Effect of parameters on roll dynamic response of an articulated vehicle carrying liquids

Directional response and roll stability characteristics of a partly filled tractor semi-trailer vehicle, with cylindrical tank, under different parameters and conditions are investigated. The effective parameters and conditions in the stability of a tractor semi-trailer carrying liquid can be considered as filled volume, kinematic viscosity of liquid cargo and vehicle steering input. The dynamic interaction of liquid cargo with the tractor semi-trailer vehicle is evaluated by integrating a dynamic slosh model of the partly filled tank with five-degrees-offreedom of a tractor semi-trailer tank model. The dynamic fluid slosh within the tank is modeled using three-dimensional Navier-Stokes equations, coupled with the volume-of-fluid equations and analyzed using FLUENT software. The coupled tank-vehicle model is subsequently analyzed to determine the roll stability characteristics for different conditions and maneuvers. The results show effects of parameter variations on roll stability of the vehicle carrying liquid and also fluid’s behavior in interaction with a vehicle’s dynamics.     

Application of multidivisional bi-level programming to coordinate pricing and inventory decisions in a multiproduct competitive supply chain

This study concerns the coordination of pricing and inventory decisions in a multiproduct two-stage supply chain that consists of one manufacturer and multiple retailers within a competitive environment. The retailers order some substitutable products from a common manufacturer. It is assumed that channel members have different market power. The purpose of this paper is to coordinate pricing and inventory decisions such that utility of all involved levels (manufacturer and retailers) is met. Hence, a nonlinear multidivisional bi-level programming model is developed. This model considers both retailers and manufacturer when deciding about the pricing and production volume (for manufacturer) or amount of purchase (for retailers). A hybrid of genetic algorithm (GA) and local search method is proposed to solve the nonlinear bi-level model. This model is reduced to a nonlinear programming by replacing the Karush–Kuhn–Tucker (KKT) conditions of followers to the lower level of the model. Then, the obtained single-level model is relaxed to a linear model to achieve an upper bound (UB). Finally, a numerical example is presented to analyze which parameters have more effect on the price, lot size and, consequently, on the profit. Results show that increasing the market scale parameter of the manufacturer increases the profit of the manufacturer, but the market scale parameter of retailers has no effect on the manufacturer’s profit, although it increases the retailers’ profit.     

A multi-product multi-period inventory control problem under inflation and discount: a parameter-tuned particle swarm optimization algorithm

In this paper, a seasonal multi-product multi-period inventory control problem is modeled in which the inventory costs are obtained under inflation and all-unit discount policy. Furthermore, the products are delivered in boxes of known number of items, and in case of shortage, a fraction of demand is considered backorder and a fraction lost sale. Besides, the total storage space and total available budget are limited. The objective is to find the optimal number of boxes of the products in different periods to minimize the total inventory cost (including ordering, holding, shortage, and purchasing costs). Since the integer nonlinear model of the problem is hard to solve using exact methods, a particle swarm optimization (PSO) algorithm is proposed to find a near-optimal solution. Since there is no bench mark available in the literature to justify and validate the results, a genetic algorithm is presented as well. In order to compare the performances of the two algorithms in terms of the fitness function and the required CPU time, they are first tuned using the Taguchi approach, in which a metric called “smaller is better” is used to model the response variable. Then, some numerical examples are provided to demonstrate the application and to validate the results obtained. The results show that, while both algorithms have statistically similar performances, PSO tends to be the better algorithm in almost all problems.     

A hybrid model of fuzzy goal programming and grey numbers in continuous project time,cost, and quality tradeoff

The purpose of this paper is to develop current mathematical models of cost, time, and quality tradeoffs in conditions that parameters of project activities are estimated uncertainly by grey numbers. In some projects like construction projects, activities can be done within a much shorter time by increasing in the resources, while project's cost may rise at the same time. In such situations, managers are usually required to determine the best combination of cost, time, and quality parameters of the activities, although their information regarding these parameters is limited and rather incomplete. The greyness of these parameters in the proposed method can aid managers to deal with these conditions. The most important aspect of the proposed model is that it considers uncertainty of the project planning data in the form of grey numbers. A combination of fuzzy goal programming and grey linear programming is also developed to solve the proposed model. Finally, this model will provide the managers with a stronger ability to face with uncertainty in project management and planning. The application of this model is examined in a numerical example. As its major finding, the model determines an optimal range in which the project managers can respond to intrinsic changes that may occur in the parameters during a project.     

A queuing approach for a tri-objective manufacturing problem with defects: a tuned Pareto-based genetic algorithm

In this research, a manufacturing facility with independent workstations to remanufacture nonconforming products is investigated. Each workstation is first modeled as an M/M/m queuing system with m being a decision variable. Then, a tri-objective integer nonlinear programming models is developed to formulate the problem. The first objective tries to minimize the waiting times of products, while the second one tries to maximize the minimum reliability of machines at the workstations. Since minimization of the waiting times results in using a large number of machines with higher idle times, the third objective is considered to minimize the mean idle time of the machines. The aim is to determine optimal number of machines at each workstation. Since the problem belongs to the class of NP-hard problems, the non-dominated sorting genetic algorithm-II (NSGA-II) is utilized to find Pareto fronts. Because there is no benchmark available in the literature to validate the results obtained, the non-dominated ranked genetic algorithm (NRGA) is used as well. In both algorithms, not only the best operators are selected but also all of their important parameters are calibrated using statistical analysis. The performances of the algorithms are statistically compared using the t test. Besides, the multiple attribute decision-making method of TOPSIS is used to determine the better algorithm. The applicability of the proposed model and the solution algorithms is demonstrated via some illustrative examples.     

