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The global water resource crisis caused by population growth, and increasing water demand has led to using treated wastewater in many regions. Effective distribution of wastewater treatment across various consumer sectors is crucial for sustainable environmental practices and resource management. Wastewater Allocation (WA) is a multi-criteria decision-making problem, and this study used modified VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method in the combination with GP (Goal Programming) technique to optimize WA in Iran. The environmental effects were evaluated using Leopold Matrix results. The proposed approach resulted in an allocation of 6912 m3/day for urban green space irrigation 10,368 m3/day for the agricultural irrigation, and 2592 m3/day for industrial water demand at the Shahreza wastewater treatment plant. The research shows that better recommendations for resource allocation may be developed by combining quantitative assessment, optimization, and mathematical models. This approach has proven successful in maximizing the reuse of wastewater for a variety of uses. [ABSTRACT FROM AUTHOR]

River restoration programmes with the goal of conserving and rehabilitating inland fishes have a multi‐decadal history, but evaluation and synthesis of past restoration actions have been limited by a lack of monitoring and reporting. Given that calls for both monitoring and systematic reviews of restoration have increased, we were interested in the influence that restoration has had on improving conditions for riverine fishes resulting from long‐standing and increasingly prominent stressors. Our objectives were to (1) identify which stressors were targeted in river restoration efforts, (2) determine the proportion of published studies that articulate restoration goals and develop comparative monitoring designs for assessing the effects of restoration on fishes, and (3) conduct a meta‐analysis to synthesise fish responses to restoration projects.We assessed restoration effectiveness for increasing fish density and richness from peer‐reviewed papers published over the past decade using a global systematic review and meta‐analysis.We found that restoration actions addressed major stressors primarily by improving in‐stream habitat (37%), increasing in‐stream longitudinal connectivity (26%) and increasing lateral floodplain connections (9%). Although 81% of studies had comparative monitoring designs (i.e., before/after and control/impact) and stated restoration goals, only 40% of those studies reported sufficient data to be included in the meta‐analysis. Projects which increased in‐stream connectivity had the largest positive effect size on fish density and richness compared to in‐stream habitat improvements and increasing floodplain connections. Time since restoration and restoration size (i.e., geographical footprint) were not strong predictors of fish response effect sizes.Restoration effectiveness was highly variable among project types. Authors of studies included in the meta‐analysis often identified spatial or temporal scale of monitoring, overriding catchment conditions, and recolonisation potential as sources of variability and effectiveness in restoration outcomes. Systematic reporting of these and other covariates may help guide processes in restoration evaluation and provide valuable research insights. Despite increased emphasis on monitoring, incomplete data reporting limited the number of studies that could be included for quantitative meta‐analysis. Persistent emphasis on setting specific criteria (e.g., measurable outcomes of fish response) for restoration goals, project monitoring, data reporting, information sharing and collaborative projects is likely to continue to improve understanding of restoration effectiveness transferable to future endeavours.Our results can be used to support river restoration practitioners with evidence‐based information to evaluate the cost–benefit ratio of competing restoration priorities, and inform restoration planning and implementation for riverine fish. [ABSTRACT FROM AUTHOR]

To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems. [ABSTRACT FROM AUTHOR]

Due to the high pollution of the transportation sector, nowadays the role of electric vehicles has been noticed more and more by governments, organizations, and environmentally friendly people. On the other hand, the problem of electric vehicle routing (EVRP) has been widely studied in recent years. This paper deals with an extended version of EVRP, in which electric vehicles (EVs) deliver goods to customers. The limited battery capacity of EVs causes their operational domains to be less than those of gasoline vehicles. For this purpose, several charging stations are considered in this study for EVs. In addition, depending on the operational domain, a full charge may not be needed, which reduces the operation time. Therefore, partial recharging is also taken into account in the present research. This problem is formulated as a multi-objective integer linear programming model, whose objective functions include economic, environmental, and social aspects. Then, the preemptive fuzzy goal programming method (PFGP) is exploited as an exact method to solve small-sized problems. Also, two hybrid meta-heuristic algorithms inspired by nature, including MOSA, MOGWO, MOPSO, and NSGAII_TLBO, are utilized to solve large-sized problems. The results obtained from solving the numerous test problems demonstrate that the hybrid meta-heuristic algorithm can provide efficient solutions in terms of quality and non-dominated solutions in all test problems. In addition, the performance of the algorithms was compared in terms of four indexes: time, MID, MOCV, and HV. Moreover, statistical analysis is performed to investigate whether there is a significant difference between the performance of the algorithms. The results indicate that the MOSA algorithm performs better in terms of the time index. On the other hand, the NSGA-II-TLBO algorithm outperforms in terms of the MID, MOCV, and HV indexes. [ABSTRACT FROM AUTHOR]

This study achieved the goal of guiding bed design and optimization by conducting multi-objective optimization research on the performance of CNC lathe beds. In this study, Morris analysis was first performed on the sensitivity of the parameters, and then out to optimize the parameters using a combination of neural network and genetic algorithm. The loss function value, RMSE error accumulation, recall, sensitivity and specificity of the ASSGA-BP optimization model were better. The maximum error between the predicted and true values of the ASSGA-BP model was 0.28 mm. In the performance study of the multi-objective optimization method based on the Morris sensitivity analysis and the improved GA algorithm, the average MAE value is 0.91 %. The average RMSE value is 0.59 %. Also, the new model is significantly better than the NSGA-II, EGA, and FGA algorithms in terms of both the number of final non-dominated solutions and the speed of reaching convergence. The above results demonstrate that the model proposed in this study has high performance, can achieve faster convergence and has the best stability of the convergence state. The innovation of this article lies in the use of the Morris method to screen and evaluate numerous parameters in order to improve the accuracy of the calculation results and ensure the effectiveness of the optimization results. The improved algorithm overcomes the problems of BP neural network and can effectively improve the generalization performance of the neural network, thereby improving the prediction accuracy of the model. [ABSTRACT FROM AUTHOR]

