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Precipitation is one of the basic components of the water cycle and it is considered as one of the most important input components of the hydrological cycle. In the current research, the accuracy of the Simulated Annealing algorithm based on Support Vector Machine (SVM-SA) was evaluated in the simulation of precipitation changes. In order to verify the results, the precipitation data of the Gorgan synoptic station during the 40-years from 1971 to 2010 were used . Based on the results, using 5 non-precipitation meteorological parameters including cloud cover, maximum temperature, water vapor pressure, maximum relative humidity, and dew point, the values of RMSE, SE and R2 in the training section are equal to 6.02 mm, 0.01 and 0.999, and in the testing section 18.72 mm, 0.03 and 0.925 mm, were calculated respectively. The results showed that the SVM-SA can be highly accurate in simulating precipitation changes in the study area.

In this research, an integrated scheduling problem of job shop systems with an assembly stage and transportation to minimize the total tardiness time is studied. In this problem, the parts are processed in a job shop system and then assembled in the assembly stage. Ultimately, the products are shipped in packages to customers. Setup time is assumed to depend on sequence. At first, a mixed-integer linear model is developed. Since the problem is NP-hard, a hybrid imperialist competitive and simulated annealing (ICA-SA) algorithm is proposed to solve the problems with the medium and large sizes. To validate the performance of the proposed algorithm, results are compared to an imperialist competitive algorithm and a hybrid imperialist competitive and tabu search (ICA-TS) algorithm. Analysis of variance random block design is used to compare the results of the algorithms. P-values of algorithms and blocks in this test are smaller than the significance level of 0.05. The computational results show that the proposed hybrid algorithm achieves better performance than the imperialist competitive algorithm and hybrid imperialist competitive and tabu search.

This paper aims at proposing a novel quadratic assignment-based mathematical model for designing an optimal facility layout in each period of the stochastic dynamic facility layout problem (SDFLP). Considering routing flexibility is the main assumption of this problem so that parts can pass through multiple routes. It is also assumed that product demands are independent, normally distributed random variables with known expected value and variance changing from period to period at random. In addition, to solve the proposed model, a new hybrid meta-heuristic algorithm is developed by combining simulated annealing (SA) and the CRAFT approaches. Finally, the proposed model and the hybrid algorithm are verified and validated using design of experiment, real case study and sensitivity analysis methods as well as solving some numerical examples.The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time perspectives. Moreover, the proposed model can be used to design the layout of facilities in both of the stochastic and deterministic environments of traditional and modern manufacturing systems.

In this research, for the first time, a simulation-optimization system dynamic-based model with Anylogic software for reservoir eutrophication management is presented. Thus, the relationships of the elements related to eutrophication were formulated in different layers of Karkheh Dam and in relation to each other in the system dynamics form. comparing the presented results with the results of the CE-QUAL-W2 and Vensim simulation models over a one-year period showed that the value of the correlation coefficient (𝑅2) for the variables phosphate, ammonium, nitrate, Dissolved oxygen and Chlorophyll-A between the two models of Anylogic and CE-QUAL-W2 is equal to 0.89, 0.81, 0.78, 0.75 and 0.86, respectively, and the value of this coefficient for the mentioned variables is equal between the two models of Anylogic and Vensim. With 0.98, 0.94, 0.90, 0.93 and 0.97, which shows the high correlation between the results of Anylogic model with the two mentioned models. Then, by combining the developed model in Anylogic environment with a simulated annealing optimization model, a simulation-optimization model was developed to determine the optimal values of water releases from different layers of reservoir in 15 years’ horizon considering the quantitative and qualitative objectives. Here, the RMSE between the results of the two models for the lower, middle and upper layers of the reservoir is 0.004, 0.007 and 0.010, respectively, and 𝑅2 is 0.91, 0.9 and 0.79. The results indicated that the results of the two models were similiar. This study showed the effectiveness of the proposed model in the management of dam eutrophication.

