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The transition to low-carbon agriculture is seen as essential for accelerating the implementation of ecologically friendly and climate-smart agricultural supply chain management strategies. Success in these efforts depends on robust policy frameworks, effective stakeholder participation, and the availability of agricultural products to customers. This study introduces a conceptual and operational structure for the incorporation and management of an agricultural supply chain with a low-carbon scheme through the establishment of a collaborative market environment involving farmers, agricultural enterprises, and consumers. Within this framework, agricultural enterprises and farmers engage in a cooperative Nash game to determine the extent to which they are willing to employ low-carbon practices in response to collaborative market prices and government policies, including subsidies and carbon taxes. The Nash game yields a unique Nash equilibrium, suggesting the possibility of cooperative negotiations among the parties with the goal of improving their financial gains. The proposed model is applied in two Brazilian sugarcane supply chain case studies, integrating with traditional biorefinery mills to produce and market ethanol as a gasoline alternative. Our results suggest that (1) successfully allocating government subsidies and taxes motivates low-carbon adoption; (2) the government taxation policy stabilizes the low-carbon supply chain, improving stakeholder market pricing and consumer affordability; and (3) reduced cost gaps between low-carbon and non-low-carbon alternatives increase low-carbon adoption. As a result, this study provides fresh insights on the Nash collaboration of stakeholders in accordance with government policies to create a cleaner and more affordable agricultural supply chain market. • A novel low-carbon application is introduced under government's regulations. • Existence of the Nash equilibrium encourages low-carbon stakeholder co-operation. • Successfully planning government subsidies and taxes promotes low-carbon adoption. • Two Brazilian sugarcane supply chain case studies show the low-carbon adaptability. [ABSTRACT FROM AUTHOR]

Abstract: An algorithm of the inductive model generation and model selection is proposed to solve the problem of automatic construction of regression models. A regression model is an admissible superposition of smooth functions given by experts. Coherent Bayesian inference is used to estimate model parameters. It introduces hyperparameters which describe the distribution function of the model parameters. The hyperparameters control the model generation process. [ABSTRACT FROM AUTHOR]

Abstract: Generalized linear models are widely used in statistical techniques. As an extension, generalized partial linear models utilize semiparametric methods and augment the usual parametric terms with a single nonparametric component of a continuous covariate. In this paper, after a short introduction, we present our model in the generalized additive context with a focus on the penalized maximum likelihood and the penalized iteratively reweighted least squares (P-IRLS) problem based on B-splines, which is attractive for nonparametric components. Then, we approach solving the P-IRLS problem using continuous optimization techniques. They have come to constitute an important complementary approach, alternative to the penalty methods, with flexibility for choosing the penalty parameter adaptively. In particular, we model and treat the constrained P-IRLS problem by using the elegant framework of conic quadratic programming. The method is illustrated using a small numerical example. [Copyright &y& Elsevier]

In this work, we obtain strong-order conditions of the Runge-Kutta method for the optimal control of the stochastic differential equations. We follow the discretize-then-optimize approach in order to get the optimality system. We compare Stratonovich-Taylor expansions of the exact solution and approximation method of the stochastic optimal control problem defined by the Runge-Kutta scheme successively to get the strong-order conditions. We derive the strong order-1.5 conditions and verify by a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to estimate fractional snow cover (FSC) from MODIS data in a complex and heterogeneous Alpine terrain is represented by using a state-of-the-art nonparametric spline regression method, namely, multivariate adaptive regression splines (MARS). For this purpose, twenty MODIS - Landsat 8 image pairs acquired between April 2013 and December 2016 over European Alps are used. Fifteen of the image pairs are employed during model training and five images are reserved as an independent test dataset. MARS models are trained by using MODIS top-of-atmosphere reflectance values of bands 1–7, normalized difference snow index, normalized difference vegetation index and land cover class as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat 8 binary snow cover maps. Multilayer feedforward artificial neural network (ANN) models are also trained by using the same input data. During the training and the testing, the effects of the training data size and the sampling type on the predictive performance of ANN and MARS models are investigated. An additional search is also conducted to reveal whether the choice of the transfer function used in the output layer of ANN has a significant contribution to the network's FSC mapping performance. The final ANN and MARS FSC products are at 500 m spatial resolution. The results on the independent test scenes indicate that the developed ANN models with linear and hyperbolic tangent transfer functions in the output layer and the MARS models are in good agreement with reference FSC data with the same average values of R = 0.93. In contrast, the standard MODIS snow fraction product, namely, MOD10 FSC, exhibits slightly poorer performance with average R = 0.88. The proposed MARS approach is statistically proven to have the same performance with ANN, yet it is computationally more efficient in model building. [ABSTRACT FROM AUTHOR]

