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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]

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]

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]

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]

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]

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]

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]

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]

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]

This Special Issue (SI) aimed to investigate the achievement of resiliency and sustainability in supply chains. The concepts of resiliency, sustainability, and agility of supply chains have been at the top of the list more than ever. Resiliency is essential because disruption and demand fluctuations are forced upon supply chains, and the effects of these on many managerial supply chains are unknown. [Extracted from the article]

The presented issue concerns publications presented as part of the Workshop 2018 of the EURO Working Group on Operational Research for Development (EWG-ORD) held in Complutense University of Madrid (Spain), July 5–7. This editorial presents an introduction on sustainable development and the Sustainable Development Goals. It contains short descriptions of the research area presented in individual publications, highlighting their contributions to the achievement of the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Carbon emissions due to economic activities are recognized to be global problem. Governments of all countries need to evolve environmental policies and practices for large-scale collective actions to regulate green house gas emission. Fuel quality standards for vehicles, stricter codes for construction, emission limits for industrial units and power plants are some of measures advocated to speed up emission control. This study investigates how far different sectors of a developing economy are able to manage green supply chain with respect to 4 aspects of environmental practices viz. Green procurement, green logistics, green products and process designs and regulatory framework. Globally, corporate social responsibility (CSR) assumes significance in recent years not only with respect to societal issues but also for environmental protection. Research suggests that CSR department creates culture for implementation of CSR activities. We investigate whether CSR departments in the sample organizations have made any difference in achieving emission control objectives. Data are from manufacturing organizations in a congested industrial region of India. We apply non-parametric Kruskal–Wallis and Mann–Whitney tests; then regression analysis is carried out to ascertain predictability of carbon reduction performance with respect to 4 environmental constructs. Results highlight positive roles of inclusion of green enablers—green procurement, green logistics, green product and process design as contributory factors for improvement in carbon performance and reveal that green logistics in the given scenario need major improvement in carbon performance. Our model also considers the impact of size of the organization on carbon performance in terms of workforce. [ABSTRACT FROM AUTHOR]

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]

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]

The delineation of the transportation network is a strategic issue for all over the place. The problem of locating new facilities among several existing facilities and minimizing the total transportation cost are the main topics of the location network system. This paper addresses the transportation-p-facility location problem (T-p-FLP) which makes a connection between the facility location problem and the transportation problem, where p corresponds to the number of facilities. The T-p-FLP is a generalization of the classical transportation problem in which we have to seek where and how we impose the p-number of facilities such that the total transportation cost from existing facility sites to the potential facility sites will be minimized. The exact approach, based on the iterative procedure, and a heuristic approach as applied to the T-p-FLP are discussed and corresponding results are compared. An experimental example is incorporated to explore the efficiency and effectiveness of our proposed study in reality. Finally, a summary is given together with suggestions for future studies. [ABSTRACT FROM AUTHOR]

Determining optimum places for the facilities and optimum transportation from existing sites to the facilities belongs to the main problems in supply chain management. The solid transportation-p-facility location problem (ST-p-FLP) is an integration between the facility location problem and the solid transportation problem (STP). This paper delineates the ST-p-FLP, a generalization of the classical STP in which location of p-potential facility sites are sought so that the total transportation cost by means of conveyances from existing facility sites to potential facility sites will be minimized. This is one of the most important problems in the transportation systems and the location research areas. Two heuristic approaches are developed to solve such type of problem: a locate-allocate heuristic and an approximate heuristic. Thereafter, the performance of the proposed model and the heuristics are evaluated by an application example, and the obtained results are compared. Moreover, a sensitivity analysis is introduced to investigate the resiliency of the proposed model. Finally, conclusions and an outlook to future research works are provided. [ABSTRACT FROM AUTHOR]

The presented paper concerns publications presented as part of XXXI International Seminar of Ergonomics. It presents basic information on ergonomics terminology. In addition, the development directions of the ergonomics research area in historical terms were shown. This is because, among other things organizing background for over 40 years International Ergonomics Seminars series in Poland. The second part of the text contains short descriptions of the research area presented in individual publications, highlighting their specificity and connections with the leading topic of human factors. [ABSTRACT FROM AUTHOR]

