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Due to the strict emission standards and fuel consumption restrictions, automotive industry is greatly interested in warm tube hydroforming of aluminum and magnesium alloys. The main shortcoming of these alloys is their inferior formability at room temperature, which can be improved by forming at temperatures below the crystallization temperature. Because of the complex nature of forming at high temperatures, the proper determination and control of forming parameters are very important in fulfillment of the process. In this paper, the effects of tube geometry, bulge height, corner fillets and strain rate are investigated on optimal internal pressure and axial feeding loading paths, which are required for successful hydroforming of annealed AA6061 tubes at 300 °C. A new method based on simulated annealing algorithm is developed for optimization of pressure and feed loading paths. Numerical results are discussed, verified and validated by experiments. A good agreement is observed between numerical and experimental results.

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In this paper, we investigate a decision support system (DSS) for the resolution of real-life vehicle routing and scheduling problem (VRSP). Scheduling the deliveries from a regional distribution centre (RDC) to large stores of a major fmcg retailer includes every possible vehicle routing complexity. Usual constraints that are seen are: size of the vehicle and the length of the driving day, loading feasibility of products in different parts of the vehicle, and also with various time windows. More importantly, in this scheduling decision-making is customer oriented, in which, Customer's value for the company is considered as one of the most important factors. The algorithms for the resolution of the distribution problems constitute a very important part of DSS. Therefore, a simulated annealing based algorithm has been developed to speed up the process by circumventing the need for the skeletal schedule.

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In this paper, a practical method of combined finite element simulation and adaptive simulated annealing (ASA) optimization was developed to design and analyze sheet hydroforming process. Process simulation using finite element code with parametric definition of process parameters creates flexibility on the proposed method in which geometrical dimensions and properties of the workpiece and the die comprise a part of input data of optimization program. Redefinition of simulated annealing parameters with respect to hydroforming process caused to achieve data convergence in a shorter time and higher precision. An intermediate MATLAB code was developed to manage data transfer automatically between optimization and simulation codes, in which there would be no need to any interference of user/designer during the optimization process. The aim of this research for presenting the combinatorial procedure of flexible simulation is to achieve optimal forming pressure loading path, determine the desired punch velocity, produce the desired workpiece with minimum thinning, and avoid wrinkling and rupturing. Two different loading paths proportionate to the ram’s stroke of press unit are proposed to synchronize optimal pressure path and desired punch velocity in forming of cup-shaped products. Using the optimization approaches of constant and variable velocity, thinning values of 12.9778 and 12.3295 for a steel part with conical shape were obtained by implementing simulation iteration of 202 and 148, respectively. This result demonstrates improvement of product quality and decrease of simulation iterations in variable velocity. Appropriate conformity between numerical and experimental results verified the reliability and accuracy of the proposed optimization method.

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In traditional covering problem, covering levelof receiving the services is independent ofdistances between nodes and facilities. However in gradual covering location problem (GCLP) the covering objective depends on the distance of customers from the service centers. Hence increasing incustomer-facility distances will results in decreasing the covering level. In most of covering problems, researchers only consider the distance factor. However; in the real word there are some other important elements such as population, availability, distance and etc. which affect the location of service center. Increasing the number of demand nodes and criteria’s will result in increasing the nodes evaluating computational time and inconsistency rate. This paper proposes a combined Simulated annealing (SA) and Artificial Neural Network (ANN) approaches to solve the multi criteria GCLP.In presence of few nodes; score of nodes are calculated usingAnalytic Hierarchy Process (AHP) method. Moreover by increasingthe number of nodes the score of every node will be calculated using ANNinstead of AHP. Statistical test of signed rank test shows that there is not a significant difference between the result of ANN and AHP methods. The comparison results between the exact solution method and the proposed algorithm confirms the efficiency of the proposed solution approach.

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 Scheduling plays an important role in the development and success of the project; this has always been one of the main issues considered in operations and management science researches. Another reason for the focus of much research into it is the difficult nature of this problem. Therefore, special techniques and methods have been proposed to solve this problem. On the other hand, in order to intensify sanctions on foreign delaying projects, timely completion of projects has been accelerated. Therefore, attention to project robustness subject to project managements. In this article, a real issue is scheduled for a robust scheduling model of a refinery project. Since project scheduling has resource constraints such NP-Hard problems, simulated annealing algorithm was proposed to solve this problem. In order to validate the model, 4 problems with small size were chosen and the solutions obtained by the proposed algorithms were compared with the exact solution obtained by Lingo8 software. The results showed that the proposed algorithm is efficient and convergent to the optimal solution.