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Organizational resilience (OR) increases when the company has the ability to anticipate, plan, make decisions, and react quickly to changes and disruptions. Thus the company should focus on the creation and implementation of proactive and innovative solutions. Proactive processing of information requires modern technological solutions and new techniques used. The main focus of this study is to propose the best technique of Machine Learning (ML) in the context of accuracy for predicting the attributes of the organizational resilience potential. Based on the calculations, the research includes estimating them through the applications of regression and machine learning methods. The dataset is obtained from the results of the our survey based on the questionnaire consisting of 48 items mainly established on OR attributes formed on ISO 22316:2017 standard. Based on the outcomes of the study, it can be stated that the optimal technique in the context of accuracy for predicting the attributes of the organizational resilience potential is ensemble methods. The k-nearest neighbor (KNN) filtering-based data pre-processing technique for stacked ensemble classifier is used. The stacking is achieved with three base classifiers namely Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM). The chosen ensemble method should be implemented in an organization systemically according to the circle of innovation, and should support the quality of managerial decision-making process by increasing the accuracy of organizational resilience potential prediction, and indication of the importance of attributes and factors affecting the potential for organizational resilience. [ABSTRACT FROM AUTHOR]

Goal programming (GP) is one of the widely used and effective method of solving real-world multi-objective decision-making problems. The term "multi-objective transportation problem" (MOTP) covers a specific class of vector maximum (minimum) linear programming problem that typically has multiple, competing, and incompatible objective functions. In this article, GP and weighted goal programming (WGP) are summarized in MOTP. Furthermore, its advantages over GP are illustrated by a theorem. Additionally, by utilizing WGP, a solution procedure is articulated for MOTP. Finally, an application on screen-panels of mobile devices is presented to explore the applicability. The paper ends with a conclusion and including an outlook to future studies, whereby we address internal and external transportation, and view the internal one with a broader understanding of logistics. [ABSTRACT FROM AUTHOR]

We introduce the concept of logarithmic square root neutrosophic normal interval-valued set (LO-SRNSNIVS) based on multiple attribute decision-making (MADM) problems that can be solved in our communication. The logarithmic square root neutrosophic sets (LO-SRNSSs) are new approach of the interval-valued neutrosophic sets (IVNSSs) and neutrosophic sets (NSSs). We will examine a number of aggregating operators (AOs). The LO-SRNSNIV using weighted (LO-SRNSNIVW) operators of this article. Now, we presented logarithmic square root NSNIVW averaging (LO-SRNSNIVWA), logarithmic square root NSNIVW geomentric (LO-SRNSNIVWG), logarithmic square root generalized NSNIVW averaging (LO-SRGNSNIVWA) and logarithmic square root generalized NSNIVW geometric (LO-SRGNSNIVWG). We also develop an algorithm to solve MADM problems. A real-world scenario is given with examples, as well as explanations of how Euclidean distance (ED) and Hamming distance (HD) are applied. We will explain the properties of these sets using various algebraic structures. The notion for the scoring function, accuracy function and operational laws of the LO-SRNSNIVS. In addition to their simplicity and practicality, they can help determine which option is best to pursue. Thus, the results of the described models are more understandable and closely related to integer $ \Psi $. A few proposed and existing models are also compared in order to demonstrate their validity and applicability. [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]

Symbolic simplification of analog circuits is a computationally challenging problem due to the exponential growth of the number of symbolic terms with the circuit size. In recent years, researchers have proposed various methods to address this problem, but these methods often require matrix- or graph-based symbolic analysis methods, which can be computationally expensive and memory-intensive, especially for real-size analog circuits. To overcome these limitations, we introduce a new methodology called Direct Simplified Symbolic analysis (DSSA). The proposed DSSA method views simplified symbolic circuit analysis as a modeling problem and directly produces the most significant terms of the transfer function, without the need for traditional circuit analysis. One of the main advantages of DSSA is that it significantly reduces computational complexity and required memory compared to the existing techniques. This is achieved by generating a dataset using the Monte-Carlo simulation method and performing a genetic algorithm to solve the established modeling problem. The objective is to minimize the average numerical error between the simplified symbolic expression and the exact numeric expression for all data points. The proposed method has been tested on five circuits in MATLAB, and the results clearly demonstrate its performance against existing methods. The findings by the DSSA algorithm across five circuits reveal 0.64 dB and 1.36 dB variations for the average and maximum dc-gain, respectively. Moreover, the DSSA algorithm exhibits an average pole/zero error of 6.8% and a maximum pole/zero displacement of 16.8%. It has the potential to improve the efficiency and accuracy of symbolic analysis, making it a promising tool for circuit designers and engineers. [ABSTRACT FROM AUTHOR]

