Performance of mines can be affected by different factors such as safety and economic factors. This study aims to analyze the influence of safety and economic factors on mines’ performance. To this purpose, a framework is proposed based on a Data Envelopment Analysis (DEA), Ro-bust Data Envelopment Analysis (RDEA) and common weight Robust Data Envelopment Anal-ysis (CWRDEA) to determine the factors affecting on performance of mines. In this study, for the first time, integrated economic and safety factors are considered for evaluation of mines per-formance. To analyze safety and economic factors, this research gathers real data from a mine with 56 sites in south of Iran. Based on different DEA models, different sites become the best site among other sites, but RDEA is much closer to real situation than basic DEA and CWRDEA is the most efficient approach in real situation.
Data Envelopment Analysis (DEA) is a decision making tool based on linear programming for measuring the relative efficiency of a set of comparable units. DEA helps us identify the sources and level of inefficiency for each of the inputs and outputs. This approach has been used to evaluate the efficiency of the safety department in five construction companies. A three-input, safety workforce, safety training, and safety budget, and two-output, Perfect days and Uptime, constant returns-to-scale (CRS) model was developed. The model indicated the necessary improvements required in the inefficient unit’s inputs and outputs to make it efficient, by identifying what factor is responsible for the low efficiency of performance, and also what factor should be improved in order to improve the efficiency of the safety department. The result shows that the safety department of firm A, B and D are efficient, but Firm C and Firm E can improve their efficiency by reducing inputs up to 3.34% and 6.05%, respectively. The inputs identified for reduction were; number of safety staffs and safety budget for Firm C and E respectively.
Measuring the relative efficiency of similar units has been a popular research especially when the units were mostly non-financial. Even, similar financial units may not be necessarily evaluated based on traditional financial figures such as return of equities, return of assets, etc. In this paper, we present an empirical investigation to measure the relative efficiency of 30 branches of an Iranian bank named Bank Mellat. The study considers four inputs including operating expenses, interest paid, capital expenditures and fixed assets. In addition, we use customers’ bank deposit, commissions and loans paid as output parameters. Using three different data envelopment analyses, the study measures the relative efficiencies of all units. The preliminary results indicate that most banks were working under desirable level of efficiency.
Measuring the relative efficiency of financial units plays essential role for making strategic decisions such as business development, downsizing, etc. This paper presents an empirical investigation to rank different branches of a credit institution named Samen in city of Semnan, Iran. The proposed study uses data envelopment analysis (DEA) for measuring the relative efficiency of 17 units. The results indicate that five units were efficient and using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the efficient units are ranked based on some inputs/outputs. The results of this study indicate that most branches of this financial unit performed poorly and a restructure in their businesses is necessary. In addition, the study has provided some evidences that considering employee wage, bank deposit and administration expenses as inputs for DEA implementation seems to provide better results than using total assets and equities.
Measuring the relative performance of firms plays an important role for investment decisions. This research presents a systematic method to evaluate the performance of accepted investment companies listed on Tehran Stock Exchange. The analysis is based on a set of criteria that are determined by experts and the values of criteria’s are extracted from the actual data reported to stock exchange. The efficiency measurement carried out by value efficiency analysis, which is an extension of data Envelopment Analysis. Finally, according to the model’s results, the efficient investment companies are introduced and they are ranked based on the defined criteria. The preliminary results indicate that both methods are capable of providing appropriate rankings for different financial firms.
Measuring the performance of governmental organizations plays essential role on making strategic decisions. In this paper, we present an empirical investigation to measure the performance of 22 different branches of municipalities in city of Tehran, Iran. The proposed study uses data envelopment analysis (DEA) for measuring the relative efficiencies of various units. The proposed DEA uses fixed assets, employee expenses and total income as input and Green Space Development, Resumption and Waste, Development of Cultural Spaces as well as Improvement of Passages and highways are considered as the output of the model. The results indicate that 9 regions were operating efficiently and 14 regions were inefficient.
Measuring the relative efficiency of banking industry has been a popular subject among both practitioners and academicians. Data envelopment analysis (DEA) has been widely applied for different purposes. This paper presents an empirical investigation to measure the relative efficiency of various banks located in province of Semnan, Iran. The proposed study uses DEA method to rank all units and using Anderson and Peterson method (1993) [Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39, 1261-1264], we provide some super efficiency for inefficient units. The study also provides reference numbers for inefficient units and gives some target values for all inefficient units.
Selection of industrial robots for the present day’s manufacturing organizations is one of the most difficult assignments due to the presence of a wide range of feasible alternatives. Robot manufacturers are providing advanced features in their products to sustain in the globally competitive environment. For this reason, selection the most suitable robot for a given industrial application now becomes a more complicated task. In this paper, four models of data envelopment analysis (DEA), i.e. Charnes, Cooper and Rhodes (CCR), Banker, Charnes and Cooper (BCC), additive, and cone-ratio models are applied to identify the feasible robots having the optimal performance measures, simultaneously satisfying the organizational objectives with respect to cost and process optimization. Furthermore, the weighted overall efficiency ranking method of multi-attribute decision-making theory is also employed for arriving at the best robot selection decision from the short-listed competent alternatives. In order to demonstrate the relevancy and distinctiveness of the adopted DEA-based approach, two real time industrial robot selection problems are solved.
For many years, supplier selection as an important multi-criteria decision has attracted both the researchers and practitioners. Recently, high incidences of natural disasters, terrorism attacks, labor strikes, and other kinds of risks, also known as disruptions, indicate the vulnerability of procurement process to these unpredicted events. In this study, a new framework is introduced to select suppliers while considering the supply risks. In the proposed framework, an expert is asked to determine the reliability of each procurement element (i.e., production, transportation, and communication) based on some proposed risk factors. Then, a distinct Multi-Layer Perceptron (MLP) network is trained to play the role of the expert opinion for estimating the reliability scores of each procurement. In addition to reliabilities, the Data Envelopment Analysis (DEA) is used to take into account the conventional selection criteria: price, delivery, quality, and capacity. A set of Pareto-optimal suppliers is obtained from the combination of efficiencies and reliability scores. Finally, the decision maker is recommended to choose between the non-dominated suppliers. Obtained experiment results indicate the effectiveness of the proposed framework.
An appropriate supply chain design helps survival in competitive markets. Achieving maximum efficiency may also help decision makers have a better selection for the supply chain network. The purpose of this paper is to design an efficient supply chain model in terms of the distribution channels under uncertain conditions. The proposed study produces multi products using different materials by considering four layers of multiple suppliers, producers, storages and customers. There are two objectives of maximizing efficiency of distributers and minimizing total cost of supply chain management. The proposed model locates producers as well as suppliers and determines the amount of orders from different suppliers. In order to measure the relative efficiency, the study uses the method developed by Klimberg and Ratick (2008) [Klimberg, R. K., & Ratick, S. J. (2008). Modeling data envelopment analysis (DEA) efficient location/allocation decisions. Computers & Operations Research, 35(2), 457-474.]. In addition, to handle the uncertainty, the study uses the robust optimization technique developed by Molvey and Ruszczy?ski (1995) [Mulvey, J. M., & Ruszczy?ski, A. (1995). A new scenario decomposition method for large-scale stochastic optimization. Operations research, 43(3), 477-490.]. The preliminary results indicate that the proposed model is capable of providing efficient solutions under various uncertain conditions.