Journal of Operational Research in its Applications
Year:2017 | Volume:14 | Issue:2 (53) |Papers Count:9

Nowadays increasing the efficiency and productivity is a way of compensating the limited production factors of industries. A precise and separate measurement of these indexes for each of industrial sub-sections will lead to a categorization and comparison between industrial subsections. Also, effective sections can help determine the future policies in weak sections. In this case, in this paper, technical efficiency and return to scale probability of all Iranian industries according to the 2-digit ISIC codes have been measured for industrial workshops with more than 10 operators; this has been according to the latest data gathered from the statistical center of Iran. Technical efficiency calculations have been done by the use of DEA approach and output-oriented BCC approach, which has considered the energy variable in addition to the work force and investment inputs. The results obtained from measurements indicate that the average efficiency of industry section in the investigation interval is equal to 65%, and generally large-scale industries, have a higher rank in terms of technical efficiency compared to small-scale industries. Furthermore, applying the Anderson–Darling test and per annum calculated technical efficiency average for estimating industries’ overall efficiency distribution, shows that, the average efficiency of industry section, follows a normal distribution with the m=.65 and s=.09.

Orienting operation management research toward the concept of agility, naturally, many attempts have been made in order to develop measurement instruments for organizations' agility; this is because the presence of a proven and compiled method to evaluate the concepts is an unavoidable requirement for every analysis and planning and then control to happen. In this direction, various methods have been suggested by researches in order to measure organizational agility. But in all of these cases, only the abilities of organizations' agility have been evaluated. Considering the role of information technology in organizational agility, in the present paper, a model of network data envelopment analysis has been proposed in order to evaluate the organizational agility through the use of ability of information technology, confronting agility stimulators. In the suggested model, agility stimulators have been considered as inputs of the first stage, abilities of information technology have been considered as the output of the second stage, and finally agility abilities have been considered as the output of second stage. The suggested model was tested in Yazd’s tile and ceramic industry, and the findings of the research showed that among the twenty studied companies, five were efficient in both stages, ten companies were inefficient in both stages, three companies were efficient in second stage, and finally two companies were efficient in the first stage.

Nowadays, one of the key issues in many organizations, especially in institutions such as banks with many branches, is that is the method for evaluating their performance is not a rational and correct method. Conventional techniques, used to evaluate performance, are single-level and cannot provide enough managerial information to identify the inefficient factors of the inefficient units and attain the benefits and disadvantages of competitive strategies. However, the multilevel data envelopment analysis can overcome this problem. One of the most important problems with classical models is to ignore the internal relations of each unit in performance evaluation. In this paper, a multilevel mathematical model is designed to evaluate the performance of bank branches using the Stokklberg game concept and multivariate programming, which solves the problems of previous models and is able to provide a more realistic assessment of the efficiency of bank branches.

A two-stage hybrid flow shop problem can be considered as part of a larger issue; when solved, it can find a partial solution of a main and bigger problem. Here and in the current paper, a two stage hybrid flow shop problem with one machine in the first stage and two machines in the second stage with three criteria such as: 1) maximum manufacturing time, 2) average manufacturing time and 3) sum of the second stage machines' idle time has been studied. Moreover, a heuristic method was introduced and compared with three algorithms based on simulated annealing (SA) across the three evaluation criteria. Finally, results as a multi-criteria decision problem were solved by Simple Additive Weighting (SAW). Results showed that the heuristic method was better than the three algorithms based on SA in 71.5% of problems. And the solving time was about 48.6 times less than the algorithms based on SA. Also, The innovative method has achieved an average of 30.5% better results in more than 93.4% of the cases.

Portfolio selection problem is one of the most important problems in finance. This problem tries to determine the optimal investment allocation such that the investment return be maximized and investment risk be minimized. Many risk measures have been developed in the literature until now; however, Conditional Drawdown at Risk is the newest one, which is a conditional risk value type problem. The classic model developed by this measure is a linear programming model and does not address lack of data uncertainty. In recent years many approaches have been used to consider data uncertainty; among them one of the most important and applied one is robust optimization. In a robust optimization, through using a set of uncertainties for nondeterministic constraints, a steady counterpart is defined. The present paper seeks to develop a portfolio selection model whose risk measure is to reduce Conditional Drawdown at Risk through robust optimization. The robust approach used in this research is the Bertsimas and Sim approach. In this approach, the firm counterpart provided for a linear programming model remains linear, which makes it possible to maintain the benefits of linear programming model. To test the research, we applied the model in Tehran Stock Exchange Market for 20 real shares’data. The results showed that the model has an acceptable performance, and the results indicated the high performance of the model in developing models under uncertainty conditions. The results also showed that if the level of conservatism increases, the value of the target function will increase.

In this paper, we present a trust region method for unconstrained optimization problems with locally Lipschitz functions. For this idea, at first, a smoothing conic model sub-problem is introduced for the objective function, by the approximation of steepest descent method. Next, for solving the conic sub-problem, we presented the modified convenient curvilinear search method and equipped it with Armijo condition such that in each repetition, the objective function is sufficiently reduced. Then, the convergence property of this method is proved. Finally, the presented method is implemented in MATLAB environment, and numerical results are compared with the non-smooth trust region method.

We consider interval linear programming (ILP) problems in the current paper. Best-worst case (BWC) is one of the methods for solving ILP models. BWC determines the values of the target function, but some of the solutions obtained through BWC may result in an infeasible space. To guarantee that solution is completely feasible (i.e. avoid constraints violation), improved twostep method (ITSM) has been proposed. Many solutions are lost in this method. By using an algorithm, we introduce closed ball method (namely, CBM) as a new method for solving ILP models. In this method, feasibility test ensures that solution space is feasible. To demonstrate the effectiveness of the proposed approach, we solve two numeric examples and we compare the results obtained through BWC, ITSM, and CBM.

In recent years, fair allocation of resources or fixed costs in DEA have been noticed by researches, in which the main objective is unchanging efficiency of decision making units, In which the main purpose is to not change the efficiency of decision-making units before and after the allocation of costs. On the other hand, all studies have been due to the fraction-scale data, and any special attention have not been paid to an interval-scale data so far, because of the ratio of the measurement on such a scale are meaningless. Therefore, the structure of this paper is to study the use of an interval-scale and fractional data in a fair allocation of costs between the units such that the principle of unchanging efficiency remains in the presence of such data.