kaveh khalili-damghani

Logistics in upstream oil industry is a critical task as rigs need consistent support for ongoing production. In this paper, a multi-period, multi-product and multi-hub routing and scheduling model is presented for offshore logistics problem. As rigs can be served in specific time intervals, time windows constraints are considered in the proposed model. Despite classic VRP models, vessels are not forced to return hubs at the end of duty days. Also, a vessel may leave and return back to hubs several times during the planning horizon. Moreover, the model determines which vessels are applied in each day. In other words, a vessel may be applied in some days and be inactive in other days of planning horizon. To develop a compromise model, fueling issue is considered in the model. As a rig can be supplied by different vessels in real world cases, the proposed model is split delivery. Based on these challenges and contributions, this research deploys an integrated optimization of routing and scheduling of vessels for offshore logistics. This paper deals with a combinatorial optimization model which is NP-hard. Hence, Genetic Algorithm is applied as the solution approach. The average gap between objective functions of GAMS and GA is only 1.18 percent while saving CPU time in GA is much more than GAMS (about 78.16 percent on average). The results confirm the applicability and efficiency of the GA.

The Flare Gas Recovery System (GR) is a critical component in preventing the release of pollutants into the atmosphere. However, these systems are expensive to install and maintain, so ensuring their reliability and effectiveness during operation is critical task. Two safety system technologies have been developed for GR, the fast opening and closing valve system (OVS) and the closed drum system (CDS), but the dynamic operating conditions and lack of historical data make reliability estimates as a crucial complicated task. To address this issue, we propose a novel approach to develop a system reliability as a response surface based on multiple operating pressure and temperature using a hybrid fault tree and a fuzzy inference system. The result reveals an average 22.4% improvement in reliability for OVS compared to CDS in various operational scenarios. Our proposed method provides an operative technique to assess the reliability of GR security systems considering various operating conditions. Results also can help decision-makers to choose the security technology that best fits their particular application needs, ultimately reducing maintenance of costs while ensuring optimal performance over the long term.

The government in Iran, similar to other developing countries after the Second World War, considered itself responsible for eliminating the backwardness of the country and tried to eliminate it by designing development plans and implementing construction projects. The huge volume of half-finished construction projects is one of the realities of public sector management in the country. Parliamentarians consider the approval of the public-private partnership bill as a solution to half-finished plans. This research, conducted in a qualitative method, tries to answer two questions by analyzing the opinions of experts: first, can all half-finished projects be transferred, and second, what are the methods of economization of projects for transfer? In this article, the text of the interviews has been coded with open coding software. The analysis results are that there is no general solution for all plans, and in each case according to the background conditions (classification of unfinished plans, the possibility of their economization, etc.), causal conditions (control of the plan's approval), intervention conditions (use of capacities transfer laws, the entry of the privatization organization into this issue, etc.), strategies (preparation of legal prerequisites, preparation of regulations, etc.) and consequences (creating incentives for project managers by creating a method of returning saved resources, etc.) a convenient method for economization and transfer should be designed.

Wheat is a strategic crop; the prosperity of production improves any country's economy. In recent years, Iran has faced a significant decrease in wheat production, so it is important to conduct a study in this sector and provide a suitable solution to improve the concerns. A dynamic network model is presented to calculate efficiency for agricultural farmlands over 6 years. This model considers the every-other-year farming method, indirect input part of that is used in one year and the rest in the following years, and the complex relations of wheat production. Finally, to prove the applicability of the presented model, a case study of wheat fields in the northwest of Iran has been selected, and the desired model has been implemented. The results show Zanjanrod performs at the highest efficiency level, followed by Takestan.

