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Organisations need to develop long-term strategies to ensure they incorporate innovation for environmental sustainability (IES) to remain competitive in the market. This can be challenging given the high level of uncertainty regarding the future (e.g., following the COVID pandemic). Supplier selection is an important decision that organisations make and can be designed to support IES. While the literature provides various criteria in the field of IES strategies, it does not identify the criteria which can be utilised to assist organisations in their supplier selection decisions. Moreover, the literature in this field does not consider uncertainty related to the occurrence of possible future events which may influence the importance of these criteria. To address this gap, this paper develops a novel criteria decision framework to assist supplier evaluation in organisations, taking into consideration different events that may occur in the future. The framework that combines three decision-making methods: the stratified multi-criteria decision-making method, best worst method, and technique for order of preference by similarity to ideal solution. The framework, proposed in this paper, can also be adopted to enable effective and sustainable decision making under uncertainty in various fields. [ABSTRACT FROM AUTHOR]

The smart supply chain innovation (SSCI) has become the key way for enterprises to enhance their competitiveness. Therefore, it is very important for the current supply chain enterprises to choose a reasonable innovation path. Through the literature review method, this paper summarizes the four main evaluation indicators of path selection for smart supply chain innovation, which are technical indicators, organizational environment indicators, operational efficiency indicators, risk prevention and control indicators. According to the characteristics of evaluation index, the improved Fuzzy Entropy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is proposed. This method considers the situation that the original data contains both interval value and fixed value data. Firstly, the standardized decision matrix is constructed, and then the optimal index weight is established by Fuzzy Entropy method. Secondly, this paper calculates the weighted decision matrix by using the method of sub item weighting of hierarchical indicators. Finally, the improved TOPSIS method is used to determine the relative closeness of each scheme. According to the weighted decision matrix, the innovation path index of the smart supply chain (SSC) is further calculated. We use the actual case of a company to analyze the evaluation method for three different SSCI paths. This paper provides a reference for the path selection of SSCI from the aspects of theory and practice. [ABSTRACT FROM AUTHOR]

Nowadays, the integration of sustainability into supply chain management (SCM) is a key issue for ensuring corporate competitiveness in face of dynamic ecological and social environments. This paper reviews 185 journal publications of the last 20 years that formalize issues related to sustainable supply chain management (SSCM) in quantitative models. In a content analysis, modeling and SCM characteristics as well as sustainability and SSCM constructs are elaborated. The models are assessed numerically by counting frequencies of occurrence and by clustering the paper sample according to selected characteristics. The findings indicate that SSCM models predominantly focus on deterministic approaches and the integration of environmental aspects of sustainability while neglecting stochastic modeling techniques and the consideration of social factors. By now, comprehensive modeling approaches are most often employed on intra-organizational levels whereas broader application areas are assessed by less complex models. The integration of pressures and incentives of external stakeholders or the formalization of sustainable supplier management and sustainability risks are identified as future research perspectives. Furthermore, the interrelationships between the triple bottom line dimensions are to be scrutinized in greater detail in order to avoid focused optimization of selected sustainability criteria. Seven modeling guidelines are derived from the reviewed literature to facilitate future model-based SSCM research. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss risk management, data envelopment analysis (DEA) and peer-to-peer (P2P) lending.

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]

The use of redundancies or spares in a system is a widely adopted technique to enhance system reliability and reduce the risk of system failure. Redundancies are typically incorporated into systems at the component or system levels. It is a significant problem to allocate appropriate redundancies into a system from a set of available options for the same. In this paper, we establish sufficient conditions to compare the reliability of coherent systems of dependent components with different sets of active redundancy, whether at the component level or the system level, based on some stochastic orders. We have obtained the results for the component lifetimes following the proportional odds (PO) model (the Marshall–Olkin family of distributions) for any lifetime distribution as a baseline distribution. We have studied the problem in the most general setup, with the consideration of coherent system that includes most of the common system structures, the consideration of non-matching spares, the consideration of dependencies of the components with different associated parameters of the copulas, and the consideration of general distribution as the baseline distribution of the PO model. We provide examples satisfying the sufficient conditions of the theoretical results. Additionally, we illustrate some of the results using real-world data. [ABSTRACT FROM AUTHOR]

