We consider the application of the ABS procedure to the Linear system arising in the primal-dual interior point method where Newton method is used to compute the path to the solution. When approaching the solution the Linear system, which has the form of normal equations of the second kind, becomes more and more ill conditioned. We show how the use of the Huang algorithm in the ABS class can reduce the ill conditioning. Preliminary numerical experiments show that the proposed approach can provide a residual in the computed solution up to sixteen orders lower.

Dam rule curves should be derived using operation models and considering reservoir storage as well as input flows, which results in better management of reservoir water. In this paper, rule curves of Doroudzan dam in Fars province, has been derived, using standard operating policy (SOP), hedging rules and Linear programming. The objective function is water deficit minimization during 2009-2013. Standard operating policies resulted in severe water shortages. To mitigate the severe water shortage in SOP, the hedging policy has been used, which accepts some present delivery deficit to reduce greater shortages in the future. The Genetic Algorithm has been used to find the hedging optimal factors. Finally, the results of SOP and hedging-GA are compared to a Linear programming model, based on hedging rules’ model. Total shortage in hedging-GA was 84. 19 MCM, hedging-LP was 219. 5 MCM and in SOP was 216. 57 MCM. The GA-hedging model by supplying 88. 33% of demands, controlled the amount of shortage as well as the shortage severity. In addition, the average reservoir storage was highest among the three methods. Comparison of the mentioned models using assessment criteria including time reliability, volume reliability and vulnerability showed that hedging-GA model was less vulnerable and more reliable. Therefore, this model performance was better than the other two models.

Goal Programming (GP) is regarded as one of the most important and powerful techniques for dealing with the complex decision problems that involve multiple and specially conflicting objectives. There are several difficulties with GP. This paper provides an appraisement to the effectiveness of GP in incorporating the decision maker (DM)s preferences. An integrated-systematic classification of different GP models is provided; Also the restrictions of each model are explained, And the General Criterion or Value Function Method is introduced. In order to increase the effectiveness of GP models and to decrease the probable mistakes in defining the DMs preferences, a systematic-interactive-procedure is suggested. This systematic procedure helps DM to select a proper GP model based on his preferences.A numerical example is provided in order to make the proposed procedure easy to understand. Finally, the conclusions andfinal remarks areprovided.

This study, offers a Goal Programming model for optimal allocation of provincial budget resources in order to achieve macroeconomic and social goals of country include unemployment and income inequalities reduction and production and employment and social capital increasing. The designed model determines optimum proportion of provinces in budgetary resources in order to achieve goals. Empirical results based on ten provincial indicators include the unemployment rate, GINI coefficient, literacy rate, internet penetration rate, economic participation rate, the province proportion of domestic production and the province proportion of industrial, mining, services and agricultural value added, show that, there is relative convergence in distribution of proportion in public budget resources, and also achieving goals in the more developed provinces needy higher levels of the budget proportion of the province of the less-developed. Therefore, in order to achieve more balanced development and regional convergence and the elimination of inequalities and spatial heterogeneity and financial recommended the allocation of budgetary resources to less developed provinces must be increase and the budget proportion of the provinces converges.

In this paper additive perturbations of all data in the Charnes-Cooper-Rhodes (CCR) model preserving efficiency of an efficient Decision Making Unit (DMU) are considered first. Sufficient conditions for an efficient DMU to preserve its efficiency after additive perturbations of all data are given. These result are applied to 10 bank branches of a commercial bank of iran using 3 inputs and producing 2 outputs are considered. For each DMU, efficient according to the additive model, a region of efficiency is obtained. In that region inputs and outputs of the corresponding efficient DMU can be changed and its efficiency is preserved.

In today's highly competitive world, the condition of survival and participation in the field of activity, functioning and efficient and effective, is not achieved except through continuous planning, monitoring, control and evaluation. In this regard, we tried to present a mathematical hybrid model for selecting and planning an optimal composition of the shares according to the goals and priorities, in order to obtain the highest compatibility between the final selection and the initial ranking of each share. The proposed model consists of three steps and several steps, the SBM method of data envelopment analysis (DEA) (for initial stock revisions, multi-factor decision-making (TOPSIS)) in uncertainty conditions, for the assessment and ranking of shares in two individual steps and categorized and integer Linear programming (IP) for choosing the best stock portfolio with increased scores according to the organization's priorities and constraints. Collect information from reputable sites of five industries active automotive, pharmacy, petrochemical, cement and food industries to the best stock portfolio for investment, due to the impact of algorithms and methods.