Abstract: In undesirable facility location problem contrary to desirable location, facilities are located far from service receiver facilities as much as possible. The problem of locating such facilities is discussed in this study. This research is focused on the ‘not in my backyard’ (NIMBY) phrase which refers to the social phenomena in which residents are opposed to locate undesirable facilities around their houses. Examples of such facilities include electric transmission lines and recycling centers. Due to the opposition typically encountered in constructing an undesirable facility, the facility planner should understand the nature of the NIMBY phenomena and consider it as a key factor in determining facility location. Because of the adverse effects of these facilities, coupled with the uncertainty in the real world, it is estimated taking into account the potential uncertainty. This problem has been considered in the discrete space. The mathematical model is presented and methods to deal with uncertainty and stochastic programing problems modeling and methods used in the study are presented. According to the NP-hard problem, Simulated Annealing suggested for solving problems in large-scale. Numerical experiments to evaluate and validate the mathematical modeling and proposed algorithm are considered and operation of the proposed algorithm to solve various problems with genetic algorithms available in the literature about the problem is compared. Introduction: From a general point of view, it is possible to investigate locational issues in two categories of optimal and undesirable facilities. There are many location models available for desirable facilities such as warehouses, service centers, police stations, and more. In such instances, customers are attracted to their facilities. Unlike desirable facilities in the location of undesirable facilities, it attempts to facilities as far away as possible from receiving areas. Undesirable facilities, while providing essential services for the people, at the same time, may have negative consequences for their neighborhoods. The proximity of undesirable facilities to residential areas due to their high pollution and hazardousness, will lead to lower quality of life in these areas and increase the health risk for residents of these areas. This type of problem is formulated in order to minimize the adverse effects of a new facility on existing facilities. Given that the degree of pollution from this facility in the real world is associated with uncertainty, in this paper, the mathematical model for the problem of locating undesirable facilities with uncertainty in degree of pollution parameters is presented. Also, the history of the problem of locating facilities in stochastic conditions and undesirable facilities has been presented. In the following, considering the mathematical model in a deterministic and stochastic manner, the proposed algorithm structure is described. Materials and Methods: The problem of locating the undesirable facilities discussed in this paper is based on the article that focuses on the purpose of the term (not in my backyard). This refers to social phenomena in which residents oppose the placement of undesirable facilities around their homes. Considering the opposition to the creation of undesirable facilities, the facility planner must understand the nature of the NIMBY phenomenona and consider it as a key factor in determining the location of the facility. In this research, the NIMBY phenomenona is directly discussed through the structure of the objective function. The purpose function structure allows the residents to speak of the fact that the hosts hosting these facilities are those who are absorbing environmental costs, while other regions enjoy the benefits of this facility. 1- First, uncertainty in the degree of pollution is considered and different scenarios are considered for it and the problem will be solved. 2- According to the mathematical model presented in stochastic mode, the problem is solved for each individual scenario and finally, three scenarios are compared and the objective hope of the target function is obtained for all scenarios. We considered the scenarios based on the degree of difference between these two parameters, which are as follows: The scenario (1): The degree of main contamination (a) 100 times the marginal contamination (b) Scenario (2): both change in close proximity. Scenario (3): The degree of main contamination (a) is 10 times the marginal contamination level (b) 3- Because of the NP-hardness of the nature of the problem studied, it is proposed to solve it in large dimensions for the large-scale simulation of Simulated annealing algorithm. A Heuristic method has been used to generate the initial answer to this question. The method of transfer from a solution to its neighboring solution is characterized by a known key factor called the neighborhood structure. Here, four operators for generating neighboring solutions are used in the Simulated annealing algorithm. Results and Discussion: The problem in small and medium sizes in all experimental samples of the Metaheuristic-algorithm has been reached to the optimal solution of the problem Cplex solving software. Due to the fact that the Cplex software was unable to solve test samples for dimensions of 1000 node and larger than this dimension of the problem, the results of numerical tests of the problem were considered by considering 3 scenarios in each sample. Sixteen types of problem in large dimensions are solved with genetic and Simulated annealing meta-heuristic algorithms, and the stopping time of both algorithms is 1 hour. Finally, the results are compared. The genetic algorithm is written according to Sang et al. (Sang et al., 2013). Due to the fact that it was not able to produce feasible answers on large dimensions, the Heuristic method used in the Simulated annealing algorithm to generate the primary solution is also used in this algorithm. In all experimental samples, the Simulated annealing algorithm has the lowest value of the target function in comparison with the genetic algorithm. Conclusion: In this paper, the problem of locating undesirable facilities with a focus on reducing the degree of pollution from these facilities was studied. Considering that in the past and also in the real world, the exact amount of this degree of pollution is uncertain, it was decided to investigate the degree of contamination resulting from this facility in the uncertainty mode. Then, by investigating the methods of dealing with uncertainty and modeling methods of random planning problems, the scenario method was used to consider the uncertainty in this problem and scenarios were designed to take into account the uncertainty in degree of pollution of the facility for the points around them. The problem was solved for various test examples in small to medium size scenarios with Cplex exact solver software and optimal solutions were obtained. Given the complex nature of this problem, a large-scale Simulated annealing algorithm was proposed to solve it in large dimensions and given that the space is large, it is used to generate an initial possible response instead of producing a random solution from an Heuristic method to produce an initial possible answer to this problem. The computational results of Optimal Optimization and Simulated annealing Algorithms in Small and Medium Dimensions were compared. Both methods achieved the optimal response and the difference was during solving these samples. For large dimensions, experimental samples were used using the Simulated annealing, and genetic algorithm available in the literature were compared and the efficiency of Simulated annealing algorithm in convergence to optimal solution and less time to solve in different samples and scenarios has been proved. References Song, B. D. Morrison, J. R. Ko, Y. D. (2013). Efficient location and allocation strategies for undesirable facilities considering their fundamental properties. Comput. Ind. Eng., 65: 475–484. Mirhasani, S. A. (2014). Stochastic Programming. First Edition. Amir Kabir University Publishers.

