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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]

In this work, we obtain weak order-2 conditions of Runge–Kutta method for the optimal control of stochastic differential equations which occurs in many areas of economics and finance and recently in cognitive sciences and neuroscience. We get the order conditions that a stochastic Runge–Kutta technique must meet to have weak order two by comparing the stochastic expansion of the approximation with the associated Taylor scheme. Moreover, we present numerical examples which verify the theoretical results. We conclude our paper by a summary and an outlook to future research and application. [ABSTRACT FROM AUTHOR]

We have Derringer and Suich's desirability functions in mind, especially, the two-sided ones in our analysis throughout this study. We propose and develop a finite partitioning procedure of the individual desirability functions over their compact and connected interval which leads to the definition of generalized desirability functions. We call the negative logarithm of an individual desirability function having a max-type structure and including a finite number of nondifferentiable points as a generalized individual desirability function. By introducing continuous selection functions into desirability functions and, especially, employing piecewise max-type functions, it is possible to describe some structural and topological properties of these generalized functions. Our aim with this generalization is to show the mechanism that gives rise to a variation and extension in the structure of functions used in classical desirability approaches. [ABSTRACT FROM AUTHOR]

We study a stochastic optimal control problem for a delayed Markov regime-switching jump-diffusion model. We establish necessary and sufficient maximum principles under full and partial information for such a system. We prove the existence-uniqueness theorem for the adjoint equations, which are represented by an anticipated backward stochastic differential equation with jumps and regimes. We illustrate our results by a problem of optimal consumption problem from a cash flow with delay and regimes. [ABSTRACT FROM AUTHOR]

In order to avoid destructive results of financial bubbles which affect the entire economy, it is important to develop an early-warning signalling. By using optimization-supported tools, we introduce a new method for an early-warning signalling which approaches the bubble concept geometrically by determining and evaluating ellipsoids. We generate a volume-based index via minimum-volume covering ellipsoid clustering method, and to visualize these ellipsoids, we utilize Radon transform from the theory of the Inverse Problems. The analyses were conducted for US, Japan and China stock markets. In our study, we observe that when the bubble-burst time approaches, the volumes of the ellipsoids gradually decrease and, correspondingly, the figures obtained by Radon transform become more 'brilliant', i.e., more strongly warning. [ABSTRACT FROM AUTHOR]

Desirability functions are increasingly used in multi-criteria decision-making which we support by modern optimization. It is necessary to formulate desirability functions to obtain a generalized version with a piecewise max type-structure for optimizing them in different areas of mathematics, operational research, management science and engineering by nonsmooth optimization approaches. This optimization problem needs to be robustified as regression models employed by the desirability functions are typically built under lack of knowledge about the underlying model. In this paper, we contribute to the theory of desirability functions by our robustification approach. We present how generalized semi-infinite programming and disjunctive optimization can be used for this purpose. We show our findings on a numerical example. The robustification of the optimization problem eventually aims at variance reduction in the optimal solutions. [ABSTRACT FROM AUTHOR]

Neuroscience is of emerging importance along with the contributions of Operational Research to the practices of diagnosing neurodegenerative diseases with computer-aided systems based on brain image analysis. Although multiple biomarkers derived from Magnetic Resonance Imaging (MRI) data have proven to be effective in diagnosing Alzheimer's disease (AD) and mild cognitive impairment (MCI), no specific system has yet been a part of routine clinical practice. This paper aims to introduce a fully-automated voxel-based procedure, Voxel-MARS, for detection of AD and MCI in early stages of progression. Performance was evaluated on a dataset of 508 MRI volumes gathered from the Alzheimer's Disease Neuroimaging Initiative database. Data were transformed into a high-dimensional space through a feature extraction process. A novel 3-step feature selection procedure was applied. Multivariate Adaptive Regression Splines method was used as a classifier for the first time in the field of brain MRI analysis. The results were compared to those presented in a previous study on 28 voxel-based methods in terms of their ability to separate control normal (CN) subjects from the ones diagnosed with AD and MCI. It was observed that our method outperformed all of the others in sensitivity (83.58% in AD/CN and 78.38% in MCI/CN classification) with acceptable specificity values (over 85% in both cases). Furthermore, the method worked for discriminating MCI patients which converted to AD in 18 months (MCIc) from non-converters (MCInc) with a sensitivity outcome better than 27 of 28 methods. Overall, it was shown that the proposed method is promising in early detection of AD. [ABSTRACT FROM AUTHOR]

