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    A bi-objective carton box production planning problem with benefit and wastage objectives under belief degree-based uncertainty
    (Springernature, 2024) Niroomand, S.; Mahmoodirad, A.; Ghaffaripour, A.; Allahviranloo, T.; Amirteimoori, A.; Shahriari, M.
    In this paper, a production planning problem for paperboard is formulated considering criteria such as yield, cost, excess production, wastage of raw sheets, etc., considering two-objective functions, and is optimized considering supplier selection decisions and raw sheet dimensions. Compared to the studies in the literature, this problem takes into account, as a novelty, the yield of the produced boxes, the waste value of the raw sheets and the inventory cost of the additional boxes produced. Another contribution is that the problem is formulated from the viewpoint of belief degree-based uncertainty, which is suitable for formulating real-world problems. The uncertain problem is transformed into a crisp form using the expected value criterion for the uncertain objective functions and the random condition form of the constraints with uncertain parameters. The obtained crisp two-objective formulation is solved by some multi-objective solution methods known from the literature. As an application, a case study is used to make some comparisons and to show the implications of the problem for management.
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    Correction to: Scale elasticity and technical efficiency measures in two-stage network production processes: an application to the insurance sector (Financial Innovation, (2024), 10, 1, (43), 10.1186/s40854-023-00578-z)
    (Springer Science and Business Media Deutschland GmbH, 2024) Amirteimoori, A.; Allahviranloo, T.; Arabmaldar, A.
    Springer 1 56 0 2024 Southwestern University of Finance and Economics 2024 No Unnumbered 2024 2 2 0 0 Regular ArchiveJournal Unnumbered OpenChoice OpenAccess OpenAccess OpenAccess OpenAccess OpenAccess OpenAccess false BodyRef/PDF/40854_2024_Article_624.pdf Typeset OnlinePDF Regular Erratum Economics Macroeconomics/Monetary Economics//Financial Economics Political Economy/Economic Systems Economics and Finance true Following publication of the original article (Amirteimoori et al. 2024), the authors reported a typesetting error in the affiliation of author Tofigh Allahviranloo. Due to a typesetting error, author Tofigh Allahviranloo was mistakenly assigned to affiliation 2: Department of Business Administration, Faculty of Business and Economics, University of Goettingen, 37073, G?ttingen, Germany. The correct affiliation for author Tofigh Allahviranloo should be affiliation 1: Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey. The original article (Amirteimoori et al. 2024) has been updated. © The Author(s) 2024.
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    A novel parallel heuristic method to design a sustainable medical waste management system
    (Elsevier Ltd, 2024) Amirteimoori, A.; Tirkolaee, E.B.; Amirteimoori, A.; Khakbaz, A.; Simic, V.
    Efficient management of waste generated in healthcare systems is crucial to minimize its environmental impact and ensure public health. Sustainable medical waste management (MWM) systems require careful network design, which can be achieved through efficient optimization techniques. This work develops a mixed-integer linear programming (MILP) to formulate the problem, a two-step MILP (TSMILP) to generate quality lower bounds, and a novel parallel heuristic algorithm to configure a sustainable waste management system including waste generation centers (WGCs), waste treatment centers (WTCs), waste recycling centers (WRCs), waste disposal centers (WDCs) and waste incineration centers (WICs). Such a hybrid methodology has not been yet offered in the literature wherein the aim is to address strategic (establishment of facilities), tactical (employment of transportation system), and operational decisions (transportation planning) optimally in large networks. As reflected in the literature, there is a huge gap in efficiency and application of combinatorial optimization, and parallel computing in sustainable MWM systems, where the suggested MILPs' solvers are not technically capable of discovering quality solutions in reasonable runtimes on large-sized instances. Thus, we suggest a novel heuristic equipped with parallel computing to share the complexity of the problem, with all the CPU cores to shorten runtime. Comparing the results generated by the parallel heuristic with those of the sequential heuristic, the MILP, and the TSMILP on three sets of benchmark instances using Nemenyi's post-hoc procedure for Friedman's test, it is inferred that the parallel heuristic is so effective in coping with the problem, and produces high-quality solutions, especially on the large-sized set. Finally, sensitivity analysis is adopted to analyze the effects of parameters on the objective values and provide useful managerial insights. © 2024 Elsevier Ltd
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    Performance and Managerial Ability Analysis in Health Sector: A Data Envelopment Analysis Approach
    (Springer Science and Business Media Deutschland GmbH, 2024) Amirteimoori, A.; Safarpour, S.; Kordrostami, S.; Khoshandam, L.
    Since the healthcare system is one of the most important key sectors in a society and as health service supply is one of the personal development factors in any country, so paying heed to this sector can result in social well-being and prosperity. To ensure a better and more qualified health care, treatment and protection services, analysis of the related performance plays a major role in any health system. In so doing, proper usage of assets is an undeniable fact. This research aims at introducing an applicable case in health system sector of all hospitals in Iran where the performance analysis is measured. To do so, the data of thirty-one state hospitals are collected and after recognizing contextual variables and undesirable factor, performance analysis and managerial ability of each hospital are measured. To measure it, first, technical performance with undesirable factor, is calculated using data envelopment analysis. Then, the technical analysis logarithm of the first stage has been applied to a set of contextual variables which impact hospitals analysis. All the results are regressed later. Next, the managerial ability is measured by the remaining regression of the previous stage. Finally, a unique ranking according to managerial ability criterion is suggested. All in all, the results are analyzed in order to give practical recommendations to managers and for more efficient management of hospitals in Iran. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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    Solving fully linear programming problem based on Z-numbers
    (Univ Sistan & Baluchestan, 2023) Joghataee, M.; Allahviranloo, T.; Lotfi, F. Hosseinzadeh; Ebrahimnejad, A.; Abbasbandy, S.; Amirteimoori, A.; Catak, M.
    Generally exploring the exact solution of linear programming problems in which all variables and parameters are Znumbers, is either not possible or difficult. Therefore, a few numerical methods to find the numerical solutions do act an important role in these problems. In this paper, we concentrate on introducing a new numerical method to solve such problems based on the ranking function. After proving the necessary theories, for more illustrations and the correctness of the topic, some theoretical and practical examples are also provided. Finally, the results obtained from the proposed method have been compared with some existing methods.
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    Stochastic resource reallocation in two-stage production processes with undesirable outputs: An empirical study on the power industry
    (Elsevier Ltd, 2024) Amirteimoori, A.; Kazemi, Matin, R.; Yadollahi, A.H.
    Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased. © 2024 Elsevier Ltd