an inadequacy of frameworks that objectively incorporates the interests of stakeholders into a single metric. To advance this significant gap, this paper proposes a multiple stakeholder-based target-oriented robust optimization (MS-TORO) model which aggregates the interests of stakeholders into a single model with account for uncertainty. The conceptual and mathematical properties of the classical TORO model are used as a part of the MS-TORO framework to generate a satisficing solution with respect to the interests of multiple stakeholders. To demonstrate the applicability and validity of the proposed model, a hypothetical case study is performed in the decision process involving the post-departure aircraft rerouting problem. The system of the rerouting process involves multiple stakeholders each with inherent interests in an uncertain environment. Implementing the model provided solutions which satisfices the interests of multiple stakeholders as represented by the target metric minimizing the deviation from the performance targets of stakeholders. The proposed model not only confirmed the preferences of stakeholders in instances when a common route is selected but also showed non-biased solutions, thereby, adequately balancing interests. [ABSTRACT FROM AUTHOR]

The vector autoregressive (VAR) model has been popularly employed in operational practice to study multivariate time series. Despite its usefulness in providing associated metrics such as the impulse response function (IRF) and forecast error variance decomposition (FEVD), the traditional VAR model estimated via the usual ordinary least squares is vulnerable to outliers. To handle potential outliers in multivariate time series, this paper investigates two robust estimation methods of the VAR model, the reweighted multivariate least trimmed squares and the multivariate MM-estimation. The robust information criteria are also proposed to select the appropriate number of temporal lags. Via extensive simulation studies, we show that the robust VAR models lead to much more accurate estimates than the original VAR in the presence of outliers. Our empirical results include logged daily realized volatilities of six common safe haven assets: futures of gold, silver, Brent oil and West Texas Intermediate (WTI) oil and currencies of Swiss Francs and Japanese Yen. Our sample covers July 2017–June 2020, which includes the history-writing price drop of WTI on April 20, 2020. Our baseline results suggest that the traditional VAR model may significantly overestimate some parameters, as well as IRF and FEVD metrics. In contrast, robust VAR models provide more reliable results, the validity of which is verified via various approaches. Empirical implications based on robust estimates are further illustrated. [ABSTRACT FROM AUTHOR]

Mutual fund (MF) is one of the applicable and popular tools in investment market. The aim of this paper is to propose an approach for performance evaluation of mutual fund by considering internal structure and financial data uncertainty. To reach this goal, the robust network data envelopment analysis (RNDEA) is presented for extended two-stage structure. In the RNDEA method, leader–follower (non-cooperative game) and robust optimization approaches are applied in order to modeling network data envelopment analysis (NDEA) and dealing with uncertainty, respectively. The proposed RNDEA approach is implemented for performance assessment of 15 mutual funds. Illustrative results show that presented method is applicable and effective for performance evaluation and ranking of MFs in the presence of uncertain data. Also, the results reveal that the discriminatory power of robust NDEA approach is more than the discriminatory power of deterministic NDEA models. [ABSTRACT FROM AUTHOR]