Supply chain sustainability is necessary for businesses today, so organizations pay attention to social and environmental goals in addition to economic goals. Therefore, to ensure the realization of these goals, measuring the sustainability efficiency of the supply chain is considered inevitable. The problem of assessing the performance of supply chain sustainability, on the one hand, is affected by the inherent conflicts of interest among sustainability goals, which increase the complexity of the problem, and on the other hand, it should be configured in a way that can simultaneously provide sufficient knowledge of the efficiency of overall supply chain sustainability and sustainability goals. In this study, a combined mathematical model of data envelopment analysis and game theory is presented to evaluate the sustainability efficiency of the supply chain and the efficiency of sustainability goals by considering the trade-offs among sustainability goals. The trade-offs among sustainability goals are formulated using the Nash bargaining game in such a way that the ability to provide simultaneous results is related to the overall efficiency of supply chain sustainability and the efficiency of each sustainability goal. The proposed model was evaluated under a case study for appraising the efficiency of supply chain sustainability of an Iranian pharmaceutical company. The results of the model implementation indicate that the proposed model is firstly capable of simultaneously providing efficiency values of supply chain sustainability and each sustainability goal, and secondly, it provides the necessary and sufficient knowledge in comparison of these values for efficient and inefficient products.

The vastness and complexity of human societies and the importance of figuring out the structure of interpersonal communications to explore the effect of these communications to achieve recognized, precise and predetermined goals have highlighted the role of consulting networks and their analyses over the past decades.  Today activities of large companies of the world are pertinent directly to the evaluation and analysis of social networks. Meanwhile, since organizations and enterprises are often formed and continue to operate to make profits, managers try so that nothing hurts their profitability. Service companies whose income is mainly derived from services they procure and are highly reliant on their human force need more than other companies to identify exact position and role of employees in formal and informal structures of the organization in attracting and maintaining customers in order to boost revenue through encouraging counseling networks and promoting knowledge and knowhow of employees in various levels of the organization. This study in an attempt to explore its prediction goals used a network structure and collected behavioral data of employees working in 36 branches of a private bank to detect their internal counseling network. Then network graph was made and profitability prediction analysis was performed by determining the association relatively strength of internal counseling networks and bank profitability via regression model. According to the results obtained from a nonlinear regression model, this study supported the association between the independent variables and the dependent variables.

As global trade flourishes, terminals endeavor to get higher income while adapting to an expanded intricacy concerning terminal administration tasks. Perhaps the most common issues such terminals encounter is the Berth Allocation Problem (BAP), which involves allotting vessels to a bunch of berths and time allotments while at the same time limiting goals, for example, total stay time or total assignment cost. Complex formats of actual terminals present spatial constraints that restrict the mooring and departure of vessels. In spite of the fact that significant research has been carried out with regard to the BAP, these real-world limitations have not been considered in an overall manner. In this paper, a stochastic Multi-Period Berth Allocation-Scheduling Problem in Different Terminals with Irregular Layouts (SBAP) considering multi-Period modes, generalized precedence relations are developed. To solve the (SBAP), a solution approach based on Stochastic Chance Constraint Programming (SCCP) and a solution approach based on Two-Stage Stochastic Linear Programming with Recourse (TSSLPR) is proposed. A mathematical model is solved to show the applicability of the suggested model and solution approach.

Time-cost-quality trade-off project scheduling problem is one of the most important and practical problems in project management discipline. Project Managers interest in making decisions to accomplish the project with minimum cost, minimum time, and maximum quality. Unfortunately, in real life problems, the estimation of time, cost, and quality may be mixed with uncertainty. These parameters may be far from their estimated values. So, considering uncertain parameters in time-cost-quality project scheduling is essential. In this paper, a probabilistic version of time-cost-quality trade –off problem is proposed. Some parameters of the proposed model are assumed to be probabilistic. The probabilistic nature of the proposed model is handled using stochastic chance constraint programming. The multi-objective nature of the problem is handled using goal programming. Finally, the stochastic chance constraint goal programming is proposed. The deterministic equivalence of proposed stochastic model is presented. A numerical example is analyzed using GAMS software and results are investigated.

With the outbreak of COVID-19 disease to reduce the risk of disease transmission, increasing the level of automation (LoA) in manufacturing and services is the concern of many managers.

The purpose of this article is to provide a new definition of LoA, considering the increasing use of enterprise resource planning (ERP) systems and modern technologies such as blockchain and the internet of things (IoT).

First, different generations of ERP systems were studied and three components of information, system, and human were identified in it, then the components of each class were studied and the solutions used in each component were examined. In the continuation of the research, the previous definitions of LoA were reviewed and the existing research gap was identified and the definition of new automation levels was presented. In this research, the Delphi method was used. For validation, the new definition of LoA was adapted to the definition performed by Verplank and Sheridan.

