Journal of Optimization in Industrial Engineering
Volume:6 Issue: 12, Winter and Spring 2013

The classical method of process capability analysis necessarily assumes that collected data are independent; nonetheless, some processes such as biological and chemical processes are autocorrelated and violate the independency assumption. Many processes exhibit a certain degree of correlation and can be treated by autoregressive models, among which the autoregressive model of order one (AR (1)) is the most frequently used one. In this paper, we discuss the effect of autocorrelation on the process capability analysis when a set of observations are produced by an autoregressive model of order one. We employ a multivariate regression model to modify the process capability estimated from the classical method, where the AR (1) parameters are utilized as regression explanatory variables. Finally, the performance of the presented method is investigated using a Monte Carlo simulation.

The development of optimization and mathematical models for closed loop supply chain (CLSC) design has attracted considerable interest over the past decades. However, the uncertainties that are inherent in the network design and the complex interactions among various uncertain parameters are challenging the capabilities of the developed tools. The aim of this paper, therefore, is to propose a new mathematical model for designing a CLSC network that integrates the network design decisions in both forward and reverse supply chain networks. Moreover, another objective of this research is to introduce an inexact-fuzzy-stochastic solution methodology to deal with various uncertainties in the proposed model. Computational experiments are provided to demonstrate the applicability of the proposed model in the CLSC network design.

In this paper, finding Dynamic Load Carrying Capacity (DLCC) of flexible link manipulators in point to-point motion was formulated as an optimal control problem. The finite element method was employed for modelling and deriving the dynamic equations of the system. The study employed indirect solution of optimal control for system motion planning. Due to offline nature of the method, many difficulties such system nonlinearities and all types of constraints can be catered for and implemented easily. The application of Pontryagin’s minimum principle to this problem was resulted in a standard two-point boundary value problem (TPBVP), solved numerically. Then, the formulation was developed to find the maximum payload and corresponding optimal path. The main advantage of the proposed method is that the various optimal trajectories can be obtained with different characteristics and different maximum payloads. Therefore, the designer can select a suitable path among the numerous optimal paths. In order to verify the effectiveness of the method, a simulation study considering a two-link flexible manipulator was presented in details.

Tendency to optimization in last decades has resulted in creating multi-product manufacturing systems. Production planning in such systems is difficult, because optimal production volume that is calculated must be consistent with limitation of production system. Hence, integration has been proposed to decide about these problems concurrently. Main problem in integration is how we can relate production planning in medium-term timeframe with scheduling in short-term timeframe. Our contribution creates production planning and scheduling framework in flexible job shop environment with respect to time-limit of each machine in order to produce different parts families in automotive industry. Production planning and scheduling have iterative relationship. In this strategy information flow is transformed reciprocative between production planning and scheduling for satisfying time-limit of each machine. The proposed production planning has heuristic approach and renders a procedure to determine production priority of different part families based on safety stock. Scheduling is performed with ant colony optimization and assigns machine in order of priority to different part families on each frozen horizon. Results showed that, the proposed heuristic algorithm for planning decreased parts inventory at the end of planning horizon. Also, results of proposed ant colony optimization was near the optimal solution. The framework was performed to produce sixty four different part families in flexible job-shop with fourteen different machines. Output from this approach determined volume of production batches for part families on each frozen horizon and assigned different operations to machines.

In this paper, a novel approach for multi response optimization is presented. In the proposed approach, response variables in treatments combination occur with a certain probability. Moreover, we assume that each treatment has a network style. Because of the probabilistic nature of treatment combination, the proposed approach can compute the efficiency of each treatment under the desirable reliability. The proposed approach has been constructed based on a network data envelopment analysis and chance constraint method. Finally, a numerical example shows the proposed method applicability on a multi response problem. The results demonstrate that the proposed approach is a capable method for analyzing the best treatment. Moreover, the results show that the proposed approach has better performance in efficient treatment determination than other existing methods.

In today''s globalization, an effective integration of production and distribution plans into a unified framework is crucial for attaining competitive advantage. This paper addresses an integrated multi-product and multi-time period production/distribution planning problem for a two-echelon supply chain subject to the real-world variables and constraints. It is assumed that all transportations are outsourced to third-party logistics providers and all-unit quantity discounts in transportation costs are taken into consideration. The problem has been formulated as a multi-objective mixed-integer linear programming model which attempts to simultaneously minimize total delivery time and total transportation costs. Due to the complexity of the considered problem, genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are developed within the LP-metric method and desirability function framework for solving the real-sized problems in reasonable computational time. As the performance of meta-heuristic algorithms is significantly influenced by calibrating their parameters, Taguchi methodology has been used to tune the parameters of the developed algorithms. Finally, the efficiency and applicability of the proposed model and solution methodologies are demonstrated through several problems in different sizes

The most well-known uni-arribute control chart used to monitor the number of nonconformities per unit is the Shewhart type C-chart. In this paper, a new method is proposed in an attempt to reduce the false alarm rate in the C-chart. lized to design an iterative method, where the belief is used to decide whether a process is in an in-control or out-of-control state. Then, a new statistic is defined based on the DOB and the chart is designed accordingly. Some simulation experiments are also performed to evaluate the performance of the proposed scheme and to compare its in-control and out-of-control average run length (ARL) with those of the C and the EWMA charts in different scenarios of mean shifts. Finally, a case study is given to illustrate the application of the proposed methodology. The results show the proposed control chart outperforms the other two charts.

Projects scheduling by the project portfolio selection, something that has its own complexity and its flexibility, can create different composition of the project portfolio. An integer programming model is formulated for the project portfolio selection and scheduling.Two heuristic algorithms, genetic algorithm (GA) and simulated annealing (SA), are presented to solve the problem. Results of calculations show that the algorithm performance of GA is better than SA in project portfolio selection to maximize the NPV of the project portfolio.