In system reliability, coherent systems play a central role since without them any system is considered flawed. A system is coherent if all the components are relevant to that system i.e. functioning of every component has an effect in the functioning of the system and the structure of the system is monotone. Monotone structure means that improving any component will not deteriorate the system. A mixed system is a stochastic mixture of coherent systems and any coherent system is a special case of a mixed system. In reliability engineering, one major problem is to compare various coherent and mixed systems so that the better system can be used to increase the overall reliability. Another important problem is to measure the complexity of systems. A highly complex system will naturally have a higher running and maintenance cost associated with it and it is desirable for a reliability engineer to have an understanding regarding the complexity of systems beforehand. In this paper, we address these two problems from an information theoretic approach. Extropy is a measure of information which is the dual of the famous Shannon entropy measure. Recently, a new measure related to extropy, called cumulative residual extropy (CREx), was introduced in the literature by Jahanshahi et al. (Probab Eng Inf Sci 1–21, 2019). This measure is based on the survival function of the underlying random variable and it has some advantages over extropy measure. In this work, we analyze the CREx measure for coherent and mixed systems and develop some comparison results among systems. We also obtain some bounds of CREx of coherent and mixed systems consisting of independent and identically distributed (iid) and dependent and identically distributed (d.i.d.) components. We propose a new divergence measure to calculate the complexity of systems having iid components. Also, we introduced a new discrimination measure to compare various coherent and mixed systems when pairwise comparisons by usual stochastic order is not possible. Finally, we discuss analysis of the CREx measure of coherent systems having heterogeneous components. We also provide applications involving redundancy allocation. Various numerical examples are considered for illustrative purposes. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

This paper studies continuous-time models for newsvendor problems with dynamic replenishment, financial hedging and Stackelberg competition. These factors are considered simultaneously and the high-dimensional stochastic control models are established. High-dimensional Hamilton-Jacobi-Bellman (HJB) equations are derived for the value functions. To circumvent the curse of dimensionality, a deep learning algorithm is proposed to solve the HJB equations. A projection is introduced in the algorithm to avoid the gradient explosion during the training phase. The deep learning algorithm is implemented for HJB equations derived from the newsvendor models with dimensions up to six. Numerical outcomes validate the algorithm's accuracy and demonstrate that the high-dimensional stochastic control models can successfully mitigate the risk. [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]

This paper compares the hedging and safe haven properties of clean (renewable) energy and unclean (non-renewable) energy stocks. Using around 20 years of monthly clean and unclean energy indices and applying the GARCH and quantile regression models with dummy variables, we find that likewise the unclean energy stocks renewable energy stocks provide a weak hedge and a safe haven against the global economic policy uncertainty and its extreme values even during the pandemic and low and high return environments. These findings suggest that clean energy stocks provide immunity to investors against unfavourable changes to economic policy uncertainty and market volatility. [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]

We consider a non-preemptive single machine scheduling problem for a non-negative penalty function f, where an optimal schedule satisfies the left-shifted property, i.e. in any optimal sequence all executions happen without idle time with a starting time t 0 ≥ 0 . For this problem, every job j has a priority weight w j and a processing time p j , and the goal is to find an order on the given jobs that minimizes ∑ w j f (C j) , where C j is the completion time of job j. This paper explores local dominance properties, which provide a powerful theoretical tool to better describe the structure of optimal solutions by identifying rules that at least one optimal solution must satisfy. We propose a novel approach, which allows to prove that the number of sequences that respect the local dominance property among three jobs is only two, not three, reducing the search space from n! to n ! / 3 ⌈ n / 3 ⌉ schedules. In addition, we define some non-trivial cases for the problem with a strictly convex penalty function that admits an optimal schedule, where the jobs are ordered in non-increasing weight. Finally, we provide some insights into three future research directions based on our results (i) to reduce the number of steps required by an exact exponential algorithm to solve the problem, (ii) to incorporate the dominance properties as valid inequalities in a mathematical formulation to speed up implicit enumeration methods, and (iii) to show the computational complexity of the problem of minimizing the sum of weighted mean squared deviation of the completion times with respect to a common due date for jobs with arbitrary weights, whose status is still open. [ABSTRACT FROM AUTHOR]