The cell formation problem and the group layout problem, both are two important problems in designing a cellular manufacturing system. The cell formation problem is consist of grouping parts into part families and machines into production cells. In addition, the group layout problem is to find the arrangement of machines within the cells as well as the layout of cells.In this paper, an integrated approach is presented to solve the cell formation, group layout and routing problems. By Considering the dimension of machines, the width of the aisles, and the maximum permissible length of the plant site, a new framework, called spiral layout, is suggested for the layout of cellular manufacturing systems. To extend the applicability of the problem, parameters such as part demands, operation sequences, processing times and machine capacities are considered in the problem formulation. The problem is formulated as a bi-objective integer programming model, in which the first objective is to minimize the total material handling cost and the second one is to maximize the total similarity between machines. As the problem is NP-hard, three metaheuristic algorithms, based on Genetic Algorithm and Simulated Annealing are proposed to solve it. To enhance the performance of the algorithms, a Dynamic Programming algorithm is embedded within them. The performance of the algorithms is evaluated by solving numerical examples from the related literature. Finally, a comparison is carried out between the proposed spiral layout and the linear multi-row layout which has recently presented in the literature

This paper aims to examine the scheduling of vehicles in a multi-product supply chain regarding to the mutual relationship between the transportation and the manufacturing units. The integration level in the supply chain consists of a manufacturer and its first tier suppliers, which are linked by a transportation fleet. The problem is determining orders allocation to the suppliers, orders production sequence at the suppliers, orders allocation to the vehicles, and orders transportation priority, in order to minimize the sum of orders delivery time. This issue has not been discussed in the literature, so far. At first, the mathematical model of the problem is presented, then the NP-Hardness of the problem is demonstrated. For solving the problem, a new combination of genetic algorithm and simulated annealing algorithm, named as Populated Simulated Annealing algorithm (PSA) is proposed. For verifying the PSA, its results are compared to results of simulated annealing algorithm (SA) and developed version of DGA algorithm, proposed for the nearest problem in the literature to our problem. Furthermore, relaxing some hypothesis, the results of PSA are compared to DGA results. All of the comparisons show that PSA is more efficient than the other algorithms. Finally, comparison of PSA with exact solution for small size problems demonstrates its proper efficiency. Introduction: The vehicle routing problem (VRP) is one of the most important issues in the world's industry, which, today, is highly regarded because of its practical applications in industries. We examined the scheduling of production and transportation in a multi-product supply chain considering the interaction between the transportation and the manufacturing units. The supply chain consists of two parts.The first part is suppliers which are located in different geographical locations handling specific orders. The second part consists of several vehicles that collect the orders processed by suppliers and deliver them to the company. The considered transportation system is similar to vehicle routing problem (VRP). The difference between VRP and the problem in this research is that in VRP the amount of goods that should be transported, and is known. However, as it is assumed in this research, the allocation of orders and sequencing of their manufacturing are the decisive variables. Problem objectives are determining the allocation of orders to suppliers, orders production sequence, orders allocation to the vehicles, and transportation sequence, in order to minimize the summation of the orders completion time. Innovation of this paper is as follows: A combination of production scheduling problem in suppliers and VRP in a supply chain when the supplier can’t process all orders. Developing a new mathematical model for solving the problem. Three algorithms have been proposed to solve this problem, including: developed DGA, simulated annealing algorithm (SA), and a new combination of these two algorithms, which is named populated simulated annealing algorithm (PSA). A supply chain consists of a set of suppliers, producers and distributors that cooperate with each other in order to satisfy customers’ need. A supply chain determines all levels in which the value is added to a product. VRP has several versions. In this study, it is considered that a number of heterogeneous vehicles are collecting orders from suppliers located in different geographical locations. With considering the integration level of companies in the supply chain, researches can be divided into four categories: 1) Researches that examine the relationship between manufacturers and suppliers; 2) Researches that examine the relationship between manufacturers and distributors or customers; 3) Researches that focus on the relationship between some manufacturers together (Outsourcing); 4) Researches that consider combination of the above scenarios. 