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Customer insurance coverage sales plan problem, in which the loyal customers are recognized and offered some special plans, is an essential problem facing insurance companies. On the other hand, the loyal customers who have enough potential to renew their insurance contracts at the end of the contract term should be persuaded to repurchase or renew their contracts. The aim of this paper is to propose a three-stage data-mining approach to recognize high-potential loyal insurance customers and to predict/plan special insurance coverage sales.

The first stage addresses data cleansing. In the second stage, several filter and wrapper methods are implemented to select proper features. In the third stage, K-nearest neighbor algorithm is used to cluster the customers. The approach aims to select a compact feature subset with the maximal prediction capability. The proposed approach can detect the customers who are more likely to buy a specific insurance coverage at the end of a contract term.

The proposed approach has been applied in a real case study of insurance company in Iran. On the basis of the findings, the proposed approach is capable of recognizing the customer clusters and planning a suitable insurance coverage sales plans for loyal customers with proper accuracy level. Therefore, the proposed approach can be useful for the insurance company which helps them to identify their potential clients. Consequently, insurance managers can consider appropriate marketing tactics and appropriate resource allocation of the insurance company to their high-potential loyal customers and prevent switching them to competitors.

Despite the importance of recognizing high-potential loyal insurance customers, little study has been done in this area. In this paper, data-mining techniques were developed for the prediction of special insurance coverage sales on the basis of customers’ characteristics. The method allows the insurance company to prioritize their customers and focus their attention on high-potential loyal customers. Using the outputs of the proposed approach, the insurance companies can offer the most productive/economic insurance coverage contracts to their customers. The approach proposed by this study be customized and may be used in other service companies.

Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions.

A dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods.

The results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method.

To the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.

A Nash bargaining game data envelopment analysis (NBG-DEA) model is proposed to measure the efficiency of dynamic multi-period network structures. This paper aims to propose NBG-DEA model to measure the performance of decision-making units with complicated network structures.

As the proposed NBG-DEA model is a non-linear mathematical programming, finding its global optimum solution is hard. Therefore, meta-heuristic algorithms are used to solve non-linear optimization problems. Fortunately, the NBG-DEA model optimizes the well-formed problem, so that it can be solved by different non-linear methods including meta-heuristic algorithms. Hence, a meta-heuristic algorithm, called particle swarm optimization (PSO) is proposed to solve the NBG-DEA model in this paper. The case study is Industrial Management Institute (IMI), which is a leading organization in providing consulting management, publication and educational services in Iran. The sub-processes of IMI are considered as players where their pay-off is defined as the efficiency of sub-processes. The network structure of IMI is studied during multiple periods.

The proposed NBG-DEA model is applied to measure the efficiency scores in the IMI case study. The solution found by the PSO algorithm, which is implemented in MATLAB software, is compared with that generated by a classic non-linear method called gradient descent implemented in LINGO software.

The experiments proved that suitable and feasible solutions could be found by solving the NBG-DEA model and shows that PSO algorithm solves this model in reasonable central process unit time.

This paper aims to propose a multi-dimensional model on the basis of the key factors of the flexibility and the complexity through structural equation modeling (SEM). Dimensions of the flexibilities and complexity, including 16 main factors and 34 sub-factors, are investigated. The sampling of the research is accomplished using both academic and industrial experts.

A huge electronic questionnaire analysis, including 1,250 samples from which 1,036 were returned, was accomplished in various universities and manufacturing companies throughout the USA, Europe and Asia. Partial least square-SEM (PLS-SEM) is used to test the hypotheses through confirmatory factor analysis.

The results reveal insightful information about the impacts of different dimensions of flexibility on each other and also the effect of the flexibility on the complexity. Finally, system of linear mathematical equations for flexibility-complexity trade-off is proposed. This can be applied to realize the trade-off among dimensions of flexibility and complexity.

Flexible manufacturing systems are formed to meet the needs of the customers. Such systems try to produce products in appropriate quality at the right time and at the specified quantity. These, in turn, require flexibility and will cause complexity. Although flexibility and complexity are both important, there is no comprehensive framework in which the multi-dimensional relationships of the manufacturing flexibility and complexity, as well as their dimensions, are demonstrated.

This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).

First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.

The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.

Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.

This study aims to investigate the interactive network of risk factors in the construction and preparation project of gas condensate storage tanks in the presence of sanctions.

First, this paper determines and weighs the project goals using the stepwise weight assessment ratio analysis method. Then, this study identifies and categorizes the project risks and form the network of the project risk factors based on the weight of the project goals by using interpretive structural modeling and decision-making trial and evaluation laboratory techniques.

