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Any failure on the recovery system will cause a lot of environmental damage as well as energy loss. Hereof two types of alternatives; fast opening valve system (FOVS) and seal drum system (SDS) may be installed. The focus of this article is on the decision stage to choose the most preferred option in terms of reliability assessment. The major challenge in the research problem is on changing the pressure and temperature during operational cycles, which significantly affect the reliability. In addition, the lack of historical data complicates the reliability assessment method. Hence, we proposed a hybrid approach using fault tree analysis (FTA) and the Mamdani fuzzy inference to estimate reliability response as a function of a few frequently operating pressure and temperature. Also, discrete-event simulation helped us to evaluate the system reliability at different operating conditions. The comparisons reveals that the FOVs outperforms on average of 22.4% than the SDS and it is recommended for putting into practice for purchasing

Purpose Delivery management of perishable products such as blood in a supply chain is a considerable issue such that the last-mile delivery, which refers to deliver goods to the end user as fast as possible takes into account as one of the most important, expensive and, polluting segments in the entire supply chain. Regardless of economic challenges, the last-mile delivery faces social and environmental barriers to continuing operations while complying with environmental and social standards, therefore incorporating sustainability into last-mile logistic strategy is no longer an option but rather a necessity. Accordingly, the purpose of this paper is to consider a last-mile delivery in a blood supply chain in terms of using appropriate technologies such as drones to assess sustainability. Design/methodology/approach The authors discuss the impact of drone technology on last-mile delivery and its importance in achieving sustainability. They focus on the effect of using drones on CO2 emission, costs and social benefits by proposing a multi-objective mathematical model to assess sustainability in the last-mile delivery. A preemptive fuzzy goal programming approach to solve the model and measure the achievement degree of sustainability is conducted by using a numerical example to show the capability and usefulness of the suggested model, solution approach and, impact of drone technology in achieving all three aspects of sustainability. Findings The findings illustrate the achievement degree of sustainability in the delivery of blood based on locating distribution centers and allocating drones. Moreover, a comparison between drones and conventional vehicles is carried out to show the preference of using drones in reaching sustainability. A sensitivity analysis on aspects of sustainability and specifications of drone technology is conducted for validating the obtained results and distinguishing the most dominant aspect and parameters in enhancing the achievement degree of sustainability. Originality/value To the best of the authors’ knowledge, no research has considered the assessment of sustainability in the last-mile delivery of blood supply chain with a focus on drone technology.

The main concern of this research is to control traffic flow and monitor highways by installing wireless sensors. Therefore, a new multi-objective model is proposed to find the optimal location of wireless sensors along highways. The sensors are paired and each pair of sensors communicates and interacts with each other to receive information from passing cars. The speed estimation of the vehicles passing through this pair of sensors must have the minimum deviation from the actual speed that is obtained by an accurate measurement. This deviation is called measurement error, which is minimized in the first objective function. In this research when traffic jams happens, some sensors located in proper distance with enough energy, move into the traffic area to reduce the measurement error which is caused by traffic congestion. In fact, for each traffic area a new location problem should be solved to relocate sensors so that the maximum decrease in error rate happens. The second objective maximizes the error reduction resulted from sensors movement. In this paper, movement of sensors is considered based on the amount of solar energy stored in the sensor at that moment. Finally, the third objective function maximizes the benefits resulted from detecting the bottlenecks in highways. Since some parameters of the objective functions such as error rates, error reduction resulted from movement of sensors and benefits are uncertain, this research employs a multi-objective robust optimization approach which results in a traffic control plan which is less sensitive to the realization of uncertain parameters.

Purpose Assessing the performance of medical laboratories plays an important role in the quality of health services. However, because of imprecise data, reliable results from laboratory performance cannot be obtained easily. The purpose of this paper is to illustrate the use of interval network data envelopment analysis (INDEA) based on sustainable development indicators under uncertainty. Design/methodology/approach In this study, each medical diagnostic laboratory is considered as a decision-making unit (DMU) and an INDEA model is used for calculating the efficiency of each medical diagnostic laboratory under imprecise inputs and outputs. The proposed model helps provide managers with effective performance scores for deficiencies and business improvements. The proposed model with realistic efficiency scores can help administrators manage their deficiencies and ultimately improve their business. Findings The results indicate that uncertainty can lead to changes in performance scores, rankings and performance classifications. Therefore, the use of DEA models under certainty can be potentially misleading. Originality/value The contribution of this study provides useful insights into the use of INDEA as a modeling tool to aid managerial decision-making in assessing efficiency of medical diagnostic laboratories based on sustainable development indicators under uncertainty.