In sequencing situations, it may affect parameters used to determine an optimal order in the queue, and consequently the decision of whether (or not) to rearrange the queue by sharing the realized cost savings. In this paper, we extend one machine sequencing situations and their related cooperative games under fuzzy uncertainty. Here, the agents costs per unit of time and processing time in the system are fuzzy intervals. In the sequel, we define sequencing fuzzy interval games and show that these games are convex. Further, fuzzy equal gain splitting rule is given. Finally, a numerical example is illustrated priority based scheduling algorithm. [ABSTRACT FROM AUTHOR]

An appropriate and sustainable waste management plan is required in different scenarios for global development. The main goal of this paper is to evaluate a waste management problem by investigating an integrated multi-objective environment through solid transportation problem. To develop sustainability, three objective functions are optimized by choosing as cost for economical opportunity, time for social satisfaction and carbon emission for environmental view. Cap and trade policy is regarded here to minimize carbon emission and to provide some economical opportunities to the system. To control hesitancy of this scenario, twofold uncertainty (type-2 intuitionistic fuzzy) is incorporated here, and this uncertainty is defuzzified by a ranking operator. A strategy is proposed here to optimize three factors of sustainability by an intellectual model formulation of solid waste management. To check the appropriateness of the proposed model, two numerical problems are evaluated using two advanced methods, namely, neutrosophic linear programming and ϵ -constraint method. The Pareto-optimal solutions are derived by the variation of cap value and fulfilling the criteria of sustainability. The obtained results indicate that cap and trade policy or waste management, or both are highly sophisticated for applying in real-world application. The overall conclusions recommend that a government or NGO should encourage transportation system, or the industry to minimize carbon emission by utilizing several carbon policies. It can assist to establish different new project of waste management in a discrete environment, based on sustainability. [ABSTRACT FROM AUTHOR]

Spatial technologies offer high flexibility to handle substantial amount of spatial data and wide range of modelling capabilities. Remotely sensed data are the most significant data source used in spatial technologies. However, it is often associated with uncertainties due to atmospheric effects (i.e. absorption and scattering by atmospheric gases and aerosols). Methods based on rigorous treatment of radiative transfer models still have some drawbacks in the inversion of top of atmospheric reflectance values to surface reflectance values on large numbers of satellite images. In this paper, our aim is to represent a more flexible (adaptive) approach for the regional atmospheric correction by employing nonparametric regression splines within the frame of inverse problems and modern techniques of continuous optimization. To achieve this objective, atmospheric correction models obtained through conic multivariate adaptive regression splines, which is an alternative method to multivariate adaptive regression splines by constructing a penalized residual sum of squares as a Tikhonov regularization problem, are applied on a set of satellite images in order to convert the top of atmospheric reflectance values into surface reflectance values. The results are compared with the ones obtained by both multivariate adaptive regression splines and a radiative transfer-based method. [ABSTRACT FROM PUBLISHER]

This paper develops an economic ordering policy model for non-instantaneous deteriorating items with selling price- and inflation-induced demand under the effect of inflation, permissible delay in payments and customer returns. Shortages are allowed and partially backlogged. The customer returns are assumed to increase with both the quantity sold and the product price. The main objective is to determine the optimal selling price, the optimal length of time in which there is no inventory shortage, and the optimal replenishment cycle simultaneously, to minimize the present value of the total profit. An efficient algorithm is presented to find the optimal solution of the developed model. Finally, a numerical example is extracted to solve the presented inventory model using the proposed algorithm. [ABSTRACT FROM AUTHOR]

