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Industrial clusters are one of the most current development models. Aggregation of firms in a geographical area has many advantages, such as cost reduction, better supply, and knowledge emission with linkage together. The linkage result will be created the networks. The industrial clusters without co-operation networking will not be developed. That must be noticed to severe changes of business environment parameters. Therefore, this paper develops an uncertain mathematical model under sustainable and dynamic conditions. The model contains four objectives, namely profit, transportation cost, employment, and environment appraisal of the cluster. The outcome of this research is to find the best/optimal solution for firms’ arrangements with/within networks that maximize the profit, employment, and environment score and so minimize the transportation cost. The assignment patterns show horizontal and vertical cooperation with/within networks. The efficiency of model clustering in sub-clusters is followed by the neighbor clustering efficiency and the one’s clustering efficiency methods.

In this paper, a new algorithm for solving bi-level optimization problems is presented. This algorithm can obtain the optimal or near-optimal solution for any bi-level optimization problem. The decision variables of the first and second level models can be both integers and continuous. In this method, by solving a certain number of the bi-objective programming model and then solving the corresponding second-level model, a bi-level feasible solution that is either optimal or near-optimal is identified. To evaluate the efficiency of the algorithm, the value of the objective function, as well as its computation time in different instances, are compared with exact methods as well as evolution-based methods. The numerical results confirm the high efficiency of the proposed algorithm.

This paper considers a stochastic location-allocation problem for a capacitated bike sharing system (S-L&A-CBSS), in which a bike demand is uncertain. To tackle this uncertainty, a sample average approximation (SAA) method is used. Because this problem is an NP-hard problem, a hybrid greedy evolutionary algorithm based on genetic algorithm (GA) and particle swarm optimization (PSO), namely greedy GA-PSO is embedded in the SAA method in order to solve the given large-sized problems. The performance of the proposed hybrid algorithm is tested by a number of numerical examples and used for empirical test based on Tehran business zone. Furthermore, the associated results show its efficiency in comparison to an exact solution method in solving small-sized problems. Finally, the conclusion is provided.

Dealing with more than one response in the process optimization is a great issue in these recent years, so multiple response optimization studies have been grown in the published works. In the common problems, there are some input variables which can affect output responses but optimization can be more complex and more real when the responses have correlation with each other. In such problems analyst should consider the correlation structure in addition to input variables effects. In some cases, responses variables may emerge from another distributions rather than normal in which can be analyzed by the proposed method. Moreover, in some problems, response variables may have different importance for the decision maker. In this study, we try to propose an efficient method to find the best treatment in an experimental design which its correlated responses have different weights, either cardinal or ordinal ones. Also a heuristic method was proposed to deal with problems that have considerable number of correlated responses or treatments. The results of some numerical examples confirm the validity of the proposed method. Moreover a real case about Tehran air pollution is studied to show theapplicability of the proposed method in the real problems.

This study aims in providing a new approach regarding design of a closed loop supply chain network through emphasizing on the impact of the environmental government policies based on a bi-level mixed integer linear programming model. Government is considered as a leader in the first level and tends to set a collection rate policy which leads to collect more used products in order to ensure a minimum distribution ratio to satisfy a minimum demands. In the second level, private sector is considered as a follower and tries to maximize its profit by designing its own closed loop supply chain network according to the government used products collection policy. A heuristic algorithm and an adaptive genetic algorithm based on enumeration method are proposed and their performances are evaluated through computational experiences. The comparison among numerical examples reveals that there is an obvious conflict between the government and CLSC goals. Moreover, it shows that this conflict should be considered and elaborated in uncertain environment by applying Min-Max regret scenario based robust optimization approach. The results show the necessity of using robust bi-level programming in closed loop supply chain network design under the governmental legislative decisions as a leader-follower configuration.