New LoA definition by considering the latest technologies in the world for use in production and service centers. Determining the LoA of a medical center and proposing the optimal level of the desired center with the available resources.

The new LoA definition can help improve the LoA of medical centers practically

The government in Iran, like other developing countries after World War II, considered itself responsible for eliminating the country's backwardness and tried to eliminate these backwardness by designing development programs and implementing development projects. The huge volume of semi-finished development projects is one of the realities of public sector management in the country at present. MPs consider the approval of the public-private partnership bill as the solution to the semi-finished projects. This qualitative research tries to answer two questions by analyzing the opinions of experts: first: can all semi-finished projects be ceded and second: What are the ways to economize plans for assignment? The text of the interviews has been opened by the coding software. The results of the analysis are that there is no general solution for all projects and in each case according to the background conditions (classification of unfinished projects, the possibility of their economization, etc.), causal conditions (control of project approval), conditions Intervention (use of the capacities of transfer laws, entry of the privatization organization into this issue, etc.), strategies (development of legal prerequisites, preparation of regulations, etc.) and outcome (motivation for project managers by creating a return method Resources saved, etc.) are necessary.

Allocating fixed shared cost and common revenue equitably to all decision making units (DMUs) ‎are two interesting issues in resource allocation using data envelopment analysis (DEA). The ‎existing methods have accomplished these distinctively. In this paper, we developed a single ‎dada envelopment analysis (DEA) approach for equitable allocation of shared costs to inputs and ‎common revenue to outputs, concurrently. The main contribution of this research in comparison ‎with existing methods are: 1) both allocation of shared costs to inputs and common revenue to ‎outputs are considered in a single model, simultaneously; 2) The computational efforts has been ‎reduced and no LP required to be solved; 3) simultaneous changes of inputs and outputs have ‎been considered in order to project a DMU towards efficient frontier. A numerical example, ‎adopted form literature, is presented and discussed in order to illustrate the applicability and ‎efficacy of proposed approach. ‎

Cryptocurrencies are considered as new financial and economic tools having special and innovative features, among which Bitcoin is the most popular. The contribution of the Bitcoin market continues to grow due to the special nature of Bitcoin. The investors' attention to Bitcoin has increased significantly in recent years due to significant growth in its prices. It is important to create a prediction system which works well for investment management and business strategies due to the high chaos and volatility of Bitcoin prices. In this study, in order to improve predictive accuracy, Bitcoin price dataset is first divided into a time interval through time window, then propose a new model based on Long Short-Term Memory (LSTM) neural networks and Metaheuristic algorithms. Chaotic Dolphin Swarm Optimization algorithm is used to optimize the LSTM. Performance evaluation indicated that the proposed model can have more effective predictions and improve prediction accuracy. In addition, the performance of the optimized model is better and more reliable than other models.

In developing countries, whereas the urban bus network is a major part of public transportation system, it is necessary to try to find the best design and routing for bus network. Optimum design of school bus routes is very important. Non-optimal solutions for this problem may increase traveling time, fuel consumption, and depreciation rate of the fleet. A new bus routing problem is presented in this study. A multi-objective mixed integer model is proposed to handle the associated problem. Minimization of transportation cost as well as traveling time is the main objectives. The main contributions of this paper are considering gender separation as well as mixed-loading properties in the school bus routing problem. Moreover, special and handicapped students are considered in this problem. The proposed model is applied in a real case study including 4 schools in Tehran. The results indicate the efficiency of the proposed model in comparison with the existing system. This comparison shows that the students’ travelling time is reduced by 28% for Peyvand middle smart school, 24% for Tehran international school, 13% for Hemmat School and 21% for Nikan High school. A customized Genetic Algorithm (GA) is proposed to solve the model. Penalty functions are used to handle the several constraints of the problem in Genetic Algorithm. The results justify the applicability and efficacy of the both proposed model and solution approach.