The United Nations Humanitarian Response Depot (UNHRD) coordinated 515 different shipments (2,420,258 km in total) from six UNHRD depots to 88 different countries to provide a 27,343 m3 volume of products in 2018. The main purpose of the proposed study is to investigate the current distribution plan and identify the potential improvements using operations research techniques. Maximization of the number of covered people, minimization of the traveled distance, and also analyzing the necessity of the response depots are the problems that need to be addressed for UNHRD. Several methods are applied to the UNHRD network for the first time in the literature, and it is aimed to provide a practical solution considering reductions in total distance, time, and cost. To achieve this, three different location-allocation models are devised and deployed on the real UNHRD distribution network, including maximum coverage, P-median, and set covering. As a result of utilizing the P-median model, the total traveled distance is reduced by 58%, and the most significant depots are identified as Accra and Dubai under various distance limitations using the maximum coverage and set covering models. When it comes to the application and managerial implications of the proposed study, the biggest part of demand was supplied by the Dubai depot before the P-median application, and the Accra depot now has the biggest part. Therefore, it is required to increase the volume of the Accra depot. In addition, Accra Depot is preferred 28 times according to the maximum covered model application, with 6 different coverage limits. From this point of view, it is necessary to have efficient and effective planning for the Accra depot in particular. With the set covering method, it is demonstrated the minimum and maximum ranges to serve demand points with the number of the opened depot(s). The most important contribution of the paper to the literature is to address and improve a real humanitarian aid logistics problem using well-known location-allocation models. The study provides a better understanding of the system to have advanced managerial insight by considering the UNHRD logistic network. [ABSTRACT FROM AUTHOR]

Currently, the presence of data uncertainty and noise raises critical issues to be handled on both theoretical and computational grounds. Therefore, robustification and robust optimization have gained attention from theoretical and practical points of view for the establishment of a modeling framework in mathematical optimization to immunize solutions against diverse uncertainties. Data of both the input and output variables, underlying the problems to be addressed, are affected by the noise of different kinds, such that standard statistical models alone may not be sufficient to ensure trustworthy results due to the complexity of the model. Consequently, we propose including parametric uncertainties reflecting future scenarios in multivariate adaptive regression splines (MARS), which, in turn, show great promise for fitting nonlinear multivariate functions, where additive and interactive effects of the predictors are employed to assess the response variable. We robustify MARS through the usage of robust optimization techniques. Due to the large complexity of the underlying model in prior studies, we applied a so-called weak robustification. Now, we exploit a geometrical and combinatorial approach to allow for a more complete robustification, by formulating Robust MARS (RMARS) under Cross-Polytope Uncertainty. In this study, we use RMARS for energy data and demonstrate its superior performance through a simulation study. [ABSTRACT FROM AUTHOR]

Regression analysis is a widely used statistical method for modelling relationships between variables. Multivariate adaptive regression splines (MARS) especially is very useful for high-dimensional problems and fitting nonlinear multivariate functions. A special advantage of MARS lies in its ability to estimate contributions of some basis functions so that both additive and interactive effects of the predictors are allowed to determine the response variable. The MARS method consists of two parts: forward and backward algorithms. Through these algorithms, it seeks to achieve two objectives: a good fit to the data, but a simple model. In this article, we use a penalized residual sum of squares for MARS as a Tikhonov regularization problem, and treat this with continuous optimization technique, in particular, the framework of conic quadratic programming. We call this new approach to MARS as CMARS, and consider it as becoming an important complementary and model-based alternative to the backward stepwise algorithm. The performance of CMARS is also evaluated using different data sets with different features, and the results are discussed. [ABSTRACT FROM AUTHOR]

Nowadays, the importance of financial crises and defaults of countries are becoming clear due to the globalization in the economic area and investments. Generalized partial linear model (GPLM) is a combination of two different regression models connecting with the mean of the dependent variable with the help of a link function. It is adequate to high-dimensional, non-normal data sets having the flexibility to reflect all anomalies effectively. The nonlinear patterns are also easily explained by the nonparametric component of the model. In this study, we introduce a newly developed conic GPLM (CGPLM) to predict default probabilities of 45 emerging markets using the contribution of a continuous model CMARS and a discrete model logistic regression. We present its application results on a data set with 13 macroeconomic variables in 25 years’ time. To predict debt crises, CGPLM gives better results than a single CMARS and a single logistic regression. In fact, we have 91.81% and 89.31% accuracy rates, computed according to the correctness of the model output, for training and validation sample, respectively. This improvement in prediction of crises can contribute to new prospects and developments in financial mathematics to make more accurate previsions for investments and to take measures due to coming risks. [ABSTRACT FROM PUBLISHER]

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]

