This paper is aimed at DESIGNing a DISTRIBUTION NETWORK of small industries in Arak City. The model presented in this paper will provide optimal rates of order quantity given by a supplier to the producer. NMFC model covers drops in prices and finance costs. Furthermore, the flexible time horizon planning permits the producer to use this model in different time lags like hour, day, and month. The genetic algorithm function has been used in Matlab for achieving the solution space and comparing the output results of the model with two EOQ and JIT models to calculate optimal order quantity. The sensitivity of all parameters has been taken into account to examine its effect on the model which indicates the higher effect of holding and warehousing costs on the total costs.

Supply chain management is taken into account as an inseparable component in satisfying customers' requirements.This paper deals with the DISTRIBUTION NETWORK DESIGN (DND) problem which is a critical issue in achieving supply chain accomplishments. A capable DND can guarantee the success of the entire NETWORK performance. However, there are many factors that can cause fluctuations in input data determining market treatment, with respect to short-term planning, on the one hand. On the other hand, NETWORK performance may be threatened by the changes that take place within practicing periods, with respect to long-term planning. Thus, in order to bring both kinds of changes under control, we considered a new multi-period, multi-commodity, multi-source DND problem in circumstances where the NETWORK encounters uncertain demands. The fuzzy logic is applied here as an efficient tool for controlling the potential customers' demand risk. The defuzzifying framework leads the practitioners and decision-makers to interact with the solution procedure continuously. The fuzzy model is then validated by a sensitivity analysis test, and a typical problem is solved in order to illustrate the implementation steps. Finally, the formulation is tested by some different-sized problems to show its total performance.

In this paper, a novel multiobjective optimization model is presented for selecting optimal locations in the water DISTRIBUTION NETWORK (WDN) with the aim of installing pressure loggers. The pressure data collected at optimal locations will be used later on in the calibration of the proposed WDN model.Objective functions consist of maximization of calibrated model prediction accuracy and minimization of the total cost for sampling DESIGN. In order to decrease the model run time, an optimization model has been developed using multiobjective genetic algorithm and adaptive neural NETWORK (MOGAANN).Neural NETWORKs (NNs) are initially trained after a number of initial GA generations and periodically retrained and updated after generation of a specified number of full model-analyzed solutions. Trained NNs are replaced with the fitness evaluation of some chromosomes within the GA progress. Using cache prevents objective function evaluation of repetitive chromosomes within GA.Optimal solutions are obtained through pareto-optimal front with respect to the two objective functions. Results show that jointing NNs in MOGA for approximating portions of chromosomes’ fitness in each generation leads to considerable savings in model run time and can be promising for reducing run-time in optimization models with significant computational effort.

Today, the high importance of achieving food security for communities has made it necessary to provide a new perspective on the DESIGN of the grain supply chain DISTRIBUTION NETWORK to better adapt to real-world uncertainties and also to take into account disruptions. To this end, in this study, a mixed-integer linear programming model has been developed for the DISTRIBUTION NETWORK DESIGN problem in the grain supply chain, which has three objectives such as minimizing costs and maximizing job opportunities, both of which are examples of sustainability. In addition, considering the importance of grain in the food basket of households and the importance of food security, the third objective function has been developed focusing on the issue of resilience to deal with any disruption in the DESIGN of the DISTRIBUTION NETWORK. The robust-possibilistic programming approach is used to deal with uncertain demand and the multi-objective problem is solved using the improved epsilon constraint method. Finally, a compromised solution is selected using the TOPSIS method.

Key DISTRIBUTION is an important problem in wireless sensor NETWORKs where sensor nodes are randomly scattered in adversarial environments. Due to the random deployment of sensors, a list of keys must be pre-distributed to each sensor node before deployment. To establish a secure communication, two nodes must share common key from their key-rings. Otherwise, they can find a key- path in which ensures that either two neighboring nodes have a key in common from source to destination. Combinatorial DESIGNs are powerful mathematical tools with comprehensive and simple structures. Recently, many researchers have used combinatorial DESIGNs as key pre-DISTRIBUTION scheme in wireless sensor NETWORKs. In this paper we consider a hybrid key pre-DISTRIBUTION scheme based on Balanced Incomplete Block DESIGN. We consider a new approach for choosing key-rings in the hybrid symmetric DESIGN to improve the connectivity and resilience. Performance and security properties of the proposed scheme are studied both analytically and computationally. The obtained results show that our scheme provides better resilience than symmetric DESIGN.