In this paper, we consider a resource overload problem and add a tardiness penalty to the objective function when a prescribed project makespan is exceeded, which enables a trade-off between a balanced resource utilization and a project delay. For the tardiness penalty, we distinguish between a constant and variable delay cost variant. Based on the structural properties of the resource overload problem, we show that the search space of the resource overload problem with tardiness penalty can also be reduced utilizing quasistable schedules. In addition, we discuss the application of these findings to further problems, which include objectives composed of a locally concave and a concave function or a reward structure for an early project completion instead of a tardiness penalty. As solution approaches, we present mixed-integer linear model formulations as well as a novel genetic algorithm with a decoding procedure, which exploits the devised structural properties. The performance of the genetic algorithm is improved by implementing learning methods and utilizing lower bounds. Finally, we present results from experiments on small to medium sized problem instances. [ABSTRACT FROM AUTHOR]

In recent years, new resource types have been established in project scheduling. These include so-called partially renewable resources, whose total capacity applies only to a subset of periods in the planning horizon. In this paper, we consider the extension of the resource-constrained project scheduling problem with those partially renewable resources as well as generalized precedence constraints with the objective of minimizing the project duration (RCPSP/max- π ). For this problem it is known that already the determination of a feasible solution is NP-hard in the strong sense. Hence, we present two different generation schemes that are able to find good feasible solutions in short time for most tested instances. The first one is a construction-based heuristic wherein the activities of the project are scheduled iteratively time- and resource-feasibly. The second one is a relaxation-based generation scheme, in which—starting from the schedule consisting of the earliest start times—resource conflicts are identified and resolved by inserting additional resource constraints. [ABSTRACT FROM AUTHOR]

This paper addresses a multi-skill resource-constrained multi-project scheduling problem (MSRCMPSP) with different types of resources and complex industrial constraints, which originates from SNCF heavy maintenance factories. Two objective functions, that have been rarely addressed in the literature, are independently considered: (i) Minimization of the sum of the weighted tardiness of the projects and (ii) Minimization of the sum of the weighted duration of the projects. A time-indexed mixed-integer linear programming model is presented with both resource assignment and capacity constraints. To solve large instances with several thousand activities, a new memetic algorithm combining a novel hybrid simulated genetic algorithm with a simulated annealing is implemented. The memetic algorithm is compared with popular solution approaches. Computational experiments conducted on real instances and benchmark instances validate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The multi-mode resource constrained project scheduling problem (MRCPSP) is an NP-hard optimisation problem involving scheduling tasks under resource and precedence constraints, while there are several modes for executing each task. In this paper, we propose a novel matheuristic based on relax-and-solve (R &S) algorithm to solve MRCPSP. In addition, a mathematical programming model, which is the generalisation of the multi-dimensional knapsack problem is developed. That model conducts the mode selection process for the purpose of generating an initial feasible solution. We evaluate the performance of the proposed algorithm by solving benchmark instances that are widely used in the literature. The results demonstrate that the proposed R &S algorithm outperforms the state-of-the-art methods for solving the MRCPSP. [ABSTRACT FROM AUTHOR]

This paper addresses a two-machine re-entrant flow shop scheduling problem with stochastic processing times where each job is expected to require a rework phase, flowing twice within the whole system. Due to the stochastic characteristics of the addressed problem, the proposed approach aims to devise robust schedules, i.e., schedules that are less sensitive to the occurrence of uncertain events, specifically, to the variability of the processing times. Two classes of approaches are proposed: the first is a branch-and-bound algorithm capable of solving the problem optimally, although with limitations regarding the size of the scheduling instances; the second is heuristic algorithms that can be applied to medium/large instances. For both approaches, the goal is to minimise the value-at-risk associated with the makespan, to assist decision-makers in balancing expected performance and mitigating the impact of extreme scenarios. A Markovian Activity Network (MAN) model is exploited to estimate the distribution of the makespan and evaluate its value-at-risk. Phase-type distributions are used to cope with general distributions for the processing times while exploiting a Markovian approach. A set of computational experiments is conducted to demonstrate the effectiveness and performance of the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper deals with a scheduling problem arising at the tactical decision level in aeronautical assembly line. It has the structure of a challenging multi-mode resource-constrained project scheduling problem with incompatibility constraints, a resource leveling objective and also a large number of tasks. We first present a new event-based mixed-integer linear programming formulation and a standard constraint programming formulation of the problem. A large-neighborhood search approach based on the constraint programming model and tailored to the resource leveling objective is proposed. The approaches are tested and compared using industrial instances, yielding significant improvement compared to the heuristic currently used by the company. Moreover, the large-neighborhood search method significantly improves the method proposed in the literature on a related multi-mode resource investment problem when short CPU times are required. [ABSTRACT FROM AUTHOR]

