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Effective supply chain management is crucial for economic growth, and sustainability is becoming a key consideration for large companies. COVID-19 has presented significant challenges to supply chains, making PCR testing a vital product during the pandemic. It detects the presence of the virus if you are infected at the time and detects fragments of the virus even after you are no longer infected. This paper proposes a multi-objective mathematical linear model to optimize a sustainable, resilient, and responsive supply chain for PCR diagnostic tests. The model aims to minimize costs, negative societal impact caused by shortages, and environmental impact, using a scenario-based approach with stochastic programming. The model is validated by investigating a real-life case study in one of Iran's high-risk supply chain areas. The proposed model is solved using the revised multi-choice goal programming method. Lastly, sensitivity analyses based on effective parameters are conducted to analyze the behavior of the developed Mixed-Integer Linear Programming. According to the results, not only is the model capable of balancing three objective functions, but it is also capable of providing resilient and responsive networks. To enhance the design of the supply chain network, this paper has considered various COVID-19 variants and their infectious rates, in contrast to prior studies that did not consider the variations in demand and societal impact exhibited by different virus variants. [ABSTRACT FROM AUTHOR]

• Propose a framework for measuring a maturity level of performance-based budgeting. • Develop a parallel network data envelopment analysis model. • Consider the hierarchical configuration of performance indicators. • Use fuzzy sets theory to deal with vagueness and ambiguity. • Present a case study to demonstrate the applicability of the developed framework. Performance-based budgeting (PBB) aims to formulate and manage public budgetary resources to improve managerial decisions based on actual performance measures of agencies. Although the PBB system has been overwhelmingly applied by various agencies, the progress and maturity of its implementation process are not satisfactory at large. Therefore, it warrants to find, evaluate and improve the performance of organisations in relation to implementing a PBB system. To do so, the composite indicators (CIs) have been proposed to aggregate multiple indicators associated with the PBB system, but their employment is contentious as they often lean on ad-hoc and troublesome assumptions. Data envelopment analysis (DEA) methods as a powerful and established tool help to contend with key limitations of CIs. Although the original DEA method ignores an internal production process, the knowledge of the internal structure of the PBB systems and indicators is of importance to provide further insights when assessing the performance of PBB systems. In this paper, we present a budget assessment framework by breaking a PBB system into two parallel stages including operations performance (OP) and financial performance enhancement (FPE) to open up the black-box structure of the system and consider the indicator hierarchy configuration of each stage. In situations of the hierarchical configuration of indicators, we develop a multilayer parallel network DEA-based CIs model to measure the PBB maturity levels of the system and its stages. It is shown that the discrimination power of the proposed multilayer model is better than the existing models with one layer and in situations of relatively small number of DMUs the model developed in this paper can be a good solution to the dimension reduction of indicators. Moreover, this research leverages fuzzy logic to surmount the subjective information that is often available in collecting indicators of the PBB systems. The major contribution of this research is to examine a case study of a PBB maturity award in Iran, as a developing country with a myriad of financial challenges, to adopt a PBB maturity model as well as point towards the efficacy and applicability of the proposed framework in practice. [ABSTRACT FROM AUTHOR]

Conventional Data Envelopment Analysis (DEA) assesses the performance of Decision-Making Units (DMUs) by measuring only one type of efficiency using definite data. However, due to the vastness of some industries, such as banks, calculating only one type of efficiency cannot confirm DMUs as efficient or inefficient. Also, real-world cases are often faced with uncertain data, and conventional DEA lacks the power to consider these uncertainties in evaluation. In this paper, we develop a multi-objective DEA model to calculate three types of efficiencies, including profitability, operational, and transactional for bank branches when there are uncertain data. First, we apply a modified DEA model which is capable of calculating the common weight of all inputs and outputs by solving only one linear programming model. Then, we employ a robust approach to handle the uncertainty in data. The uncertainty in data is described with discrete scenarios. Discrete scenarios require a set of possible values for each parameter with uncertain nature. Finally, we apply a fuzzy programming method to convert the proposed multi-objective model into a single-objective one. Our main goal is to calculate three types of efficiencies for bank branches under four different scenarios. To validate the accuracy of the proposed model, a real case of 45 Agriculture bank branches located in West Azerbaijan in Iran is examined. The results show that the proposed model can produce accurate results under different scenarios. We also perform a comparative analysis on each efficiency aspect to specify the benchmark branches and also inefficient branches. Comparative analysis can help managers recognize where improvement should be prioritized. [ABSTRACT FROM AUTHOR]

• Developing a framework for the wellness tourism supply chain. • Designing a mathematical model for the wellness tourism supply chain. • Observing the concept of sustainability in wellness tourism. • Using the 3 V big data concept in data collection. • Considering a real case study from Sarein spa in Iran. Nowadays, the most successful and prominent wellness tourism centers are those that, consider the aspects of sustainable development, while providing memorable services with the best quality and reasonable prices. A wellness tourism center is seeking to meet the purposeful journey of wellness tourists by improving their mental, physical, and spiritual happiness. This paper presents a three-objective mathematical model to formulate the supply chain of services provided by a wellness tourism center. The purpose of this model is to establish a balance between sustainability aspects in this supply chain. The first objective seeks to maximize profits from the supply chain and the second and third objectives are to maximize customer satisfaction concerning environmental and social impacts. The proposed model includes quantitative parameters of operating costs, capacity increase cost, service capacity, and service demand. Due to the multi-objective nature of the model, the improved multi-choice goal programming method is used. Real data are collected from the Ardabil-Sarein wellness tourism center in Iran and used as a case study to validate and analyze the model. In collecting the dataset for the parameters, the three aspects of big data (3 V's) are considered. Findings show that the model offers different types of discounts for various periods following the tourist demand. Additionally, the wellness tourism center can use the original cost to expand its range of services by offering different tours. Finally, the limitations and managerial suggestions are discussed. [ABSTRACT FROM AUTHOR]