The results of the current work divide the risks of such projects into five financial, technical, managerial and contractual, social and environmental and political categories. The analyzes indicate the complicated risk network and the cause-and-effect interactions between the risks. The findings identify the financial and political risk clusters as the most effective category and select the technical, managerial and contractual and social and environmental risk clusters as the most affected categories. The technical risk has the least important among the others under the sanction conditions.

This paper model the domino effect of the risk factors considering the complicated interactions and the cause-and-effect relations in a network. Moreover, this study discusses the importance of the risk factors in oil and gas megaprojects in the presence of sanctions. Finally, this paper applies the proposed approach to a real case study in the field of gas projects.

Enterprise resource planning (ERP) is assumed as a commonly used solution in order to provide an integrated view of core business processes, including product planning, manufacturing cost, delivery, marketing, sales, inventory management, shipping and payment. Selection and implementation of a suitable ERP solution are not assumed a trivial project because of the challenging nature of it, high costs, long-duration of installation and customization, as well as lack of successful benchmarking experiences. During the ERP projects, several risk factors threat the successful implementation of the project. These risk factors usually refer to different phases of the ERP projects including purchasing, pilot implementation, teaching, install, synchronizing, and movement from old systems toward new ones, initiation and utilization. These risk factors have dominant effects on each other. The purpose of this paper is to explore the hybrid reliability-based method is proposed to assess the risk factors of ERP solutions.

In this regard, the most important risk factors of ERP solutions are first determined. Then, the interactive relations of these factors are recognized using a graph based method, called interpretive structural modeling. The resultant network of relations between these factors initiates a new viewpoint toward the cause and effect relations among risk factors. Afterwards, a fuzzy fault tree analysis is proposed to calculate Failure Fuzzy Possibility (FFP) for the basic events of the fault tree leading to a quantitative evaluation of risk factors.

The whole proposed method is applied in a well-known Iranian foodservice distributor as a case study. The most impressive risk factors are identified, classified and prioritized. Moreover, the cause and effect diagram between the risk factors are identified. So, the ERP leader can plan a low-risk project and increase the chance of success.

According to the authors’ best knowledge, such approach was not reported before in the literature of ERP risk assessments.

One of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.

One of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.

The proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.

The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

This paper addresses a location-routing problem (LRP) under uncertainty for providing emergency medical services (EMS) during disasters, which is formulated using a robust optimization (RO) approach. The objectives consist of minimizing relief time and the total cost including location costs and the cost of route coverage by the vehicles (ambulances and helicopters).

A shuffled frog leaping algorithm (SFLA) is developed to solve the problem and the performance is assessed using both the ε-constraint method and NSGA-II algorithm. For a more accurate validation of the proposed algorithm, the four indicators of dispersion measure (DM), mean ideal distance (MID), space measure (SM), and the number of Pareto solutions (NPS) are used.

The results obtained indicate the efficiency of the proposed algorithm within a proper computation time compared to the CPLEX solver as an exact method.

In this study, the planning horizon is not considered in the model which can affect the value of parameters such as demand. Moreover, the uncertain nature of the other parameters such as traveling time is not incorporated into the model.

The outcomes of this research are helpful for decision-makers for the planning and management of casualty transportation under uncertain environment. The proposed algorithm can obtain acceptable solutions for real-world cases.

A novel robust mixed-integer linear programming (MILP) model is proposed to formulate the problem as a LRP. To solve the problem, two efficient metaheuristic algorithms were developed to determine the optimal values of objectives and decision variables.

The purpose of this paper is to determine the criteria weights of outsourcing and their key role in ranking outsourcing suppliers.

A new hybrid multi-criteria decision-making approach merges three tools, namely, decision making trial and evaluation (DEMATLE), fuzzy analytic network process (FANP) and ordinal/cardinal data envelopment analysis (DEA) model. Afterwards, experts’ opinions were gathered from a Pipe and Fittings company. Finally, their opinions were incorporated in three-stage approach for outsourcing suppliers’ selection.

The findings of this study show that among the selective criteria for outsourcing, business development, focus on basic activities and order delays are the three most important criteria. Also, the proposed approach ranks suppliers to facilitate decision making for selection.

The number of suppliers, selection criteria and the number of members of the respondents’ team have been identified as some of the limitations of the present study.

The study has significant and practical implications for the managers and for the organizations which have to choose top suppliers, particularly in the case of dealing with numerous and qualitative/quantitative criteria.

This paper proposed a new three-stage approach that incorporates outputs of previous as inputs of next stage to increasing results accuracy. Also, it showed that by incorporating results of FANP method into DEA model, key role of experts’ opinions as a qualitative and quantitative criteria can be caused by increasing flexibility of decision process.