seyed meysam mousavi

Meticulous project planning is pivotal for ensuring successful project outcomes, with strategic decision-making being a core component of this process. Among these decisions, selecting the right contractor is of paramount importance, particularly in the oil and gas sector where complexity, stringent safety regulations, and significant financial stakes are prevalent. The choice of contractor can significantly affect the project's success, highlighting the need for a meticulous selection process. This study presents a new approach for supplier selection in the oil and gas industry, utilizing an Interval-Valued Fuzzy MULTIMOOSRAL (IVF-MULTIMOOSRAL) method. This advanced methodology synthesizes the strengths of MOOSRA, MOORA, and MULTIMOORA techniques with interval-valued fuzzy sets to manage uncertainties in decision-making. Through a case study involving the evaluation of ten potential suppliers for a Vietnamese petroleum company, the IVF-MULTIMOOSRAL method demonstrates its practical application. The study assesses suppliers based on fifteen sub-criteria within five main categories: reliability, capability, agility, effective asset management, and cost. By providing a comprehensive framework for evaluating suppliers amidst uncertainty, this method facilitates more informed and adequate decision-making in the oil and gas supply chain. The IVF-MULTIMOOSRAL approach distinguishes itself by evaluating and ranking options across multiple criteria, ultimately integrating these assessments into a unified rating. This multifaceted approach not only enhances the precision of the selection process but also underscores the critical nature of the factors being evaluated.

Critical path method is one of the most widely used approaches in planning and project control. Time is considered a determinative criterion for the critical path. But it seems necessary to regard other criteria in addition to time. Besides time criterion, effective criteria such as quality, cost, risk and safety are considered in this paper. Then, the developed problem is solved as a multi-attribute decision making problem by a new extension of MULTIMOORA method. Moreover, type-2 fuzzy sets are utilized for considering uncertainties. Type-2 fuzzy sets are more flexible and capable than type-1 fuzzy sets in reflecting uncertainties. Eventually, SWARA method is developed for determining the weights of efficient criteria such as time, cost, quality, risk and safety under type-2 fuzzy environment. Finally, an applied example has been solved to illustrate the calculations and the ability of the proposed approach. Based on the example, it is clear that the longest path in terms of time criterion is not a critical path, and other influential criteria are involved in determining the critical path.

Brain drain is a phenomenon that has become more widespread in recent decades. This issue is gradually becoming a crisis in Iran. Considering the importance of maintaining the elites in the country, this study endeavors to offer solutions to manage this crisis.

In this qualitative study, the opinions of experts on the issue of brain darin were received and analyzed using a crowdsourcing system. Coding method, content analysis and Maxqda10.2 qualitative data analysis software were used to analyse data.

Key results were classified into three main categories: causes of brain drain, its consequences and management strategies. Data analysis lead to extraction of three main categories including: 1) causes of elite migration with two subcategories of repulsive factors of origin and gravity of destination countries;  2) consequences of elite migration with 2 subcategories of elite migration threats and elite migration opportunities; and 3) elite migration strategies with six subcategories of accurate problem recognition, reforming the nurturing system and supporting the elites, establishing and strengthening the system of innovation, reforming the administrative system and meritocracy, promoting the status and dignity of human resources,  fulfilment of the obligationof macro-policymakers.

Elites tend to migrate due to repulsion of the country of origin and multiplicity ofattractions in the destination country. Policymakers in various fields are expected to develop clear programs and laws to manage and eliminate retention of elites and strengthen attractions to prevent the brain drain.

Multi-criteria decision-making (MCDM) challenges are expanding rapidly. Introducing a comprehensive decision model under uncertainty that considers the weight of criteria, the importance of experts, and the ranking of alternatives is essential for solving the MCDM problems. This paper aims to introduce a comprehensive decision model. For this purpose, a developed version of the grey relational analysis (GRA) method by using reference point approach is presented for ranking of alternatives. Moreover, an extension of the best-worst method (BWM), namely G-BWM, is applied for criteria weight determination. Furthermore, the multi-attributive border approximation area comparison (MABAC) method is enhanced by the average ideal concept to specify the weight of experts. The comprehensive model is enriched by employing grey numbers to cope with the uncertainty. To represent the usability of the proposed method, an illustrative example is solved. The outcomes illustrate the reliability of the comprehensive approach, and it can be applied to various MCDM problems.