In this paper, the research and development project scheduling problem (RDPSP) under uncertain failure of activities is formulated where an activity's failure results in the project's overall failure. A scenario-based bi-objective model to maximize the expected net present value (eNPV) and to minimize the NPV's risk by conditional value-at-risk (CVaR) measurement is presented. For this purpose, different modes of failure or success of activities have been considered as a stochastic parameter by a set of scenarios. To formulate the problem, a nonlinear model is first presented, then a mixed-integer programming (MIP) model of the problem is developed by piecewise approximation. Some valid inequalities are presented to improve the performance of the MIP model. A sequential sampling procedure is also used to approximate the solution of the MIP model with a large number of scenarios. The experimental results have shown that the sequential sampling procedure attains high-quality solutions in a reasonable CPU time. [ABSTRACT FROM AUTHOR]

This paper addresses a stochastic job shop scheduling problem with sequence-dependent setup times, aiming to minimize the expected maximum lateness. The stochastic nature is modeled by considering uncertain times between failures (TBF) and uncertain times to repair (TTR). To tackle this problem, a simheuristic approach is proposed, which combines a tabu search (TS) algorithm with Monte Carlo simulation. A total of 320 instances were used to conduct multiple experiments. Instances were generated with two distributions to study the behavior of stochastic TTR and TBF under log-normal and exponential distributions. Firstly, the performance of the simheuristic was evaluated for small instances by comparing it with the simulation of optimal solutions obtained with a mixed-integer linear programming (MILP) model. The simheuristic approach demonstrated an average improvement of around 7% compared to the simulation of MILP model solutions. Secondly, the simheuristic performance was evaluated for medium and large-size instances by comparing it with the simulation of the solutions obtained by the earliest due date (EDD) and process time plus work in the next queue plus negative slack (PT + WINQ + SL) dispatching rules. The results showed an average improvement of around 11% compared to EDD and 14% compared to PT + WINQ + SL. Furthermore, the results highlight that even when the two distributions have the same expected value and coefficient of variation, they can yield different expected maximum lateness values. This emphasizes the importance of precise distribution fitting when solving real cases to achieve effective scheduling performance. [ABSTRACT FROM AUTHOR]

This paper involves a centralised resource-constrained multiproject scheduling problem with four different payment patterns. In the problem, renewable resources can be shared by all the individual projects, and the task is to arrange activities' start times to minimise the contractor's maximal cash flow gap. First, based on the problem formulation, we construct an optimisation model for the problem, and through the analysis of the constructed model, we propose some properties of the problem. Then, due to the NP-hardness of the problem, we develop a simulated annealing algorithm and, according to the proposed properties, design a measure to enhance its searching efficiency of the algorithm. Finally, we conduct a computational experiment on an existing dataset to evaluate the performance of the developed algorithm and analyse the effects of key parameters on the objective function value. From the research results, we draw the following conclusions: The improved simulated annealing algorithm is the most promising algorithm for solving the problem being studied, and the effect of the improvement measure varies as the project parameters change. The maximal cash flow gap increases along with the network complexity, modified average utilisation factor of renewable resources, or discount rate per period while it decreases with the increase in the normalised average resource loading factor, variance in modified average utilisation factors, number of milestone activities, advance payment proportion, progress payment proportion, or deadline of projects. Among the four payment patterns, the expense-based and milestone activity-based payment patterns may be the best and worst, respectively, for the contractor to reduce the maximal cash flow gap. [ABSTRACT FROM AUTHOR]