Definition: The field of Water Resources Management (WRM) is becoming increasingly interdisciplinary, realizing its direct connections with energy, food, and social and economic sciences, among others. Computationally, this leads to more complex models, wherein the achievement of multiple goals is sought. Optimization processes have found various applications in such complex WRM problems. This entry considers the main factors involved in modern WRM, and puts them in a single optimization problem, including water allocation from different sources to different uses and non-renewable and renewable energy supplies, with their associated carbon emissions and costs. The entry explores the problem mathematically by presenting different optimization approaches, such as linear, fuzzy, dynamic, goal, and non-linear programming models. Furthermore, codes for each model are provided in Python, an open-source language. This entry has an educational character, and the examples presented are easily reproducible, so this is expected to be a useful resource for students, modelers, researchers, and water managers. [ABSTRACT FROM AUTHOR]

The fleet management system (FMS) plays a vital role in truck and shovel open-pit mining operations. The list of expectations from the FMS ranges from data recording to making operational decisions. This article proposes a nested fleet management system (N-FMS) for open-pit mining operations. The primary contribution of the proposed FMS is connecting the operation to the strategic plan by incorporating shovel allocation and plant feed optimization. Another contribution of the proposed N-FMS is that it simultaneously optimizes the utilization of the shovel fleet and truck fleet. The proposed FMS makes decisions using two nested multiple-objective mixed-integer linear goal programming models. Results of the implementation of the developed N-FMS in a metal mining case study show that compared to a locked-in operation, the mine experienced a 14.6% improvement in its required truck fleet capacity to meet the production target. [ABSTRACT FROM AUTHOR]

The most common situation encountered in the operating room scheduling is the uncertainty of operation times. This situation may cause the operations to be delayed or canceled. In this study, stochastic operating room scheduling is discussed under the uncertainty of operation times. In real life, variability may vary depending on many factors from operation to operation/patient to patient. These factors are the surgeon's experience, the difficulty of the operation, the patient's weight, age, disease history, etc. In this study, separate coefficients of variability were determined for each operation, taking into account the variability factors. Operations are scheduled, taking into account the operation-specific coefficients of variation. To evaluate the variability factors, analytical network process method was used considering the interaction between them. The level of uncertainty/coefficient of variation of each operation was determined with the PROMETHEE method. Finally, the logical modeling power of constraint programming is used to solve the operating room scheduling problem. In the proposed constraint programming model, the flexibility of the goal programming method was utilized. For the modeling of uncertainties, a chance-constrained approach was used. The case study demonstrates that the proposed approach is a novel and outstanding technique, and the proposed CP model is efficient in solving the problem. As a result of the study, the uncertainty in the operation time of each patient was calculated as the variability according to the factor weights, and the tables were reconstructed according to this situation. The performance and effectiveness of the new schedules obtained under variability are shown. [ABSTRACT FROM AUTHOR]

The essential goal of this work is to suggest applying the multi-dimensional Sumdu generalized Laplace transform decomposition for solving pseudo-parabolic equations. This method is a combination of the multi-dimensional Sumudu transform, the generalized Laplace transform, and the decomposition method. We provided some examples to show the effectiveness and the ability of this approach to solve linear and nonlinear problems. The results show that the proposed method is reliable and easy for obtaining approximate solutions of FPDEs and is more precise if we compare it with existing methods. [ABSTRACT FROM AUTHOR]

This paper aims to realize multi-objective optimization of cycloid-pin gears to improve the positioning accuracy and load-carrying capacity of the rotary vector (RV) reducer, via the consideration of backlash, transmission error, and torsional stiffness. Initially, the analytical models of the RV transmission backlash and transmission error are developed by using both purely geometrical and equivalent model methods individually. Based on the generalized Hooke's law, a torsion angle model is established to characterize the torsional stiffness of the system, utilizing methods such as Hertzian contact theory and bearing stiffness models. Subsequently, employing the Monte Carlo method, extremum method, and quality loss function, mapping objective functions for dimensional accuracy (tolerance) and transmission performance (backlash, transmission error, and torsional stiffness) are constructed. The geometry dimensions, dimensional accuracy, and modification of the cycloid-pin gear are considered as design variables to create a multi-objective optimization model. The improved Parallel Adaptive Genetic Algorithm using Deferential Evolution (PAGA-DE) is used for multi-objective solutions. Through example calculations, the impact of cycloid-pin gear parameters on transmission performance before and after optimization is determined. The reliability of backlash after optimization within 1.5′ reaches 99.99%, showing an increase of 8.24%. The reliability of transmission error within 1′ reaches 98.52%, demonstrating an increase of 1.35%. The torsional angle is reduced by 8.9% before optimization. The results indicate that the proposed multi-objective optimization design method for cycloid-pin gears can achieve the goal of improving the transmission performance of the RV reducer. [ABSTRACT FROM AUTHOR]