A centrally important research area of computational biology, biotechnology – and life sciences at all – is devoted to modelling, prediction and dynamics of gene-expression patterns. However, this enterprise cannot be investigated in a satisfying way without the role of the environment, including the societies, members of the international community of nations. For a representation of the past, present and predicted future states, we also acknowledge the existence of uncertainties in modern technology and decision making, and the negotiations in solving societal problems and in international collaboration. We survey and closer explain recent advances in understanding the mathematical foundations and interdisciplinary implications of the newly introduced gene-environment networks; the main basis of our article is (Weber, Alparslan-Gök and Söyler [to appear]). We integrate the important theme of environmental protection by joint international projects into the context of networks and their dynamics. As an example of environmental protection, we study CO2 emissions, their implications for global warming by green-house effect, the reduction of both and the joint implementation requested for this purpose by Kyoto protocol. Given data from DNA microarray experiments and environmental records, we extract nonlinear ordinary differential equations which contain parameters that have to be determined. This is done by modern approximation and optimization. After this, time-discretized dynamical systems are studied by a combinatorial algorithm which detects the region of parametric stability. Finally, we analyze the ‘landscape’ of gene-environment networks. Its structure and stability have a very important meaning for the understanding of life and social system and of the conductibility of common enterprises, for example, in the environmental sector. To represent the interactions between the project participants, we imply modern collaborative game theory, where the players may be individuals or companies or, in particular, nations. This pioneering work is practically motivated and theoretically elaborated; it is devoted to support improvements in the living conditions of people all over the world, especially, in health care, medicine, education, environmental protection and public awareness. The authors invite the interested readers to future research. [ABSTRACT FROM AUTHOR]

This article contributes to a further introduction of continuous optimization in the field of computational biology which is one of the most challenging and emerging areas of science, in addition to foundations presented and the state-of-the-art displayed in [C.A. Floudas and P.M. Pardalos, eds., Optimization in Computational Chemistry and Molecular Biology: Local and Global Approaches, Kluwer Academic Publishers, Boston, 2000]. Based on a summary of earlier works by the coauthors and their colleagues, it refines the model on gene-environment patterns by a problem from generalized semi-infinite programming (GSIP), and characterizes the condition of its structural stability. Furthermore, our paper tries to detect and understand structural frontiers of our methods applied to the recently introduced gene-environment networks and tries to overcome them. Computational biology is interdisciplinary, but it also looks for its mathematical foundations. From data got by DNA microarray experiments, non-linear ordinary differential equations are extracted by the optimization of least-squares errors; then we derive corresponding time-discretized dynamical systems. Using a combinatorial algorithm with polyhedra sequences we can detect the regions of parametric stability, contributing to a testing the goodness of data fitting of the model. To represent and interpret the dynamics, certain matrices, genetic networks and, more generally, gene-environment networks serve. Here, we consider n genes in possible dependence with m special environmental factors and a cumulative one. These networks are subject of discrete mathematical questions, but very large structures, such that we need to simplify them. This is undertaken in a careful optimization with constraints, aiming at a balanced connectedness, incorporates any type of a priori knowledge or request and should be done carefully enough to be robust against disturbation by the environment. In this way, we take into account attacks on the network, knockout phenomena and catastrophies, but also changes in lifestyle and effects of education as far as they can approximately be quantified. We characterize the structural stability of the GSIP problem against perturbations like changes between data series or due to outliers. We give explanations on the numerics and the use of splines. This study is an attempt to demonstrate some beauty and applicabilty of continuous optimization which might together one day give a support in health care, food engineering, biomedicine and -technology, including elements of bioenergy and biomaterials. [ABSTRACT FROM AUTHOR]

In this work, we provide discrete optimality conditions of the optimal control problems of stochastic differential equations. Euler and Runge–Kutta methods are used for discretization. A Lagrange multiplier method for a discrete-time stochastic optimal control problem is formulated. The discrete adjoint process p n is obtained in terms of conditional expectations E [ p n + 1 ] and E [ p n + 1 Δ W ] for both methods. To estimate these nested conditional expectations at each time step via simulation, we use a very powerful new approach, least-squares Monte-Carlo method, developed by Longstaff–Schwartz. This is the first time to solve a stochastic optimal control problem by calculating the nested conditional expectations numerically with the help of a least-squares Monte-Carlo method. Some examples are studied to test and demonstrate the efficiency of the Lagrange multiplier combined with the least-squares Monte-Carlo method. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue including one by Ralf Korn and Serkan Zeytun on optimal investment problems, one by J.H. Maruhn on static hedging, and one by Martin Rainer on stochastic models.