Knowledge of protein-protein interactions (PPI) is essential for studying protein functions and understanding the biological processes. Previously, most of the works on PPI in the BioNLP domain rely solely on textual data. With the availability of different information (structure, sequence, gene ontology) about proteins, researchers have started to use other details with textual data to predict PPI more accurately. This paper reports the first attempt in integrating gene ontology(GO)-based information with the features extracted from other two modalities of proteins namely 3D structure and existing textual information. Existing two popular text-based benchmark PPI corpora, i.e., BioInfer and HRPD50 are first extended to integrate with the structure and GO-based information. Finally, some deep learning-based techniques are employed to extract features from three modalities and those are concatenated for final prediction of protein interaction. The experimentation on generated multi-modal datasets illustrates that the proposed deep multi-modal framework outperforms the baselines (uni-modal, bi-modal and multi-modal) and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Concomitant with people beginning to understand their legal rights or entitlement to complain, complaints of offensive odors and smell pollution have increased significantly. Consequently, monitoring gases and identifying their types and causes in real time has become a critical issue in the modern world. In particular, toxic gases that may be generated at industrial sites or odors in daily life consist of hybrid gases made up of various chemicals. Understanding the types and characteristics of these mixed gases is an important issue in many areas. However, mixed gas classification is challenging because the gas sensor arrays for mixed gases must process complex nonlinear high-dimensional data. In addition, obtaining sufficient training data is expensive. To overcome these challenges, this paper proposes a novel method for mixed gas classification based on analogous image representations with multiple sensor-specific channels and a convolutional neural network (CNN) classifier. The proposed method maps a gas sensor array into a multichannel image with data augmentation, and then utilizes a CNN for feature extraction from such images. The proposed method was validated using public mixture gas data from the UCI machine learning repository and real laboratory experiments. The experimental results indicate that it outperforms the existing classification approaches in terms of the balanced accuracy and weighted F1 scores. Additionally, we evaluated the performance of the proposed method in various experimental settings in terms of data representation, data augmentation, and parameter initialization, so that practitioners can easily apply it to artificial olfactory systems. [ABSTRACT FROM AUTHOR]

In this paper, by using a lifecycle perspective, four stages related to the extraction, refining and processing of copper were identified. The different behaviors of countries in the import/export networks at the four stages synthetically reflect their position in the global network of copper production and consumption. The trade flows of four commodities related to the extraction, refining and processing of copper of 142 nations with population above 2 millions based on the UN Comtrade website (https://comtrade.un.org/data/), in five years from 2017 to 2021, were considered. The observed trade flows in each year have been modelled as a directed multilayer network. Then the countries have been grouped according to their structural equivalence in the international copper flow by using a Multilayer Stochastic Block Model. To put further insight in the obtained community structure of the countries, a deep learning model based on adapting the node2vec to a multilayer setting has been used to embed the countries in an Euclidean plane. To identify groups of nations that play the same role across time, some distances between the parameters obtained in consecutive years were introduced. We observe that 97 countries out of 142 consistently occupy the same position in the copper supply chain throughout the five years, while the other 45 move through different roles in the copper supply chain. [ABSTRACT FROM AUTHOR]

Rating prediction is a crucial element of business analytics as it enables decision-makers to assess service performance based on expressive customer feedback. Enhancing rating score predictions and demand forecasting through incorporating performance features from verbatim text fields, particularly in service quality measurement and customer satisfaction modelling is a key objective in various areas of analytics. A range of methods has been identified in the literature for improving the predictability of customer feedback, including simple bag-of-words-based approaches and advanced supervised machine learning models, which are designed to work with response variables such as Likert-based rating scores. This paper presents a dynamic model that incorporates values from topic membership, an outcome variable from Latent Dirichlet Allocation, with sentiment analysis in an Extreme Gradient Boosting (XGBoost) model used for rating prediction. The results show that, by incorporating features from simple unsupervised machine learning approaches (LDA-based), an 86% prediction accuracy (AUC based) can be achieved on objective rating values. At the same time, a combination of polarity and single-topic membership can yield an even higher accuracy when compared with sentiment text detection tasks both at the document and sentence levels. This study carries significant practical implications since sentiment analysis tasks often require dictionary coverage and domain-specific adjustments depending on the task at hand. To further investigate this result, we used Shapley Additive Values to determine the additive predictability of topic membership values in combination with sentiment-based methods using a dataset of customer reviews from food delivery services. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) architectures such as recurrent neural networks and transformers display outstanding performance in modeling sequential unstructured data. However, little is known about their merit to model customer churn with time-varying data. The paper provides a comprehensive evaluation of the ability of recurrent neural networks and transformers for customer churn prediction (CCP) using time-varying behavioral features in the form of recency, frequency, and monetary value (RFM). RFM variables are the backbone of CCP and, more generally, customer behavior forecasting. We examine alternative strategies for integrating time-varying and non-variant customer features in one network architecture. In this scope, we also assess hybrid approaches that incorporate the outputs of DNNs in conventional CCP models. Using a comprehensive panel data set from a large financial services company, we find recurrent neural networks to outperform transformer architectures when focusing on time-varying RFM features. This finding is confirmed when time-invariant customer features are included, independent of the specific form of feature integration. Finally, we find no statistical evidence that hybrid approaches (based on regularized logistic regression and extreme gradient boosting) improve predictive performance—highlighting that DNNs and especially recurrent neural networks are suitable standalone classifiers for CCP using time-varying RFM measures. [ABSTRACT FROM AUTHOR]

