Papers by Elham Haji-Sami
Depleting fossil fuel reserves will increase the use of electric vehicles. The need to recharge e... more Depleting fossil fuel reserves will increase the use of electric vehicles. The need to recharge electric vehicles has addressed this issue. In this regard, finding an optimal charging station design is critical. This study introduces a multi-objective battery electric vehicle charging station location problem (BEVCP). As one of the main novelties of this study, we divided the city into two zones, namely inside and outside the central business district (CBD) zone, and designed the charging station network taking these zones into consideration. Furthermore, with regard to the CBD, desired upper and lower bounds of distance (miles) between charging stations are also considered. The proposed two objectives minimize the capital cost of establishing a station and the distance between two stations. In this way, reducing carbon dioxide (CO2) emissions is considered as well. The obtained results confirmed the efficiency of the proposed model.
Robust DEA under discrete uncertain data: An application for Iranian hospital emergency departments
DESCRIPTION This paper presents a Data Envelopment Analysis (DEA) model with uncertain data for p... more DESCRIPTION This paper presents a Data Envelopment Analysis (DEA) model with uncertain data for performance evaluation of the emergency department in Hospitals. The application of mathematical programming models in the important case study such as considering efficiency of emergency departmentis main contribution to this study.The paper used model basis of DEA with 4 scenarios for calculating the efficiency of 6 hospitals in Tehran.The result from the model indicates that 2nd DMU (hospital number 2) has better performance compared with other hospitals ED.
Wholesale-retail pricing strategies under market risk and uncertain demand in supply chain using evolutionary game theory
Kybernetes, 2018
Purpose Nowadays, uncertainty in market demand poses considerable risk to the retailers that supp... more Purpose Nowadays, uncertainty in market demand poses considerable risk to the retailers that supply the market. On the other hand, the risk-averse behaviors of retailers toward risk may have evolved over time. Considering a supply chain including a manufacturer and a population of retailers, the authors intend to investigate how the population of retailers tends to evolve toward risk-averse behavior. Moreover, this study aims to evaluate the effects of wholesale-retail price of manufacturer on evolutionary stable strategy (ESS) of the retailers. Design/methodology/approach Due to market uncertainty, a supply chain with a population of risk-averse and risk-neutral retailers was investigated. The wholesale pricing strategy is determined by a manufacturer acting as a leader, while retailers who make order quantity decisions act as followers. An integrated Cournot duopoly equilibrium and evolutionary game theory (EGT) approach has been used to model this situation. Findings A numerical ...
Journal of Industrial Engineering International, 2014
Crisp input and output data are fundamentally indispensable in traditional data envelopment analy... more Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the real-world problems often deal with imprecise or ambiguous data. In this paper, we propose a novel robust data envelopment model (RDEA) to investigate the efficiencies of decision-making units (DMU) when there are discrete uncertain input and output data. The method is based upon the discrete robust optimization approaches proposed by that utilizes probable scenarios to capture the effect of ambiguous data in the case study. Our primary concern in this research is evaluating electricity distribution companies under uncertainty about input/output data. To illustrate the ability of proposed model, a numerical example of 38 Iranian electricity distribution companies is investigated. There are a large amount ambiguous data about these companies. Some electricity distribution companies may not report clear and real statistics to the government. Thus, it is needed to utilize a prominent approach to deal with this uncertainty. The results reveal that the RDEA model is suitable and reliable for target setting based on decision makers (DM's) preferences when there are uncertain input/output data.
Expert Systems with Applications, 2015
Determining the optimal scale size of a combined cycle power plant is inherently a complex proble... more Determining the optimal scale size of a combined cycle power plant is inherently a complex problem often with multiple and conflicting criteria as well as uncertain factors. The complexity of the problem is compounded by the production of undesirable outputs and the presence of natural and managerial disposability. We propose a customized data envelopment analysis (DEA) method for solving the return to scale (RS) problem in the presence of uncertain data and undesirable outputs. A combined cycle power plant is considered a decision making unit (DMU) which consumes fuels to produce electricity and emissions. The uncertainty of the inputs and outputs are modeled with interval data and the emissions are assumed to be undesirable outputs. The proposed DEA method determines the interval efficiency scores of the DMUs and offers a practical benchmark for enhancing the efficiency scores. We demonstrate the applicability of the proposed method and exhibit the efficacy of the procedure with a six-year study of 17 combined cycle power plants in Iran. The main contributions of this paper are six fold: we (1) model the uncertainties in the input and output data using interval data; (2) consider undesirable outputs; (3) determine the efficiency scores of the DMUs as interval values; (4) develop a group of indices to distinguish between the efficient and inefficient DMUs; (5) determine the most economic scale size for the efficient DMUs; and (6) determine practical benchmarks for the inefficient DMUs.