The modeling of electricity generation plays a crucial role in investment and long-term planning in power systems, primarily due to the significant volatility associated with wind and solar energy sources. Nevertheless, forecasting wind speeds for wind turbines based on weather conditions over an extended period is difficult and not feasible. This study provides long-term projections for wind power generation derived from a 2 MW wind turbine for the upcoming year and subsequent years utilizing the Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), Classification And Regression Tree (CART), Linear Regression (LR) and Random Forest (RF) techniques. The research is carried out in two distinct phases. During Phase 1 all considered predictive methods are compared. The research demonstrates that the MARS algorithm is a robust and efficient predictor for wind-based power generation, exhibiting strong competitiveness in its performance. During Phase 2, the MARS algorithm is employed to forecast the future 30-year wind power generation capacity lifespan hourly for nine cities in Texas, USA. It is projected that El Paso and Dallas will witness a mean rise of 8.6% in wind power capacity over three decades, while the remaining seven cities are anticipated to have an average decline of 7.7%. Hence, it is imperative to do a comprehensive and extended evaluation employing the MARS technique compared to ANN, CART, LR and RF before installing a wind turbine. This analysis would serve as a crucial resource for investors, engineers, and researchers involved in decision-making processes on wind turbine projects. [ABSTRACT FROM AUTHOR]

This study explores a Robust, Risk-aware, Resilient, and Sustainable Closed-Loop Supply Chain Network Design (3RSCLSCND) to tackle demand fluctuation like COVID-19 pandemic. A two-stage robust stochastic multiobjective programming model serves to express the proposed problems in formulae. The objective functions include minimising costs, CO2 emissions, energy consumption, and maximising employment by applying Conditional Value at Risk (CVaR) to achieve reliability through risk reduction. The Entropic Value at Risk (EVaR) and Minimax method are used to compare with the proposed model. We utilise the Lp-Metric method to solve the multiobjective problem. Since this model is complex, the Lagrange relaxation and Fix-and-Optimise algorithm are applied to find lower and upper bounds in large-scale, respectively. The results confirm the superior power of the model offered in estimating costs, energy consumption, environmental pollution, and employment level. This model and algorithms are applicable for other CLSC problems. [ABSTRACT FROM AUTHOR]

The social arrangement of organizations determines the potential possibilities of all group of agents in several economic and Operations Research (OR) situations. In this paper, a class of cooperative games in which agents with potential individual possibilities are connected via a hierarchy within an organization is studied. The leader, the agent, and any intermediate agents in the specified hierarchy constitute an important group refering to a peer group and form peer group situations. We consider uncertanty in payoffs in peer group situations. In the sequel peer group settings with fuzzy uncertainty are proposed. Further the associated games are modeled and some game-theoretical solution concepts are provided Futhermore, an application related with an auction situation is given. Finally, we conclude with some remarks and future work. [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]

In this paper, we will analyze the phenomenon of strategic cooperation between companies competing with each other so far. The analysis will concern the real situation of establishing a strategic alliance between companies. The analysis will consist in assessing the effects of cooperation for each of the companies, depending on whether and in what composition the companies would decide to cooperate. For the analysis, we will use a mathematical model of a cooperative game with fuzzy characteristic functions. Finally, we propose fuzzy interval solutions are a timely extension to classical game theoretic solutions. [ABSTRACT FROM AUTHOR]

This document discusses the concept of a circular economy (CE) and its importance in addressing resource crises and promoting sustainability. The integration of CE practices into supply chains is seen as crucial for developing sustainable supply chains. The document also highlights the role of digitalization and smart technologies in promoting a sustainable circular economy (SCE). The special issue of the journal "Sustainability" focuses on various research topics related to CE in emerging economies, including sustainability criteria, sustainable operations in the context of Industry 4.0, and the application of CE and industry 4.0 technologies in the food supply chain. The issue includes six published manuscripts that provide insights into effective practices within the framework of emerging economies. [Extracted from the article]