This paper describes a maintenance scheduling model for digester banks. Digester banks are network-connected assets that lie on the critical path of the Bayer process, a chemical refinement process that converts bauxite ore into alumina. The banks require different maintenance activities at different due times. Furthermore, the maintenance schedule is subject to production-related constraints and resource limitations. Given the complexity of scheduling maintenance for large fleets of digester banks, a continuous-time, mixed-integer linear program is formulated to find the cost-minimising maintenance schedule that satisfies all required constraints. A solution approach that employs lazy constraints and Benders decomposition is proposed to solve the model. Unlike generic implementations of Benders decomposition, we show that the subproblems can be solved explicitly using a specialist algorithm. We solve the scheduling model for realistic scenarios involving two Bayer refineries based in Western Australia. [ABSTRACT FROM AUTHOR]

In some production processes, the effort associated with a certain activity for its execution can vary over time. In this case, the amount of work per time unit devoted to each activity, so as its duration, is not univocally determined. This kind of problem can be represented by an activity project network with the so-called feeding precedence relations, and activity variable execution intensity. In this paper, we propose a forward recursion algorithm able to find the earliest start and finish times of each activity, in O (m log n) time, with n and m being the number of activities and the number of precedence relations, respectively. In particular, this requires the calculation of the (optimal) execution intensity profile, for each activity, that warrants the earliest start schedule and the minimum completion time of the project. [ABSTRACT FROM AUTHOR]

This special issue of the I Annals of Operations Research i comprises a selection of papers from worldwide researchers in the field. We encouraged submissions from the 3rd Symposium on Quantitative Finance and Risk Analysis (QFRA) held in June 2017 at the island of Corfu in Greece, although the call for papers was open to all researchers in the field. We extend our gratitude to all the referees for their devotion and time to reading, assessing, and providing high-quality reports for the papers they reviewed that definitely helped the authors enhance their papers and us to make our final decision. [Extracted from the article]

In this paper, a mixed-integer linear programming model is discussed to provide joint decision making for facility location and production–distribution across countries for both forward and reverse logistics. A hybrid facility network is considered for cost-cutting and equipment sharing where the facilities of forward logistics are also equipped to provide reverse logistics services. The model considers the dynamic production and storage capacity of the facilities which can be expanded if required. Furthermore, the effectiveness of the model is tested to deal with disruptions due to man-made or natural disasters. The dynamic facility allocation enables the model to withstand the demand/supply disruptions in a disaster-affected zone. Besides this, the model considers carbon emissions caused due to manufacturing, remanufacturing, repair, storage and transportation. These emissions are regulated using cap and trade policy Thus, the proposed model balances resilience and sustainability under uncertain market demand and product returns. The chance-constrained approach is used to obtain the deterministic equivalence of the stochastic demand and returns. The paper also investigates the changes in emission and production level in each country under demand and supply disruptions. The parameters of the model are mapped with the various dimensions of big data such as volume, velocity and variety. The proposed model is solved using randomly generated data sets having realistic parameters with essential big data characteristics. [ABSTRACT FROM AUTHOR]

This paper surveys the increasing use of statistical approaches in non-parametric efficiency studies. Data Envelopment Analysis (DEA) and Free Disposable Hull (FDH) are recognized as standard non-parametric methods developed in the field of operations research. Kneip et al. (Econom Theory, 14:783–793, 1998) and Park et al. (Econom Theory, 16:855–877, 2000) develop statistical properties of the variable returns-to-scale (VRS) version of DEA estimators and FDH estimators, respectively. Simar & Wilson (Manag Sci 44, 49–61, 1998) show that conventional bootstrap methods cannot provide valid inference in the context of DEA or FDH estimators and introduce a smoothed bootstrap for use with DEA or FDH efficiency estimators. By doing so, they address the main drawback of non-parametric models as being deterministic and without a statistical interpretation. Since then, many articles have applied this innovative approach to examine efficiency and productivity in various fields while providing confidence interval estimates to gauge uncertainty. Despite this increasing research attention and significant theoretical and methodological developments in its first two decades, a specific and comprehensive bibliometric analysis of bootstrap DEA/FDH literature and subsequent statistical approaches is still missing. This paper thus, aims to provide an extensive overview of the key articles and their impact in the field. Specifically, in addition to some summary statistics such as citations, the most influential academic journals and authorship network analysis, we review the methodological developments as well as the pertinent software applications. [ABSTRACT FROM AUTHOR]

