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The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

In this paper, the authors study the nonlinear matrix equation Xp=Q±A(X-1+B)-1AT, that occurs in many applications such as in filtering, network systems, optimal control and control theory.

The authors present some theoretical results for the existence of the solution of this nonlinear matrix equation. Then the authors propose two iterative schemes without inversion to find the solution to the nonlinear matrix equation based on Newton's method and fixed-point iteration. Also the authors show that the proposed iterative schemes converge to the solution of the nonlinear matrix equation, under situations.

The efficiency indices of the proposed schemes are presented, and since the initial guesses of the proposed iterative schemes have a high cost, the authors reduce their cost by changing them. Therefore, compared to the previous scheme, the proposed schemes have superior efficiency indices.

Finally, the accuracy and effectiveness of the proposed schemes in comparison to an existing scheme are demonstrated by various numerical examples. Moreover, as an application, by using the proposed schemes, the authors can get the optimal controller state feedback of $x(t+1) = A x(t) + C v(t)$.

To investigate the impact of board characteristics (board gender diversity, board chair age, board subcommittees, board meetings, board skill, board size and board independence) on corporate social responsibility disclosures (CSRD) of state-owned enterprises (SOEs) in Kenya during the period 2015–2018.

The study employed fixed-effects balanced panel data to examine the impact of board characteristics on CSRD. The analysis is repeated using two regression estimators (robust least square and random effects) and the four CSRD subcomponents to evaluate the robustness of the main analysis.

The results established that board gender diversity, board chair age and board subcommittees had significant negative effects on CSRD. The impact of the remaining board characteristics was found to be insignificant.

The study was limited to the disclosures included in the annual reports, which means that information disclosed in other media, like websites, was not considered. The second limitation concerns mediating and moderator variables that were not considered.

There is a need for a stricter corporate governance implementation mechanism, as opposed to the “comply or explain” principle, since results suggest that most of the board characteristics do not appear to be impactful. Additionally, the low level of reported CSRD calls for the establishment of Corporate Social Responsibility or related committees.

The evidence suggests that SOEs are reluctant to report on issues such as ethics, health and safety initiatives, environment and social investments.

The paper extends the literature on the impact of board characteristics on CSRD in unlisted non-commercial SOEs in a developing country context.

The purpose of this paper is to investigate a more robust ring oscillator. Less sensitivity to power supply variations is a target. This is important since low-quality ring oscillators could be exploited in numerous systems to reduce die costs.

The method in this work is large signal analysis. Delay time as the large signal parameter is calculated symbolically to explore dependency on a power supply voltage. Then simulations are performed to make a comparison. In this work, mathematical justifications are verified via HSPICE circuit simulator outputs, while 0.18 µm TSMC CMOS technology is exploited.

At least two combined configurations are presented with higher robustness. These circuits are more appropriate in noisy conditions. Both theoretical calculations and simulation results verify less sensitive oscillation against supply voltage ripples and temperature variations.

Introducing a band-switched inverter in combined configurations is contribution. In this way, three structures are presented which both show higher stability in oscillation frequency. The band switched delay time calculations are quite new and also the validity of the symbolical delay time approach is verified by circuit simulations.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

The purpose of this paper is to select the best maintenance policy for different types of equipment of a manufacturer integrating the fuzzy analytic hierarchy process (FAHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) models.

The decision hierarchy of this research includes three levels. The first level aims to choose the best maintenance policy for different types of equipment of an acid manufacturer. These equipment pieces include molten sulfur ponds, boiler, absorption tower, cooling towers, converter, heat exchanger and sulfur fuel furnace. The second level includes decision criteria of added-value, risk level and the cost. Lastly, the third level comprises time-based maintenance (TBM), corrective maintenance (CM), shutdown maintenance and condition-based maintenance (CBM) as four maintenance policies.

The best maintenance policy for different types of equipment of a manufacturer is the main finding of this research. Based on the obtained results, CBM policy is suggested for absorption tower, boiler, cooling tower and molten sulfur ponds, TBM policy is suggested for converters and heat exchanger and CM policy is suggested for a sulfur fuel furnace.