In this paper, a computational approach is suggested to obtain unknown space–time-dependent source term of the fractional diffusion equations. We assign a time-dependent source term and a linear space with the zero components which represent a series of boundary functions. In linear space, an energy border functional equation is obtained. After that, some numerical examples are provided to verify the accuracy of the method. Also, two tables are presented to display the values of solutions. [ABSTRACT FROM AUTHOR]

In this work, we propose a new method for modeling human reasoning about objects' similarities. We assume that similarity depends on perceived intensities of objects' attributes expressed by natural language expressions such as low, medium, and high. We show how to find the underlying structure of the matrix with intensities of objects' similarities in the factor-analysis-like manner. The demonstrated approach is based on fuzzy logic and set theory principles, and it uses only maximum and minimum operators. Similarly to classic eigenvector decomposition, we aim at representing the initial linguistic ordinal-scale (LOS) matrix as a max–min product of other LOS matrix and its transpose. We call this reconstructing matrix a neuromatrix because we assume that such a process takes place at the neural level in our brain. We show and discuss on simple, illustrative examples, how the presented way of modeling grasps natural way of reasoning about similarities. The unique characteristics of our approach are treating smaller attribute intensities as less important in making decisions about similarities. This feature is consistent with how the human brain is functioning at a biological level. A neuron fires and passes information further only if input signals are strong enough. The proposal of the heuristic algorithm for finding the decomposition in practice is also introduced and applied to exemplary data from classic psychological studies on perceived similarities between colors and between nations. Finally, we perform a series of simulation experiments showing the effectiveness of the proposed heuristic. [ABSTRACT FROM AUTHOR]

Summary: One of the key factors in the establishment of power plants is proper location, which entails addressing various criteria. Power plant establishment operations are included in the fundamental designs; in turn, comprehensive studies in this field seem to be a prerequisite. The most important innovation of the present study consists of applying the robust bi‐level programming technique and game theory (Stackelberg competition) for locating renewable energy sites. Robust stochastic method is implemented for the robustification of the model against variations (eg, noise and perturbation) and uncertain conditions. The Karush‐Kuhn‐Tucker (KKT) method is used for solving bi‐level programming. The obtained results reveal that the incorporation of uncertainties can enhance energy generation and supplier's profit. Furthermore, the objective functions of the proposed model are compared with those under conditions without uncertainties. A sensitivity analysis of major parameters is performed to validate the proposed model. By increasing uncertainty, generated energy decrease, and supplier's profit increase. The supplier's profit gradually diminishes by raising the discounting rate. Moreover, as scale of problems increases, the generated energy and supplier's profit are boosted. [ABSTRACT FROM AUTHOR]

Due to various disasters and catastrophic phenomena, we have seen different disruptions in goods supply in recent years. Such disruptions usually create a level of panic among the people. Besides this panic, the communications and interactions of people with each other affect their purchasing behavior. Our paper investigates the panic effect on the customers’ demand and retailer’s profit by considering social learning and updating customer's beliefs. Based on our findings, the retailer’s optimal strategy can be determined by the customers’ purchasing decisions. Our results show that in high level and low level of panic intensity exploitation of social learning tools and techniques is not recommended for the retailer, whether the warehouse capacity of the retailer is large or small. In the mid-range level of panic intensity, the retailer should exploit social learning tools and techniques to gain more profit. In situations with high-level customer information on stockpiling reasons and moderate panic intensity, employing social learning enhancement tools and techniques may not benefit the retailer. Finally, the conclusion and directions for future research are presented. [ABSTRACT FROM AUTHOR]

Recent techniques such as optogenetics, in vitro and in vivo multielectrode recording, neuro- and brain imaging produce high-quality data that can be used to facilitate the fundamental mechanisms of information processing in our neural domains. As Guest Editors, we will be glad if the selected topics could offer a good coverage of international research dealing with the emerging and complex problems of neuroscience and its associated fields in medicine using OR. I Neuroscience i is a multidisciplinary science concerned with the study of the structure and function of the nervous system. [Extracted from the article]

