OPTIMIZATION of underground stope is defined as looking for a boundary with the highest profit considering technological and geometrical constraints. The first ALGORITHMs were presented about five decades ago for OPTIMIZATION of mining limits. Most of ALGORITHMs, presented till now, have been applied for OPTIMIZATION of open pit limits. Few ALGORITHMs have been presented for Underground cases, most of which have been tailored for a specific mining method or lack rigorous techniques. There are, however, some rigorous ALGORITHMs but they fail to provide 3D solution,. In this paper, a new ALGORITHM is introduced to optimize stope layout using INTEGER programming (IP) technique. The ALGORITHM is implemented on a specific model, derived from conventional block model. It is a rigorous ALGORITHM and provides a realistic optimum solution, compared to its alternatives. For the first time, two more constraints, namely the minimum width of rib pillars and the maximum stope height, are considered in the proposed ALGORITHM.

Electricity demand is forecasted to double in 2035, and it is vital to address the economics of electrical energy generation for planning purposes. This study aims to examine the applicability of Gravitational Search ALGORITHM (GSA) and the newly improved GSA (IGSA) for OPTIMIZATION of the MIXED-INTEGER non-linear electricity generation expansion planning (GEP) problem. The performance index of GEP problem is defined as the total cost (TC) based on the sum of costs for investment and maintenance, unserved load, and salvage. In IGSA, the search space is sub-divided for escaping from local minima and decreasing the computation time. Four different GEP case studies are considered to evaluate the performances of GSA and IGSA, and the results are compared with those from implementing particle swarm OPTIMIZATION ALGORITHM. It is found that IGSA results in lower TC by 7.01%, 4.08%, 11.00%, and 6.40%, in comparison with GSA, for the four case studies. Moreover, as compared with GSA, the simulation results show that IGSA requires less computation time, in all cases.

This paper presents a covering ALGORITHM for solving bound-constrained global minimization problems with a differentiable cost function. In the proposed ALGORITHM, we suggest to explore the feasible domain using a one-dimensional global search ALGORITHM through a number of parametric curves that are relatively spread and simultaneously scan the search space. To accelerate the corresponding ALGORITHM, we incorporate a multivariate quasi-Newton local search ALGORITHM to spot the lowest regions.  The proposed ALGORITHM converges in a finite number of iterations to an $\varepsilon$-approximation of the global minimum. The performance of the ALGORITHM is demonstrated through numerical experiments on some typical test functions.

Integrated water resources management is a systematic process for sustainable development, allocation and monitoring of water resources that is used for social, economic and environmental purposes. In this study, a multi-period MIXED-INTEGER linear programming (MILP) model for urban water supply network management is proposed. The proposed model considers all echelons of water supply chain from supply centers to wastewater treatment centers. Also, the model optimizes the decisions such as selecting the suitable water supply centers and capacity level OPTIMIZATION. To verify and validate the proposed model a real case study is conducted in Urmia. The model is solved by the General Algebraic Modeling System (GAMS) software and its results have been analyzed. According to the results, the optimal water supply centers, optimal water flow, optimal water inventory, and optimal capacity levels of wastewater treatment centers in different periods are determined. Also, in case of transferring the remaining additional treated water to Urmia lake, its level is increased by about 0. 007 cm.

Increased environmental pollution due to the industrial, municipal wastewaters and considerable power consumption of desalination units has increased the need of wastewater treatment technologies. Oil export quality and reduce costs of the electrostatic desalting, important issues in the upstream oil and gas. In this paper, the variety of technology packages used in the different energy levels in the upstream, regarding to the environmental costs, as a function of investment and operational cost have been compared using a MIXED INTEGER mathematical programming models. Technology consists of four technology packages such as electrostatic desalination, industrial, municipal wastewater treatment and water desalination. Technology indexes in each package determine the superior technology timescales during the year. This model developed in Sirri Island and the priority of technology determined by the environmental conditions.

Index tracking is an important issue in portfolio theory. Index tracking is a passive approach in the portfolio OPTIMIZATION problem based on which finite stock should be selected to track the benchmark index. Enhanced index tracking is a selection of the portfolio with limited stock so that its return is maximized and track error is minimized without buying all stock in benchmark portfolio. The main aim of this paper was to propose a two-stage MIXED INTEGER model for enhancing portfolio performance. In order to show the approach performance, top 50 companies were traced. Return, tracking error, excess return and information ratio were used as Portfolio performance measurement. Genetic ALGORITHM and Pattern Search ALGORITHM were also used to solve the models. The findings showed that the two-stage model was better than one stage model. Likewise, pattern search enjoyed higher performance than Genetic ALGORITHM in the two-stage model. Therefore, twostage model had higher performance during pattern search ALGORITHM compared to one stage model or Genetic ALGORITHM.

The Vehicle Routing Problem (VRP), a famous problem of operation research, holds a central place in combinatorial OPTIMIZATION problems. In this problem, a fleet vehicles with Q capacity start to move from depot and return after servicing to customers in which visit only ones each customer and load more than its capacity not at all. The objective is to minimize the number of used vehicles and total distance traversed. This paper presents an application of Imperialist Competitive ALGORITHM (ICA)) in VRP. Unlike other evolutionary OPTIMIZATION ALGORITHMs, ICA is inspired from a socio political process, the competition among imperialists and colonies. Comparison between this method and famous meta-heuristic ALGORITHMs shows the effectiveness of the proposed approach.Computational experience with two groups of instances involving from 50 to 200 confirms that proposed ALGORITHM is competitive in compared to the famous meta-heuristic ALGORITHMs in terms of the quality of generated solutions. In addition, this ALGORITHM finds closely the best known solutions (BKS) for most of the instances.

modelled by mathematical programming and genetic ALGORITHM. Since green supply chain issues become important and new legislations are taken into account, carbon emissions costs are included in the total costs of the supply chain. The optimisation model has the ability to minimise the total costs and provides the best solutions, which are both cost-effective and environmentally friendly.Moreover, a mathematical model and genetic ALGORITHM are used to simulate the developments when carbon emission costs are changed and when companies are committed penalty charge. Finally, the result is shown the genetic ALGORITHM technique is feasible in transportation network planning.