The optimal combination of assets can be selected by the traditional portfolio theory which uses historical quantitative data to represent the future return of assets. However, quantitative information is inaccessible in most cases and experts can help investors and fund managers by providing qualitative information. According to above discussion, a new multi-stage qualitative approach is proposed to select the optimal portfolio under linguistic Z-number environment. To achieve this aim, this study firstly develops the Bonferroni mean (BM) operator and the geometric Bonferroni mean (GBM) operator under the linguistic Z-number environment, and introduces linguistic Z-number Bonferroni mean (LZBM) operator and linguistic Z-number geometric Bonferroni mean (LZGBM) operator to aggregate the qualitative evaluation information. Then, using the developed aggregation operators, two qualitative portfolio selection models are proposed based on the max-score rule and the score-accuracy trade-off rule for the general investors and risky investors, respectively. Finally, to illustrate the validity of the proposed models, a case study including 20 corporations of Tehran stock exchange market in Iran is provided and the obtained results are analyzed. Moreover, the qualitative proposed models are compared with another available model. The obtained results indicate that the qualitative proposed approach can help investors and fund managers to make more credible decisions so that they can select the optimal assets with considering different criteria when experts are assured about their assessments or opinions. Therefore, the qualitative proposed models are superior and more general in comparison with the other ones due to capturing the reliability of information. Also, the obtained results show the influence of reliability measures in investment processes.

Rationale, aims, and objectives Creating networked business models is one of the innovative approaches that have the ability and potential for meeting market needs. The purpose of this study is to provide a decision-making model for a fair profit sharing among the members of a diagnostic laboratory network while providing a distinctive value for the patients. Methods To identify the members of the network of laboratories, a suitable approach to calculate members' efficiency scores is proposed. Then, the network members are classified into three groups based on their performance scores. The three groups help administrators identify eligible members, members who need to improve their performance in order to meet the minimum requirements, and members who do not qualify for admission to the network. Since the performance of the members should play a significant role in the fair profit-sharing mechanism, the fair allocation of profits among network members is done by the use of Shapely value based on the efficiency scores of members. Results The results show that for such a fair mechanism, the efficiency and sample size (the number of samples [blood and urine] taken from the patients by the laboratories), as the two effective factors, have a decisive role in the share of profit of laboratory units of the network. In the Laboratory Services Network, members receive a number of samples according to their performance. As a result, the sample size received has a direct impact on the net income of each member. Conclusion In conclusion, it is evident that the use of Shapely value may help managers in the process of sharing profits among network members in a fair way, thereby improving network performance. In this way, incentive strategies may be created for the members of the network, and long-term survival of the network may be achieved.

Rationale, aims, and objectives The main purpose of this paper is to measure the efficiency and ranking of medical diagnostic laboratories by applying a network data envelopment analysis (NDEA). Methods In this study, each medical diagnostic laboratory is considered as a decision-making unit (DMU), and an NDEA model is utilized to calculate the efficiency of each medical diagnostic laboratory. Therefore, we design a series of four-stage system composed of three main laboratory processes (the pretest process, the test process, and the posttest process). We also consider sustainability criteria in order to cover social, economic, and environmental problems of health care organizations. Results The results show that three of the 22 considered laboratories are efficient. Therefore, the NDEA approach can lead to performance scores and ultimately real ranking. Also, the average efficiency scores show that the decrease of the reception unit's efficiency results in a decrease of the efficiency of each laboratory. Therefore, the laboratories can increase the number of patients. Along with the intermediate values of the reception unit and the sampling unit, the efficiency of the reception unit increases, which results in an increase for the overall efficiency of each laboratory. Conclusion The proposed model can appropriately help the administrators and managers to identify inefficient units in their laboratory and ultimately improve the laboratory performance.

