Thermo-mechanical characterization of polyurea variants

Mechanics of Time-Dependent Materials - The aim of this study is to investigate the role of chain length and hard segment dispersion within the soft segment on thermo-mechanical properties of a...     

An efficient hybrid self-learning method for stochastic cellular manufacturing problem: A queuing-based analysis

This paper addresses a new version of Stochastic Mixed-Integer model to design cellular manufacturing systems (CMSs) under random parameters described by continues distributions. In an uncertain environment processing time, part demand, product mix, inter-arrival time and etc. may change over the period of time. Thus, during planning horizon since any of the parameters of the problem may vary widely, design decisions may be in effect. So, in this research to overcome such drawback, it’s assumed that processing time for parts on machines and arrival time for parts to cells are stochastic and described by continues distribution which yields more flexibility to analyze manufacturing framework. In such case, there are some approaches such as stochastic programming (SP), robust optimization (RO) and queuing theory which can formulate and analyze this problem. In this paper, it’s assumed that each machine works as a server and each part is a customer where servers should service to customers. Therefore, formed cells define a queue system which can be optimized by queuing theory. In this way, by optimizing a desired queue system measurement such as maximizing the probability that a server is busy, the optimal cells and part families will be formed. To solve such a stochastic and non-linear model, an efficient hybrid method based on new combination of genetic algorithm (GA) and simulated annealing (SA) algorithm will be proposed where SA is a sub-ordinate part of GA under a self-learning rule (SLR) criterion. This integrative combination algorithm is compared against global solutions obtained from branch-and-bound algorithm and a benchmark heuristic algorithm existing in the literature. Also, sensitivity analysis will be performed to illustrate behavior of the model.     

Developing a new model for simultaneous scheduling of two grand projects based on game theory and solving the model with Benders decomposition

Grand infrastructure projects, such as dam, power plant, petroleum, and gas industry projects, have several contractors working on them in several independent sub-projects. The concern of reducing the duration of these projects is one of the important issues among various aspects; thus, our aim is to fulfill the requirements by using the game theory approach. In this study, a mixed-integer programming model consisting of game theory and project scheduling is developed to reduce the duration of projects with a minimum increase in costs. In this model, two contractors in successive periods are entered into a step-by-step competition by the employer during dynamic games, considering an exchange in their limited resources. The optimum solution of the game in each stage are selected as the strategy, and the resources during the game are considered to be renewable and limited. The strategy of each contractor can be described as follows: 1) share their resources with the other contractor and 2) not share the resources with the other contractor. This model can act dynamically in all circumstances during project implementation. If a player chooses a non-optimum strategy, then this strategy can immediately update itself at the succeeding time period. The proposed model is solved using the exact Benders decomposition method, which is coded in GAMS software. The results suggest the implementation of four step-by-step games between the contractors. Then, the results of our model are compared with those of the conventional models. The projects’ duration in our model is reduced by 22.2%. The nominal revenue of both contractors has also reached a significant value of 46078 units compared with the relative value of zero units in the original model. Moreover, we observed in both projects the decreases of 19.5%, 20.9%, and 19.7% in the total stagnation of resources of types 1, 2, and 3, respectively.     

Designing integrated cellular manufacturing systems with tactical decisions

In this paper, we aim to design cellular manufacturing systems that optimize the performance of a manufacturing system subject to the optimization aspects of production planning. Consequently, the demand for each part – one of the production planning features – plays a vital role in determining the part families and the formation of machine cells in each period. In our study, holding and backorder costs follow a probabilistic structure, and they are described by a set of stochastic scenarios. In this model, five objective functions are employed: one of them minimizes the expected total holding and backorder costs in order to evaluate the risk in the model. The aim of this model is to select and optimize the assignment of parts and machines to different cells as well as the number of each produced part in each period. A new heuristic algorithm based on the optimization method is established to yield the best solution for this complicated mathematical formulation. Further, the performance of the proposed algorithm is verified using certain test problems in which the obtained results are compared with those obtained using the branch-and-bound algorithm and heuristic procedures.     

Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time

This paper addresses a new mathematical model for cellular manufacturing problem integrated with group scheduling in an uncertain space. This model optimizes cell formation and scheduling decisions, concurrently. It is assumed that processing time of parts on machines is stochastic and described by discrete scenarios enhances application of real assumptions in analytical process. This model aims to minimize total expected cost consisting maximum tardiness cost among all parts, cost of subcontracting for exceptional elements and the cost of resource underutilization. Scheduling problem in a cellular manufacturing environment is treated as group scheduling problem, which assumes that all parts in a part family are processed in the same cell and no inter-cellular transfer is needed. Finally, the nonlinear model will be transformed to a linear form in order to solve it for optimality. To solve such a stochastic model, an efficient hybrid method based on new combination of genetic algorithm (GA), simulated annealing (SA) algorithm, and an optimization rule will be proposed where SA and optimization rule are subordinate parts of GA under a self-learning rule criterion. Also, performance and robustness of the algorithm will be verified through some test problems against branch and bound and a heuristic procedure.     

Endothelial cell culture model for replication of physiological profiles of pressure, flow, stretch, and shear stress in vitro

The phenotype and function of vascular cells in vivo are influenced by complex mechanical signals generated by pulsatile hemodynamic loading. Physiologically relevant in vitro studies of vascular cells therefore require realistic environments where in vivo mechanical loading conditions can be accurately reproduced. To accomplish a realistic in vivo-like loading environment, we designed and fabricated an Endothelial Cell Culture Model (ECCM) to generate physiological pressure, stretch, and shear stress profiles associated with normal and pathological cardiac flow states. Cells within this system were cultured on a stretchable, thin (～500 μm) planar membrane within a rectangular flow channel and subject to constant fluid flow. Under pressure, the thin planar membrane assumed a concave shape, representing a segment of the blood vessel wall. Pulsatility was introduced using a programmable pneumatically controlled collapsible chamber. Human aortic endothelial cells (HAECs) were cultured within this system under normal conditions and compared to HAECs cultured under static and "flow only" (13 dyn/cm(2)) control conditions using microscopy. Cells cultured within the ECCM were larger than both controls and assumed an ellipsoidal shape. In contrast to static control control cells, ECCM-cultured cells exhibited alignment of cytoskeletal actin filaments and high and continuous expression levels of β-catenin indicating an in vivo-like phenotype. In conclusion, design, fabrication, testing, and validation of the ECCM for culture of ECs under realistic pressure, flow, strain, and shear loading seen in normal and pathological conditions was accomplished. The ECCM therefore is an enabling technology that allows for study of ECs under physiologically relevant biomechanical loading conditions in vitro.