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Showing 7 results for Salmasnia

Ali Salmasnia, Hossein Fallah Ghadi, Hadi Mokhtari,
Volume 27, Issue 3 (IJIEPR 2016)
Abstract

Achieving optimal production cycle time for improving manufacturing processes is one of the common problems in production planning. During recent years, different approaches have been developed for solving this problem, but most of them assume that mean quality characteristic is constant over production run length and sets it on customer’s target value. However, the process mean may drift from an in-control to an out-of-control at a random point in time. This study aims to select the production cycle time and the initial setting of mean quality characteristic, so that the expected total cost, consisting of quality loss and maintenance costs as well as ordering and holding costs, already considered in the classic models is minimized. To investigate the effect of mean process setting, a computational analysis on a real world example is performed. Results show the superiority of the proposed approach compared to the classical economic production quantity model.


Ali Salmasnia, Ebrahim Ghasemi, Hadi Mokhtari,
Volume 27, Issue 4 (IJIEPR 2016)
Abstract

This study aims to select optimal maintenance strategy for components of an electric motor of the National Iranian Oil Refining and Distribution Company. In this regard, a method based on revised multi choice goal programming and analytic hierarchy process (AHP) is presented. Since improving the equipment reliability is an important issue, reliability centered maintenance (RCM) strategies are introduced in this paper. Furthermore, on one hand, we know that maintenance cost consists of a considerable percentage of production cost; on the other hand, the risk of equipment failure is a main factor on personnel’s safety. Consequently, the cost and risk factors are selected as important criteria of maintenance strategies.


Samrad Jafarian-Namin, Mohammad Saber Fallahnezhad, Reza Tavakkoli-Moghaddam, Ali Salmasnia, Mohammad Hossein Abooei,
Volume 32, Issue 4 (IJIEPR 2021)
Abstract

In recent years, it has been proven that integrating statistical process control, maintenance policy, and production can bring more benefits for the entire production systems. In the literature of triple-concept integrated models, it has generally been assumed that the observations are independent. However, the existence of correlated structures in some practical applications put the traditional control charts in trouble. The mixed EWMA-CUSUM (MEC) control chart and the ARMA control chart are effective tools to monitor the mean of autocorrelated processes. This paper proposes an integrated model subject to some constraints for determining the decision variables of triple concepts in the presence of autocorrelated data. Three types of autocorrelated processes are investigated to study their effects on the results. Moreover, the results of the MEC and ARMA charts are compared. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to select optimal decision variables. An industrial example and extensive comparisons are provided
Hadi Mokhtari, Ali Salmasnia, Ali Fallahi,
Volume 33, Issue 1 (IJIEPR 2022)
Abstract

This paper designs a Scenario analysis approach to determine the joint production policy for two products under possible substitution. The Scenario analysis is designed to improve decision making by considering possible outcomes and their implications. The traditional multi-products production models assume that there is no possible substitution between products. However, in real-world cases, there are many substitutable products where substitution may occur in the event of a product stock-out. The proposed model optimizes production quantities for two products under substitution with the aim of minimizing the total cost of inventory system, including setup and holding costs, subject to a resource constraint. To analyze the problem, four special Scenarios are derived and discussed in detail. Furthermore, the total cost functions are derived for each Scenario separately, and then a solution procedure is suggested based on the Scenarios developed. The numerical examples are implemented, and the results are discussed in detail.
Ali Salmasnia, Mohammad Reza Maleki, Esmaeil Safikhani,
Volume 34, Issue 2 (IJIEPR 2023)
Abstract

In some applications, the number of quality characteristics is larger than the number of observations within subgroups. Common multivariate control charts to monitor the variability of such high-dimensional processes are unsuitable because the sample covariance matrix is not positive semi-definite and invertible. Moreover, the impact of gauge imprecision on detection capability of multivariate control charts under high-dimensional setting has been clearly neglected in the literature. To overcome these shortcomings, this paper develops a ridge penalized likelihood ratio chart for Phase II monitoring of high-dimensional process in the presence of measurement system errors. The developed control chart departures from the assumption of sparse variability shifts in which the assignable cause can only affects a few elements of the covariance matrix. Then, to compensate for the adverse impact of gauge impression, the developed chart is extended by employing multiple measurements on each sampled item. Simulation studies are carried out to study the impact of imprecise measurements on detectability of the developed monitoring scheme under different shift patterns. The results show that the gauge inability negatively affects the run-length distribution of the developed control chart. It is also found that the extended chart under multiple measurements strategy can effectively reduce the error impact.
Mahdi Rezaei, Ali Salmasnia, Mohammad Reza Maleki,
Volume 34, Issue 3 (IJIEPR 2023)
Abstract

This article develops an integrated model of transmitting strategies and operational activities to enhance the efficiency of supply chain management. As the second objective, this paper aims to improve supply chain performance management (SCPM) by employing proper decision-making approaches. The proposed model optimizes the performance indicator based on SCOR metrics. A process-based method is utilized for high-level decisions, while a mathematical programming method is proposed for low-level decisions. The suggested operational model takes some major supply chain properties such as multiple suppliers, multiple plants, multiple materials, and multiple produced items over several time periods into account. To solve the operational multi-objective optimization model, a goal programming approach is applied. The computational results are explained in terms of a numerical example, and a sensitivity analysis is performed to investigate how the performance of the supply chain is influenced by strategic scenario planning.
 
Ali Salmasnia, Elahe Heydarnezhad, Hadi Mokhtari,
Volume 35, Issue 2 (IJIEPR 2024)
Abstract

One of the important problems in managing construction projects is selecting the best alternative for activities' execution to minimize the project's total cost and time. However, uncertain factors often have negative effects on activity duration and cost. Therefore, it is crucial to develop robust approaches for construction project scheduling to minimize sensitivity to disruptive noise factors. Additionally, existing methods in the literature rarely focus on environmentally conscious construction management. Achieving these goals requires incorporating the project scheduling problem with multiple objectives. This study proposes a robust optimization approach to determine the optimal construction operations in a project scheduling problem, considering time, cost, and environmental impacts (TCE) as objectives. An analytical algorithm based on Benders decomposition is suggested to address the robust problem, taking into account the inherent uncertainty in activity time and cost. To evaluate the performance of the proposed solution approach, a computational study is conducted using real construction project data. The case study is based on the wall of the east coast of Amirabad port in Iran. The results obtained using the suggested solution approach are compared to those of the CPLEX solver, demonstrating the appropriate performance of the proposed approach in optimizing the time, cost, and environment trade-off problem. 


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