This paper considers a Capacitated Location-Arc Routing Problem (CLARP) with Deadlines (CLARPD) and a fleet of capacitated heterogeneous vehicles. The proposed mixed integer programming model determines a subset of potential depots to be opened, the served roads within predefined deadlines, and the vehicles assigned to each open depot. In addition, efficient routing plans are determined to minimize total establishment and traveling costs. Since the CLARP is NP-hard, a Genetic Algorithm (GA) is presented to consider proposed operators, and a constructive heuristic to generate initial solutions. In addition, a Simulated Annealing (SA) algorithm is investigated to compare the performance of the GA. Computational experiments are carried out for several test instances. The computational results show that the proposed GA is promising. Finally, sensitivity analysis confirms that the developed model can meet arc routing timing requirements more precisely compared to the classical Capacitated Arc Routing Problem (CARP). First published online26 September 2019

Purpose The purpose of this paper is to introduce a reconfigurable model that is a combination of a schedule model and a queuing system M/M/m/K to reduce the duration of the wind turbine construction project closure phase and reduce the project documentation waiting time in the queue. Design/methodology/approach This research was implemented in a wind farm project. The schedule model deals with reducing the duration of the turbines closure phase by an activity overlapping technique, and the queuing system deals with reducing the turbine documentation waiting time in the queue, as well as reducing the probability of server idleness during the closure phase. Findings After the implementation of the model, the obtained results were compared to those of similar previously conducted projects in terms of duration, and the model was found effective. Research limitations/implications Project closure is an important and mandatory process in all projects. More often than not, this process is faced with problems including prolonged project duration, disputes, lawsuits, and also in projects like the implementation of wind farms, a queue of documents at closing stage may also cause difficulties in project closure phase. Originality/value The contributions of this research are twofold: first, a combination of project management and queuing system is presented, and second, a reconfigurable model is introduced to enhance the performance and productivity of the closure phase of the project through reducing the implementation time and reducing the turbine documentation waiting time in the queue, as well as reducing the probability of server idleness during the closure phase of the wind farm project.

PurposeThe purpose of this paper is to propose an integrated earned value management (EVM) approach to control quality, cost, schedule and risk of projects.Design/methodology/approachThis study represents a new EVM framework by considering a quality control index. Particularly, some control indices and cumulative buffers are defined by two proposed, methods, namely the linear- and Taguchi-based methods. These methods are implemented in three different projects in different industries.FindingsAccording to the results, integration of the quality index creates a better control situation by providing more accurate information. Hence, project managers could comprehensively monitor the status of important factors to make more precise decisions while maintaining the simplicity of their analysis.Originality/valueFrom the methodological and theoretical features, this paper offers new visions because, to the best of authors’ knowledge, no comparable study has been conducted before.

Herein, nano-kaolin/Ti4+/Fe3O4 as a new magnetic nano-catalyst was synthesized, and its structural properties were characterized using various techniques such as Fourier transform infrared (FTIR) spectroscopy, field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), a vibrating sample magnetometer (VSM), thermogravimetric analysis (TGA) and energy-dispersive X-ray spectroscopy (EDX). This catalyst was used for the synthesis of pyrimido[2,1-b]benzothiazoles via the one-pot condensation of 2-aminobenzothiazole, an aldehyde and β-keto ester under solvent-free conditions at 100 °C. This simple protocol has many advantages such as easy workup, high product yields, short reaction times and reusability of the catalyst.

Facility layout problem is one of the most important problems in a huge range of industries and services organizations. Simultaneous study of some qualitative and quantitative parameters like closeness relationship between facilities, physical constraints such as input/output points and how to arrange facilities can play a key role to determine the facility layout. Considering these parameters can lead to reduce production costs, increase production capacity, and remove additional displacements. A two-stage approach is proposed to achieve these goals. In the first stage, a goal programming model is proposed to determine weights of the attributes based on the experts' opinions with different preference representation structures. Afterwards, the closeness ratings of the facilities are calculated using weights of attributes. In the second stage, an efficient layout is designed by determining facilities placement sequence, location of the next facility adjacent to the previous one, location of input/output points, and rectilinear feasible shortest path between facilities. Two meta-heuristic algorithms including particle swarm optimization and genetic algorithm are designed due to computational complexity. The objective function is to minimize the sum of products distance between facilities and closeness rating and also to minimize the dead space. A case study of an Auto Body Parts company is demonstrated to verify the efficiency of the proposed two-stage approach. Furthermore, in order to assess the performance of the proposed approach, a comparison is drawn between the proposed approach and five existing approaches in the literature to solve different problems by using the two meta-heuristic algorithms.