A game is a situation which conceives and concludes through reality involving a set of players, may be two or more. Conclusions of such situations are not always very easy, e.g., if one wins other loses. Sometimes, when considering the totality, the outcome of game is not linguistically zero, and we utter the term non-zero-sum game. Prisoners' dilemma game is one of most cited examples in non-zero-sum game literature. In this paper, human trafficking, one of the most rising problems of today's society is viewed through Prisoners' dilemma game using hesitant interval-valued intuitionistic fuzzy-linguistic term set, where linguistic terms in interval are expressed by linguistic semantics first, and then corresponding indices are used. Finally, Nash equilibrium is derived from the given definition, and the achieved result establishes a close contact with reality using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Dominance property of matrix game theory. [ABSTRACT FROM AUTHOR]

Transversal competences constitute a set of the knowledge, skills, and attitudes required for various positions and in different professions. Such competences include: entrepreneurship, teamwork, creativity, and communicativeness; they are increasingly listed by employers in different countries as the key requirements in the labor market. The article presents the model of accelerating the process of acquiring transversal competences, developed based on the analysis of data collected in four countries of the European Union: Poland, Finland, Slovakia, and Slovenia. In the analysis, multivariate additive regression spline method was used, along with artificial neural networks, in order to create the best model describing the influence of different variables on the acceleration of acquiring transversal competences. Herewith, we demonstrated that by accelerating the acquisition of the transversal competence of entrepreneurship is influenced by the following factors: rank of the training method in the developed matrix, student numbers and the weighted average of the pace of acceleration regarding the acquisition of the remaining transversal competences, i.e., teamwork, communicativeness and creativity by the given student. The results validate our new method of the acceleration of acquiring transversal competences by students. Students may be from various higher education institutions in different countries. Developed results may be used in the course of education within the framework of the already planned vocational courses and for developing the skills required by employers for various positions and in different professions. [ABSTRACT FROM AUTHOR]

Aim of this study is examining the logistic activities of enterprises with the job analysis method; to be organized and designed in such a way that it can be done most effectively, efficiently and ergonomicaly in terms of time and method studies. In this context, in the first phase of this study which was designed as four stages, the company to be investigated in terms of process optimization was determined. In second step, logistics activities of the selected large scale cereal production companies which operate in Gaziantep were collected within the scope of the study. Data were analyzed by job analysis method and a new method was proposed for the process that the related activities formed in the third phase of study. In the fourth step, new process is proposed and proven to increase the efficiency, effectiveness and ergonomy of the business is presented. As a result, it has been calculated that the proposed process saves 75% of the labor cost of enterprise in logistic activities. In addition, with the proposed new process, the total time of packaging and loading processes was reduced by 47%. The method and conclusions that are observed in this study are thought to be of the benefit of all businesses and people with similar activities. [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]

Electric energy plays an irreplaceable role in nearly every person's life on earth; it has become an important subject in operational research. Day by day, electrical load demand grows rapidly with increasing population and developing technology such as smart grids, electric cars, and renewable energy production. Governments in deregulated economies make investments and operating plans of electric utilities according to mid- and long-term load forecasting results. For governments, load forecasting is a vitally important process including sales, marketing, planning, and manufacturing divisions of every industry. In this paper, we suggest three models based on multivariate adaptive regression splines (MARS), artificial neural network (ANN) and linear regression (LR) methods to model electrical load overall in the Turkish electricity distribution network, and this not only by long-term but also mid- and short-term load forecasting. These models predict the relationship between load demand and several environmental variables: wind, humidity, load-of-day type of the year (holiday, summer, week day, etc.) and temperature data. By comparison of these models, we show that MARS model gives more accurate and stable results than ANN and LR models. [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]

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]