Nowadays, healthcare waste (HCW) management has been received attention by increasing the rate of the population and the usage of services. Meanwhile, one of the significant challenges is to select the appropriate treatment technology for decision-makers (DMs) in the HCW industry. In this respect, this paper proposes a new multi-criteria decision-making (MCDM) approach to compute criteria weights, DMs' weights, and alternative ranking methods for assessing and selecting the best HCW treatment technology from various stakeholders. The proposed structure deals with uncertain evaluations of alternatives by using intuitionistic fuzzy (IF)’ linguistic variables to show criteria weights and to extend two new weighting and ranking methods to obtain DMs' weight and rank the HCW disposal alternatives based on uncertain conditions. Eventually, an empirical case in Shanghai, China, from the recent literature, is applied to determine the feasibility, validation, and effectiveness of the proposed model. Results demonstrate that the introduced model is proper and efficient to handle the HCW treatment technology selection problem under an uncertain information condition. According to the final comparative results, the first alternative and the first DM have a high preference than others, respectively.  Furthermore, the sensitivity analysis determines that the final ranking results are reliable with changing the criteria' weights regarding four various kinds of states.

Critical path method (CPM) is categorized as a popular tool for scheduling mega projects. In this paper, to enjoy the advantages of interval type-2 fuzzy sets (IT2FSs) and better address uncertainty for the activities’ attributes, a new analysis model is presented to determine the critical path under an IT2F-environment. Also, new efficient factors on specifying critical paths, such as time, cost, risk, safety, and quality (TCRSQ), are presented to achieve a more robust plan assisting in megaproject success. Moreover, an IT2F weighting approach is suggested for specifying the weights of TCRSQ factors. Furthermore, a new IT2F-approach employing the relative preference relation is expressed for identifying the importance of each expert. Consequently, a new model for critical path determination procedure by considering efficient factors is developed under the IT2FSs environment. Finally, to demonstrate the suggested model's capability and the calculation process, an application from the previous research is solved.

In today's world, organ donation has been identified as a life-giving process worldwide and has been welcomed by many people in different societies. This process can increase the people's quality of life in various countries and also increase the level of social security. In this study, to investigate the issue of organ donation, a supply chain network is presented that includes three sections: donation hospitals, transplant centers, and recipient zones. Besides, to make the appropriate decision to select the best receiver, a new multi-criteria decision-making (MCDM) method is used under intuitionistic fuzzy conditions. Then the proposed mathematical model is presented. In this model, the requirements of climate change and its effects on the transportation system, the quality of organs affected by cold ischemic time, and queuing in transplant centers are examined. A new decision-making method to select the best type of organ recipient and benefit from mathematical modeling provides to apply the issues of organ quality, climate change risk, and the concept of queuing that are the strengths points and innovations of this paper. Then, after presenting the model, using a compromise solution approach, the proposed multi-objective model with the objectives of cost, time, and quality of the organs becomes an equivalent one-objective model. Then, a golden eagle meta-heuristic algorithm is used to solve the problem. Finally, a practical example validates the decision-making method and the proposed mathematical model.

The concept regarding green supply chain management developed as a response in order to increase public awareness from environmental stability which regularly holds both the environment and supply chain management. In the past few years, green supply chain management has become a challenging issue in advanced operations research. In a manufacturing company, selecting an appropriate supplier for their long-term growth prospects by the supply chain managers not only proceed with the business strategy but also help the company remain in a competing position. Considering multiple criteria group decision-making (MCGDM) problem in order to solve the green supplier selection (GSS) includes many unmeasurable and contradictory criteria. This research presents a new group decision approach via interval-valued 2-tuple linguistic preferences and compromises solution to appraise the GSP. The falsification and loss of information which happens formerly in the probabilistic linguistic information process are avoided. Next, a real application is provided via the introduced approach under uncertain conditions regarding the recent literature in the manufacturing industry. Finally, for validation, sensitivity and comparative analyses and a discussion on the effectiveness and benefits of the introduced method are completed and provided.

In this paper, an inbound and outbound truck scheduling problem is investigated in a multi-door cross-dock system considering due date assumptions for outbound trucks as a strict constraint. Part of the products is placed inside outsourcing trucks to ensure timely delivery, customer satisfaction, and product quality enhancement. The problem under consideration is formulated as a bi-objective mathematical programming problem whose objectives are to minimize operational costs and maximize the quality of products delivered in cross-dock. In this paper, the sequence of trucks behind the inbound and outbound doors in the cross-dock is also regarded. A mixed-integer programming model is formulated for the proposed problem. Since the real-world uncertainty is inevitable; therefore, an improved IVF-SO fuzzy method is presented in this paper to solve the model. In this paper, to illustrate the efficiency of the proposed model and also the introduced approach, a real-world example is presented to justify the performance. The numerical results of the case study demonstrate the superiority of the proposed approach over previous methods.