5) Considering the examination level of supply chain, researches have been divided in two categories: 1) Researches that have a macro planning and coordinating in the completion chain; 2) Researches that have an operational scheduling and coordinating in the supply chain. The literature shows that the combination of VRP with scheduling problem in supply chains possessing constraint on allocating the orders to suppliers has not been studied. Materials and Methods:Step 1) Developing a new mathematical model for this problem. Step 2) Developing the PSA algorithm to solve the problem. Step 3) Validating PSA algorithm as follows: Step 3-1) Producing random samples with different structures. Step 3-2) Comparing PSA with SA and developing DGA. Step 3-3) Comparing PSA with DGA after adding a relaxation assumption. Step 3-4) Solving small samples with PSA and comparing with exact solution. Step 4) Doing sensitivity analysis on the three main parameters. (Number of orders, Number of suppliers, and Number of vehicles) Results and Discussion: The results of the comparison demonstrate that the populated simulated annealing algorithm shows better results than the other two. This method shows that the combination of genetic algorithm and simulated annealing in this specific way can adapt advantages of both methods. The results show that the mean of answers is increased by increasing number of orders,. With increasing suppliers, the objective function is improved because the orders allocate to different suppliers and the delivery time is decreased. By increasing orders processing time, the objective function value gets worse because the waiting time for processing orders is increased. By increasing transport times, the average solution is increased. It’s because vehicles should spend more time along the way. Conclusion This issue has not been discussed in the literature. At first, the mathematical model is presented and then it is shown that the problem is NP-Hard. Three algorithms have been proposed to solve this problem: Developed DGA, Simulated Annealing Algorithm, and a new combination of these two algorithms, which is named Populated Simulated Annealing Algorithm. Random samples with different structures is created and solved by these three algorithms. Also, relaxation of distance assumption between suppliers that are in the same location has been discussed at Zegordi and Beheshti Nia (2009) and is compared with PSA which shows that PSA is more efficient than the other algorithms. Finally, the comparison of PSA with exact solution for small size problems demonstrates its proper efficiency. References Archetti, C., Jabali, O., & Speranza, M. G. (2015). Multi-period vehicle routing problem with due dates. Computers & Operations Research, 61, 122-134. Ray, S., Soeanu, A., Berger, J., & Debbabi, M. (2014). The multi-depot split-delivery vehicle routing problem: Model and solution algorithm. Knowledge-Based Systems, 71, 238-265. Zegordi, S., & Beheshti Nia, M. (2009). Integrating production and transportation scheduling in a two-stage supply chain considering order assignment. The International Journal of Advanced Manufacturing Technology, 44(9-10), 928-939. doi: 10.1007/s00170-008-1910-x

Introduction: Parkinson's disease has become an increasing public health issue that its symptoms become more severe over time. Early diagnosis and treatment of this disease leads to improving the skills, abilities and performance of patients in daily life. In order to diagnose the disease early, it is necessary to produce clinical decision-making assistance systems that are able to detect the diagnostic rules of the disease. Methods: This study provides an automatic way to extract novel diagnostic rules for Parkinson's disease. The proposed method is based on logic regression and simulated annealing algorithm. To evaluate the method, the Oxford Parkinson's data set was used, which contains 22 biomedical voice measurements from 31 people, 23 with Parkinson's disease. The dataset has 195 voice recording from these individuals. Results: The results include two diagnostic rules; If high accuracy was the main concern, a new rule has been proposed that includes 21 logical statements that have an accuracy of 92.31%, a sensitivity of 85.42%, and a specificity of 94.56%. However, for real-time systems and clinical decision-making assistance with high interpretability, a rule consisting of 3 logical statements has been proposed, which has an accuracy of 78.97%, a sensitivity of 77.08% and a feature of 79.59%. Conclusion: The results show the high power of interpretability and reliability of the proposed rules in the diagnosis of Parkinson's disease, which can be used in the implementation of remote diagnostic systems. [ABSTRACT FROM AUTHOR]

Hub location problem is one of the new issues in location problems. This kind of location problem is widely used in transportation systems. In this paper, we investigate p-hub center location allocation problem under capacity constraint. The aim of the proposed model is to determine the location of hub nodes and the allocation of non-hub nodes to the hub in such a way that the maximum traveling time is minimized. In addition, since the problem is an NP hard problem, two metaheuristic algorithms including simulated annealing algorithm and ant colony are developed for solving large size real world problems. The performances of the proposed algorithms are examined via some numerical examples taken from known related benchmark sets (AP dataset). The best solutions found using metaheuristic algorithms are also compare to the results achieved using Lingo software. The results demonstrate that the proposed algorithms are able to find optimum or near optimum solutions in acceptable run times.