In this paper, an Economic Production Quantity (EPQ) model is formulated with an investment in green operations and deterioration. In the proposed model, the demand rate depends on selling-price as well as green concerning level of the products. The main objective of the study is to find an optimal replenishment time and an optimal green concerning level by considering profit maximization. The effect of multiple prepayments is depicted with a figure. Applying green operation, the manufacturer attracts more customers to earn more profits. Not only that, profit also increases, if the fraction of purchasing cost is paid as multiple prepayments will increase. Multiple prepayments give relief to the customer to gather a huge amount of money at a time that increases the sell of the product. Multiple prepayments generate a surplus cash flow to run the business. Carbon emission is included. Carbon tax is calculated, and government subsidy for green investment is included. A mathematical model is formulated and analysed by considering numerical examples to describe the real-life problems and to give solutions of that problem. Sensitivity analysis with respect to major parameters is performed to analyse the stability of the model. The paper ends with conclusions and a possible outlook of future research directions. [ABSTRACT FROM AUTHOR]

Water distribution networks (WDNs) are defined as the planning for the development, distribution, and utilization of water resources. The main challenge of WDNs is to preserve limited water resources while providing effective benefits from these resources in accordance with environmental considerations. Water distribution networks use hydraulic components to connect water resources to consumers. The diameter of each pipe, the layout of the pipe network, and the total length of pipes all contribute to the most effective layout for a water distribution system. This study considers the assurance that the flow (discharge) of water is in accordance with what is expected, with such aspects apt to be described as a particular form of reliability. As a result, this study proposes a stochastic optimization model with non-linear probability constraints for overcoming the challenges of water distribution networks while taking water flow reliability into account. The pressure drop equation causes the non-linear shape. The stochastic model of the opportunity constraint is changed to a deterministic multi-objective model using an approach based on integer programming and sample averaging to solve the resulting model. The direct search approach (neighbourhood search) is then applied to tackle the integer part. [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]

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

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]

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]

Municipal solid waste (MSW) management is known as one of the most crucial activities in municipalities that requires large amounts of fixed/variable and investment costs. The operational processes of collection, transportation and disposal include the major part of these costs. On the other hand, greenhouse gas (GHG) emission as environmental aspect and citizenship satisfaction as social aspect are also of particular importance, which are inevitable requirements for MSW management. This study tries to develop a novel mixed-integer linear programming (MILP) model to formulate the sustainable periodic capacitated arc routing problem (PCARP) for MSW management. The objectives are to simultaneously minimize the total cost, total environmental emission, maximize citizenship satisfaction and minimize the workload deviation. To treat the problem efficiently, a hybrid multi-objective optimization algorithm, namely, MOSA-MOIWOA is designed based on multi-objective simulated annealing algorithm (MOSA) and multi-objective invasive weed optimization algorithm (MOIWOA). To increase the algorithm performance, the Taguchi design technique is employed to set the parameters optimally. The validation of the proposed methodology is evaluated using several problem instances in the literature. Finally, the obtained results reveal the high efficiency of the suggested model and algorithm to solve the problem. [ABSTRACT FROM AUTHOR]

Nowadays, logistics is one of the most important tools in disaster relief operations. The logistic planning is essential and a key component in covering the initial needs in the immediate aftermath of any disaster. Planning is both necessary and practical, as it is generally possible to predict the types of disasters that should affect a given location and the needs that such disasters will be likely to cause. Transport planning, reception and distribution of emergency supplies, type and quantity of the resources, the way of procurement and storage of the supplies, the tools of the tracking and means transportation to the stricken area, the specialization of teams participating in the operation and plan of cooperation between these teams, are some vital life-saving coordination roles after natural disasters are connected directly to logistic planning. Turkey is located in one of the most active earthquake and volcanic regions which causes to many major earthquake-prone, in the world with a majority of the population living in these earthquake-prone areas. Earthquakes are one of the major disasters that require emergency logistic planning strategies due to their devastating effects, the large-scale natural disasters could cause major problem on commodities such as food, medicine etc. In this paper, a game theoretical model for emergency logistic planning is developed. To do this a cooperative game model is constructed from a flow problem which occurred after an earthquake in Istanbul. Several solution concepts for maximizing the transferred commodity are given. The paper ends with a conclusion and outlook to future studies. [ABSTRACT FROM AUTHOR]