The earned value technique is a crucial and important technique in analysis and control the performance and progress of mega projects by integrating three elements of them, i.e., time, cost and scope. This paper proposes a new version of earned value analysis (EVA) to handle uncertainty in mega projects under interval-valued fuzzy (IVF)-environment. Considering that uncertainty is very common in mega projects’ activities, the proposed IVF-EVA model is very useful and applicable in evaluating the progress of projects. In this paper, analyzing earned value indices and calculating them with linguistic terms have been discussed. They are then converted into interval-valued fuzzy numbers (IVFNs) for the evaluations. Finally, an application example from the recent literature is presented and steps of the proposed IVF-EVA are elaborated.The earned value technique is a crucial and important technique in analysis and control the performance and progress of mega projects by integrating three elements of them, i.e., time, cost and scope. This paper proposes a new version of earned value analysis (EVA) to handle uncertainty in mega projects under interval-valued fuzzy (IVF)-environment. Considering that uncertainty is very common in mega projects’ activities, the proposed IVF-EVA model is very useful and applicable in evaluating the progress of projects. In this paper, analyzing earned value indices and calculating them with linguistic terms have been discussed. They are then converted into interval-valued fuzzy numbers (IVFNs) for the evaluations. Finally, an application example from the recent literature is presented and steps of the proposed IVF-EVA are elaborated.

To demonstrate the importance of customer satisfaction can mention numbers of the service providers that attempt to differentiate themselves by satisfied their customers, witnessed high growth. In this paper, some factors that increase retailers and customers’ satisfaction, such as driver consistent services and delivering fresh products, are considered in a perishable inventory routing problem (PIRP) under possibility and necessity class of fuzzy uncertainty measures. In a typical inventory routing problem (IRP), a distribution center delivers products to a set of customers through a limited time horizon, and simultaneously makes a decision about inventory and routing to minimize the total cost. The proposed model is formulated as mixed-integer programming. Two types of consistent driver services are regarded for different kinds of customers, including particular and typical customers. To investigate the validity of the model, the problem is solved for two values of possibility and necessity measures.

Competition among organizations to gain more market share and higher profit encourages managers to use new and cost-effective strategies. Returned and surplus goods are always a significant part of stores and company's inventory that deciding whether or not to re-commercialize these goods can have tangible effects on their profits and losses. In this paper, a mixed integer linear programming model for maximizing profit with the cross-docking system in unsold products redistribution process is proposed. This model also takes into account the new considerations of deciding whether or not to re-commercialize products in the reverse logistics operation scheduling. Given the uncertainties in factors such as sales revenue, costs and time, the parameters of the problem are considered in terms of gray numbers and an approach to solve the gray mathematical programming model is used to deal with uncertainties. Moreover, the reverse logistics process in one of the chain stores in Tehran is considered as a case study. Implementing the proposed approach and validating the results by a sensitivity analysis on important parameters indicated that the proposed method has a high performance in decision-making process of the studied company.

Earned value management (EVM) is a well-known tool in the project control phase. Upon running the projects, it is critical to control the project to determine the amount of deviation from the plan. Most employers expect the project to be completed according to their requirements and at the expected cost and time. In traditional earned value management, the employer does not present his/her plan, but in the proposed approach the employer gives his/her plan and asks the project manager to offer their time, cost, and quality plan of project based on this plan. The proposed method, called earned incentive metric (EIM), is an extension of the EVM approach that is introduced with triangular intuitionistic fuzzy sets. The plan of project team is compared to the employer’s plan, and the project results are finally compared to the employer's plan. The difference between the two comparisons indicates project performance. In the conventional approaches of EVM, the project is controlled in terms of time and cost, but in the presented approach, the quality criterion is controlled along with time and cost criteria. For the quality values of each activity in each work period, a new group decision-making process is provided. Finally, an application example is given, in which the cost, quality, and progress percentages of each activity in each period are regarded as triangular intuitionistic fuzzy numbers and accordingly performance of time, cost, and project quality are calculated.

Proposing a hierarchical group compromise method can be regarded as a one of major multi-attributes decision-making tool that can be introduced to rank the possible alternatives among conflict criteria. Decision makers’ (DMs’) judgments are considered as imprecise or fuzzy in complex and hesitant situations. In the group decision making, an aggregation of DMs’ judgments and fuzzy group compromise ranking is more capable and powerful than the classical compromise ranking. This research extends a new hierarchical group compromise ranking methodology under a hesitant fuzzy (HF)environment to handle uncertainty, in which for the margin of error, the DMs could assign the opinions in several membership degrees for an element. The hesitant fuzzy set (HFS)is taken into account for the process of the proposed hierarchical group compromise ranking methodology, namely HFHG-CR, and for avoiding the data loss, the DMs’ opinions with risk preferences are considered for each step separately. Also, the Euclidean–Hausdorff distance measure is utilized in a new proposed index for calculating the average group score, worst group score and compromise measure regarding each DM. A new ranking index is presented for final compromise solution for the evaluation. Proposed HFHG-CR methodology is applied to a practical example for a facility location selection problem, i.e. cross-dock location problem, to show the validation and application.

