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Supply chain is a network of suppliers, manufacturers, distributors, and retailers that act together to control, manage, and improve the overall supply chain performance. The most important and critical part of decision making is identifying the different sides of supply chain's performance. Evaluating the performance of the whole supply chain is a complex task, due to the complexity inherent in the structure and operations of the supply chain. This study presents a suggestion for a comprehensive system to evaluate the performance of the supply chain in eight dimensions (i.e., financial, customer, internal operations, learning and growth, people, environmental, and political perspectives). The proposed performance evaluation system (PES) suggests a procedural framework to explain the application methodology of that PES. Moreover, the study offers a simulation modelling methodology for modelling the complex system of supply chain. It also provides a real world case study to clarify the applicability of the proposed PES.

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Supply chains, always, face increased uncertainty in demand. For that reason, inventory control presents a critical issue of supply chain management. Controlling inventories with proper policies can enhance customer service levels, smooth production plans, and reduce operation costs. In this paper, a framework is suggested for evaluating and comparing different types of inventory control policies. Four distinct inventory control policies are discussed and modeled. Different types of measures are used to evaluate the performance of the supply chains which implement these inventory control policies; performance measures used are; fill rate, as an example of desired measures (to be increased), and inventory level, as an example of undesired measures (to be decreased). A framework for evaluating and comparing the overall performance of the inventory policy is developed and applied. A discrete event simulation with ARENA simulation package is used for developing a simplified supply chain model consists of two echelons, with one supplier that prepares and supplies raw materials to a production/inventory system, which has two different inventories, one for supplied raw materials, and the other for finished products. A numerical example is provided to illustrate the applicability of the developed framework. The applied numerical example clarified the ability of the evaluation framework to deal with different types of inventory control policies, and different practice scenarios.

Control analysis and design for robot manipulators require the knowledge of their dynamic model. In the first part of this paper, the Lagrange-Euler (L-E) formulaion has been developed to separate the generalized forces/torques due to rotational motion from that due to the translational motion. Therefore a new form for the dynamic equations of the robot manipulators has been obtained. In the second part, the generalized d’Alembert (GD) method has been used to develop the equations of motion for robot manipulators with revolute and/or prismatic joints. Also, the rotational effect and the translational effect in the new form of the GD method are separated. The develpoed equations, by both methods, when applied to robot manipulators result in an efficient and explicit set of closed form second order nonlinear differential equations. They give fairly well structured equations of motion suitable for control analysis and manipulator design. Using the proposed models, the rotational effects and translational effects have been studied separately and a simplified model has been obtained, a computational algorithm has been established and a computer software has been developed to perform the necessary calculations to obtain the generalized force required for each joint to follow any pre-specified trajectory. As an application of the proposed methods, a simplified model for a Stanford manipulator has been obtained and it is found that the results of the normal and the simplified models are very close and the simplified model can be accepted even with end-effector load variation and with different trajectories. The simplified model has the advantage of less computational time which is more appropriate for control purposes. Also, the results obtained by the developed L-E and GD methods are the same.