In the recent strategic queueing literature, there is a large number of papers that study the join-or-balk dilemma in queueing systems with server's on-off periods, modeling vacations and failures. These studies consider the customers as discrete units and adopt the assumption that reneging is not permitted. In the present paper, we depart from this framework and study the effect of the reneging option in such systems. We consider the fluid on-off model of the basic queue with vacations/failures and study reneging vs. no-reneging when customers are strategic. We derive the equilibrium customer strategies and the corresponding performance measures of the system, and we use them to study the equilibrium throughput and social welfare. The main finding is that the existence of the reneging option is very beneficial for overloaded systems, i.e., for such systems balking alone is not sufficient to achieve good outcomes. On the contrary, for underloaded systems the reneging option is not particularly valuable. [ABSTRACT FROM AUTHOR]

Supply Chain Finance (SCF), an important way of integrating industry and finance that has emerged in recent decades, has attracted the interest of both industry and academia. A large number of conceptual studies on SCF support the theory that SCF benefits supply chains by alleviating financing problems and maintaining stability. The focal firms which are the main SCF practices provider help alleviating the financing problem of their partners by weakening part of the working capital management. However, SCF-oriented practice on the focal firm and traditional corporate finance theory which earing profit by improving working capital management are not aligned. This paper attempts to provide empirical evidence to explain this phenomenon on the SCF-oriented perspective. Secondary panel data analysis is employed. The impact of focal firms providing SCF services is used to represent SCF practices in the supply chain since focal firms dominate SCF activities. Cash Conversion Cycle (time perspective), trade credit and prepayment (volume perspective) of the focal firms are independent variables of the analysis. Three type of performances, financial, risk and operations, are dependent variables of the analysis. This study found that a focal firm providing SCF can conditionally improve their firm's financial performance, risk levels and operations management. Results from different industrial analyses suggest that tertiary industries are best suited to implementing SCF activities. Providing SCF also benefits the firm-level performance of state-owned enterprises. This is the first paper that comprehensively analyses the effects of SCF implementation with different firm characteristics from both a time perspective and a volume perspective. [ABSTRACT FROM AUTHOR]

With the rapid growth of the global digital economy, supply chain finance has entered the stage of platform development in view of the history, policy environment, market status and other factors. Supply chain finance relies on multiple supply chain stakeholders to carry out financial business, which needs to solve a variety of financial risk control and pricing issues. The financing model in supply chain finance can be differentiate from the traditional credit model. The service mode and leading mode of supply chain finance need to be adjusted in accordance with the changes of the industrial operation. Further, the business mode of supply chain finance shows a diversification trend, which includes traditional supply chain financial models such as accounts receivable, advance payment, inventory financing, and supply chain credit financing models. In this paper, we investigate the similarities between supply chain finance and options, and further introduce American call options to supply chain financial products under the mode of small and medium-sized enterprises' (SMEs) accounts receivable financing. The price of supply chain financial products is derived through the trinomial tree option pricing model, which determines the corporate financing interest rates. The rationality of the proposed pricing model is validated in comparison with the medium and long-term load bank interest rates. The contribution of the paper is in providing SMEs with supply chain financial products under the accounts receivable model to resolve financing difficulties and the pricing the products through the trinomial budget pricing model. [ABSTRACT FROM AUTHOR]

This paper explores the mispricing relative to the capital asset pricing model through an equilibrium model. We find that both the strong risk preference dependent on good market states and strong risk aversion dependent on bad market states can produce high mispricing. Choosing the China stock market, the largest emerging market dominated by individual investors and known for its volatile nature in a short history as our sample, the empirical results also support our theoretical findings. Overall, our paper sheds light on the mispricing caused by the investor's risk preference reference-dependent on market states. [ABSTRACT FROM AUTHOR]