In order to manage the expenditures in a road transportation network in which the transport demands between cities are less than a truckload capacity, one needs to determine the location of hubs at first, and then collect the cargo from the cities in some routes which are assigned to the appropriate hubs. In this paper, a special case of hub location-routing issue was considered that is suitable for the particular conditions of Iran as cities are located in the sparse and distant places. A mixed integer mathematical programming model was proposed. As the model is NP-hard in nature, a two-phase hybrid method including genetic algorithms and simulated annealing was designed to solve the model. The results of the comparison between the model and the outputs demonstrated the accuracy and speed of the proposed solution method. Finally, a real case including all 31 capital cities of Iran provinces was solved to illustrate the appropriate performance of the solution method.

In this research, optimization of examinations' timetable for university courses, based on a real problem in one of the universities in Iran is studied. The objective function defined for this problem is more practical and realistic than the other objective functions that have been utilized by previous researchers in literature and effectively reflects the real objective of the problem. In order to define the objective function, we have made use of Coulomb's law in electricity that says the magnitude of the electrostatic force of interaction between two point charges is directly proportional to the scalar multiplication of the magnitudes of the charges and inversely proportional to the square of the distance between them. We have defined a repulsive force between any pair of Examinations. The optimum solution is achieved when the sum of all forces is minimized. Hence, the obtained mathematical model is a non-linear programming with binary variables, similar to the quadratic assignment problem (QAP) which is an NP-Hard problem. This sort of problems can be solved exactly only if they are in small sizes. For solving this problem in medium and large scale, some methods are used based on Simulated Annealing (SA) algorithm and Imperialist Competitive algorithm (ICA). These algorithms can reach good sub-optimal solutions in a short period of time. Practical results of this mathematical model are already used in one of the national universities in Iran. The practical results demonstrate the high efficiency and effectiveness of this model.

Purpose: The main purpose of this paper is to propose a multi-objective model for assigning service/product to clustered customers. The main practical objectives of this model from the perspective of the bank are reduced cost and risk and increased customer satisfaction. Design/methodology/approach: In this paper, five indicators of recency, frequency, monetary, loan and deferred have been identified and customers have been clustered, accordingly using K-means approach. Then, a three-objective mathematical model has been designed to assign optimal service/product as response to customer. Finally the model has been solved by simulation based optimization. Findings: In the case study, all information about five characteristics of customers was extracted from the database, 31953 customers were placed in seven clusters and the validity of these clusters was measured. A three-objective mathematical model was designed based on the characteristics of 13 types of bank products/services. Then, the simulation modeling solutions were improved using the simulated annealing algorithm. In this study, Weka and R-Studio, Arena and Longo were used for data mining, simulation and optimization, respectively. Research limitations/implications: The limitations of this study include inability of simulation instruments for drawing, solving all probable states (more scenarios) and solving the model for those states. It is recommended to develop the mathematical model with respect to customer, so that after problem solving, the bank would be able to make decision on providing services and products to its customers. Simultaneously, the objective functions would be fitted within their most reasonable states and ultimately, using a model, the parameters related to each product can be set for the new customer referring to the bank. Practical implications: Products/services were assigned according to customer needs in a way that cost and risk were reduced and the utility of assignment was increased through the proposed model and simulating the behavior of each cluster of customers. Social implications: Paradigm shift in the banking industry is changing from e-banking to digital banking. In digital banking, assigning/customizing products/services, regarding the needs of customers, is very difficult .The banking industry is not well equipped to respond to the digital banking expectations of most consumers. One of the most important challenges of banks is recognizing customers, clustering and assigning a service/product to each of the different clusters. The main policy in the banking industry is to increase customer satisfaction and reduce cost and risk in sales service. Therefore, each customer should have a dedicated service/product. Originality/value: In this paper, authors attempted to use one of the clustering approaches in multiobjective programming. In addition, they proposed an approach for assigning product/service to customer by simulating and analyzing the behavior of each customer cluster. [ABSTRACT FROM AUTHOR]