The most important strategic issue for several industries is where to find facilities so as to discover a transportation path for optimizing the objectives at the same time. This paper acquaints a streamlining model with incorporate the facility location problem, solid transportation problem, and inventory management under multi-objective environment. The aims of the stated formulation are multi-fold: (i) to seek the optimum locations for potential facilities in Euclidean plane; (ii) to find the amount of distributed commodities; and (iii) to reduce the overall transportation cost, transportation time, and inventory cost along with the carbon emission cost. Here, variable carbon emission cost is taken into consideration because of the variable locations of facilities and the amount of distributed products. After that, a new hybrid approach is introduced dependent on an alternating locate-allocate heuristic and the intuitionistic fuzzy programming to get the Pareto-optimal solution of the proposed formulation. In fact, the performances of our findings are discussed with two numerical examples. Sensitivity analysis is executed to check the resiliency of the parameters. Ultimately, managerial insights, conclusions and avenues of future studies are offered at the end of this study. [ABSTRACT FROM AUTHOR]

Urgency of fresh items is increasing day by day everywhere. During transportation over a long distance, the rate of deterioration is also increasing for perishable items, and if the items are not reaching at destination within a specified time, then they are no longer fresh. Based on the fact, the customers loose the quality and quantity of items; as a result, suppliers are penalized for the occurrence of any such a situation. This problem is resolved by introducing two criteria such as time window restrictions and preservation technology in the proposed study. To prevent the deterioration rate, we find a strategy by formulating a model for multi-objective fixed-charge solid transportation problem with two criteria and then finalize one of them. To ensure that the necessary fresh items are to reached at the destination just in time, we introduce several objectives such as transportation cost, transportation time, preservation cost, penalty charge for time window, carbon emission with cap policy, deterioration, etc., such that these are optimized simultaneously. Again source, demand and conveyance capacity are not precisely estimated for different real-life situations. Here, we address such situation by choosing type-2 zigzag uncertain variable. Expected value operator is initiated to convert such uncertainty into crisp form, and then three approaches, namely, fuzzy programming, Pythagorean hesitant fuzzy programming and global criterion method are utilized to find Pareto-optimal solution. Finally two numerical examples are put to check the appropriateness of the formulated study. The results along with discussions and conclusions are described at last. The main contribution is that the deterioration of transported perishable items is reduced by imposing one of the proposed criteria according to the economical or other conditions of systems. [ABSTRACT FROM AUTHOR]

Challenges connected with neuroscience and the use of machine learning to support analytical processes encompass more and more areas, thus supporting practitioners and managerial decisions. These changes can also be seen in the area of human resource management and support for decisions on key future spending on the remuneration of future employees. The article presents an original spatial data enrichment and spatial data mining methodology used for the analysis of primary data based on a sample of 1149 young candidates from generation Z to measure the effectiveness of data mining learning methods. The studies used data collected directly from surveys that were "enriched" with spatial geolocation. The fact that the spatial context was taken into account in the studies made it possible to develop a model explaining the spatio-temporal differentiation of professional expectations of respondents from generation Z who were studying professions connected with broadly understood IT. The analyzes used modeling with linear polynomial regression, the neural network of a multi-layer perceptron type and the multivariate adaptive regression splines method in the variant with and without spatial data filtration. The use of different spatial data mining methods made it possible to compare the reliability of models of knowledge extraction from the data and to explain the significance of individual factors which affected the respondents' beliefs. The analysis shows that spatial filtering of the data generates twice lower mean squared error while effective application of machine learning methods requires the use of explanatory spatial data. [ABSTRACT FROM AUTHOR]

