This paper presented a new two-stage green supply chain network, in which includes two innovations. Firstly, it presents a new Multi-Objective model for a two-stage green supply chain problem that considers the amount of shortage in the network, reworking, and carbon-trading cost produced in the green supply chain. Secondly, because of the complexity of this model, it uses a new Multi-Objective interior Search Algorithm (MOISA) to solve the presented model. The obtained results of the proposed Algorithm were compared with the results of other Multi-Objective meta-heuristics, namely MOPSO, SPEA2, and NSGA-II. The outcomes demonstrate that the proposed MOISA gives better Pareto solutions and indicates the superiority of the proposed Algorithm in most cases.

Given the increasing human need for health systems and the costs of using such systems, the problem of optimizing health-related systems has attracted the attention of many reSearchers. One of the most critical cases in this area is the operating room scheduling. Much of the cost of health systems is related to operating room costs. Therefore, planning and scheduling of operating rooms can play an essential role in increasing the efficiency of health systems as well as reducing costs. Given the uncertain factors involved in such matters, attention to uncertainty in this problem is one of the most critical factors in the results. In this study, the problem of the daily scheduling of the operating room with uncertain surgical time was investigated. For minimizing overhead costs and maximizing the number of surgeries to reduce patients' waiting time, after introducing a mathematical model, a chance-constrained programming approach is used to deal with its uncertainty. In this study, also, a harmony Search Algorithm is proposed to solve the model because of its NP-Hardness. By performing the numerical analysis and comparing the presented Algorithm result with a genetic Algorithm, the results show that the proposed Algorithm has a better performance.

In recent years, with the high-paced development of technology, diversity seeking of the customers, and the rise of global rivalry, the traditional systems are not sufficient anymore to respond to the demands of the customers. In order to supply the requests of the customers, overtaking the rivals, and a longer survival in the area of production, companies need new production systems so that while the quality of their products, the costs of production as well as the prices of the products be decreased. A new production system is called mixed model assembly line (MMAL). Mixed-model assembly lines are a type of production line wherein a variety of product models with common base characteristics are assembled on it. In this paper we intend to study these kinds of lines with the goal of minimizing the total utility work cost and total idle cost in conditions in which operation time is considered as a Stochastic parameter. For having a Stochastic modeling, the chance constraint approach has been applied. First, we use the GAMS software to check the feasibility of the model, then because of NP-hardness nature of the problem, the improved harmony Search (IHS) Algorithm is proposed to solve it. Finally, having an attention to the results, it can be deduced in comparison with the exact answer from the Gams software that the results which were gotten from IHS Algorithm have trivial calculation mistakes and present acceptable responses that signify the efficiency of the Algorithm.

Introduction: Water quality protection along rivers involves water quality monitoring and assessment, establishing water quality goals, and controlling pollutant discharges, so that an acceptable level of water quality is maintained.The control of water quality at various locations along a river requires determining the optimal pollutant removal levels at point and nonpoint pollution sources that would yield a satisfactory water quality response in a cost-effective, equitable, and efficient manner (Burn and McBean 1985; Burn and Yulianti 2001). This is known as the “Optimal Waste Load Allocation.” Typical multi objective optimal waste load allocation problems address minimization of the total treatment cost and minimization of the inequity among the pollutant dischargers, subject to constraints on satisfaction of a specified dissolved oxygen (DO) standard at all the check points along the river (Burn and Yulianti 2001). In this reSearch, the capability of Multi objective Ant Colony Optimization Algorithm is applied to a multi objective waste load allocation problem.

Random based inventive Algorithms are being widely used for optimization. An important category of these Algorithms comes from the idea of physical processes or the behavior of beings. A new method for achieving quasi-optimal solutions related to optimization problems in various sciences is proposed in this paper. The proposed Algorithm for optimizing the orientation game is a series of optimization Algorithms that are formed with the idea of an old game and the Search operators are an arrangement of players. These players are displaced in a certain space, under the influence of the referee's orders. The best position would be achieved by following the game laws. In this paper, the real version of the Algorithm is presented. The optimization results of a set of standard functions confirm the optimal efficiency of the proposed method, as well as the superiority of the proposed method over the other well-known metaheuristic Algorithms.