Stochastic differential equations (SDEs) rapidly become one of the most well-known formats in which to express such diverse mathematical models under uncertainty such as financial models, neural systems, behavioral and neural responses, human reactions and behaviors. They belong to the main methods to describe randomness of a dynamical model today. In a financial system, different kinds of SDEs have been elaborated to model various financial assets. On the other hand, economists have conducted research on several empirical phenomena regarding the behaviour of individual investors, such as how their emotions and opinions influence their decisions. All those emotions and opinions are described by the word Sentiment. In finance, stochastic changes might occur according to investors' sentiment levels. In our study, we aim to represent the mutual effects between some financial process and investors' sentiment with constructing a coupled system of non-autonomous SDEs, evolving in time. These equations are hard to assess and solve. Therefore, we express them in a simplified manner by discretization and Multivariate Adaptive Regression Splines (MARS) model. MARS is a strong method for flexible regression and classification with interactive variables. Hereby, we treat time as another spatial variable. Eventually, we present a modern application with real-world data. This study finishes with a conclusion and an outlook towards future studies. [ABSTRACT FROM AUTHOR]

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]

The world is moving towards a environmental restricted scenario where excellence and growth in business is contingent upon good governance and excellent green sustainability related policies, strategies and long-term values to stay ahead in competition. Green supply chain management (GSCM) practices play an essential role in shaping the way of offering environmental sustainable products and services to customers. Existing research insists that multiple pressure push organizations to navigate towards 'eco design', 'green purchasing' and 'cooperation with customers' leading to green supply chain management. But how these factors exactly impact the organisational performance based on green supply chain management is being explored in this research. Hypothesis developed is tested by using multivariate canonical correlation analysis to model the regression relationship of 54 manufacturing organizations. Green purchasing and eco-design are the main contributors of GSCM's organisational performance. Cooperation with customer as major roadblock for organisational's performance and requires management, to ponder over for improved results. The findings coincide with published research articles that were conducted in developing countries, such as Brazil and Jordan, to ensure environmental sustainability with the integration of GSCM practices. [ABSTRACT FROM AUTHOR]

Neuroscience is a multidisciplinary science concerned with the study of the structure and function of the nervous system. New techniques such as optogenetics, in vitro and in vivo multi-electrode recording, neuroimaging and brain imaging, produce high-quality data that can be used to elucidate the fundamental mechanisms of information processing in the nervous system. This second special issue covers a broad range of topics, advancing the field of neuroscience as well as operations research. [Extracted from the article]

In recent years, attention to blood supply chain in disaster circumstances has significantly increased. Disasters, especially earthquakes, have adverse consequences such as destruction, loss of human lives, and undermining the effectiveness of health services. This research considers a six-echelon blood supply chain which consists of donors, blood collection centers (permanent and temporary), regional blood centers, local blood centers, regional hospitals, and local hospitals. For the first time, we considered that helicopters could carry blood from regional hospitals to local hospitals and return injured people that cannot be treated in local hospitals to regional hospitals due to the limited capacity. In addition to the above, different transportations with limited capacities regarded, where the optimal number of required transportations equipment determined after the solution process. This research aims to avoid the worst consequences of a disaster using a neural-learning process to gain from past experiences to meet new challenges. For this aim, this article considers three objective functions that are minimizing total transportation time and cost while minimizing unfulfilled demand. The model implemented based on a real-world case study from the most recent earthquake in the Iran–Iraq border which named the deadliest earthquake of 2017. Based on our results, we learned how to design an efficient blood supply chain that can fulfill hospitals blood demand quickly with the lowest cost using simulation and optimization processes. Moreover, we performed in-depth analyses and provided essential managerial insights at last. [ABSTRACT FROM AUTHOR]

Nowadays, transportation and logistics are considered as the drivers of economic development in the countries due to their impacts on the main variables of the country's economy such as production, employment, price, and the cost of living. Statistics indicate that fuel consumption constructs a major part of transportation costs, where its optimization leads to the creation of an energy‐efficient and sustainable transportation system. On the other hand, vehicles' traffic is also one of the main criteria affecting the travel time of vehicles between demand nodes in a supply chain, increasing fuel consumption, and, consequently, damaging effects of greenhouse gasses. In this paper, a novel robust mixed‐integer linear programming model is developed for a green vehicle routing problem with intermediate depots considering different urban traffic conditions, fuel consumption, time windows of services, and uncertain demand for perishable products. To validate and solve the suggested model, CPLEX solver of GAMS software is employed as an exact method. Finally, a case study problem is investigated to evaluate the applicability of the proposed model and determine the optimal managerial insights and policies in the real‐world conditions using sensitivity analyses. Moreover, a novel robustness threshold comparison is conducted to find the optimal level of budget assignment. [ABSTRACT FROM AUTHOR]