Process adjustment, also known as process targeting, is one of the classical problems in the field of quality control and production economics. In the process adjustment problem, it is assumed that process parameters are variables and the aim is to determine these parameters such that certain economic criteria are optimally satisfied. The aim of this paper is to determine the optimal process adjustment in a two-stage production system with rework loops. An absorbing Markov chain model is developed in which all items are inspected for conformance with their specification limits. The cycle time of production process is included in the model for optimizing total profit of the system. Also, effects of inspection errors are investigated.

In production environments, multi-route Resource-Constrained Project Scheduling Problem (RCPSP) is more complex and consists of two types of flexible and fixed parts. The flexible parts comprise the semi-finished products and each part has multiple routes denoted independently with activities and predictive relationships. This research develops a new Mixed‐Integer Nonlinear Programming (MINLP) model to minimize the makespan. The proposed mathematical model identifies the optimal routes and, consequently, determines the optimal project network. Also, it allocates renewable resources to each production activity. Production sequencing of activities is optimized by the proposed model. A new hybrid approach by regarding GA and PSO in a binary solving space is introduced to handle two main sub-problems of RCPSP-MR in production environments, namely route selection and production scheduling. To evaluate the presented optimization model and algorithm, 60 test problems in various sizes are reported in detail.

Project portfolio management consists of repetitive cycles of project evaluation, selection and execution. Project portfolio selection is the main part of project portfolio management. Organization identifies and prioritizes projects that are mostly aligned with stated strategic goals while considering real-world restrictions and considerations that are related to scheduling and resource allocation. In this paper, a new comprehensive model for the project portfolio selection problem over a planning horizon with multiple periods by maximizing profit is developed in which simultaneous selection, scheduling and resource allocation of projects are considered. The model incorporates project interdependency and strategies of project divisibility, reinvestment, external investment and resourcing in different ways at the same time in choosing the best execution schedule for the projects in real-life applications. In addition, resource and budget constraints, cardinality restriction, precedence relationship and scheduling, setup and resource costs are included in the model. Numerical examples under eight scenarios are presented to highlight the characteristics of the proposed model.

Forming limit diagrams (FLDs) are useful tools for prediction of the instability of sheet in metal forming. The goal of this study is to evaluate the formability of 260 brass alloy sheets under various strain rates (particularly at high strain rate). Three types of experimental procedure were developed: Nakazima test (for determination of the FLD at quasi-static condition), hydrodynamic forming (for determination of the FLD at intermediate strain rate) and Electrohydraulic forming (for determination of high strain rate FLD). Electrohydraulic forming (EHF) is a high velocity sheet metal forming process in which two or more electrodes are positioned in a water filled chamber and a high-voltage discharge between the electrodes generates a high pressure to form the sheet. Arbitrary Lagrangian Eulerian (ALE) formulations coupled with fluid–structure interaction (FSI) algorithms (that are available in the advanced finite element code LS-DYNA) were used to the numerical simulation of process and design of sheet metal specimen geometries. It was found that the forming limits of brass 260 in EHF increased more than 11% relative to the quasi-static. In addition, the formability of this material under the hydrodynamic loading is 4% higher than quasi-static values.

Nowadays high velocity forming methods have become popular among industrial companies due to their capability at improving formability in various materials in comparison to conventional methods. Electrohydraulic forming (EHF) is a high velocity sheet metal forming process in which two electrodes are positioned in a water filled chamber and a high-voltage discharge between the electrodes generates a high-pressure to form the sheet metal. In this work, extensive experimental tests have been designed based on design of experiments (DOE) technique to investigate the effective parameters in EHF (with bridge wire between electrodes). Discharge energy, material, length and diameter of bridge wire have been considered as effective input parameters. Response surface methodology (RSM) has been used to model and optimize the EHF performance with respect to drawing depth for Brass 260. Base on the results, it can be stated that maximum drawing depth is obtained when discharge energy is maximum. It was found that the aluminum wire was more efficient than copper and tungsten. There also exists an optimum amount of length and diameter of bridge wire determined according to the process conditions.