Assessing the performance of health systems assists health decision-makers to ensure the accountability for their decisions. Medical diagnostic laboratories are one of the most important and sectors in the healthcare system of all countries. Thus, an assessment of the performance of medical diagnostic laboratories is of particular importance. This paper aims to propose a network data envelopment analysis (NDEA) model to assess the performance of medical diagnostic laboratories and decomposing the efficiency of the system with intricate internal structure based on sustainable development indicators. In addition, the proposed model is designed according to the internal structure of the medical diagnostic laboratory, which includes three main laboratory processes (the pre-test, the test and the post-test) with a combination of additional inputs and outputs (including both desirable and undesirable). The proposed model is a multiplicative DEA approach to estimate and decompose the efficiencies of system. Thus, a heuristic method is used as a suitable solution to convert a multiplicative NDEA approach into an equivalent linear program. The performance proposed model is shown through a real study in Iran. The computational results demonstrate the applicability of the proposed model in determining the most efficient laboratory using undesirable sustainability indicators.

Traditionally the performance of firms is evaluated by financial criteria, but this study presents a new qualitative comprehensive framework that incorporates the ethical criteria into the portfolio models and it is widely matched with the preferences of socially responsible investors. The increase of corporate deceptions has caused investors or fund managers consider the ethical assessments in their investment management. Therefore, it is essential to develop models that capture the ethical criteria along with the financial criteria in the investment processes. In this study, a multi-stage methodology is proposed and linguistic Z-numbers are applied to represent the evaluation information, and the Muirhead mean (MM) aggregation operators are employed to fuse the input data under linguistic Z-number environment. Hence, we firstly develop four linguistic Z-number Muirhead mean operators. Then, using the max-score rule and the score-accuracy trade-off rule, three qualitative portfolio models are proposed. These models have been aimed to maximize the financial performance of portfolio as main objective and have been distinguished by the ethical goal that the investor follows. The obtained results of numerical example validates the capability of the models for constructing more diversified portfolio based on a trade-off between financial and ethical criteria according to investors’ preferences.

Z-numbers can generate a more flexible structure to model the real information because of capturing expert’s reliability. Moreover, various semantics can flexibly be reflected by linguistic terms under various circumstances. Thus, this study aims to model the portfolio selection problems based on aggregation operators under linguistic Z-number environment. Therefore, a multi-stage methodology is proposed and linguistic Z-numbers are applied to describe the assessment information. Moreover, the weighted averaging (WA) aggregation operator, the ordered weighted averaging (OWA) aggregation operator and the hybrid weighted averaging (HWA) aggregation operator are developed to fuse the input arguments under the linguistic Z-number environment. Then, using the max-score rule and the score-accuracy trade-off rule, three qualitative portfolio models are presented to allocate the optimal assets. These models are suitable for general investors and risky investors. Finally, to illustrate the validity of the proposed qualitative approach, a real case including 20 corporations of Tehran stock exchange market in Iran is provided and the obtained results are analyzed. The results show that combining linguistic Z-numbers with portfolio selection processes can increase the tendencies and capabilities of investors in the capital market and it helps them manage their portfolios efficiently.

In this study, a new integrated approach is presented to solve the facility layout problem with unequal areas in an open field. This approach includes a two-stage method and a bi-level genetic algorithm. In the first stage of the proposed method, the arrangement of departments adjacent to each other is determined by an improved zone algorithm; in the second stage, the layout obtained from the first stage is improved by using a linear programming model, and the final locations of the departments are thus determined. This two-stage method is used in a bi-level genetic algorithm to achieve an appropriate layout. The orientation and aspect ratios of the departments are determined at the first level of the algorithm, and at the second level, the sequence of the departments is determined for the layout. The proposed approach is compared with the four approaches in the literature, by using 10 datasets in order to evaluate efficiency and performance. The quality of the proposed approach is confirmed by improving the best solutions obtained from previous studies in seven datasets. Furthermore, the presented approach obtains the best solutions from previous studies in the other three datasets. 'n Geïntegreerde benadering om die fasiliteitsbeplanningsprobleem met ongelyke areas in 'n oop veld op te los word voorgestel. Die benadering behels 'n tweeledige metode en 'n dubbele fase genetiese algoritme. In die eerste fase van die voorgestelde metode word die rangskikking van naburige departemente bepaal deur middel van n verbeterde sone algoritme. Die tweede fase verbeter die eerste fase se uitleg met 'n lineêre programmeringsmodel. Die uitset is die finale plasings van die departemente. Die tweeledige metode is gebruik binne n dubbelevlak geneties algoritme om die gewenste uitleg te bepaal. Die oriëntasie en aspekverhouding van die departemente word in die eerste vlak bepaal en die volgorde van die departemente in die tweede vlak. Die voorgestelde uitleg word met vier gepubliseerde benaderings vergelyk deur middel van tien datastelle. Die gehalte van die voorgestelde benadering is bevestig aangesien daar n verbetering in vergelyking met die beste bestaande oplossings getoon word in sewe van die datastelle. Die voorgestelde metode behaal ook dieselfde uitleg in die oorblywende drie datastelle.