Business process management systems (BPMS) are vital complex information systems to compete in the global market and to increase economic productivity. Workload balancing of resources in BPMS is one of the challenges have been long studied by researchers. Workload balancing of resources increases the system stability, improves the efficiency of the resources and enhances the quality of their products. Workload balancing of resources in BPMS is considered as an important factor of the performance and the stability in systems. Setting the workload of each source at a certain level increases the efficiency of the resources. The main objectives of this research are the concept of resource workload balance and uniformity of the workload for each source at a specified level. To optimize the balance workload and uniformity of each source, the ​ ​ setting multi-process concurrency was offered and studied. Also, the regulation of multi-process concurrency was mentioned as an optimization problem. In this paper, tuning concurrency of the business process is introduced as a problem in BPMS, which is an application issue to improve at workload balance of resources and uniformity in the workload of each resource. To solve this problem, a delay vector is defined, each element of delay vector makes the synthetic delay at the first of each business process, then a dynamic optimization algorithm is presented to compute delay vector and the speed of the proposed algorithms is compared with and state-space search algorithm and evolutionary algorithm of PSO. The comparison shows that the speed of the proposed algorithm is 37 hours to 5. 8 years compared to the state-space search algorithm, while the POS algorithm solves the same problem in just 3 minutes. The experimental results on a real dataset show 21. 64 percent improvement in the performance of the proposed algorithm.

The purpose of this study was to investigate reforestation projects based on the principles of sustainable development in the Darabkola watershed in Mazandaran province. Therefore, a goal programming model was used for the consideration of multiple goals. A fuzzy hierarchical process was used to compute the weights of different criteria. The criteria and indicators were determined according to principles of the sustainable development, including those related to economy, environment, production, biodiversity conservation, forest health, and forest cover (e. g., various indicators such as net present value, carbon balance, annual increment of diameter, stem biomass, basal area, diameter variation, mass stability, and ecological productivity). The tree species comprised Acer velutinum, A. cappadocicum, Fraxinus excelsior, Juglans regia, Quercus castaneifolia, Alnus subcordata and Pinus brutia. The results of goal programming model showed that the areas of afforestation with different species were changed compared to the ecological capability assessments. The results showed a zero ha potential for maple, which was initially defined 590. 197 ha based on the assessment of ecological potential. In addition, the results revealed 120. 17 ha increase (547 vs. 667. 21) for F. excelsior, 470. 02 ha increase (535. 96 vs. 1005. 99) for A. cappadocicum and 89. 72 ha reduce (416. 30 vs. 326. 57) for Quercus castaneifolia. The reforested area was unchanged for the other species such as J. regia, A. subcordata and P. brutia. Therefore, a total area of 3091. 01 ha was suggested for the afforestation according to the results of goal programming model.

Purpose: Using the data envelopment analysis method to determine the most efficient companies listed on the Tehran Stock Exchange. Methodology: In this study, three industries of banking, petrochemical and pharmaceutical have been studied to determine efficient companies. Then, using the ideal planning method, the investment percentage of each company's share in the portfolio is calculated. In this method, once the return and share risk as model variables and with another liquidity variable is added to them. Findings: The results showed that after ranking the companies, their sensitivity can be analyzed and by determining the weaknesses and recognizing the impact of variables, to increase the efficiency of companies. Originality/Value: Using the portfolio of efficient companies for investment leads to reducing investment risk and choosing the right portfolio.

Non-cooperative intelligent control agents (ICAs) with dedicated cost functions, can lead the system to poor performance and in some cases, closed-loop instability. A robust solution to this challenge is to place the ICAs at the feedback Nash equilibrium point (FNEP) of the differential game between them. This paper introduces the designation of a robust decentralized infinite horizon LQR control system based on the FNEP for a Linear time-invariant system. For this purpose, two control strategies are defined. The first one is a centralized infinite horizon LQR (CIHLQR) problem (i.e. a supervisory problem), and the second one is a decentralized control problem (i.e. an infinite horizon Linear-quadratic differential game). Then, while examining the optimal solution of each of the above strategies on the performance of the other, the necessary and sufficient conditions for the equivalence of the two problems are presented. In the absence of the conditions, by using the least-squares error criterion, an approximated CIHLQR controller is presented. It is shown that the theorems could be extended from a two-agent control system to a multi-agent system. Finally, the results are evaluated using the simulation results of a Two-Area non-reheat power system.