This research develops a novel model by integrating FAHP and an interval TOPSIS with concurrent consideration of added-value, risk level and cost to select the best maintenance policy. According to the highlights of the previous studies conducted on maintenance policy selection and related tools and techniques, an operative integrated approach to combine risk, added-value and cost with integrated fuzzy models is not developed yet. The majority of the previous studies have considered classic fuzzy approaches such as FAHP, FANP, Fuzzy TOPSIS, etc., which are not completely capable to reflect the decision makers’ viewpoints.

The purpose of this research is to describe the importance of the Cattaneo–Christov theory of heat conduction in a triangular enclosure with a semi-circular heater. Analysis subjected to Fe₃O₄-H₂O nanofluid is reported. Viscosity dependent on magnetic field is taken into consideration to simulate ferrofluid viscosity. Besides, heat generation and shape factor of nanoparticles are also considered.

The well-known control volume finite element method is used for simulations.

The outcomes reveal that the magnetic field can be introduced to the system as a controlling element.

No such analysis exists in the literature.

The purpose of this study is to conduct a numerical analysis of flow and heat transfer of water–aluminum oxide nanofluid in a channel with extended surfaces in the presence of a constant magnetic field. The channel consists of two parallel plates and five obstacles of constant temperature on the lower wall of the channel. The upper wall and the inlet and outlet lengths of the lower wall are insulated. A uniform magnetic field of the magnitude B0 is located beneath the obstacles. The nanofluid enters the channel with a uniform velocity and temperature, and a fully developed flow leaves the channel.

The control volume-based finite difference and the SIMPLE algorithm were used for numerical solution. In addition to examining the effect of the Reynolds number, the effects of Hartman number, the volume fraction of nanoparticles, the height of obstacles, the length of obstacles and the distance between the obstacles were investigated.

According to the results, the heat transfer rate increases with an increasing Reynolds number. As the Hartmann number increases, the heat transfer rate increases. The heat transfer rate also increases with an increase in the volume fraction of nanoparticles. The mean Nusselt number is reduced by an increasing height of obstacles. An increase in the distance between the obstacles in the presence of a magnetic field does not have a significant impact on the heat transfer rate. However, the heat transfer rate increases in the absence of a magnetic field, as the distance between the obstacles increases.

This paper is original and unpublished and is not being considered for publication elsewhere.

This research aims to model the social dimension of sustainability in construction projects. A new hybrid system dynamic (SD)–fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is proposed to analyze the various factors affecting social aspect of sustainability taking into account their complex interactions.

The various factors affecting the social dimension of sustainable development are identified based on the opinions of 12 experts with an extensive experience in highway construction projects and a thorough knowledge and/or professional experience in the sustainability area. The qualitative model of social sustainability is constructed using SD approach and the complex inter-related structure of the various influencing factors are modeled using cause and effect feedback loops. Fuzzy set theory is applied to model the uncertainty of human judgments. The importance of various influencing factors is then determined quantitatively taking account of their complex interactions using the proposed SD-fuzzy DEMATEL method. The most significant influencing factors are finally determined.

To evaluate the performance of the proposed method, it is implemented on a real highway project and the importance of various factors affecting the social sustainability is determined. A set of complex interrelated factors affecting social sustainability are divided into cause and effect groups, and the root causes affecting the social sustainability performance of the project are determined. Therefore, the required managerial actions can be taken to improve the social sustainability.

Well-qualified experts with a well-developed mental model of social sustainability are necessary to provide required input data for modeling social sustainability using the proposed approach. The absence of such experts could be a limitation for the implementation of the proposed model on a new project.

The proposed Hybrid SD-fuzzy DEMATEL method provides a practical and robust tool to analyze the various factors affecting social sustainability taking into account their complex interactions.

The proposed method offers a more precise and accurate analysis of various factors affecting social sustainability of construction projects since the complex inter-related structure of influencing factors as well as the vague and imprecise nature of experts' judgment is taken into account efficiently.