Faced with dynamic and increasingly diversified public transport requirements, bus operators are urged to propose operational innovations to sustain their competitiveness. In particular, ordinary bus operations are heavily constrained by well-established route options, and it is challenging to accommodate dynamic passenger flows effectively and with a good level of resource utilization performance. Inspired by the philosophy of sharing economy, many of the available transport resources on the road, such as minibuses and private vehicles, can offer opportunities for improvement if they can be effectively incorporated and exploited. In this regard, this paper proposes a metric learning-based prediction algorithm which can effectively capture the demand pattern and designs a route planning optimizer to help bus operators effectively deploy fixed routing and cooperative buses with traffic dynamics. Through extensive numerical studies, the performance of our proposed metric learning-based Generative Adversarial Network (GAN) prediction model outperforms existing ways. The effectiveness and robustness of the prediction-supported routing planner are well demonstrated for a real-time case. Further, managerial insights with regard to travel time, bus fleet size, and customer service levels are revealed by various sensitivity analysis. [ABSTRACT FROM AUTHOR]

Police patrol is an effective crime prevention tool and boosts public confidence in urban security. Many interesting decision making problems appear in route design, resource allocation and jurisdiction planning. Many cities across the world have adopted a structured and intelligent method of police patrol due to the presence of a variety of operational and resource constraints. In this paper, we present a comprehensive review of the state-of-the-art in this domain, especially from the practice of operations research (OR) point of view. This is the first-of-its-kind review on police patrol presenting a classification scheme based on the type of problem, objective and modelling approach. In this novel scheme, one can track any paper almost readily to find the specific contribution. The applicability of OR in this domain is set to grow significantly as the governments formulate policies related to smart city planning and urban security. This study reveals many practical challenges in police patrolling for future research. [ABSTRACT FROM AUTHOR]

Industrial relocation (IR) is a business strategy consisting of moving operations locations. The purpose of this paper is to present how to assess, with multi-attribute decision-making (MADM), alternatives for IR. With MADM, IR strategies can be assessed not only based on a single attribute, as costs, or profits. This paper presents the application of MADM in a real case of IR. Four leading methods of MADM were applied: analytic hierarchy process (AHP), multi-attribute utility theory (MAUT), multi-attribute value theory (MAVT), and technique of order preference by similarity to ideal solution (TOPSIS). Results of AHP, MAUT, MAVT, and TOPSIS were quite similar, indicating the decision for the company not to relocate. A joint comparison of results with compatibility indices and correlation coefficients is the major novelty presented by this paper to the field of Operations Research, known as MADM. [ABSTRACT FROM AUTHOR]

I Seddigh et al. i in their work I "Approaching towards sustainable supply chain under the spotlight of business intelligence" i develop a theoretical model to explain how business intelligence capabilities influence the company's supply chain sustainability. They apply SmartPLS 3.0, which uses partial least squares structural equation modelling (PLS-SEM), to test the correlation between supply chain risks and supply chain performance, investigate the mediating effect of supply chain risk management on the correlation between supply chain risks and supply chain performance. The COVID-19 pandemic has created a lot of disturbances in supply chains around the globe leading to disturbances in the availability of logistics infrastructure (e.g., ports closure, boarders' control, etc.) and workforce absence. They propose a novel robust-heuristic optimization approach which support the supply chain managers in decision-making in selecting of carbon emission policies in supply chains with different vehicle types, demand and economic uncertainty in large scale-problems. [Extracted from the article]