Global warming and climate change become a sensitive issue worldwide, and many countries try to control the CO2 emissions by investigating in many projects. This study proposes a sustainable inventory model to reduce carbon emission. It is generally assumed that demand is increasing at starting of an inventory level and decreasing at ending for a particular newly launched product. In this situation, demand is usually represented by Normal distribution or imprecise fuzzy demand. However, in reality, those assumptions do not always hold. In most of the inventory models, it is seen that demand is a linearly increasing function of time before starting deterioration; then, the demand decreases inversely proportional to the deterioration rate after starting the deterioration. To describe the possible situations more clearly, we introduce here a new type of demand, called as composite demand. Most of the products have a fixed life span for maintaining the quality in original condition which is known as non-instantaneous deterioration. Therefore, we design an inventory model for non-instantaneous deteriorating items with composite demand function. The deterioration rate is assumed to follow an exponential distribution; the holding cost is considered as time dependent. The model is formulated based on retailer's perspective and, thereafter, the total cost for the retailer is calculated. Numerical examples are provided for better understanding of this model. The results show that the total profit for non-instantaneous deteriorating items is higher than for instantaneously deteriorating items. A sensitivity analysis is conducted based on the important parameters. Finally, the paper ends with conclusions and an outlook to future research. [ABSTRACT FROM AUTHOR]

Optimization challenges are becoming increasingly complex, especially in supply chain planning; nowadays, traditional methods are not effective enough to solve complex optimization problems [[1]]. Optimization plays an important role in several branches of the engineering sciences. Apparel supply chain management based on reorder decision making was addressed by Baghbadorani et al. using a unified modelling language to delineate the relationship between agents and simulate the supply chain processes. [Extracted from the article]

Discussions of the resiliency, sustainability, and agility of supply chains are important in the research and management of supply chains in these difficult times, considering the ongoing pandemic of COVID-19. A viable supply chain is often characterized by resiliency, sustainability, and agility in its network design. Resiliency is essential because disruption and demand fluctuations are forced upon SCs, and the effects of these for many managerial supply chains are unknown. In addition, applying novel technology in the supply chain, such as blockchain, Internet-of-Things (IoT), and artificial intelligence (AI) as agility tools can assist and enable the transition to lean production. This special issue of the Foundations of Computing and Decision Sciences is dedicated to advancements in this fields. Besides, the special issue covers instructional information about OR techniques which are useful for addressing real-world applications on such challenges. [ABSTRACT FROM AUTHOR]

Medical waste management (MWM) is an important and necessary problem in the COVID-19 situation for treatment staff. When the number of infectious patients grows up, the amount of MWMs increases day by day. We present medical waste chain network design (MWCND) that contains health center (HC), waste segregation (WS), waste purchase contractor (WPC), and landfill. We propose to locate WS to decrease waste and recover them and send them to the WPC. Recovering medical waste like metal and plastic can help the environment and return to the production cycle. Therefore, we proposed a novel viable MWCND by a novel two-stage robust stochastic programming that considers resiliency (flexibility and network complexity) and sustainable (energy and environment) requirements. Therefore, we try to consider risks by conditional value at risk (CVaR) and improve robustness and agility to demand fluctuation and network. We utilize and solve it by GAMS CPLEX solver. The results show that by increasing the conservative coefficient, the confidence level of CVaR and waste recovery coefficient increases cost function and population risk. Moreover, increasing demand and scale of the problem makes to increase the cost function. [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]

Human resources (HR) have a key impact on the creation and implementation of modern products, solutions and concepts. Relatively new and rarely undertaken research challenge in enterprise is optimization of HR in the context of their location and requirements for working conditions. A great challenge here is the transparency and reliability of the collected data. In the article, we present a modern approach to knowledge extraction based on Artificial Intelligence (AI) and Multivariate Adaptive Regression Splines optimizing the availability of HR with a high innovation rate, taking into account their availability time and location. This study was conducted on a group of 5095 young people from the Z generation. A total of 11 variables were analyzed in the context of innovation and presented in this article. The effect of research using machine learning methods is the analysis of the characteristics of generation Z representatives, whose desire is to work in innovative companies. Research results indicate that some regions offer candidates with a higher level and commitment to innovation, and thus make HR more available for the development of innovative products. Chosen models designed by using AI and Operational Research Analytics were presented in the graphic visualization, which is a novelty in the presentation of similar issues in relation to HR. [ABSTRACT FROM AUTHOR]