In financial markets with high uncertainties, the trade-off between maximizing expected return and minimizing the risk is one of the main challenges in modeling and decision making. Since investors mostly shape their invested amounts towards certain assets and their risk aversion level according to their returns, scientists and practitioners have done studies on that subject since the beginning of the stock markets' establishment. In this study, we model a Robust Optimization problem based on data. We found a robust optimal solution to our portfolio optimization problem. This approach includes the use of Robust Conditional Value-at-Risk under Parallelepiped Uncertainty, an evaluation and a numerical finding of the robust optimal portfolio allocation. Then, we trace back our robust linear programming model to the Standard Form of a Linear Programming model; consequently, we solve it by a well-chosen algorithm and software package. Uncertainty in parameters, based on uncertainty in the prices, and a risk-return analysis are crucial parts of this study. A numerical experiment and a comparison (back testing) application are presented, containing real-world data from stock markets as well as a simulation study. Our approach increases the stability of portfolio allocation and reduces the portfolio risk. [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]

Productivity, profitability and sustainability have become the essence of business survival; thus, modern industrial establishments are turning into frameworks consistent with environmental objectives by adopting cleaner technologies, green practices have ultimately resulting in generation of fewer pollutants. Urbanization and burgeoning technological advancement in different sector within India have brought the concept of green supply chain management, to highlight the importance of responsible consumption and production to maintain environmental quality, reduce wastage and bring out economic growth. This paper aims to segregate the sustainable and competitive performers from the average ones in the Indian manufacturing sector and to understand the degree of the impact of green practices of supply chain management based on their contribution towards sustainable environment. Using survey method, data are collected from 54 manufacturing organizations from Pune Nashik area, and a comprehensive framework of sustainability measurement is developed through successive applications of Chi-square Automatic Interaction Detector analysis, CHAID analysis. This paper is unique as it has applied the concept of CHAID analysis for first time to identify green logistics as main driver to achieve a reduction of ecological damage and improve business performance. [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]

Recently, crypto-cloud computing has become an interesting research area with many technical, security, commercial and financial aspects, goals and consequences. The original intention of the cloud is to link computing stations (services) for collaboration. Sharing and coordination of computational resources is important because of the activities between service providers and service requesters. Considering the cooperative functionality of crypto-cloud computing, the use of game theory in that area has became very beneficial. In the sequel, we mathematically associate that area with game theory, i.e., the bargaining and compromising of interests of various 'players', by using a game-theoretical approach which arises from networks, servers, operating systems, storage devices, etc. Further, we propose a novel efficient encryption system by using XTR (effective and compact subgroup trace representation) which has the property of semantic security. Those interactions has been constructed in the direction of how cryptographic tools can be used to address a natural optimization problem in the fields of game theory and financial economics. It is believed that game theory and its optimization is going to provide a suitable framework for the design of a crypto-cloud computing system that will be perceived as a strong technique and satisfy the needs of many participants and users of the cloud. The paper ends with a conclusion and an outlook to future studies. [ABSTRACT FROM AUTHOR]

In this paper, a deterministic inventory control model with deterioration is developed. Here, the deterioration rate follows stochastic deterioration, especially Weibull distribution deterioration. A time-dependent demand approach is introduced to show the applicability of our proposed model and to be up-to-date with respect to time. The main purpose of the paper is to investigate the optimal retailer's replenishment decisions for deteriorating items including time-dependent demand for demonstrating more practical circumstances within economic-order quantity frameworks. Keeping in mind the criterion of modern era, we consider that the holding cost is totally dependent on time, and shortages are allowed for this model. Subject to the formulated model, we minimize the total inventory cost. The mathematical model is explored by numerical examples to validate the proposed model. A sensitivity analysis of the optimal solution with regard to important parameters is also carried out to elaborate the quality, e.g., stability, of our result and to possibly modify our model. The paper ends with a conclusion and an outlook to future studies. [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]

An introduction is presented in which the editors discuss various reports within the issue on topics related to neuroscience including intracellular signaling and regulation, behavioral and cognitive functions and neuroimaging.