Showing 3 results for Izadbakhsh
Rassoul Noorossana, Abbas Saghaei, Hamidreza Izadbakhsh, Omid Aghababaei,
Volume 24, Issue 2 (IJIEPR 2013)
Abstract
In certain statistical process control applications, quality of a process or product can be characterized by a function commonly referred to as profile. Some of the potential applications of profile monitoring are cases where quality characteristic of interest is modelled using binary,multinomial or ordinal variables. In this paper, profiles with multinomial response are studied. For this purpose, multinomial logit regression (MLR) is considered as the basis.Then, the MLR is converted to Poisson GLM with log link. Two methods including Multivariate exponentially weighted moving average (MEWMA) statistics, and Likelihood ratio test (LRT) statistics are proposed to monitor MLR profiles in phase II. Performances of these three methods are evaluated by average run length criterion (ARL). A case study from alloy fasteners manufacturing process is used to illustrate the implementation of the proposed approach. Results indicate satisfactory performance for the proposed method.
Mohammad Mehdi Dehdar, Mustafa Jahangoshai Rezaee, Marzieh Zarinbal, Hamidreza Izadbakhsh,
Volume 29, Issue 4 (IJIEPR 2018)
Abstract
Human-based quality control reduces the accuracy of this process. Also, the speed of decision making in some industries is very important. For removing these limitations in human-based quality control, in this paper, the design of an expert system for automatic and intelligent quality control is investigated. In fact, using an intelligent system, the accuracy in quality control is increased. It requires the knowledge of experts in quality control and design of expert systems based on the knowledge and information provided by human and equipment. For this purpose, Fuzzy Inference System (FIS) and Image Processing approach are integrated. In this expert system, the input information is the images of the products and the results of processing on images for quality control are as output. At first, they may be noisy images; the pre-processing is done and then a fuzzy system is used to be processed. In this fuzzy system, according to the images, the rules are designed to extract the specific features that are required. At second, after the required attributes are extracted, the control chart is used in terms of quality. Furthermore, the empirical case study of copper rods industry is presented to show the abilities of the proposed approach.
Simin Dargahi Darabad, Maryam Izadbakhsh, Seyed Farid Ghannadpour, Siamak Noori, Mohammad Mahdavi Mazdeh,
Volume 35, Issue 1 (IJIEPR 2024)
Abstract
The construction supply chain is presently the focus of considerable interest among numerous project-related businesses. Strong project management is essential for the effective completion of a project, since restricted budgets and time constraints are considered for each project. The research uses multi-objective linear programming to create a mathematical model of the building supply chain. The primary aims of the present investigation are to limit the expenses associated with logistics and to diminish the release of greenhouse gases caused by transportation. Given the reality of managing several projects concurrently, the model provided comprises a network of projects. Following the completion of each project, an inspection is arranged to assess its level of success. Estimating the costs of a project relies on several variables. In reality, there are always uncertainties highlighted in several studies about the uncertainty of cost and time parameters. This research incorporates many characteristics concurrently to simulate real-world settings and address the issue of uncertainty. The expression of uncertainty for all costs, activity length, inspection, supplier capacity, and resource demand are represented by triangular fuzzy numbers. Ultimately, the precision of the model's performance has been verified using a numerical illustration.