Waste management involved in various fields of global ecosystem that provides several positive effects for green environment and sustainable development. We devise a multi-objective solid transportation model of waste management problem in agriculture field and forest department for urban or rural development. Starting to end point of the problem covered by considering the objective functions as transportation cost, job opportunity and carbon emission. Carbon emission is restricted by the combination of several policies of carbon mechanism (carbon tax, cap-and-trade and offset policy). Various critical sitchs appear in such realistic process and uncertainty attached with related data. Here we prefer Pythagorean hesitant fuzzy environment to overcome deep uncertainty rather than single uncertainty. After that, we initiate a ranking approach to convert uncertain data into crisp data. To justify the appropriateness of the formulated model and to select the best policy of carbon mechanism, we study two industrial applications with various cases of such mechanism. To derive the Pareto-optimal solution of the problems, two fuzzy techniques, namely, fuzzy programming and Pythagorean hesitant fuzzy programming, are utilized here. Comparative study, model validation, sensitivity analysis, managerial insights and conclusions with future research scopes are outlined at last. [ABSTRACT FROM AUTHOR]

Desirable performance of sustainable pharmaceutical supply chain plays a key role in health attainment and performance evaluation is an essential element of effective pharmaceutical supply chain. Several models have been developed for performance evaluation of supply chains. The important point is that the model should be comprehensive and produces the reliable results. For this purpose, comprehensive criteria for evaluation of all levels at the supply chain is identified based on the revised perspectives of Balanced Scorecard. Considering the network nature of the supply chain, Anderson Peterson Network Data Envelopment Analysis (AP-NDEA) model is used to measure efficiency and rank efficient units. To overcome the weakness of this model, this paper for the first time integrates the predictive Neural Network with the AP-NDEA model called Neuro-AP-NDEA. The proposed model estimates the efficiency measurement function in the shortest time, results in computational savings in memory and is more resistant to statistical disturbances. To make the evaluation model more effective and realistic, Interval Evidential Reasoning with linguistic Interval Fuzzy Belief degree (IFB-IER approach) is applied. A numerical example is provided to illustrate the model. The analytical results indicate that the Neuro-AP-NDEA model allows for an accurate prediction and more efficient performance evaluation than the AP-NDEA model. [ABSTRACT FROM AUTHOR]

This special issue of the Foundations of Computing and Decision Sciences, titled "Computational Performance Analysis based on Novel Intelligent Methods: Exploration and Future Directions in Production and Logistics", is devoted to the application of Computational Performance Analysis (CPA) for real-life phenomena. The special issue and its editorial present novel intelligent methods as they meet with various research topics in production and logistics, especially in terms of challenges, limitations and future trends. This special issue aims to bring together current progress on the CPA, organization management, and novel models and solution techniques that can contribute to a better understanding of the CPA systems and delineate useful practical strategies. Methodologically interesting and well-documented case studies are highly recommended. Additionally, the special issue covers innovative cutting-edge research methodologies and applications in the related research field. [ABSTRACT FROM AUTHOR]

Rumours are a kind of information that has a impact on social life and economies, which spread quickly and widely, especially, via internet. Recently, the spread of information affects our daily lives due to the increasing number of social media users. Rumours are defined in social areas which are delivered by gossips, fake news, marketing, social media all the way even to revolutions. In this paper, we study the dynamics of a rumour propagation model with a numerical approach. By using an algorithmic technique with an error analysis, the validity of the numerical technique is described. We investigate the model with this numerical approach to explain the dynamics of rumour propagation. Besides, we explain sensitivity analyses of the model of parameters. Then by numerical simulations efficiency of the technique is shown. Finally, the results are displayed and discussed with the help of figures and tables. The paper ends with a conclusion and an outlook to future studies. [ABSTRACT FROM AUTHOR]