In the past, traditional channel or retailer was used for selling products but with the development of ecommerce, a large company in the world considers another sale channel like websites. Considering the existence of two channels for sale, choosing the right strategy for pricing has become important. In pricing and production planning, risk is a very important factor. In this paper, outsourcing policies have been used to deal with risks and a new mathematical model is presented for simultaneous decision-making on pricing and outsourcing in a threelevel and two-channel supply chain despite uncertainty. In this paper, a nonlinear model is presented for supply chain profit function. According to the complexity of profit function, a meta-heuristic algorithm based on simulated annealing and scenario-based stochastic model are used to solve the proposed model. The initial parameters of this algorithm are set by Taguchi method. The computational results and sensitivity analysis indicate the effectiveness of the proposed solving method for problem solving. [ABSTRACT FROM AUTHOR]

In undesirable facility location problem contrary to desirable location, facilities are located far from service receiver facilities as much as possible. The problem of locating such facilities is discussed in this study. This research is focused on the ‘not in my backyard’ (NIMBY) phrase which refers to the social phenomena in which residents are opposed to locate undesirable facilities around their houses. Examples of such facilities include electric transmission lines and recycling centers. Due to the opposition typically encountered in constructing an undesirable facility, the facility planner should understand the nature of the NIMBY phenomena and consider it as a key factor in determining facility location. Because of the adverse effects of these facilities, coupled with the uncertainty in the real world, it is estimated taking into account the potential uncertainty. This problem has been considered in the discrete space. The mathematical model is presented and methods to deal with uncertainty and stochastic programing problems modeling and methods used in the study are presented. According to the NP-hard problem, Simulated Annealing suggested for solving problems in large-scale. Numerical experiments to evaluate and validate the mathematical modeling and proposed algorithm are considered and operation of the proposed algorithm to solve various problems with genetic algorithms available in the literature about the problem is compared. [ABSTRACT FROM AUTHOR]

In this paper hybrid flowshop scheduling problem where some jobs, not all, have to follow no-wait restriction (that is, the operations of that job must be processed with no stop) is examined. In the literature, all papers assume that all jobs of the shops have to follow no-wait restrictions. First, this paper mathematically formulates the problem with two different mixed integer linear models under proposed considerations. The models are evaluated using two performance measures of size complexity and computational complexity. The small instances of the problem are solved using commercial software of mathematical programming. To solve larger instances of problem, two solution algorithms have been developed. These two algorithms are based on imperialist competitive algorithm and simulated annealing. A comprehensive numerical experiment including small and large instances is conducted to evaluate the models and algorithms. The results show that the imperialist competitive algorithm outperforms simulated annealing

Jobshop manufacturing system is a suitable system for economical manufacturing of parts family and Jobshop scheduling is completely efficient in successfully running in improvement of productivity of system. The jobshop scheduling model has multiple objectives: Minimizing makes pan (Cmax) and Minimizing the Weighted Sum of Earliness and Tardiness penalties (WSET). In this study to achieve these objectives at the same time, Goal Programming (GP) has being used. This model from as computational point of view is NP-Hard, so in this paper we apply the Simulated Annealing (SA) meta-heuristic approach for solve it. One array structure of solution (family parts or parts in family) is used in common methods that lead to decrease of solution space, but in this study hybrid selection of neighborhood structures has been used for determaine the structure of solution; Directed Interchange Scheme (DIS) and Random Interchange Scheme (RIS). The results of research indicate solving goal programming model of Job shop Scheduling by SA is efficient to achieve goals of model.

Nowadays organization especially R&D centers are dealing with project portfolio selection decisions under uncertainty. Moreover in the most of the past research, project portfolio selection and scheduling are often considered to be independent problem. This leads to insufficient result in real world. So in this research simultaneous project portfolio selection and scheduling problem is modeling whose objectives are maximizing expected profit and minimizing risk. Moreover there is autocorrelation among annual earnings. Therefore an efficient time series methodology is used for forecasting. Another advantage of proposed model is considering uncertainty of project success and earnings and also risk of dealing with budget deficiency. Due to the complexity of problem, especially for large size, practical swarm, simulated annealing and genetic algorithm are presented and their efficiency is compared by a hypothetical example. The results show efficiency of simulated annealing algorithm in terms of quality and execution time. Finally the model is validated via its application to a knowledge based company in Ferdowsi University of Mashhad.

Collaborative optimization is one of bi-level multidisciplinary optimization methods which consists of system level and discipline level and is applied for complex engineering problems. since this method is rigidly convergent at discipline level because of noisy constraints at system level on one hand and minimizing objective function necessity at system level on the other hand, this optimizationmethod is forced to use evolutionary algorithms in order to minimize objective function at system level, also, It has been proved that, applying this algorithms according to their nature is expensive and time consuming. This paper with performed inspections is a new method for applying innovated optimization algorithms through which considerable results are obtained in solving sample problems. It is shown that using this method will decrease function calls number or problem solving time and therefore calculating costs will decrease considerably. Also it is shown that this method sometimes increase accuracy.