Geometrically nonlinear vibration analysis of piezoelectrically actuated FGM plate with an initial large deformation

A theoretical model for geometrically nonlinear vibration analysis of piezoelectrically actuated circular plates made of functionally grade material (FGM) is presented based on Kirchhoff’s-Love hypothesis with von-Karman type geometrical large nonlinear deformations. To determine the initial stress state and pre-vibration deformations of the smart plate a nonlinear static problem is solved followed by adding an incremental dynamic state to the pre-vibration state. The derived governing equations of the structure are solved by exact series expansion method combined with perturbation approach. The material properties of the FGM core plate are assumed to be graded in the thickness direction according to the power-law distribution in terms of the volume fractions of the constituents. Control of the FGM plate’s nonlinear deflections and natural frequencies using high control voltages is studied and their nonlinear effects are evaluated. Numerical results for FG plates with various mixture of ceramic and metal are presented in dimensionless forms. In a parametric study the emphasis is placed on investigating the effect of varying the applied actuator voltage as well as gradient index of FGM plate on vibration characteristics of the smart structure. This paper was recommended for publication in revised form by Associate Editor Eung-Soo Shin Farzad Ebrahimi received his B.S. and M.S. degree in Mechanical Engineering from University of Tehran, Iran. He is currently working on his Ph.D. thesis under the title of “Vibration analysis of smart functionally graded plates” at Smart Materials and Structures Lab in Faculty of Mechanical Engineering of the University of Tehran. His research interests include vibration analysis of plates and shells, smart materials and structures and functionally graded materials.     

A new mathematical modeling and a genetic algorithm search for milk run problem (an auto industry supply chain case study)

The idea of Milk Run has been used in the context of logistic and supply chain problems in order to manage the transportation of materials. In this paper, we propose a new Milk Run method, as a mixed integer approach, to manage supply chain problems. Since the resulted problem formulation is NP-Hard, we use some meta-heuristic and compare the results with the optimal solutions of the proposed Milk Run method. The mathematical modeling of this paper is purposely customized for a special case of an auto industry. We implement the mathematical formulation and the meta-heuristic using some actual data and compare the results with the current strategy. The preliminary results indicate that the proposed method could provide a practical tool to significantly reduce the cost of logistic.     

Numerical simulation of the effects of elastic anisotropy and grain size upon the machining of AA2024

Turning modeling and simulation of different metallic materials using the commercially available Finite Element (FE) softwares is getting prime importance because of saving of time and money in comparison to the costly experiments. Mostly, the numerical analysis of machining process considers a purely isotropic behavior of metallic materials; however, the literature shows that the elastic crystal anisotropy is present in most of the ‘so-called’ isotropic materials. In the present work, the elastic anisotropy is incorporated in the FE simulations along with the effect of grain size. A modified Johnson-Cook ductile material model based on coupled plasticity and damage evolution has been proposed to model the cutting process. The simulation results were compared with experimental data on the turning process of Aluminum alloy (AA2024). It was found that the elastic anisotropy influences the average cutting force up to 5% as compared to the isotropic models while the effect of grain size was more pronounced up to 20%.     

Detection of debonding in composite-aluminum joints using gamma-ray Compton scattering

Gamma-ray Compton scattering is used for the detection of debonding in adhesively bonded composite-aluminum joints. A collimated narrow beam of monochromatic photons, generated by a ¹³⁷CS source, is directed towards the joint and scattered photons are recorded, using a detector located on the same side as the source. The energy of the scattered radiation is measured and related to the angle of scattering. The occurrence of debonding is indicated by a change in the count rate at an energy corresponding to its location. The performance of the technique is successfully demonstrated experimentally for joints of different adhesive-bonding thicknesses and for artificially induced debonds.     

Introducing a dimensionless number as tank selector in hybrid solar thermal energy storage systems

Using hybrid energy storage system is a method for increasing the storage capability of solar thermal energy. If multiple energy storage devices with complementary performance characteristics are used together, the resulting system will be a ‘Hybrid Energy Storage System’. In other words, a Hybrid Energy Storage System (HESS) has several media available for storage at any time. In this way, increase in storable energy is obtained without increasing collectors’ area. When there are more than one storage mediums, the system should be able to choose the best medium for storing energy according to the conditions. In the previous works, an optimizer program was used to find the proper medium along time. But running a computer optimizer program is almost time consuming. So, in this work, a recognition parameter is introduced which helps to select the proper tank in different conditions. Using this dimensionless parameter, the hybrid storage system won’t be always dependent to the optimizer. The results have been checked theoretically and experimentally.