In this paper, we first propose the model of multi-leader–follower games with fuzzy goals involving fuzzy parameters and introduce its α -FNS equilibrium. Next, we shift our attention to the existence of α -FNS equilibrium and prove it by Kakutani's fixed point theorem. Finally, we illustrate an example to show that the equilibrium existence result is valid. [ABSTRACT FROM AUTHOR]

The main target of health institutions is to provide the health services needed by society at the desired quality with the lowest possible cost. Considering the total number of employees in health institutions, nurse assignment and scheduling have an essential role in increasing efficiency and improving service quality due to the one-to-one interaction of nurses with patients. This study proposes a nurse scheduling model based on nurses' skill levels incorporated into a decision support system. The skill level of nurses is assessed using Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution method based on eight criteria. The nurse scheduling problem is then modeled with 0-1 Goal Programming, considering the skill assessment as a constraint. The practicality of the proposed model is examined for the assignment and scheduling conditions of nurses at the 3rd level of surgical intensive care in a general hospital, and the valuable aspects of the proposed approach are discussed. When the proposed solution is compared with the current situation, it is realized that one nurse is saved without worsening the constraints. [ABSTRACT FROM AUTHOR]

In this study, faculty-level university course timetabling problem with double major and minor program constraints where classrooms are shared with several faculties is taken into account. This is the first study considering all these constraints together. A goal programming model is proposed to solve the considered problem. Since it is not possible to find a feasible solution for large-size problems with the proposed model in a time limit, a simulated annealing algorithm is developed. The performance of the proposed solution methods is tested by using randomly generated test problems. In addition, a case study is performed at the engineering faculty of a private university. Computational results show the success of the proposed simulated annealing algorithm to solve large-sized problems. An 83% improvement was achieved with the proposed algorithm for the real-life problem. [ABSTRACT FROM AUTHOR]

Road Safety has always been a factor of great concern in India as well as across the world. Road Traffic Accidents have been consistently increasing, and to address this issue, the world's governments declared 2021 to 2030 as the 'Decade of Action' for Road Safety. This rise in accidents can be attributed to the increasing motorization escalation, increasing individual modes of transport, soaring speeds, drinking and driving, increasing transportation network patterns and inefficient enforcement, to name a few. If compendiously scrutinized, the losses due to road traffic incidences are wholly avoidable. A comprehensive study was thus conducted to identify the key aspects which can be attributed to road safety. The six key factors were identified, namely the design of roads, the design of vehicles, the role of the law enforcement agency, the part of the Indian judicial system, the function of the Indian medical system, and general public awareness for developing road safety index. Primary data collection among various population segments identified the relative importance of these factors. The weightage based on the importance of these factors was computed using the Logarithmic Goal Programming Model, and thus, the Road Safety Index was developed. [ABSTRACT FROM AUTHOR]

In recent years, battery swapping stations have become increasingly popular in smart energy networks. Its advantages include reducing the time required for recharging energy, balancing the grid's load, and extending the battery's lifespan. Despite efforts focused on the placement and operation of battery swapping stations (BSSs), there is still a lack of a comprehensive and systematic examination that covers all aspects of both the economic and technical aspects of the power network. This encompasses considerations such as power quality and reliability, particularly in terms of where these stations should be located. This paper introduces a novel framework for strategically positioning BSS within smart microgrids that integrate distributed energy resources (DERs). It takes into account various technical factors (such as reliability and power quality) and economic factors (like the cost of generation and operation), which have been overlooked in the previous research. To achieve this goal, a unique hybrid optimization strategy is developed, incorporating a combination of epsilon-constraint and lexicographic (DECL) optimization methods. This approach tackles a multi-objective challenge, treating factors like the number, locations, and sizes of BSS as independent variables, while operational costs and technical power quality metrics are considered dependent variables. To validate this approach, it is tested on standard benchmark distribution power networks such as IEEE 33, 69, and 118 bus systems. The simulation results, showcasing the strengths and capabilities of this innovative strategy, are compared to the findings of previous research studies. [ABSTRACT FROM AUTHOR]

The solid transportation problem (STP) is a three-dimensional optimization problem concerned with minimizing total costs while meeting supply and demand limitations by utilizing a variety of transportation modes, referred to as conveyances. This study examines the application of the STP to a logistic problem related to the milk distribution problem. Here, the goal is to efficiently deliver milk from a production facility to multiple retail outlets, considering both the cost of transportation and time of delivery as the two primary objectives of the solid transportation problem. Hence, the bi-objective solid transportation problem (BOSTP) is solved, supposing its constraints are fuzzy due to various unpredictable factors during the transportation period. These uncertain values are initially de-fuzzified into the equivalent crisp values using the α-cut approach. Later, the STP is solved with single objectives at once using Vogel's approximation method. This mathematical model is then formulated to optimize the total transportation cost and time and solved using the hierarchical order goal programming technique to determine the most cost-effective and time-efficient route. The results show that the hierarchical order goal programming technique minimizes the total transportation cost and time while improving the effectiveness of milk distribution. [ABSTRACT FROM AUTHOR]

We propose that the global environmental crises of the Anthropocene are the outcome of a ratcheting process in long-term human evolution which has favoured groups of increased size and greater environmental exploitation. To explore this hypothesis, we review the changes in the human ecological niche. Evidence indicates the growth of the human niche has been facilitated by group-level cultural traits for environmental control. Following this logic, sustaining the biosphere under intense human use will probably require global cultural traits, including legal and technical systems. We investigate the conditions for the evolution of global cultural traits. We estimate that our species does not exhibit adequate population structure to evolve these traits. Our analysis suggests that characteristic patterns of human group-level cultural evolution created the Anthropocene and will work against global collective solutions to the environmental challenges it poses. We illustrate the implications of this theory with alternative evolutionary paths for humanity. We conclude that our species must alter longstanding patterns of cultural evolution to avoid environmental disaster and escalating between-group competition. We propose an applied research and policy programme with the goal of avoiding these outcomes. This article is part of the theme issue 'Evolution and sustainability: gathering the strands for an Anthropocene synthesis'. [ABSTRACT FROM AUTHOR]