Expert Systems with Applications, 2015
Determining the optimal scale size of a combined cycle power plant is inherently a complex proble... more Determining the optimal scale size of a combined cycle power plant is inherently a complex problem often with multiple and conflicting criteria as well as uncertain factors. The complexity of the problem is compounded by the production of undesirable outputs and the presence of natural and managerial disposability. We propose a customized data envelopment analysis (DEA) method for solving the return to scale (RS) problem in the presence of uncertain data and undesirable outputs. A combined cycle power plant is considered a decision making unit (DMU) which consumes fuels to produce electricity and emissions. The uncertainty of the inputs and outputs are modeled with interval data and the emissions are assumed to be undesirable outputs. The proposed DEA method determines the interval efficiency scores of the DMUs and offers a practical benchmark for enhancing the efficiency scores. We demonstrate the applicability of the proposed method and exhibit the efficacy of the procedure with a six-year study of 17 combined cycle power plants in Iran. The main contributions of this paper are six fold: we (1) model the uncertainties in the input and output data using interval data; (2) consider undesirable outputs; (3) determine the efficiency scores of the DMUs as interval values; (4) develop a group of indices to distinguish between the efficient and inefficient DMUs; (5) determine the most economic scale size for the efficient DMUs; and (6) determine practical benchmarks for the inefficient DMUs.
A robust optimization model for coordinating pharmaceutical reverse supply chains under return strategies
Annals of Operations Research
A robust optimization model for coordinating pharmaceutical reverse supply chains under return strategies
Annals of Operations Research
Productivity of steam power-plants using uncertain DEA-based Malmquist index in the presence of undesirable outputs
International Journal of Information and Decision Sciences
هدف از این نوشتار مطالعه و ارزیابی عملکرد سیستم جامع مدیریت دانش در میان شرکت های تابعه شرکت مادر... more هدف از این نوشتار مطالعه و ارزیابی عملکرد سیستم جامع مدیریت دانش در میان شرکت های تابعه شرکت مادر تخصصی توسعه معادن و صنایع معدنی خاورمیانه (میدکو) می باشد. تحقیق حاضر در راستای اندازه گیری میزان کارایی نسبی استقرار مدیریت دانش و همچنین اهمیت مدیریت دانش در فرایندهای سازمانی و نقش موثر آن در تولید دانش ، صورت گرفته است. در این پژوهش به منظور اندازه گیری کارایی نسبی با استفاده از روش تحلیل پوششی داده ها DEA از مدل پوششی CCR استفاده شده است. بدین منظور شاخص حجم سرمایه گذاری (میلیون ریال) و تعداد پرسنل (نفر) به عنوان ورودی و شاخص نسبت بسته دانشی ثبت شده به تعداد دانش به کار گرفته شده نیز به عنوان خروجی در نظر گرفته شده اند. نتایج بدست آمده حاکی از این است که از میان دوازده شرکت تابعه پنج شرکت کاراتر از بقیه میب اشند. همچنین در ادامه بر اساس R.F به دست آمده برای آن دسته از از واحدهایی که کارایی کمتری دارند، الگویی جهت رسیدن به مرز کارایی ارائه می شود. در این پژوهش جامعه آماری و نمونه های آماری همان شرکت های دوازده گانه تابعه میدکو می باشد. همچنین برای جمع آوری داده ها از اطلاعات حاصل از نرم افزار جامع مدیریت دانش (MT Ashare) استفاده شده است. مدیریت ارشد شرکت با آرزوی داشتن شرکتی پویا و متعالی و با اعتماد قلبی به اینکه بزرگترین سرمایه سازمان، سرمایه انسانی است، بر آن شد تا سیستم جامع مدیریت دانش را در سازمان نهادینه کند و شعار خود را "سازمانی یادگیرنده و یاد دهنده" قرار دهد.
In this paper, a procedure based on DEA is proposed to measure the efficiency and return to scale... more In this paper, a procedure based on DEA is proposed to measure the efficiency and return to scale (RTS) of
decision-making units (DMUs) in presence of uncertain data and undesirable outputs. A combined cycle-power
plant is assumed as a DMU which consumes fossil fuels to produce electricity as desirable output and
emissions as undesirable outputs. The proposed procedure is applied on a real case of 10 combined cycle power
plants during a five-year planning period. Uncertainty is modeled using interval data. The input-oriented
envelopment form of CCR model is used to develop the proposed models if these paper. Finally, the efficiency
scores and RTS of DMUs are calculated and determined, respectively. The reference set is also presented for
inefficient DMUs.