There are many sources of risk affecting the network elements may lead to network failure, so planners need to consider them in the network design. One of the most important strategies for disruption risk management is the static resilience. In this strategy, the network functionality is maintained after the disruption event by the prevention and hardening actions. In this paper, a resilient capacitated fixed-charge location-allocation model is proposed. Both facility hardening and equipping of the network to backup facilities for disrupted elements are considered together to avoid supply network failure due to random disruption. Facilities are decided to be hardened in multiple levels before disruption events. The problem is formulated as a non-linear integer programming model, then its equivalent linear form is presented. A Lagrangian decomposition algorithm (LDA) is developed to solve large-scale instances. Computational results confirm the efficacy of the proposed solution approach comparing to classical solution approaches in large-scale problems. Moreover, the superiority of the proposed model is confirmed by comparing to the classical models.

In some processes, quality of a product should be characterized by functional relationships between response variables and a signal factor. Hence the traditional methods cannot be used to find the optimum solution. In this paper, we propose a method which considers two different dispersion effects, i.e. in domain and between replicates variations in the functional responses. Besides, we propose an integral based measure to find the deviation from t

The hub location problem is used for many applications, including cargo delivery systems, airline systems, telecommunication network design and so on. Each area has its own characteristics in hub location. In this paper, we study the hub covering problem with different coverage type over complete hub networks. Furthermore, hub set and maximal covering are expressed with single and multiple allocation strategies. First, a quadratic formulation is proposed for single allocation hub set covering problem. Then a linearization idea is considered for it and applied for multiple allocation hub set, single and multiple allocation hub maximal covering problems. The aim of these models is to find the location of hubs and allocate non-hub nodes to the located hub nodes subject to the travel time (cost or distance) between two nodes in origin-destination doesn’t exceed a given bound. The formulations with this coverage constraint have not been remarked in the literature. Two heuristic procedures are proposed to handle these models in an agreeable solution quality and computational time. The computational experience of Turkish dataset was presented for better illustration of proposed model. And a special application on Iranian hub airports location is discussed.

An artificial Neural Network (ANN) is an efficient approach applied to solving a variety of problems. The main problem in using ANN is parameter tuning, because there is no definite and explicit method to select optimal parameters for the ANN parameters. In this study, three artificial neural network performance measuring criteria and also three important factors which affect the selected criteria have been studied. Moreover, central composite design has been used to design experiments and also analyze network behavior according to identified parameters, by using the overall desirability function. Then the Genetic Algorithm has been proposed to find optimal parameter status. For this purpose, the proposed method has been illustrated by the numerical example of a well known mathematical function. The results show that the designed ANN, according to the proposed procedure, has a better performance than other networks by random selected parameters and also parameters which are selected by the Taguchi method. In general, the proposed approach can be used for tuning neural network parameters in solving other problems.

Control charts are widely used for monitoring the quality of a product or a process. Their implementation cost motivates researchers to design them with the lowest cost and most desirable statistical properties. Usually, the cost function is optimized subject to statistical properties. However, the cost function also depends on statistical properties, and minimizing it as the only objective is not an efficient method of economic statistical design of control charts. In this paper, cost function, as well as statistical properties, including probability of Type I error, power of X¯ control chart, and Average Time to Signal (ATS), are considered as objectives; the corresponding constraints are also used. Then, a Multi-Objective Genetic Algorithm for Economic Statistical Design (MOGAESD) is proposed for identifying the Pareto optimal solutions of control chart design. The preferred solution is selected by the designer. The performance of the proposed method is compared through some numerical examples reported in the literature. The results show that the proposed approach is effective.

The one median location problem with stochastic demands can be solved as a deterministic problem by considering the mean of weights as demands. There are also some other approaches in consideration of this problem. However, it is better to find the probability for each node that shows the chance of the node being in the optimal location, especially when demands are correlated to each other. With this approach, alternative answers with their optimality probability can be found. In small networks with a few nodes, it is not so difficult to solve the problem, because a multivariate normal probability for each node should be calculated. But, when the number of nodes increases, not only do the number of probability calculations increase, but also, the computation time for each multivariate normal distribution grows exponentially. In this paper, a meta-heuristic algorithm, based on modified Simulated Annealing (SA), with consideration of a short term memory module is proposed to find the optimality probability more efficiently. The algorithm was performed on some sample networks with correlated demands.