Proposed closed-loop supply chain network for the olive industry. [Display omitted] • Designing a sustainable closed-loop supply chain network for the olive industry. • Developing a multi-objective optimization framework for analyzing economic, environmental and social factors. • Proposing two hybrid optimization algorithms consisting of four meta-heuristics for the first time. • Studying a real-case study to implement the sustainability goals. A closed-loop option for reusing, remanufacturing, and recycling waste products in the olive industry creates a high added value for this business network. This fact motivates the implementation of closed-loop supply chains for adopting an efficient and sustainable strategy in the olive industry in Iran. As one of the developing countries, Iran must redesign their supply chain networks to meet the standards of sustainable development goals. In this regard, the triple bottom line approach focuses on a sustainable design considering all economic, environmental, and social factors in supply chain networks. The main novelty of this paper is to merge the sustainable Closed-Loop Supply Chain Network (CLSCN) design and the olive industry. Hence, a multi-objective optimization framework is proposed to make location, allocation, and inventory decisions for the considered problem. Based on the triple bottom line approach, the objectives of the optimization model are to minimize the total cost and carbon dioxide (CO 2) emissions and maximize job opportunities. In small instances, the model is solved by an epsilon-constraint method. To address the high complexity of large-scale networks, this study innovates new hybrid optimization algorithms. In this regard, a hybrid of Virus Colony Search Algorithm (VCS) and Simulated Annealing (SA) and a combination of Electromagnetism-like Algorithm (EMA) and Genetic Algorithm (GA) are proposed for the first time. To confirm their efficiency, an extensive comparison with individual algorithms is carried out by different multi-objective optimization metrics. Finally, some sensitivity analyses are performed to discuss some practical insights for the supply chain managers working in the olive industry in Iran. [ABSTRACT FROM AUTHOR]

• Machine-learning algorithms were adopted to predict the cost of maintenance policies. • The Markov Chain model was used to predict the post-maintenance network condition. • Optimal policies were found based on the optimization of cost and network condition. • The paper contributes to the sustainable development goals #9, #11, and #13. A framework was proposed to find optimal maintenance policies in a road network. The framework included: identifying factors that contribute to policy-making; clustering the network based on these factors; identifying criteria that impact optimal policies; and determining optimal policies and periods using these criteria. To test the framework applicability, it was applied to Iran roads network step-by-step. First, road functional class and climate were identified as factors that contribute to policy-making. Second, the network was clustered into six sub-networks based on two road functional classes and three climates. Third; maintenance cost, network condition, and maintenance period were identified as criteria that impact optimal policies. Fourth, 96 maintenance policies were applied to each sub-network considering two-year, four-year, six-year, and twelve-year maintenance periods. To quantify policies cost, seven machine-learning algorithms including Gradient Boosting Regression, Lasso, Ridge, Random Forest Regression, Elastic Net, Neural Network, and Multiple Linear Regression were tested. Using the coefficient of determination (R2) as the accuracy metric, it was found that in all sub-networks the Gradient Boosting Regression had the highest accuracy on testing set (greater than 90%) while that of other algorithms was between 50% and 90%. Sub-networks condition was modeled using the Markov Chain model and was measured by the average Pavement Condition Index (PCI). Having policies cost and sub-networks PCI, the optimal policy was selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). It was concluded that in all sub-networks, the four-year maintenance period was optimal. Roads in warm zone demanded the most intense policies, followed by those in cold and humid zones. The same applied to arterial roads followed by local ones. [ABSTRACT FROM AUTHOR]

This work focuses on the problem of constructing accurate prediction models using an adaptive neuro-fuzzy inference system (ANFIS) from data that are scarce and poorly scaled. Many real-world problems have limited data to work with and yet require models with high prediction accuracy. Due to the fuzzy nature of many data sets, it is appropriate to construct prediction models using fuzzy inference systems (FIS), rather than traditional artificial neural network (ANN) models. However, the scarcity of the data raises serious concerns when attempting to construct models of this type. In this paper, we address the problem of constructing accurate prediction models using ANFIS in circumstances where capturing large amounts of data is difficult. We propose a new approach, using robust data expansion methods to enhance the data carefully. This approach provides the learning algorithm with enough representative data to allow all of the parameters (premise and consequent) to be optimised. We compare and contrast the effectiveness in reducing prediction error for three specific expansion models using radial basis function (RBF) kernels. When expanding data, for which the underlying smoothness properties are not known, there is often a danger of over-smoothing. This is usually noticeable at turning points and can occur when fitting the data using polynomials or cubic splines for example. To avoid over-smoothing, we introduce a modified multiquadric approach that allows smooth, near-interpolation while maintaining the desirable shape-preserving properties of the data. The proposed method is explained through two case studies — the first to predict gasoline consumption for the oil/fuel industry in Iraq and the second for predicting natural gas consumption in Iran. This is the first study that uses adjustable, shape-preserving re-sampling methods for optimising ANFIS models in low data environments. • ANFIS networks capture complexities not easily found using traditional ANNs. • Model complexity versus data sample size is a significant practical problem. • Data augmentation methods can be constructed using robust continuous models. • Multiquadric RBFs can be easily tuned to provide robust, accurate data expansion. • Complex models can be built with confidence using reliable data expansion. [ABSTRACT FROM AUTHOR]