In this paper, we made an investigation about an uncertain interval supply chain network model with risk and visibility (UISCNMwRV). We consider the available budget for supply chain visibility, production capacity, cost of reducing supply risk, cost of enhancing supply chain visibility, demand for each product in each period, the expected value of demand, minimum order quantity, purchase price, the impact of supply risk, supply risk, maximum allowable supply risk, supply chain visibility, minimum visibility are uncertain interval parameters for the effect of resenting pandemic COVID-19. To model the proposed UISCNMwRV, we have developed two different models namely the expected value model (EV model) and the chance-constrained model (CC model) using uncertain interval programming techniques. These two models are formulated under the framework of uncertain interval theory. Then, the equivalent deterministic transformations of these models are formulated and are solved in two different methods namely goal programming method and linear weighted method. Finally, a real-life example of the UISCNMwRV is included to show the effectiveness and usefulness of our proposed study. [ABSTRACT FROM AUTHOR]

Batch processes are relevant to a wide variety of industries in chemical processes. In batch operations, water sources are almost not directly reused/recycled in process sinks without considering time constraints and storage tanks. However, storage tanks are usually expensive and thus a cost-effective water system has to be synthesized. Rescheduling the water network can contribute to reducing the cost of storage tanks by reducing their number and capacity. In the current research work, a goal programming is used to reschedule the water network in batch processes considering the time and storage tanks. A Mixed Integer Non-Linear Program model is introduced using the Lingo optimization program. This model is used to optimize multiple objectives, which are freshwater usage, wastewater discharge, the number and capacity of tanks, the degree of shifting streams, and the total cost of the water network. Three case studies are presented in this study to demonstrate the effectiveness of the proposed procedure, considering both single and multi-contaminants problems. The results of the first case study show a reduction in the network cost and the freshwater flowrate by 26.4% and 42%, respectively. Regarding the rescheduled water network results of the second case study, the cost is reduced by 24.6% and the freshwater flowrate is decreased by 21.8% with no requirement of storage tanks. The third case study highlights the model's applicability to multi-contaminants problem, revealing a 15.1% cost reduction and a 25.7% decrease in freshwater flow. [ABSTRACT FROM AUTHOR]

Flavodiiron nitric oxide reductases (FNORs) equip pathogens with resistance to nitric oxide (NO), an important immune defense agent in mammals, allowing these pathogens to proliferate in the human body, potentially causing chronic infections. Understanding the mechanism of how FNORs mediate the reduction of NO contributes to the greater goal of developing new therapeutic approaches against drug-resistant strains. Recent density functional theory calculations suggest that a second coordination sphere (SCS) tyrosine residue provides a hydrogen bond that is critical for the reduction of NO to N2O at the active site of FNORs [J. Lu, B. Bi, W. Lai and H. Chen, Origin of Nitric Oxide Reduction Activity in Flavo-Diiron NO Reductase: Key Roles of the Second Coordination Sphere, Angew. Chem., Int. Ed., 2019, 58, 3795–3799]. Specifically, this H-bond stabilizes the hyponitrite intermediate and reduces the energetic barrier for the N–N coupling step. At the same time, the role of the Fe⋯Fe distance and its effect on the N–N coupling step has not been fully investigated. In this study, we equipped the H[BPMP] (= 2,6-bis[[bis(2-pyridylmethyl)amino]methyl]-4-methylphenol) ligand with SCS amide groups and investigated the corresponding diiron complexes with 0–2 bridging acetate ligands. These amide groups can form hydrogen bonds with the bridging acetate ligand(s) and potentially the coordinated NO groups in these model complexes. At the same time, by changing the number of bridging acetate ligands, we can systematically vary the Fe⋯Fe distance. The reactivity of these complexes with NO was then investigated, and the formation of stable iron(II)–NO complexes was observed. Upon one-electron reduction, these NO complexes form Dinitrosyl Iron Complexes (DNICs), which were further characterized using IR and EPR spectroscopy. [ABSTRACT FROM AUTHOR]

Shift scheduling, which has a direct impact on the productivity of enterprises, is a problem that should always be considered. On the basis of the problem, it is aimed to assign people to shifts according to their competencies, to have a fair distribution of overtime expected from each other and to have minimum deviation from each other. In this study, nurse scheduling chose for the assignment problem. In determining the constraints, the mandatory situations determined by the law, the needs determined by the hospital, the competencies/responsibilities of the employees and annual leave situations taken into consideration. Goal programming has been used in order to achieve multiple goals that may conflict with each other, where all the constraints are met, with minimum deviation. In addition, stochastic parameters were added to the model in order to respond to the demands in different scenarios based on probabilistic real-life data. In the content of the selected problem, a modeling that provides fair distribution by providing the necessary constraints for twenty-eight nurses with different competencies working in two different units and can provide the appropriate response to uncertainty situations has been made. The model was solved by using mixed integer programming (MIP) with GAMS program. [ABSTRACT FROM AUTHOR]