Nowadays green retail practices are increasing in number. In this paper, an inventory model is formulated with retail investments in green operations including variable holding cost. In the proposed model, the demand rate depends on selling-price as well as green concern level. The main objective of the study is to find optimal replenishment time and optimal green concern level by considering profit maximization. Here, the effect of greening-level on purchasing-price and selling-price is shown. By investing in green operations, a retailer creates considerable impact on profitability. As the greening level increases, the profit of a retailer increases. Product with lower deterioration rate would increase the profit. Based on the above consideration, the mathematical model is designed by allowing carbon tax. Then, the model is explored by numerical examples. Mathematica software is used to obtain global maximum solution to the optimal cycle time and optimal greening-level. A sensitivity analysis with respect to major parameters is performed in order to access the stability of the model. The paper concludes with conclusions and an outlook of possible future directions is depicted. [ABSTRACT FROM AUTHOR]

Sustainable development requires scheduling and implementation of projects by considering cost, environment, energy, and quality factors. Using a robust approach, this study investigates the time-cost-quality-energy-environment problem in executing projects and practically indicates its implementation capability in the form of a case study of a bridge construction project in Tehran, Iran. This study aims to take into account the sustainability pillars in scheduling projects and uncertainties in modeling them. To model the study problem, robust nonlinear programming (NLP) involving the objectives of cost, quality, energy, and pollution level is applied with resource-constrained. According to the results, as time diminished, the cost, energy, and pollution initially decreased and then increased, witha reduction in quality. To make the model close to the real world by considering uncertainties, the cost and quality tangibly improved, and pollution and energy consumption declined. We applied the augmented ε ε -constraint method to solve the proposed model. According to the result of the research, with regard to the time-cost, time-quality, time-energy, and time-pollution charts, as uncertainty increases, the cost and quality will improve, and pollution and energy will decrease. The proposed model can be employed for all industrial projects, including roads, construction, and manufacturing. [ABSTRACT FROM AUTHOR]

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]

Nowadays, using Blockchain Technology (BCT) is growing faster in each country. It is essential to apply BCT in Supply Chain Network Design (SCND) and is considered by the designer and manager of SC. This research indicates Viable Supply Chain Network Design (VSCND) by applying BCT. A new form of two-stage robust optimization is suggested. Facility locations and activation BCT for VSCND is the first stage of decisions; finally, we determine flow transshipment between components in the next stage. The GAMS-CPLEX is used for solving the model. The results show that running BCT will decrease 0.99% in costs. There is an economic justification for using BCT when demand is high. A fix-and-optimize and Lagrange relaxation (LR) generate lower and upper bound to estimate large scale in minimum time. The gap between the main model and fix-and-optimize is better than the LR algorithm. Finally, this research suggests equipping VSCND by BCT that becomes more resilient against demand fluctuation, sustainable, and agile. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, using renewable energy (RE) is faster growing by each country. The managerial and designer of supply chain network design (SCND) have to plan to apply RE in pillars of supply chain (SC). This research indicates resilience and sustainable SCND by considering RE (RSSCNDRE) for the first time. A two‐stage new robust stochastic optimization is embedded for RSSCNDRE. The first stage locates facility location and RE and the second stage defines flow quantity between SC components. We solve the model by GAMS‐CPLEX solver to locate components of SC and RE. Effects of changing conservative coefficient and demand are investigated and by increasing 20% for conservative coefficient, the cost function increase by 0.5%. Also, when demand is high, activating RE is economically feasible and we cannot buy and supply energy by the government power network and have to supply energy by RE. After activating RE, by increasing 20% for demand, the cost function increases by 6%. We contribute fix‐and‐optimize strategy to define the upper bound for a large‐scale problem. The proposed upper bound for the main model is less than 10% and appropriate for estimating the cost of large‐scale problems. This research suggested to equip SC by RE that SC becomes resilient against demand fluctuation and sustainable energy resource compatible with sustainable development goal (SGD7). [ABSTRACT FROM AUTHOR]

Neuroscience is a combination of different scientific disciplines which investigate the nervous system for understanding of the biological basis. Recently, applications to the diagnosis of neurodegenerative diseases like Parkinson's disease have become very promising by considering different statistical regression models. However, well-known statistical regression models may give misleading results for the diagnosis of the neurodegenerative diseases when experimental data contain outlier observations that lie an abnormal distance from the other observation. The main achievements of this study consist of a novel mathematics-supported approach beside statistical regression models to identify and treat the outlier observations without direct elimination for a great and emerging challenge in humankind, such as neurodegenerative diseases. By this approach, a new method named as CMTMSOM is proposed with the contributions of the powerful convex and continuous optimization techniques referred to as conic quadratic programing. This method, based on the mean-shift outlier regression model, is developed by combining robustness of M-estimation and stability of Tikhonov regularization. We apply our method and other parametric models on Parkinson telemonitoring dataset which is a real-world dataset in Neuroscience. Then, we compare these methods by using well-known method-free performance measures. The results indicate that the CMTMSOM method performs better than current parametric models. [ABSTRACT FROM AUTHOR]