This study deals with a bi-objective flexible flow shop problem (BOFFSP) with transportation times and preventive maintenance (PM) on transporters via considering limited buffers. The PM actions on transporters are a missing part of the literature of the flexible flow shop problem (FFSP) which before the breakdown occurs, each transporter at each stage is stopped and the PM action is performed on it. The capacity of each intermediate buffer is limited and each job has to wait in the intermediate buffers. By including all these features in the proposed BOFFSP, not only processing times affect the objective functions, but also, the transportation times of jobs, the waiting time of jobs in the intermediate buffers, and availability of transporters in the system are considered in the model and make it a sample of a real-world FFSP. The presented BOFFSP has simultaneously minimized the total completion time and the unavailability of the system. As the problem is NP-hard, a non-dominated sorting genetic algorithm II (NSGA-II) and a multi-objective particle swarm optimization (MOPSO) is proposed to solve the model for large size problems. The experimental results show that the proposed MOPSO relatively outperforms the presented NSGA-II in terms of five different metrics considered to compare their performance. Afterwards, two one-way ANOVA tests are performed. It can be observed MOPSO achieves relatively better results than NSGA-II. Finally, sensitivity analysis is conducted to investigate the sensitivity of the objective functions to the number of jobs and their transportation time at each stage.

One of the goals of financial institutions is to strengthen the economic infrastructure in developing the financial sphere. In this regard, financial institutions should take the necessary planning to increase their incomes, and if they do not pay attention, the consequences can be predicted for this group of economic activists Increasing income and reducing the risk of bankruptcy are among the most important goals for financial institutions and enterprises. Therefore, considering the increase of income and the integration approach based on the selection of partners in the field of banking, this paper presents a mathematical model based on reducing the risk of bankruptcy. The multi-objective genetic algorithm method has been used to solve and optimize the model. The proposed method was implemented on real data related to ten Iranian banks and the results led to the formation of a financial firm with a combination of banks to maximize the income and minimize the bankruptcy risk.

Avoiding the passengers extra waiting time is a vital task for rail planners. The current research focused on minimizing the passenger waiting time on the presence of real frequently random occurred disturbances. Details of the proposed model are on the 1st line of Tehran underground rail rapid transit. All fitness functions are validated using the analysis of variance (ANOVA) by applying the hypothesis testing method. Also, a validated discrete-event computer simulation model is applied to examine the average waiting time per passenger as the key performance measure under different scenarios generated using full factorial design of experiments. The validity of the obtained optimal solution, i.e., train headway times is confirmed at a 95% level of reliability. Also, simulation outcomes indicated that the proposed response surface meta-model could efficiently provide a more reliable train operation plan to ensure a desirable level of system resiliency on the presence of random disturbances. The numerical results indicated that wait time could be reduced by 14.8% for passengers as compared with the baseline train headway plan.

The main objective of sustainable supply chain is to balance the economic, environmental, and social goals that companies have to use closed-loop supply chains for cost reduction and increasing the efficiency of the supply chain. According to the research literature, considering the risk in supply chains, especially the return supply chain, is one of the topics that has been little studied. Therefore, the aim of this study is to locate the components of a three-objective, sustainable closed-loop, multi-commodity, and multi-period supply chain, considering uncertainty and market scenarios with a risk approach. Location in the sustainable closed-loop supply chain, considering the risk, and also paying attention to the quality of manufactured products and different scenarios of demand are among the innovations of this research. Due to the NP-Hard nature of the problem, the model is solved by the nondominated sorting genetic algorithm II (NSGA-II). Sensitivity analysis has been performed on the parameters of the problem, and the efficiency of the studied methods has been investigated. The average Pareto points obtained from the first objective function is 56789.9, the average Pareto points for the second objective function is 1828.8 and for the third objective function is 77365.32, and also the average solution time of the model is 15.9 seconds.