This paper presents a group decision-making (GDM) method based on q-rung orthopair fuzzy preference relations (q-ROFPRs). Firstly, the multiplicative consistent q-ROFPRs (MCq-ROFPRs) and the normalized q-rung orthopair fuzzy priority weight vectors (q-ROFPWVs) are introduced. Then, to obtain q-ROFPWVs, a goal programming model under q-ROFPRs is established to minimize their deviation from the MCq-ROFPRs and minimize the weight uncertainty. Further, a group goal programming model of ideal MCq-ROFPRs is constructed to obtain the expert weights using the compatibility measure between the ideal MCq-ROFPRs and the individual q-ROFPRs. Finally, a GDM method with unknown expert weights is solved by combining the group goal programming model and the simple q-rung orthopair fuzzy weighted geometric (Sq-ROFWG) operator. The effectiveness and practicality of the proposed GDM method are verified by solving the crucial factors in crowdsourcing task recommendation. The results show that the developed GDM method effectively considers the important measures of experts and identifies the crucial factors that are more reliable than two other methods. [ABSTRACT FROM AUTHOR]

In light of the rapid rate of change and unforeseen occurrences seen in the realms of technology, market dynamics, and the wider business landscape, there is a growing need for the inclusion of uncertainty and risk factors in the realm of supply chain planning. Supplier evaluation and selection (SES) is a major strategic decision area where the impact of uncertainty and risk can be more proactively dealt with. A review of extant literature reveals that there is a strong need for developing practitioner-oriented and more comprehensive frameworks and models to mitigate both the capability- and performance-related risks, in the context of SES decisions. This paper presents an integrated model to support SES decisions involving quantity discounts and multiple planning periods under stochastic conditions. The proposed model employs the Fuzzy Analytical Hierarchy Process (FAHP), Fuzzy Evaluation Based on Distance from Average Solution EDAS (EDAS-F), and fuzzy stochastic goal programming (FSGP) to effectively address the above requirements. A case study from a garment manufacturing industry is used to demonstrate the efficacy of the proposed model. The findings of the study provide confirmation that the suggested FSIM has the ability to provide substantial advantages in the context of making choices related to quantity discounts in SES. The proposed FSIM model incorporates the use of FAHP and EDAS-F techniques to effectively reduce the number of suppliers to a manageable level, taking into consideration capability-based risks. Additionally, fuzzy stochastic goal programming (FSGP) is employed to mitigate performance-based risks, enabling the selection of suppliers and the allocation of orders among them. The paper contributes to the literature by proposing a comprehensive framework to solve the SES problem, considering certain practical situations faced by organizations. [ABSTRACT FROM AUTHOR]

Due to globalization and the new characteristics of the business, companies face various challenges to ensure their continuity and competitive advantages. COVID-19 pandemic can be an extreme event that will eventually force many businesses and all industries to redesign and transform their global supply chain model? Challenges concerning mainly reducing the operating cost which is based on selecting the optimal suppliers to provide a reliable product. This study contributes to solving a supplier selection problem under disruption risk due to the lack of literature reviews with a lack of multi-methodological perspective for the fuzzy stochastic notions and quantitative techniques for the quantification of risk alternatives. Prior studies are neglecting to consider the value of risk and prefer to discover chances for optimizing anticipated costs or profits. This study proposed a fuzzy stochastic goal programming approach for selecting the optimal supplier under disruption risk. The proposed model incorporates multiple criteria such as capacity, stochastic demand, and probability of disturbance. The problem of stochastic combinatorial optimization obtained is presented as a program of fuzzy random aim by integrating techniques of value at risk and conditional risk value. Numeric samples and calculation results are included. The results of the models help the decision-maker to optimize the selection of suppliers in the event of a disturbance risk problem by an estimated value at risk and by simultaneously minimizing the conditional value of the risk and demonstrate the efficacy and acceptability of the created risk-averse technique as well as the effects of risk factors on our model behavior. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing significance of research on humanitarian logistics for both researchers and practitioners. This research is crucial for aiding relief operations. While there has been extensive study of mathematical models for disaster operations management in the preparedness and response phases, the recovery phase models still need more attention. One of the significant challenges during the recovery phase is the spread of contagious diseases in the affected area, which necessitates the timely and cost-effective transportation of both contagious and non-contagious populations while preventing further casualties and disease spread. The paper proposes a multi-objective solid transportation model with different conveyance types for the relocation process to address these challenges. The proposed multi-objective model seeks to minimize two essential objectives: the cost and time required for relocation, and includes factors such as transportation, penalties, accommodations, medical expenses, halts, refueling, and maintenance. To account for the unpredictability and vagueness of input data in post-disaster scenarios, the proposed model incorporates fuzzy inputs and introduces a novel defuzzification technique that is validated by comparing it with an existing methodology. The research employs optimization techniques using the LINGO optimizing solver and presents a case study and particular cases that provide valuable management insights for improving decision support systems. Among the optimization techniques, namely the Neutrosophic compromise approach, Goal programming, Fuzzy goal programming, and Global criterion method, the optimal solution is obtained using the Neutrosophic compromise approach. The cost and time objective values obtained using the Neutrosophic compromise approach are 2034725 and 3923, respectively. [ABSTRACT FROM AUTHOR]