Supplier selection is one the main concern in the context of supply chain networks by considering their global and competitive features. Resilient supplier selection as generally new idea has not been addressed properly in the literature under uncertain conditions. Therefore, in this paper, a new multi-criteria group decision-making (MCGDM) model is introduced with interval-valued fuzzy sets (IVFSs) and fuzzy possibilistic statistical concepts. Then, a new weighting method for the supply chain experts or decision makers (DMs) is presented under uncertainty in supply chain networks. Additionally, a modified version of an entropy method is extended for computing the weight of each assessment criterion. Possibilistic mean, standard deviation, and the cube-root of skewness are proposed within the MCGDM. In addition, a new fuzzy ranking method based on relative-closeness coefficients are proposed to rank the resilient supplier candidates. Finally, a resilient supplier selection problem is solved by the proposed group decision model to demonstrate its validity and is compared with a recent study.

Effective project management requires reliable knowledge of cash required in different stages of project life cycle. Getting this knowledge is highly dependent on sophisticated consideration of project environment. Nature of projects and their environments are associated with uncertain conditions. In this paper, a new project cash flow assessment method based on project scheduling is proposed to foresee project's cash flow in their different stages. Interval-valued fuzzy sets (IVFSs) are applied to address the uncertainty of activity durations and costs. First, an IVF-project scheduling method is proposed to calculate early start time and early finish time of activities under IVF-environment and based on that, a new method of cash flow assessment is introduced under IVF-environment. For the purpose of illustration, the proposed method is implemented to generate cash flow of main activities of a large-scale project. The results show the flexibility of presented assessment method in expressing uncertainty, in addition to its capability in risk evaluation. Furthermore, using alpha-cuts to address different levels of uncertainty and risk provides a comprehensive insight of the cash required in different stages of project life cycle under different levels of risk and uncertainty. Finally, the results are discussed and the proposed method is believed to be useful in the project evaluation.

Electrohydraulic forming (EHF) is a high velocity sheet metal forming process in which two or more electrodes are positioned in a water filled chamber and a high-voltage discharge between the electrodes generates a high-pressure to form the sheet. In this study extensive experimental tests were carried out to investigate the effect of different parameters (such as discharge energy, stand off distance and electrodes gap) on the maximum drawing depth and implicit on shock wave maximal pressure in electrohydraulic free forming. EHF is a complex phenomenon and experimental work alone is not sufficient to properly understand this process. To explain different aspects of the problem, Arbitrary Lagrangian Eulerian (ALE) formulations coupled with fluid–structure interaction (FSI) algorithms that are available in the advanced finite element code LS-DYNA were used to the numerical simulation. In order to model the effect of the electrical discharge, two different approaches were implemented; explosive equivalent mass and energy leak. In the first approach, According to the similarity between explosion and electrical discharge in the water, electrical discharge energy was converted to equivalent TNT mass. In the second approach electrodes gap is replaced by a plasma channel and electrical discharge energy was leaked to it in a short amount of time which makes the channel expand and generate shock waves propagating toward the workpiece .Finally, it was found a good correlation between the experimental and simulation results.

In this paper, firstly by using a mixed linear programming a new model of locating facilities with limited capacity is presented to design a closed-loop supply chain in a multi-product and multi-period mode. Then, using a robust optimization approach, the proposed model decreases in non-deterministic expansion. The results show that the proposed model can handle facility capacity in a closed loop logistics network. In addition, the results showed that the cost and time of test problems for the robust model is higher than the deterministic model.

Choosing the right set of projects is the first step of project oriented firms in strategic project portfolio management. Since economic growth depends on environmental and social issues, sustainable development has been an essential part of firm's plans to keep their competitive advantage. Moreover, market conditions, fast worldwide changes and other similar issues have given these issue ever-growing uncertain elements. As a result, this paper provides a method of sustainable construction-project portfolio selection under interval-valued type-2 fuzzy sets. This method consists of two main parts. The first level evaluates the proposed projects and omits the unsuitable ones. Then, in the second level, the portfolio is selected by means of mathematical programming. Eventually, to display applicability of the method, an application example is presented and solved.

This paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. The output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (LLNF) model, is useful for assessing a project status at different time horizons. Being trained by a locally linear model tree (LOLIMOT) learning algorithm, the model is intended for use by members of the project team in performing the time control of projects in the construction industry. The present paper addresses the effects of different factors on the project time and schedule by using both fuzzy sets theory (FST) and artificial neural networks (ANNs) in a construction project in Iran. The construction project is investigated to demonstrate the use and capabilities of the proposed model to see how it allows users and experts to actively interact and, consequently, make use of their own experience and knowledge in the estimation process. The proposed model is also compared to the well-known intelligent model (i.e., BPNN) to illustrate its performance in the construction industry.