The purpose of this study is to provide a comprehensive model for the assignment of unfinished construction projects with their economicization approach. This research was conducted qualitatively with the data theory of the foundation. For this purpose, a researcher with 26 experts and specialists in the fields of Management and Planning Organization, the Majlis Research Center and the provincial civil affairs department coordination that purposive sampling (snowball) were interviewed. The text of the interviews was uploaded in MAXQDA10 software and then open coded using the software. After extracting the codes, the main categories were extracted using axial coding. Then, by discovering the relationship between the central categories, and then by performing selective coding and identifying the central category, the desired model based on data theory is presented as a paradigm. The results of data analysis in the form of a template are: Underlying conditions (classification of unfinished construction projects, their economization and assignment of unfinished construction projects), causal conditions (the first step is to control and limit the approval of new projects, the next step is to classify and present a schedule, economic For all incomplete development projects and transfers based on the existing legal instruments of transfers and participation), intervention conditions (lack of proper use of the capacities of existing laws and failure to specify the general policy law of the forty-fourth principle in the transfer of projects, lack of Effective entry of the Privatization Organization due to the difficulties of assigning development projects to companies, lack of accurate information on development projects of state-owned companies and provincial projects, between the systemic nature of the issue and the unclear method of pricing projects), strategies (codification of legal prerequisites, preparation of regulations and systems related to the transfer, preparation of a list of the most probable cases of transferable projects, coordination of responsible bodies due to the inter-institutional nature of the issue and determination of prices through auctions, negotiations, etc. Depending on the application of each method) and the outcome (creating sufficient motivation for managers, operators and current stakeholders of projects and projects by creating a method of returning the saved resources from investment with the participation of the private sector, aggregation and refinement A set of relevant and effective regulations and the availability of all laws and regulations governing private sector investment in development projects, the dissemination of information on all projects in the electronic system and public access to all investable applications as well as contract terms and all subsequent contract developments. The entire duration of the construction, operation or transfer, full transparency of the transfer process to reduce rent-seeking behaviors and pave the way for private sector activity).

Supply chain problems have many ambiguous parameters, and decisions about these types of problems, which are usually multi-objective, should be made according to the constraints and priorities of the objectives. In this paper, we will examine the integrated model of supply chain network with supply, production and distribution levels, considering the logistics costs and service level simultaneously under uncertainty. In multi-objective Mixed Integer Linear Programming (MILP) model, objectives are considered as fuzzy and with different priorities and to eliminate the ambiguity in membership values of fuzzy objectives, priorities are adjusted with fuzzy relations. The model is solved by two approaches of Fuzzy Goal Programming (FGP) and their results are compared. Presenting a multi-period multi-level multi-product multi-objective model in the field of designing and distribution of supply chains and presenting two methods of fuzzy goal programming and the results are compared to provide a suitable method to convert the proposed model into a fuzzy model are the contributions of this paper. The computational results show that the first method in the criterion of cumulative weight of fuzzy membership values ​​and the second method in determining the cumulative weight of ambiguous preferences of decision-maker have had a good performance. The results of ANOVA and Mann-Whitney tests, show that  of all three criteria is less than acceptable level (0.05) and e first method had a good performance in determining the criterion of membership value of cumulative weight of fuzzy objectives.

One of the main critical steps that should be taken during natural disasters is the assignment and distribution of resources among affected people. In such situations, this can save many lives. Determining the demands for critical items (i.e., the number of injured people) is very important. Accordingly, a number of casualties and injured people have to be known during a disaster. Obtaining an acceptable estimation of the number of casualties adds to the complexity of the problem. In this paper, a location-routing problem is discussed for urgent therapeutic services during disasters. The problem is formulated as a bi-objective Mixed-Integer Linear Programming (MILP) model. The objectives are to concurrently minimize the time of offering relief items to the affected people and minimize the total costs. The costs include those related to locations and transportation means (e.g., ambulances and helicopters) that are used to carry medical personnel and patients. To address the bi-objectiveness and verify the efficiency and applicability of the proposed model, the ε-constraint method is employed to solve several randomly-generated problems with CLEPX solver in GAMS. The obtained results include the objective functions, the number of the required facility, and the trade-offs between objectives. Then, the parameter of demands (i.e., number of casualties), which has the most important role, is examined using a sensitivity analysis and the managerial insights are discussed.