Purpose In the past two decades, the growth in the number and severity of disasters causes a rapid increase in the presence of NGOs for more effective response and efficient management of disasters. The NGOs must spend part of their resources on attracting funds to fulfill their humanitarian goals. However, limited number of donors and received contributions leads to a competition among NGOs for fundraising. Therefore, managing the relationship between these organizations and donors is very important. This paper aims to examine the competitive and coopetitive behavior of NGOs to model the interaction. Design/methodology/approach To achieve this purpose, by using game theory, two mathematical programing models are presented to examine the two inter-organizational interactions among NGOs. Findings The results show that if the NGOs work together, all the organizations, donors and affected people will benefit, and the accrued disaster will be managed more efficiently. Practical implications The expressed benefits of coopetition of NGOs can be an incentive for them to work together to manage disasters effectively. Originality/value To the best of authors’ knowledge, no research has considered the impact of the coopetition of NGOs in achieving their social mission successfully. Therefore, this paper can be seen as a valuable resource in this field.

Mitigating the poaching pressure on food webs under multiple constraints (including financial and ecological ones) remains an open problem within conservation. Within this field, mathematically modeling the effects of poaching threats on managerial decision-making is a novel approach. To fill this scientific gap, the present paper uses a security game approach to model the interactions between an environmental manager (defender) and a group of profit-seeking pursuit poachers (attackers) who target species which are nodes of the food web. Based upon the non-cooperative Stackelberg game, the objective of the defender (as leader) is to keep the food web at or near equilibrium through optimally manipulating the populations of an optimal subset of species. In contrast, each attacker strives to maximize monetary profit by hunting an optimal population size of the selected species. The model is validated by a numerical example examining the food web of the endangered Persian leopard (Panthera pardus saxicolor), which lives in Golestan National Park (GNP), Iran. The model provides an overarching biotic intervention strategy to keep the (1 predator-4 prey) food web near equilibrium, while only 2 prey species (the urial (Ovis vignei) and the red deer (Cervus elaphus)) are directly threatened by poachers. The examination revealed that both species population data and poaching data should be taken into account to set effective multi-species conservation prioritization levels. In a sensitivity analysis approach, it was found that, despite the fact that red deer is endangered and preferred by poachers, the deterrent penalty measure should be 1.5 times greater for poaching urial than red deer. The output analysis illustrated that, in order to bring the urial deterrent penalty measure closer to the red deer one, enforcement measures should be about 2.5 times stricter for poaching urial than red deer. The results specifically yield insight into how to optimally conserve a food web equilibrium under poaching pressure and within several constraints.

This paper proposes a hybrid goal programming-data envelopment analysis (GP-DEA) model in a network structure to present improvement solutions and rank units of a supply chain. The improvement solutions are presented for all efficient and inefficient units based on experts’ requirements. Therefore, the goals are considered as fuzzy values. To deal with uncertainty, a suitable fuzzy possibilistic approach is employed. Given robust optimization approach, the units of the supply chain are ranked based on penalties of deviations from goals as feasibility robustness and also the average and standard deviation of deviations as optimality robustness. One advantage of the proposed model is that it can determine balancing values and improvement solutions for flows among the units of a supply chain which have dual-roles factors so that the deviations from their goals are minimized. The proposed robust network GP-DEA model is run in a case study. The outcome of this paper can be used to evaluate and rank all types of supply chains with different network structures.

This study makes use of the artificial intelligence approaches combined with some nonlinear optimization techniques for optimization of a well-known problem in financial engineering called yield curve. Yield curve estimation plays an important role on making strategic investment decisions. In this paper, we use two well-known parsimonious estimation models, Nelson-Siegel and Extended Nelson-Siegel, for the yield curve estimation. The proposed models of this paper are formulated as continuous nonlinear optimization problems. The resulted models are then solved using some nonlinear optimization and meta-heuristic approaches. The optimization techniques include hybrid GPSO parallel trust region-dog leg, Hybrid GPSO parallel trust region-nearly exact, Hybrid GPSO parallel Levenberg-Marquardt and Hybrid genetic electromagnetism like algorithm. The proposed models of this paper are examined using some real-world data from the bank of England and the results are analyzed.