A particle swarm optimization approach for constraint joint single buyer-single vendor inventory problem with changeable lead time and (r,Q) policy in supply chain

In this paper, the chance-constraint joint single vendor-single buyer inventory problem is considered in which the demand is stochastic and the lead time is assumed to vary linearly with respect to the lot size. The shortage in combination of back order and lost sale is considered and the demand follows a uniform distribution. The order should be placed in multiple of packets, the service rate limitation on each product is considered a chance constraint, and there is a limited budget for the buyer to purchase the products. The goal is to determine the re-order point and the order quantity of each product such that the chain total cost is minimized. The model of this problem is shown to be an integer nonlinear programming type and in order to solve it, a particle swarm optimization (PSO) approach is used. To assess the efficiency of the proposed algorithm, the model is solved using both genetic algorithm and simulated annealing approaches as well. The results of the comparisons by a numerical example, in which a sensitivity analysis on the model parameters is also performed, show that the proposed PSO algorithm performs better than the other two methods in terms of the total supply chain costs.     

A new three-level fault tolerance arithmetic and logic unit based on quantum dot cellular automata

Microsystem Technologies - Quantum-dot cellular automata (QCA) as a technology in the nanoscale is used for designing the future circuits. It has high density, speed, and low power dissipation....     

Structural stabilization of linear 2D discrete systems using equivalence transformations

Multidimensional Systems and Signal Processing - We consider stability and stabilization issues for linear two-dimensional (2D) discrete systems. We give a general definition of structural...     

Operating condition diagnosis based on HMM with adaptive transition probabilities in presence of missing observations

A new approach for modeling and monitoring of the multivariate processes in presence of faulty and missing observations is introduced. It is assumed that operating modes of the process can transit to each other following a Markov chain model. Transition probabilities of the Markov chain are time varying as a function of the scheduling variable. Therefore, the transition probabilities will be able to vary adaptively according to different operating modes. In order to handle the problem of missing observations and unknown operating regimes, the expectation maximization algorithm is used to estimate the parameters. The proposed method is tested on two simulations and one industrial case studies. The industrial case study is the abnormal operating condition diagnosis in the primary separation vessel of oil‐sand processes. In comparison to the conventional methods, the proposed method shows superior performance in detection of different operating conditions of the process. © 2014 American Institute of Chemical Engineers AIChE J, 61: 477–493, 2015     

Analysis of Transient Heat Conduction in a Hollow Sphere Using Duhamel Theorem

In this article, analytical modeling of two-dimensional heat conduction in a hollow sphere is presented. The hollow sphere is subjected to time-dependent periodic boundary conditions at the inner and outer surfaces. The Duhamel theorem is employed to solve the problem where the periodic and time-dependent terms in the boundary conditions are considered. In the analysis, the thermophysical properties of the material are assumed to be isotropic and homogenous. Moreover, the effects of the temperature oscillation frequency, the thickness variation of the hollow sphere, and thermophysical properties of the sphere are studied. The temperature distribution obtained here contains two characteristics, the dimensionless amplitude (A) and the dimensionless phase difference (j{\varphi}). Moreover, the obtained results are shown with respect to Biot and Fourier numbers. Comparison between the present results and the findings from a previous study for a hollow sphere subjected to the reference harmonic state show good agreement.     

Warm forming limits of rare earth-magnesium alloy ZEK100 sheet

Forming limit curves were developed for a rare earth-magnesium alloy, ZEK100-F, at temperatures between 25 and 350 °C in both the rolling (RD) and transverse directions (TD) of the sheet. ZEK100-F contains additions of zirconium (Zr) as a grain refining alloying element and a rare earth addition, neodymium (Nd), that promotes a weakened basal texture allowing enhanced slip activity at lower temperatures. Warm formability measurements were also performed on non-rare earth containing AZ31B-O to examine the relative performance of these two alloys. The ZEK100 material exhibited significantly better room temperature formability over AZ31B-O with a limiting dome height of 29 mm for ZEK100 compared to only 12 mm for the AZ31B-O. At elevated temperatures (250 °C) the difference in formability between the two alloys becomes less pronounced with a LDH of 40 and 36 mm for ZEK100 and AZ31B-O, respectively. What is particularly striking is the pronounced benefit of the rare earth alloyed material at intermediate temperatures, with an LDH of 37 mm at 150 °C which equals the LDH of AZ31B at 250 °C. Similar trends were determined in the measured forming limit curves reported here for the two alloys. The relative performance of the two alloys is largely attributed their initial textures. ZEK100-F also exhibits strongly anisotropic formability (RD versus TD) which can again be attributed to its’ initial texture.     

A fuzzy lot-sizing problem with two-stage composite human learning

Due to the repetitive nature of inventory planning over the planning horizon, the operator in charge has to perform planning tasks repetitively, and consequently s/he becomes more familiar with the tasks over time. Familiarity with the tasks suggests that learning takes place in inventory planning. Even though the operator’s learning over time might improve his/her efficiency, prior research on fuzzy lot-sizing problems mostly overlooked the effect of human learning in their models and its impact on the operator’s performance. To close the research gap in this area, this paper models the operator's learning in a fuzzy economic order quantity model with backorders. The paper models a situation where the operator applies the acquired knowledge over the cycles in setting the fuzzy parameters at the beginning of every planning cycle, where his/her learning ability includes the cognitive and motor capabilities of a human being. Subsequently, a mathematical model which takes account of a two-stage human learning over the planning cycles is developed, which is then analytically investigated using sample data-sets. The results indicate that both operator’s capabilities, cognitive and motor, affect the efficiency of the fuzzy lot-sizing inventory model, but the influence of the cognitive capability is more profound, which in turn suggests the importance of training programmes for the workforces. The results of the sensitivity analysis also draw some managerial insights for the case that some model parameters vary over the planning horizon.