Abstract: To construct an adequate regression model one has to fulfill the set of measured features with their generated derivatives. Often the number of these features exceeds the number of the samples in the data set. After a feature generation process the problem of feature selection from a set of highly correlated features arises. The proposed algorithm uses an evidence maximization procedure to select a model as a subset of generated features. During the selection process it rejects multicollinear features. A problem of European option volatility modeling illustrates the algorithm. Its performance is compared with the performances of similar well-known algorithms. [Copyright &y& Elsevier]

In recent decades, global climate change has become one of the most critical environmental issues, leading to increased environmental and social concerns about the sustainability of logistics networks. This study proposes a novel multi-objective, multi-facility location–allocation model for a two-stage solid logistics network that supports green practices. To account for uncertainty in real-world problems, some parameters of the model are represented as triangular type-2 neutrosophic numbers to deal with hesitant situations. The fuzzy model is then converted into a deterministic one using a new ranking approach. Three conflicting objective functions – minimizing total financial costs, maximizing customers' satisfaction levels, and ensuring sustainable and effective conveyances – are simultaneously optimized using the ϵ -constraint approach. The proposed model and solution approach are tested with two numerical examples. A comparative study is also presented, comparing the proposed approach with other Pareto-based multi-objective approaches, such as the global criterion approach, multi-objective fuzzy programming, and weighted Chebychev programming. Finally, the paper concludes with some remarks. • A model integrating location–allocation, two-stage, and solid logistics problems. • Analyzed mixed-integer non-linear multi-objective model under two-fold uncertainty. • De-neutrosophic based on a novel ranking function for sustainable decision-making. • Conducted a comparative analysis of the proposed model against existing approaches. • Demonstrated the impact of carbon emissions constraint with two applications. [ABSTRACT FROM AUTHOR]

The performance of waste management system has been recently interrupted and encountered a very serious situation due to the epidemic outbreak of the novel Coronavirus (COVID-19). To this end, the handling of infectious medical waste has been particularly more vital than ever. Therefore, in this study, a novel mixed-integer linear programming (MILP) model is developed to formulate the sustainable multi-trip location-routing problem with time windows (MTLRP-TW) for medical waste management in the COVID-19 pandemic. The objectives are to concurrently minimize the total traveling time, total violation from time windows/service priorities and total infection/environmental risk imposed on the population around disposal sites. Here, the time windows play a key role to define the priority of services for hospitals with a different range of risks. To deal with the uncertainty, a fuzzy chance-constrained programming approach is applied to the proposed model. A real case study is investigated in Sari city of Iran to test the performance and applicability of the proposed model. Accordingly, the optimal planning of vehicles is determined to be implemented by the municipality, which takes 19.733 h to complete the processes of collection, transportation and disposal. Finally, several sensitivity analyses are performed to examine the behavior of the objective functions against the changes of controllable parameters and evaluate optimal policies and suggest useful managerial insights under different conditions. Unlabelled Image • Addressing the sustainability of location-routing problem under the pandemic situation • Applying fuzzy chance-constrained programming technique to address the uncertainty • Implementing weighted goal programming method to deal with the mutli-objectiveness • Investigating applicability of the proposed methodology using a real case study. [ABSTRACT FROM AUTHOR]