The special issue: "Numerical Techniques Meet with OR" of the Foundations of Computing and Decision Sciences consists of two parts which are of the main theme of numerical techniques and their applications in multi-disciplinary areas. The first part of this special issue was already collected in the FCDS Vol. 46, issue 1. In this second part of our special issue editorial, a description of the special issue presents numerical methods which can be used as alternative techniques for Scientific Computing and led Operational Research applications in many fields for further investigation. [ABSTRACT FROM AUTHOR]

This paper suggests a novel robust formulation designed for optimizing the parameters of the turning process in an uncertain environment for the first time. The aim is to achieve the lowest energy consumption and highest precision. With this aim, the current paper considers uncertain parameters, objective functions, and constraints in the offered mathematical model. We proposed several uncertain models and validated the results in real-world case studies. In addition, several artificial intelligence-based solution techniques are designed to solve the complex nonlinear problem. We determined the most efficient solution approach by solving various test problems. Then, simulated several scenarios to demonstrate the robustness of our results. The results showed that the solutions provided by the offered model significantly reduce energy consumption in different setups. To ensure the reliability of the results, we carried out worst-case sensitivity analyses and found the most critical parameters. The results of the worst-case analyses indicated that the offered robust model is efficient and saves a significant amount of energy comparing to traditional models. It is shown that the provided solution by the presented robust formulation is reliable in all situations and results in the lowest energy and the best machining precision. [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]

We present a new method based on unification of fictitious time integration (FTI) and group preserving (GP) methods. The GP method is applied in numerically discretized ordinary differential equations obtained from application of FTI method to a given partial differential equation (PDE). The algorithm is applied to hyperbolic telegraph equation and utilizes the Cayley transformation and the Pade approximations in the Minkowski space. It avoids unauthentic solutions and ghost fixed points which is one of the advantages of the present method over other related numerical methods in the literature. The technique is tested on three specific examples for various parameter values appearing in the telegraph equation and discretization steps. Such solutions of the telegraph equation are obtained first time in this paper. Illustrative figures are provided. Efficiency of the method is determined by an error analysis which is achieved by comparing numerical solutions with exact solutions. [ABSTRACT FROM AUTHOR]

Game theory plays an important role in numerous decision-oriented real-life problems. Nowadays, many such problems are basically characterized by various uncertainties. Uncertainties come to happen due to decision makers' collection of data, intuition, assumption, judgement, behaviour, evaluation and lastly, due to the problem itself. Fuzzy concept with membership degree made an initialization towards the treatment of uncertainty, but it was not enough. Intuitionistic fuzzy concept was evolved concerning with both membership and non-membership degrees but failed to express reality more accurately. Then, neutrosophy logic was developed with a new degree in uncertainty, say, indeterminacy degree besides membership and non-membership degrees. Multi-objective optimization is an area of multiple-criteria decision making related with mathematical optimization problems involving more than one objective function to be optimized at the same time. Game theory (matrix game) problems with imprecise, vague information, like neutrosophic, can be formed with multiple objective functions. We develop and analyse a matrix game with multiple objectives, and solve the problem under a single-valued neutrosophic environment in linguistic approach. The main achievement of our study is that we here introduce a problem-oriented example to justify our designed methodologies with a successful real-life implications using linguistic neutrosophic data rather than crisp data as used in previous researches. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1007/s10479-021-04486-2.Correction to: Annals of Operations Research https://doi.org/10.1007/s10479-021-04486-2This erratum published as several formatting issues were noticed with Tables and equations.Original article has been corrected.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.By Erfan Babaee Tirkolaee; Alireza Goli; Selma Gütmen; Gerhard-Wilhelm Weber and Katarzyna SzwedzkaReported by Author; Author; Author; Author; Author [Extracted from the article]

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]