Abstract This paper presents a mathematical model for solving dynamic cell formation problem, operator assignment and the inter-cellular and intra-cellular layouts simultaneously. The proposed model includes three objectives, the first objective seeks to minimize inter and intra-cell part movement, machine relocation, second objective minimize operator related cost, third objective maximize ratio of consecutive forward flows. The model is Multi-objective; therefore, the LP-metric approach is used to solve it. In order to validate the model, the proposed model has been solved by using Lingo software. Then, due to NP-hardness of the cell formation problem, for solving large scale problems, a multi-objective simulated annealing algorithm proposed. Several numerical examples solved by Lingo software and multi-objective simulated annealing algorithm. Results show that the proposed multi-objective simulated annealing algorithm solved considerably time less than the software Lingo and also none of the answers obtained by the two methods are not dominated

The Markowitz issue of optimization can’t be solved by precise mathematical methods such as second order schematization, when real world condition and limitations are considered. On the other hand, most managers prefer to manage a small Portfolio of available assets in place of a huge Portfolio. It can be analogized to cardinal constrains, that is, constrains related to minimum and maximum current assets on Portfolios. This study aims to solve the problem of optimizing Portfolios with cardinality constrains, using simulated annealing algorithm. Therefore, by using the information of 50 companies which have been more active in Tehran’s exchange stock from April 2010 to April 2012, Portfolios’ efficient frontier has been supposed from 10 to 50. Results shows that first, simulated annealing algorithm has been successful in solving the above problem, and second, by selecting shares appropriately and determining suitable weights from it, smaller Portfolios with more suitable performances can be selected.

This paper aims to examine the scheduling of vehicles in a multi-product supply chain regarding to the mutual relationship between the transportation and the manufacturing units. The integration level in the supply chain consists of a manufacturer and its first tier suppliers, which are linked by a transportation fleet. The problem is determining orders allocation to the suppliers, orders production sequence at the suppliers, orders allocation to the vehicles, and orders transportation priority, in order to minimize the sum of orders delivery time. This issue has not been discussed in the literature, so far. At first, the mathematical model of the problem is presented, then the NP-Hardness of the problem is demonstrated. For solving the problem, a new combination of genetic algorithm and simulated annealing algorithm, named as Populated Simulated Annealing algorithm (PSA) is proposed. For verifying the PSA, its results are compared to results of simulated annealing algorithm (SA) and developed version of DGA algorithm, proposed for the nearest problem in the literature to our problem. Furthermore, relaxing some hypothesis, the results of PSA are compared to DGA results. All of the comparisons show that PSA is more efficient than the other algorithms. Finally, comparison of PSA with exact solution for small size problems demonstrates its proper efficiency. [ABSTRACT FROM AUTHOR]

Hub location problem is further used in transportation and telecommunication networks (airlines, post delivery services, etc.) so origin-destination pairs, receive or send commodities via special facilities called Hub. Hub median problem with multiple allocation is an NP-hard problem which includes both locating hub facilities and allocating non-hub nodes to hubs as minimizes total transportation and location costs. In this paper, the hub median problem is considered in an environment which network flow varies during the time periods and the capacities of hubs and arcs are unlimited. Also opening and closing hubs in different periods of planning horizon are permitted. The model and the proposed algorithm for this problem were considered to Iran airlines network using real passenger flows data. Computational results state that the dynamic network compared with static model has lower cost and whatever the number of time periods in dynamic case increases, the cost will be reduced as well.

In this paper, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) with objective of minimizing the makespan is investigated. Under studied problem is NP-hard and mainly includes three sub-problems. The first one is to assign each operation to a machine out of a set of capable machines, the second one is to determine a worker among a set of skilled workers for processing each operation on the selected machine and the third one deals with sequencing the assigned operations on the machines considering workers in order to optimize the performance measure. In this paper, we provide a mathematical model for this problem and then propose a hybrid meta-heuristic algorithm for solving the problem. The proposed hybrid algorithm uses variable neighborhood search and simulated annealing algorithms to search in the solution space. Computational study with randomly generated test problems is performed to evaluate the performance of the proposed algorithm. The results show the proposed algorithms are effective approaches for solving the DRCFJSP.