This article explores the development of previous models to determine hubs in a competitive environment. In this paper, by comparing parameters of the ticket price, travel time and the service quality of hub airports, airline hubs are divided into six categories. The degree of importance of travel time and travel cost are determined by a multivariate Lagrange interpolation method, which can play an important role in allocating travelers to follower airline hubs. Then, based on the seasonal demand of travelers, we consider travel demand as uncertain parameters. To determine the robust counterpart of this category of hub location models, a robust optimization method is used. Finally, models are tested in a case study. The central results show that the follower airline's income has a considerable growth and can absorb nearly 2% of travelers of the leader airline due to lower travel costs and travel time compared to that of leader airline.

The present article formulates the scenarios that the organization will be probably facing with, using the uncertain factors in business environment, and it also selects the most robust strategies of organization for dealing with the formulated scenarios using the fuzzy information expressed by the experts in fuzzy inference system. The present article aims to provide a method enabling the scenario programmers to employ robustness philosophy using the scenario planning potentials and fuzzy inference system at the decision-making stage of the general process of strategy formulation. The process helps the strategic managers of the organizations to determine their business future clearly and enables them to select their robust scenario in the current market that is uncertain. After the introduction of the robust strategic planning methodology and illustrating its different stages, the selected strategies of them will be compared at the end of the article by implementing the strategic planning method in a practical case. The results of the research have been examined as a case study for carpet industry.

In this paper, decisions about different phases of crisis management cycle are modeled in the form of an integrated mathematical programming model based on the assumption of the real situation of the crisis. Goals are minimizing the number of injured people who are not serviced and minimizing the cost of relief supplies in affected areas. Simultaneous optimization of locating problems of relief bases, allocation of resources, distribution and delivery of relief supplies and evacuation of injured (pre and post-crisis situations) are among the innovations of this research. Therefore, scenarios based on existing faults (four faults) in region one of city of Tehran are considered. In this study, first, we present a binary integer programming model. To validate the model, the Epsilon Constraint method in software environment of GAMS with the CPLEX solver has been used to solve the problem in small scale. To solve the problem in large scale, we have investigated a case study using the data of relief bases in region one of Tehran city. The case study was also investigated using non-dominant sorting Genetic approach. The results of the research show that the non-dominant sorting Genetic approach can solve the model with the least error than the exact solution and in less time.

The purpose of the present research is to provide a multi-period portfolio optimization model in a fuzzy credibility environment, aimed for end-of-period wealth maximization and risk minimization. The investor’s risk was measured using the Average Value at Risk (AVaR) as a coherent risk measure.
The model is designed in such a way that, in addition to considering transaction costs, the investor will have the opportunity to allocate part of his wealth to a risk-free asset. In designing the model, in addition to the cardinality constraints, constraints such as the minimum “proportion entropy” (as the portfolio of diversification degree) and the expected returns of the portfolio in each period are considered.
The results of the model running by MOPSO algorithm indicated that the model objectives in the optimum portfolios were better suited than those when the model was run with random weights. The results also indicated that an increase in the portfolio diversification degree reduced the amount of the final wealth.

Current dynamic and complicated environment has made organizations and industries compete at an international level. If a world-class level production is pursued, they have to manufacture their products in a world-class level. To meet the world-class production indicators and remain competitive, they need to be more integrated at the level of organization, partners and supply chain. The present study aims to investigate the relationship between supply chain integration (SCI) and world class manufacturing (WCM). This study uses a descriptive-correlative method and its research population covers Electronic Domestic Appliance Industry companies. The samples in this research were selected using simple random sampling method. Fuzzy Delphi method was used to identify the dimensions and indicators of production in the world class level and to collect data, a researcher-made questionnaire was used. AMOS software was used to test the hypotheses through a statistical test of Structural Equation Modeling (SEM). The results showed that three dimensions of SCI namely, internal integration, supplier and customer have significantly positive impacts on obtaining products in world class level. The impact of customer integrity on delivery was not confirmed while high level of integral integrity is claimed to have more effects on cost and on-time delivery rather than other integration dimensions of supply chain integration. As the results showed, the companies in the industry are required to take actions on integrating supply chain comprising internal, supplier and customer integration in order to obtain WCM dimensions such as cost, innovation, quality, delivery, flexibility and services. Also result of. This paper helps managers to distinguish the effects of the different dimensions of SCI on obtaining WCM.