In real world, we often face with uncertainty in data and for determining Malmquist productivity ... more In real world, we often face with uncertainty in data and for determining Malmquist productivity index (PMI), the observed values are often imprecise or vague. In this paper, a procedure based on DEA is proposed to measure Malquist productivity index of decision-making units (DMUs) in presence of imprecise data and undesirable outputs. Uncertainty is modeled using interval data. The proposed procedure is applied on a real case of 10 steam power-plants during a six year planning period. Each steam power-plan considered as a DMU. By using interval data, the MPI would be interval then calculated the special index to determine productivity of each DMU.
Crisp input and output data are fundamentally
indispensable in traditional data envelopment analy... more Crisp input and output data are fundamentally
indispensable in traditional data envelopment analysis
(DEA). However, the real-world problems often deal with
imprecise or ambiguous data. In this paper, we propose a
novel robust data envelopment model (RDEA) to investigate
the efficiencies of decision-making units (DMU) when
there are discrete uncertain input and output data. The
method is based upon the discrete robust optimization
approaches proposed by Mulvey et al. (1995) that utilizes
probable scenarios to capture the effect of ambiguous data
in the case study. Our primary concern in this research is
evaluating electricity distribution companies under uncertainty
about input/output data. To illustrate the ability of
proposed model, a numerical example of 38 Iranian electricity
distribution companies is investigated. There are a
large amount ambiguous data about these companies. Some
electricity distribution companies may not report clear and
real statistics to the government. Thus, it is needed to utilize
a prominent approach to deal with this uncertainty. The
results reveal that the RDEA model is suitable and reliable
for target setting based on decision makers (DM’s) preferences
when there are uncertain input/output data.
This paper presents a Data Envelopment Analysis (DEA) model with uncertain data for performance e... more This paper presents a Data Envelopment Analysis (DEA) model with uncertain data for performance evaluation of the emergency department in Hospitals. The application of mathematical programming models in the important case study such as
considering efficiency of emergency departmentis main contribution to this study.The paper used model basis of DEA with 4
scenarios for calculating the efficiency of 6 hospitals in Tehran.The result from the model indicates that 2nd DMU (hospital
number 2) has better performance compared with other hospitals ED.
Please cite this article as: Khalili-Damghani, K., Tavana, M., Haji-Saami, E., A data envelopment... more Please cite this article as: Khalili-Damghani, K., Tavana, M., Haji-Saami, E., A data envelopment analysis model with interval data and undesirable output for combined cycle power plant performance assessment, Expert Systems with Applications (2014), doi: http://dx.
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Papers by Elham Haji-Sami
decision-making units (DMUs) in presence of uncertain data and undesirable outputs. A combined cycle-power
plant is assumed as a DMU which consumes fossil fuels to produce electricity as desirable output and
emissions as undesirable outputs. The proposed procedure is applied on a real case of 10 combined cycle power
plants during a five-year planning period. Uncertainty is modeled using interval data. The input-oriented
envelopment form of CCR model is used to develop the proposed models if these paper. Finally, the efficiency
scores and RTS of DMUs are calculated and determined, respectively. The reference set is also presented for
inefficient DMUs.
indispensable in traditional data envelopment analysis
(DEA). However, the real-world problems often deal with
imprecise or ambiguous data. In this paper, we propose a
novel robust data envelopment model (RDEA) to investigate
the efficiencies of decision-making units (DMU) when
there are discrete uncertain input and output data. The
method is based upon the discrete robust optimization
approaches proposed by Mulvey et al. (1995) that utilizes
probable scenarios to capture the effect of ambiguous data
in the case study. Our primary concern in this research is
evaluating electricity distribution companies under uncertainty
about input/output data. To illustrate the ability of
proposed model, a numerical example of 38 Iranian electricity
distribution companies is investigated. There are a
large amount ambiguous data about these companies. Some
electricity distribution companies may not report clear and
real statistics to the government. Thus, it is needed to utilize
a prominent approach to deal with this uncertainty. The
results reveal that the RDEA model is suitable and reliable
for target setting based on decision makers (DM’s) preferences
when there are uncertain input/output data.
considering efficiency of emergency departmentis main contribution to this study.The paper used model basis of DEA with 4
scenarios for calculating the efficiency of 6 hospitals in Tehran.The result from the model indicates that 2nd DMU (hospital
number 2) has better performance compared with other hospitals ED.