In this paper, modeling and solving an open selective vehicle routing problem with pricing are introduced. We will discuss optimal pricing when using a homogeneous fleet of vehicles. Furthermore, in some real world applications, companies prefer to distribute their products using rented vehicles so returning to the depot is not required. Therefore we face an open routing problem. Despite the applicability of such problem, we did not find any published research that examines it.also an Improved Imperialist Competitive Algorithm (IICA) is proposed to solve proposed model. For validating this method, some small scale problems are solved and results are compared to the results of an exact method and Simulated Annealing (SA) algorithm. The comparison of results shows that the proposed method is suitable for solving the model. For investigating its efficiency in dealing with real world problems, some large scale problems are solved and the results are compared to the results of Simulated Annealing (SA) algorithm. Results show that IICA is more efficient than SA. [ABSTRACT FROM AUTHOR]

Hub location problem is one of the new issues in location problems. This kind of location problem is widely used in transportation systems. In this paper, we investigate p-hub center location allocation problem under capacity constraint. The aim of the proposed model is to determine the location of hub nodes and the allocation of non-hub nodes to the hub in such a way that the maximum traveling time is minimized. In addition, since the problem is an NP hard problem, two metaheuristic algorithms including simulated annealing algorithm and ant colony are developed for solving large size real world problems. The performances of the proposed algorithms are examined via some numerical examples taken from known related benchmark sets (AP dataset). The best solutions found using metaheuristic algorithms are also compare to the results achieved using Lingo software. The results demonstrate that the proposed algorithms are able to find optimum or near optimum solutions in acceptable run times. [ABSTRACT FROM AUTHOR]

Today, the use of meta-heuristic methods to obtain satisfying responses in compound optimization has grown dramatically. Due to the approach of problems to real-world situations due to the increasing complexity of the problems and the inability of current mathematical methods to provide optimal points with reasonable resources, this has intensified. The development of meta-heuristic methods is usually done by exploring the nature of optimization and its inspiration, including the ant algorithm and refrigeration simulation. The proposed algorithm of this paper is developed by investigating the interesting behavior of two functions x(Cos)(x) and tanh(x) in iterative loops and presents a method for finding neighborhoods in continuous functions that resembles the optimization algorithm. Refrigeration Modeling and Cloud Theory Based Refrigeration Simulation Algorithm performs better in terms of accuracy and speed. The superiority of the proposed algorithm to the two mentioned algorithms was proved by comparing the performance of these algorithms to find the optimal point (points) of seven known continuous functions.

This research studies resource constrained project scheduling problem in a part of a refinery construction project in real world. In real world, most of the activities are new and we have problems such as activity duration uncertainty. This problem causes a change in a project makespan. The RCPSP is NP-hard. Hence, we proposed an optimization method based on simulated annealing to solve the RCPS Problem. In this paper, Fuzzy sets theory is used to represent this activity duration uncertainty. Used schedule generation scheme, in the proposed simulated annealing algorithm, is a fuzzy parallel scheduling generation method. Proposed algorithm generates the minimum project makespan while considers renewable resource-constrained and activity precedence and also has ability to perform with fuzzy numbers for presenting project details such as start and final time of activities and project whole time with fuzzy numbers. Finally, the results of the algorithm will be evaluated and it will be represented that the proposed algorithm is very efficient and can be used by managers and scheduling programmers in real projects. [ABSTRACT FROM AUTHOR]

Hub location problem is further used in transportation and telecommunication networks (airlines, post delivery services, etc.) so origin-destination pairs, receive or send commodities via special facilities called Hub. Hub median problem with multiple allocation is an NP-hard problem which includes both locating hub facilities and allocating non-hub nodes to hubs as minimizes total transportation and location costs. In this paper, the hub median problem is considered in an environment which network flow varies during the time periods and the capacities of hubs and arcs are unlimited. Also opening and closing hubs in different periods of planning horizon are permitted. The model and the proposed algorithm for this problem were considered to Iran airlines network using real passenger flows data. Computational results state that the dynamic network compared with static model has lower cost and whatever the number of time periods in dynamic case increases, the cost will be reduced as well. [ABSTRACT FROM AUTHOR]

This research studies resource constrained project scheduling problem in a part of a refinery construction project in real world. In real world, most of the activities are new and we have problems such as activity duration uncertainty. This problem causes a change in a project makespan. The RCPSP is NP-hard. Hence, we proposed an optimization method based on simulated annealing to solve the RCPS Problem. In this paper, Fuzzy sets theory is used to represent this activity duration uncertainty. Used schedule generation scheme, in the proposed simulated annealing algorithm, is a fuzzy parallel scheduling generation method. Proposed algorithm generates the minimum project makespan while considers renewable resource-constrained and activity precedence and also has ability to perform with fuzzy numbers for presenting project details such as start and final time of activities and project whole time with fuzzy numbers. Finally, the results of the algorithm will be evaluated and it will be represented that the proposed algorithm is very efficient and can be used by managers and scheduling programmers in real projects.