Selective maintenance refers to the problem of decision‐making on how to maintain a multi‐component complex system that can be repaired or replaced. The goal is generally to plan ahead in order to improve the likelihood of success for future maintenance tasks. In this article, we proposed a multi‐objective decision‐making model for maximizing the system reliability of a multi‐component systems. The objective functions represent the reliability of the sub‐systems arranged in series comprising of components arranged in parallel forms. The cost and total time incurred in the repair/replacement activities are considered as two sets of constraints in the proposed model. Fuzzy logic is incorporated to deal with the inherent uncertainty that exists in the parameters involved in the proposed model. To solve the uncertain multi‐objective selective maintenance model, we have proposed four‐valued neutrosophic technique. The proposed solution methodology combines the concept of four‐valued neutrosophic fuzzy set with fuzzy goal programming technique. Four linear membership functions for the degrees of truth, uncertainty, contradiction, and false are used to obtain the compromise solution through the proposed technique. To determine the feasibility and applicability of the proposed model and solution methodology, a case problem of multi‐component system of an aircraft gas turbine engine is studied. [ABSTRACT FROM AUTHOR]

By increasing world food demand, the agricultural food supply chain has become a key element in the global business competitive environment. Also, the growing population causes increased demand for edible vegetable oils. Edible oil is one of the main products in the home food basket, which shows the importance and attention to the supply chain network of edible vegetable oils. As a result, the present research aims to design a sustainable supply chain network of edible vegetable oils under uncertainty. A mixed-integer linear programming model has developed with three objective functions, including minimizing costs, minimizing environmental impacts, and maximizing social impact. The multi-objective mathematical model has been solved by the preemptive fuzzy goal programming method. In this study, the demand for final edible oil and meals is considered uncertain. A case study has been considered in the supply chain network design of Iranian edible vegetable oils to validate the proposed model. In order to collect the dataset of the input parameters, we considered the three aspects of big data, namely variety, velocity, and volume. Furthermore, some sensitivity analyses have been provided for the main parameters of the present research. The obtained results show that the most important component of the cost objective function is production costs. Furthermore, import tariff and its value have a significant impact on the objective functions and decision variables, including area under cultivation, water consumption, and number of workers. Finally, some suggestions are presented for managers and experts in this industry. [ABSTRACT FROM AUTHOR]

In facility layout problems, ergonomic issues are generally not considered in the first place; instead, traditional layout aspects are taken into account. However, with the developing technology, legal responsibilities, and awareness of both the public and private sector to ergonomic issues, ergonomic concerns in designing any production area are getting more attention. The aim of this paper is to find an optimal single row facility layout arrangement taking into account both ergonomic and traditional layout aspects. The considered ergonomic feature is the noise exposure. If one worker exposures continuous high level of noise, temporary or permanent hearing loss may occur, which is not desired in the working life. To show the effect of noise exposure on layout, a mathematical model with noise constraint was developed. Moreover, a goal programming model which takes noise exposure, closeness ratings and material flow aspects as objectives was proposed. Traditional Analytical Hierarchy Process was utilized to gain the weights of the objectives. The applicability of the model was demonstrated by the solution of a hypothetical problem. The proposed models were tested and validated on different conditions to find out the effect of noise exposure. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty. Design/methodology/approach: A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches. Findings: The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO2 emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed. Originality/value: Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design. [ABSTRACT FROM AUTHOR]

The evaluation of teaching quality plays a crucial role in promoting the improvement of education quality and ensuring the healthy development of education. This study presents a novel teaching quality evaluation model based on improved interval-valued intuitionistic fuzzy Best-Worst method (IVIF-BWM) and interval-valued intuitionistic fuzzy weighted Maclaurin symmetric mean operators (IVIFWMSM). The study is divided into three parts. Firstly, to derive the optimal interval-valued intuitionistic fuzzy weights of criteria, we develop an improved IVIF-BWM by establishing a goal programming model based on the multiplicative consistent interval-valued intuitionistic fuzzy preference relation(IVIFPR), and then we propose the new consistency index (CI) and the consistency ratio (CR) under interval-valued intuitionistic fuzzy environment to verify the reliability of the derived results. Secondly, with regard to the importance and interaction relationships among criteria, IVIFWMSM is used to aggregate evaluation values of alternatives on each evaluation criteria in multi-criteria decision making process. Finally, the proposed teaching quality evaluation model is applied to a case of teaching quality evaluation in higher education and a comparison study with other existing methods are performed. The results demonstrate that the proposed teaching quality evaluation model not only overcomes the shortcomings of previous methods, but also is more accuracy, effective and reasonable for dealing with the teaching quality evaluation under the intuitionistic fuzzy environments. [ABSTRACT FROM AUTHOR]

Nowadays, the issue of traffic flow assignment has become an interdisciplinary topic that concerns multiple research areas and branches of science. This work is focussed on the mathematical and computational aspects of the equilibrium traffic assignment problem in the case when route flows are considered to be decision variables. Firstly, we obtain a fixed-point mapping with the explicit contraction operator, which is proven to equilibrate the journey times on feasible routes between a single origin-destination pair of nodes with the quadratic rate. Remarkable that from mathematical perspectives, the developed operator generalizes most path-equilibration operators already exploited by researchers. Secondly, we use the obtained fixed-point procedure to run the sequential path-equilibration algorithm for traffic flow assignment on well-known test urban road networks with arc-additive travel time functions. Our computational results appear to demonstrate higher accuracy of user-equilibrium traffic assignment solutions than the best ones known to us. In other words, developed within this paper sequential path-equilibration algorithm leads to solutions with less goal function values compared to the best solutions for today, according to our knowledge. [ABSTRACT FROM AUTHOR]

