Mohammadsadegh Mobin

Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS

We use NSGA-III and MOPSO algorithms to solve a multi-objective X-bar control chart design problem.NSGA-III and MOPSO are modified to handle a constrained multi-objective problem with discrete and continuous variables.Four DEA models are proposed to reduce the number of Pareto optimal solutions to a manageable size.TOPSIS is used to prioritize the efficient optimal solutions.Several metrics are used to compare the performance of NSGA-III and MOPSO algorithms. X-bar control charts are widely used to monitor and control business and manufacturing processes. This study considers an X-bar control chart design problem with multiple and often conflicting objectives, including the expected time the process remains in statistical control status, the type-I error, and the detection power. An integrated multi-objective algorithm is proposed for optimizing economical control chart design. We applied multi-objective optimization methods founded on the reference-points-based non-dominated sorting genetic algorithm-II (NSGA-III) and a multi-objective particle swarm optimization (MOPSO) algorithm to efficiently solve the optimization problem. Then, two different multiple criteria decision making (MCDM) methods, including data envelopment analysis (DEA) and the technique for order of preference by similarity to ideal solution (TOPSIS), are used to reduce the number of Pareto optimal solutions to a manageable size. Four DEA methods compare the optimal solutions based on relative efficiency, and then the TOPSIS method ranks the efficient optimal solutions. Several metrics are used to compare the performance of the NSGA-III and MOPSO algorithms. In addition, the DEA and TOPSIS methods are used to compare the performance of NSGA-III and MOPSO. A well-known case study is formulated and solved to demonstrate the applicability and exhibit the efficacy of the proposed optimization algorithm. In addition, several numerical examples are developed to compare the NSGA-III and MOPSO algorithms. Results show that NSGA-III performs better in generating efficient optimal solutions.

Maintenance decision making, supported by computerized maintenance management system

This paper is written based on the need for Computerized Maintenance Management Systems (CMMS) decision analysis capability to achieve world class status in maintenance management. Investigations indicate that decision analysis capability is often missing in existing CMMSs and collected data in the systems are not completely utilized. How to utilize the gathered data to provide guidelines for maintenance engineers and managers to make proper maintenance decisions has always been a crucial question. In order to provide decision support capability, the aim of this paper is to provide and examine three different decision making techniques which can be linked to CMMS and add value to collected data. This research has been conducted within a global project in a large manufacturing site in Sweden to provide a new maintenance management system for the company. The data from the main studies were collected through document analysis complemented by discussions with maintenance engineers and managers at the case company to verify the data. Methods including a Multiple Criteria Decision Making (MCDM) technique called TOPSIS, k-means clustering technique, and one decision making model borrowed from the literature were used. The results indicate the most appropriate maintenance decision for each of the selected machines/parts according to factors such as frequency of breakdowns, downtime, and cost of repairing. The paper concludes with a comparison of results obtained from the different decision making techniques and also a discussion on possible improvements needed to increase the capability of the maintenance decision making models.

Multi-Objective and Multi-Stage Reliability Growth Planning in Early Product-Development Stage

This paper proposes a multi-objective multi-stage reliability growth planning method in the early product-development stage. Multi-stage reliability growth planning is common in practice, and it aligns well with multiple developmental stages of a new product such as concept design, detail design, prototype design, and final production version design. The multi-objective formulation reflects the needs of product developments multiple objectives, such as program cost, schedule, and reliability. Pareto optimal solutions of the multi-objective multi-stage formulation for reliability growth planning are searched using a modified nondominated sorting genetic algorithm (NSGA-II). To reduce the large size of Pareto optimal solutions to a workable size of efficient solutions for plan implementation, both constant return-to-scale and variable return-to-scale data envelopment analysis (DEA) methods are used for determining the efficient solutions. Based on tradeoff and sensitivity analysis, insights and guidelines are presented for choosing appropriate reliability growth plans in terms of optimal allocation of testing time and testing units, and the timing for new technology introduction. The growth rate in each product-development stage and its impact on the development cost, schedule, and reliability are also discussed. An illustrative example is given to demonstrate the approach for planning the reliability growth for a next-generation engine development.

A Multiobjective Approach for Multistage Reliability Growth Planning by Considering the Timing of New Technologies Introduction

This paper proposes a new multiobjective multiple stage reliability growth planning (MO-MS-RGP) model. The model is based on multiobjective consideration of developing a new product, including the cost, time, and product reliability. The number of test units, test time, and the percentage of introduced new technologies are considered as decision variables in the model. Varying reliability growth rates are considered for each subsystem in each stage. Product new technologies or contents can be completely introduced in one stage or partially introduced to the product over multiple stages. New product development time limit and budget are considered as constraints in the MO-MS-RGP model. An integrated approach is developed to formulate and solve the proposed MO-MS-RGP problem. The approach starts with a multiobjective evolutionary algorithm, called multipleobjective particle swarm optimization to find a set of Pareto optimal solutions. Then, clustering methods are applied to cluster the solutions obtained by the evolutionary algorithm. Finally, the clustered solutions are ranked using a multiple criteria decision making method. A numerical example illustrates the application of the proposed MO-MS-RGP model for the reliability growth planning optimization of a next generation engine development.