• Developing a novel mathematical model for the robust green LAIP Problem. • Analyzing different MSW requirements within the waste management system. • Designing an efficient economical eco-friendly collection and disposal network. • Introducing the concept of RWT considering the budget constraint. The excessive expansion of cities and the consequent increase in urban population, especially in recent years, have led to a significant increase in consumptions and generations of different types of municipal solid waste (MSW). In this research, a robust green location-allocation-inventory problem (LAIP) is investigated to design an efficient MSW management system. Since the exact amount of MSW composition in different regions is not known and is uncertain, robust optimization technique is applied to formulate the problem as a mixed-integer linear programming (MILP) model. The objective is to minimize the total cost including fixed locational costs of collection and processing/disposal facilities, operational costs of facilities, transportation costs, penalty costs of non-collected waste and costs arising from pollution emissions. The validation of the proposed model is performed by different problems based on real-life data in deterministic and uncertain conditions using CPLEX solver of GAMS software. Then, the effects of greenness are evaluated by performing a sensitivity analysis on the parameter of pollution cost. Finally, the concept of robustness worthiness threshold (RWT) under budget constraint is introduced and discussed. [ABSTRACT FROM AUTHOR]

The increasing in industry as well as usage of vehicles for transporting products generate many problems to the environment such as greenhouse gas emission and global warming. As nowadays environmental pollution has become a crucial problem, production companies have now shifted their attention to lower the carbon emission level along with their financial goal. In reality, the values of some parameters such as production cost, holding cost, demand rate, deterioration rate may not fixed due to several factors. Reworking of defective products reduces production cost. This study aims at developing a multi-objective supply chain inventory management by considering deteriorating products together with imperfect quality production under neutrosophic environment. Several carbon reduction policies such as carbon tax, carbon cap-and-trade and carbon cap-and-offset and green technology are implemented here to mitigate the influence of carbon emission by curbing amount of carbon emission. To reduce the deterioration rate, preservation technology is utilized. In real-world situations, all parameters related to an inventory management are not handled as a crisp value, a fuzzy number or an intuitionistic fuzzy number. To confrontation this issue, single valued trapezoidal neutrosophic number is chosen for investigating this type of an inventory problem. A ranking approach is designed to transform all uncertain parameters into deterministic values. A comparison between two types of reworking processes, and a comparison among crisp, intuitionistic fuzzy and neutrosophic models are drawn. A sensitivity analysis regarding important parameters is also taken into consideration to accentual managerial intuitions. • Two SCIM models with synchronous and asynchronous rework processes are developed. • Preservation technology is utilized to reduce deterioration rate. • Carbon tax, offset, cap-and-trade policies are involved to curb carbon emission. • Neutrosophic set is utilized in this study to handle real-life ambiguity. • Neutrosophic compromise programming is used to solve the proposed models. [ABSTRACT FROM AUTHOR]

• We propose a Gradient-based Grey Wolf Optimizer for complex optimization problems. • We use Gaussian walk and Lévy flight that improve the exploration ability of GGWO. • We apply GGWO on several benchmarks to show its efficiency. • We apply GGWO for predicting the COVID-19 pandemic in the US. • We predicted the peak of infected, recovered, ICU admitted, and death cases. This research proposes a new type of Grey Wolf optimizer named Gradient-based Grey Wolf Optimizer (GGWO). Using gradient information, we accelerated the convergence of the algorithm that enables us to solve well-known complex benchmark functions optimally for the first time in this field. We also used the Gaussian walk and Lévy flight to improve the exploration and exploitation capabilities of the GGWO to avoid trapping in local optima. We apply the suggested method to several benchmark functions to show its efficiency. The outcomes reveal that our algorithm performs superior to most existing algorithms in the literature in most benchmarks. Moreover, we apply our algorithm for predicting the COVID-19 pandemic in the US. Since the prediction of the epidemic is a complicated task due to its stochastic nature, presenting efficient methods to solve the problem is vital. Since the healthcare system has a limited capacity, it is essential to predict the pandemic's future trend to avoid overload. Our results predict that the US will have almost 16 million cases by the end of November. The upcoming peak in the number of infected, ICU admitted cases would be mid-to-end November. In the end, we proposed several managerial insights that will help the policymakers have a clearer vision about the growth of COVID-19 and avoid equipment shortages in healthcare systems. [ABSTRACT FROM AUTHOR]