Objective: To develop a healthy diet for Ethiopian women closely resembling their current diet and taking fasting periods into account while tracking the cost difference. Design: Linear goal programming models were built for three scenarios (non-fasting, continuous fasting and intermittent fasting). Each model minimised a function of deviations from nutrient reference values for eleven nutrients (protein, Ca, Fe, Zn, folate, and the vitamins A, B1, B2, B3, B6, and B12). The energy intake in optimised diets could only deviate 5 % from the current diet. Settings: Five regions are included in the urban and rural areas of Ethiopia. Participants: Two non-consecutive 24-h dietary recalls (24HDR) were collected from 494 Ethiopian women of reproductive age from November to December 2019. Results: Women's mean energy intake was well above 2000 kcal across all socio-demographic subgroups. Compared to the current diet, the estimated intake of several food groups was considerably higher in the optimised modelled diets, that is, milk and dairy foods (396 v. 30 g/d), nuts and seeds (20 v. 1 g/d) and fruits (200 v. 7 g/d). Except for Ca and vitamin B12 intake in the continuous fasting diet, the proposed diets provide an adequate intake of the targeted micronutrients. The proposed diets had a maximum cost of 120 Ethiopian birrs ($3·5) per d, twice the current diet's cost. Conclusion: The modelled diets may be feasible for women of reproductive age as they are close to their current diets and fulfil their energy and nutrient demands. However, the costs may be a barrier to implementation. [ABSTRACT FROM AUTHOR]

This paper proposes an economical reliability‐oriented solution to determine the optimal number of cascade cells and redundancies for STATCOM using a Non‐Dominated Sorting Genetic (NSGA‐II) algorithm based on different redundancy configuration schemes. A multi‐objective optimization framework is presented, to minimize the total cost and maximize its reliability index by considering power quality issues. In the presented model, a new cost modelling is introduced considering the cost of inductor loss. The multilevel cascade topology with staircase modulation strategy is also taken into account. The effectiveness of the proposed optimization framework is validated by the NSGA‐II algorithm compared to the results of goal attainment and goal programming as decomposition single‐objective optimization methods. The results verify that the proposed multi‐objective optimization framework could be useful in the optimal selection of high‐voltage STATCOM levels in the presence of redundancies while ensuring the required level of reliability at the minimum cost. Also, the presented NSGA‐II optimization algorithm is applicable to maintain both objective functions at an acceptable value. [ABSTRACT FROM AUTHOR]

Nowadays, the amounts of infectious medical waste (IMW) have surged considerably so waste management has become a critical emergency in many developing countries. However, most large medical waste generation centers (MWGC) are equipped with treatment facilities, small MWGC faces the waste management problem. It reveals the significance of having a proper program for small health centers. This is an indisputable difficulty that governments bordered because it imposes great costs on societies, also the environmental problems caused by improper treatment are irreparable. To attend to all the essential aspects of the problem, this paper recommended a location-routing model with four objective functions to minimize the total costs, environmental pollution, the risk imposed on the population around disposal sites, and the total violation from the expected arrival time. Considering a multi-period problem with a maximum acceptable delay plays a key role to connect the assumptions to the real-world problem. In addition, for solving mathematical models based on case studies, the role of uncertainty is undeniable. The demand for dental waste treatment is not definite and is changed based on the different conditions thus fuzzy chance-constrained programming is proposed for this problem to tackle the uncertainty. The revised multi-choice goal programming method is considered to solve the model and a real case study for dental clinics in Babol city of Iran is investigated to illustrate the validation of the proposed model. The results indicate that the solution method can create a balance between four objective functions. Finally, sensitivity analyses are performed for some parameters to analyze the behavior of the objective functions. [ABSTRACT FROM AUTHOR]

Uncertainty in real-world fractional transportation problem is the major issue. In this paper, a multi-choice fractional stochastic multi-objective transportation problem (MCFS-MOTP) is investigated. Due to the uncertainty, in the introduced model, the coefficients of the fractional objective functions are a multi-choice type also, the parameters of the constraints are treated as multi-choice independent normally distributed random variables. Firstly, an interpolating polynomial is detailed utilizing practical qualities at nonnegative integer nodes to deal with any multi-choice parameters based on Newton divided difference method. Secondly, stochastic programming approach is applied to transform the probabilistic constraints into crisp ones. Moreover, we introduced a linearization methodology to work out the linear form of the problem. Finally, a fuzzy goal programming approach and the ϵ -constrain method were utilized to think through the issue. Applicability of the proposed model was verified through a numerical example. [ABSTRACT FROM AUTHOR]

The purpose of this study is to form several optimal portfolios based on the proportion of funds invested in stocks, then the most optimal portfolio will be selected among the optimal portfolios that have been formed. The method used in this study is the lexicographic goal programming method, which is to determine the optimal portfolio based on the proportion of invested funds, then the selection of the most optimal portfolio is determined using the expected shortfall method. This study used data from 5 companies contained in Indonesia Stock Exchange (IDX). The results showed that from the proportion of invested funds, 11 optimal portfolios were obtained. Using the expected shortfall method, the most optimal portfolio of 11 portfolios was obtained, namely type 5 portfolio. [ABSTRACT FROM AUTHOR]