This paper aims to develop a robust multi-item EPQ formulation considering rework process and imperfect items. The mathematical model consists of two objective functions targeting minimization of total inventory costs and the total required warehouse space, respectively. Since, in real-world applications, parameters of the mathematical formulation are due to uncertainty, in this paper, Basic Chance Constraint Programming and Robust Fuzzy Chance Constraint Programming models are developed to handle uncertainties in both objective function and constraints. By solving examples, the superiority of the RFCCP over the BCCP model is showed and its ability to provide risk-averse solutions is ensured. Due to the nonlinearity of the RFCCP model and to provide the decision maker with the Pareto front, two algorithms, namely, Multi-Objective Grey Wolf Optimizer and Multi-Objective Water Cycle Algorithm, are used to optimize the decision variables. The performance of the algorithms is evaluated within different large-size test problems, using diversity, Spacing, number of non-dominated solutions and CPU-time. In the end, to investigate significant differences and to determine the best algorithm ANOVA test is implemented. In the end, we also offer new research directions, e.g. in terms of further ways of uncertainty modelling and risk management. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a multi-objective multi-item fixed-charge solid transportation problem (MOMIFCSTP) with fuzzy-rough variables as coefficients of the objective functions and of the constraints. The main focus of the paper is to analyze MOMIFCSTP under a fuzzy-rough environment for a transporting system. In practical situations, the parameters of a MOMIFCSTP are imprecise in nature, due to several uncontrollable factors. For these reasons, we introduce the fuzzy-rough variables in MOMIFCSTP to tackle vague data which are different from fuzziness and roughness. Fuzzy-rough expected-value operator is employed to convert fuzzy-rough MOMIFCSTP into deterministic MOMIFCSTP. Thereafter, we develop a methodology to solve the deterministic MOMIFCSTP by technique for order preference by similarity to ideal solution (TOPSIS). Three distinct approaches, namely extended TOPSIS, weighted goal programming (WGP) and fuzzy programming, are used to derive Pareto-optimal solution from the suggested model. A comparison is drawn among the optimal solutions which are derived from different approaches. It is observed from the extracted results that TOPSIS provides a better optimal solution than WGP and fuzzy programming. TOPSIS also overcomes some difficulties which arise in WGP. Finally, a real-world (industrial) problem is incorporated to show the applicability and feasibility of the proposed problem. [ABSTRACT FROM AUTHOR]

Networked materials and micro-architectured systems gain increasingly importance in multi-scale physics and engineering sciences. Typically, computational intractable microscopic models have to be applied to capture the physical processes and numerous transmission conditions at singularities, interfaces and borders. The topology of the periodic microstructure governs the effective behaviour of such networked systems. A mathematical concept for the analysis of microscopic models on extremely large periodic networks is developed. We consider microscopic models for diffusion–advection–reaction systems in variational form on periodic manifolds. The global characteristics are identified by a homogenization approach for singularly perturbed networks with a periodic topology. We prove that the solutions of the variational models on varying networks converge to a two-scale limit function. In addition, the corresponding tangential gradients converge to a two-scale limit function for vanishing lengths of branches. We identify the variational homogenized model. Complex network models, previously considered as completely intractable, can now be solved by standard PDE-solvers in nearly no time. Furthermore, the homogenized coefficients provide an effective characterization of the global behaviour of the variational system. [ABSTRACT FROM AUTHOR]

In the inverse electrocardiography (ECG) problem, the goal is to reconstruct the heart's electrical activity from multichannel body surface potentials and a mathematical model of the torso. Over the years, researchers have employed various approaches to solve this ill-posed problem including regularization, optimization, and statistical estimation. It is still a topic of interest especially for researchers and clinicians whose goal is to adopt this technique in clinical applications. Among the wide range of mathematical tools available in the fields of operational research, inverse problems, optimization, and parameter estimation, spline-based techniques have been applied to inverse problems in several areas. If proper spline bases are chosen, the complexity of the problem can be significantly reduced while increasing estimation accuracy. However, there are few studies within the context of the inverse ECG problem that take advantage of this property of the spline-based approaches. In this paper, we evaluate the performance of Multivariate Adaptive Regression Splines (MARS)-based method for the solution of the inverse ECG problem using two different collections of simulated data. The results show that the MARS-based method improves the inverse ECG solutions and is "robust" to modeling errors, especially in terms of localizing the arrhythmia sources. Graphical Abstract Multivariate adaptive non-parametric model for inverse ECG problem. [ABSTRACT FROM AUTHOR]