The world is moving towards carbon restricted scenario where excellence and growth in business is contingent upon good governance and excellent low carbon emission related policies, strategies, and long-term values to stay ahead in competition. Existing research insists that multiple external pressures push organizations to navigate towards sustainable internal practices leading to low carbon performance. Rooted in the theoretical concepts of institutional and contingency theory, this study explores the impact of uncertainties of external business environment on complexities of business internal environment. Hypothesis developed is tested by taking data from 134 manufacturing organizations based on their size and sector, and sustainability practices. The data is analyzed using correlation and multiple linear regression analysis (MLR). Chi-square, Cramer's V and MLR is used for regression analysis and correlation whereas ANN is applied for validating the robustness of the MLR model. Comparison of results of MLR and ANN provide similar outcomes. Results rule out the impact of size of organizations on carbon performance. 'Environmental regulations activities' and 'political and legal regulations' along with efficient implementation of Corporate Social Responsibility (CSR)/Sustainability activities backed by top management are the main contributors of low carbon performance. The study identified the 'interest of foreign investors' in the region as one of areas requiring further exploration for improved carbon performance. Mode of technology came as major roadblock for low carbon performance and requires top management to ponder over for improved results. Also, found wanting for improvement are-modes of transport, employee motivation and external stakeholders. Image 1 [ABSTRACT FROM AUTHOR]

Cross-docking practice plays an important role in improving the efficiency of distribution networks, especially, for optimizing supply chain operations. Moreover, transportation route planning, controlling the Greenhouse Gas (GHG) emissions and customer satisfaction constitute the major parts of the supply chain that need to be taken into account integratedly within a common framework. For this purpose, this paper tries to introduce the reliable Pollution-Routing Problem with Cross-dock Selection (PRP-CDS) where the products are processed and transported through at least one cross-dock. To formulate the problem, a Bi-Objective Mixed-Integer Linear Programming (BOMILP) model is developed, where the first objective is to minimize total cost including pollution and routing costs and the second is to maximize supply reliability. Accordingly, sustainable development of the supply chain is addressed. Due to the high complexity of the problem, two well-known meta-heuristic algorithms including Multi-Objective Simulated-annealing Algorithm (MOSA) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are designed to provide efficient Pareto solutions. Furthermore, the ε-constraint method is applied to the model to test its applicability in small-sized problems. The efficiency of the suggested solution techniques is evaluated using different measures and a statistical test. To validate the performance of the proposed methodology, a real case study problem is conducted using the sensitivity analysis of demand parameter. Based on the main findings of the study, it is concluded that the solution techniques can yield high-quality solutions and NSGA-II is considered as the most efficient solution tool, the optimal route planning of the case study problem in delivery and pick-up phases is attained using the best-found Pareto solution and the highest change in the objective function occurs for the total cost value by applying a 20% increase in the demand parameter. • Developing a novel bi-objective MILP model to integrate reliable PRP and CDS. • Studying the effects of traffic conditions in transportation and pollution emission. • Designing high-quality algorithms including NSGA-II and MOSA to solve the problem. • Applying the Taguchi design method to enhance the performance of the algorithms. • Evaluating the applicability of the proposed methodology using a case study in Iran. [ABSTRACT FROM AUTHOR]

Recently, large companies have shown a growing tendency to enhance the reliability and sustainability of their supply chains to increase customers' satisfaction in terms of on-time fulfillment of demands and to be compatible with environmental regulations. Therefore, finding the best approaches to achieve companies' goals is a crucial concern in supply chain management, and the majority of organizations prefer to cooperate with reliable and sustainable companies. In designing a supply chain, the supplier selection problem, order size identification, and order allocation are midterm decisions that are needed to be made separately. To this end, three levels of the supply chain, i.e., suppliers, central warehouses, and wholesalers are considered. In the first level, to address the sustainable supplier selection problem, a novel hybrid approach based on the fuzzy logic is implemented. This approach applies the Fuzzy Analytic Network Process (FANP) method to ranking criteria and sub-criteria, the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is applied to identification of the relationships among the main criteria, and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prioritizing the suppliers. After prioritizing the suppliers, the obtained weights are considered as the input of a tri-objective model designed to optimize the proposed supply chain. The objectives are minimization of the total cost of the chain, maximization of the weighted value of products by taking the account of suppliers' priorities, and maximization of the reliability of the supply chain. Weighted Goal Programming (WGP) method is then used to deal with multi-objectiveness. To assess the applicability of the suggested methodology, a case study of the lamp supply chain was considered and solved using GAMS/CPLEX solver and optimal policies based on suppliers' sustainability. The reliability of the supply chain was tested, and sensitivity analyses were also performed on the main parameters of the model. [ABSTRACT FROM AUTHOR]