The primary goal of this work is to solve the problem of drug diffusion through a thin membrane using a differential quadrature approach with drastically different shape functions, such as Lagrange interpolation and discrete singular convolution (the delta Lagrange kernel and the regularized Shannon kernel). A nonlinear partial differential equation with two time- and space-dependent variables governs the system. To reduce the two independent variables by one, the partial differential equation is transformed into an ordinary differential equation using a one-parameter group transformation. With the aid of the iterative technique, the differential quadrature methods change this equation into an algebraic equation. Then, using a MATLAB program, a code is created that solves this equation for each shape function. To ensure the validity, efficiency, and accuracy of the developed techniques, the computed results are compared to previous numerical and analytical solutions. In addition, the L∞ error is applied. As a consequence of the numerical outcomes, the differential quadrature method, which is primarily based on a discrete singular convolution shape function, is an effective numerical method that can be used to solve the problem of drug diffusion through a thin membrane, guaranteeing a higher accuracy, faster convergence, and greater reliability than other techniques. [ABSTRACT FROM AUTHOR]

Goal Programming (GP), as a generalization of linear programming, is a proper technique for handling Multi-Objective Decision-Making (MODM) problems. Using this technique, we can determine multiple aspiration levels for each objective. The Neutrosophic Set (NS) theory is a powerful tool to deal with indeterminacy and inconsistent data. This paper provides a new idea to formulate Neutrosophic Multi-Choice Goal Programming (NMCGP) problems to simulate real-life situations more realistically. Also, to show the low computational complexity of the proposed model, the Space and Time complexity criteria are used in the Python language. Eventually, a practical mathematical example is solved to present the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

A globally aging population results in the long-term care of people with chronic illnesses, affecting the living quality of the elderly. Integrating smart technology and long-term care services will enhance and maximize healthcare quality, while planning a smart long-term care information strategy could satisfy the variety of care demands regarding hospitals, home-care institutions, and communities. The evaluation of a smart long-term care information strategy is necessary to develop smart long-term care technology. This study applies a hybrid Multi-Criteria Decision-Making (MCDM) method, which uses the Decision-Making Trial and Evaluation Laboratory (DEMATEL) integrated with the Analytic Network Process (ANP) for ranking and priority of a smart long-term care information strategy. In addition, this study considers the various resource constraints (budget, network platform cost, training time, labor cost-saving ratio, and information transmission efficiency) into the Zero–one Goal Programming (ZOGP) model to capture the optimal smart long-term care information strategy portfolios. The results of this study indicate that a hybrid MCDM decision model can provide decision-makers with the optimal service platform selection for a smart long-term care information strategy that can maximize information service benefits and allocate constrained resources most efficiently. [ABSTRACT FROM AUTHOR]

We introduce the concepts of a learning algorithm, an objective function, a recognition system, a class of patterns, a training set, a reward algorithm, a finitely convergent algorithm, an adaptive control system, a control goal, control tactics, adaptation time, etc., related to the problem of artificial intelligence in the processes of learning and adaptation. The general problem of self-learning (unsupervised learning)—about the separation of sets—in terms of the classical calculus of variations is posed. The generality of the problem is due to the introduction of an additional time variable into the analysis. The problem is solved by determining extremal conditions under which the minimization of the overall average risk functional is achieved. Problems corresponding to nonfixed and fixed time intervals are considered. For these two cases, expressions are found for calculating variations in cost functionals. Necessary conditions are indicated for determining the extremal values of the self-learning process (separation of classes of a set of patterns) in time. [ABSTRACT FROM AUTHOR]

Fuzzy analytical hierarchy process (FAHP) is one of the most popular multi-criteria decision making (MCDM) techniques in which the weights of criteria and the evaluation measures of alternatives are generated based on fuzzy pairwise comparisons. There are many approaches for determining the weights from pairwise comparison matrices (PCM). Some of them have calculated the interval weights at all alphacuts. However, in some MCDM techniques (like technique for order preference by similarity to ideal solution (TOPSIS), vlsekriterijumska optimizacija i kompromisno resenje (VIKOR), and FAHP), it is needed to calculate normalized crisp weights. In this paper, the authors extend an index where interval weights obtained at all alphacuts are converted into the crisp weight. Finally, several illustrative examples in the literature are considered to show the verification of the proposed index and the obtained results are then compared with weights extracted by fuzzy preference programming (FPP). The results show that our approach is suitable for obtaining the crisp weights and also, has the similar ranking orders with those of the others. [ABSTRACT FROM AUTHOR]

For multi-attribute group decision-making (MAGDM) problems, this paper proposes a three-way consensus model based on regret theory (RT) under the framework of probabilistic linguistic term sets (PLTSs), i.e., the PL-RT-GTWD model. Specifically, the PL-RT-GTWD model mainly consists of the following two components: (1) The consensus measurement considering the relativity among decision-makers (DMs); (2) A three-way consensus feedback mechanism with the minimum adjustment. First, the relative relationship between DMs is supplemented, and the consensus degree of the DM evaluation matrix is measured by a newly developed distance measure. Second, an optimization model with the minimum adjustment for all DMs to modify and reach the final desired goal (group consensus opinions) is constructed, so that DMs can achieve a balance between reaching consensus and maintaining individual independence. Then, taking the minimum adjustment before and after the adjustment as the objective function, the increase of consensus degrees and the initial range of adjustment parameters as constraints, the goal programming on adjustment parameters is constructed. It is noteworthy that the initial range of adjustment parameters is divided via three-way decision (TWD) tools that consider the regret generated during the adjustment, so as to avoid the subjectivity of the adjustment and reduce adjustment costs. Finally, comparative and sensitivity analyses are carried out to verify the feasibility and superiority of the constructed model. • A three-way consensus model with regret theory is designed. • A new distance measure in PLTSs is defined. • A PL-RT-GTWD model is established. • A feedback adjustment mechanism is considered. • An optimization model with minimum cost is explored. [ABSTRACT FROM AUTHOR]