Abdul Rahman Abdul Rahim

Application of quality function deployment (QFD) method for pultrusion machine design planning

Machine design and manufacture is the key to the advancement of manufacturing industry. Before any machine can be designed, it is important to establish requirements of the machine. Identifies quality function deployment (QFD) as one of the tools that can be used to identify customer needs and link the needs to product design. By bringing forward customers’ requirements into the design process, design rework and unnecessary iteration between design and manufacture can be reduced. In this project, QFD for a pultrusion machine design has been chosen as a case study. QFD was applied at the front end of the design process. The QFD process started with identifying the customers and determining their needs. These needs were translated into engineering requirements which were then used to formulate general specifications of the machine. A step by step approach was introduced to make the QFD process more manageable. Reports on the first phase of a research project in which QFD was used to capture all vital information from the customers and translated that into engineering requirements.

A tuned NSGA-II to optimize the total cost and service level for a just-in-time distribution network

Distribution network planning has attracted the attention of many studies during last decades. Just-in-time (JIT) distribution has a key role in efficient delivery of products within distribution networks. In modeling of JIT distribution networks, the most frequently applied objectives are related to cost and service level. However, evaluating the impact of simultaneously minimizing total costs and balance between distribution network entities in different echelons still rarely complies with the current literature. To remedy this shortcoming and model reality more accurately, this paper develops a multi-objective mixed-integer nonlinear optimization model for a JIT distribution in three-echelon distribution network. The aims are minimization of total logistics cost along with maximization of capacity utilization balance for distribution centers and manufacturing plants. A non-dominated sorting genetic algorithm-II (NSGA-II) with three different mutation operators namely swap, reversion and insertion is employed to provide a set of near-optimal Pareto solutions. Then, the provided solutions are verified with non-dominated ranked genetic algorithm (NRGA) as well. The Taguchi method in design of experiments tunes the parameters of both algorithms, and their performances are then compared in terms of some multi-objective performance measures. In addition, a genetic algorithm is used to assess Pareto optimal solutions of NSGA-II. Different problems with different sizes are considered to compare the performance of the suggested algorithms. The results show that the proposed solution approach performs efficiently. Finally, the conclusion and some directions for future research are proposed.

Carbon-capped Distribution Planning

We use a hybrid NSGA-II to obtain the near Pareto front solutions in a Just-In-Time distribution network.We investigate different carbon constraints namely periodic, cumulative and global.We present the complexity analysis for the proposed model.We analyze the solution approach as well as identify some managerial and policy insights. Products distribution and transportation is one of the largest sources of CO2 emission in supply chains. To date, a number of researchers have argued that intensive transportation activities through popular distribution strategies such as Just-In-Time (JIT) could significantly increase carbon emissions within logistics chains. However, a systematic understanding of how JIT distribution affects carbon emissions is still lacking in current literature. In this study, we develop a bi-objective optimization model for a carbon-capped JIT distribution of multiple products in a multi-period and multi-echelon distribution network. The aims are to jointly minimize total logistics cost and to minimize the maximum carbon quota allowed per period (carbon cap). The considered problem is investigated under three different carbon emission constraints namely periodic, cumulative and global. Since the studied problem is NP-Hard, a non-dominated sorting genetic algorithm-II (NSGA-II) is developed and its parameters are tuned by Taguchi method. For further quality improvement of the developed solution approach, a novel local search approach called modified firefly algorithm incorporates NSGA-II. Different sizes of the problem are considered to compare the performances of the proposed hybrid NSGA-II and the classical one. Finally, the results are presented along with some policy and managerial insights. For policy makers, the findings show the impact of varying the carbon emission cap on total cost and total emissions under JIT distribution concept. From managerial perspectives, we analyze the relationships between average inventory holding and backlog level per period which can assist mangers to identify critical decisions for JIT distribution of products in carbon-capped environment.

Production Planning and Inventory Control in Automotive Supply Chain Networks

This paper addresses a non-linear optimization model by integrating production planning and inventory control in the automotive industry at the strategic and operational level. In order to provide an effective modeling, we developed a framework to integrate manufacturing system and suppliers within an automotive supply chain network. The numerical experiments demonstrate the efficiency of the proposed model on minimization of total delivery cost and due date delivery.

Multi-objective genetic algorithm in green just-in-time logistics

This paper addresses a mixed-integer linear programming model by integrating just-in-time delivery along with green objectives in a logistics network. Multi-objective genetic algorithm optimization has been applied in order to minimize the number of delivery and lead-time as well as environmental impact of logistic network. This evolutionary based algorithm incorporates non-dominated sorting genetic algorithm, so as to allow heuristic for parallel optimization of the objective functions. Computational results demonstrate efficiency of the proposed model for minimizing the objective functions. Finally, the conclusion and some areas of further research are proposed.

Issues in Total Productive Maintenance (TPM) Implementation: Justification of Employing Case Study Methodology

This paper aims to review the common research design employed to investigate the various issues in TPM implementation. Concurrently, it is also aimed to analyze the contribution from Malaysian –based researchers towards increasing the literature of TPM. Attempt was made to discuss the available literature related to TPM published from year 1992 until 2012. The outcomes from this review would serve as useful guideline for the future research in TPM particularly for Malaysian researcher.

A Review on the Total Productive Maintenance (TPM) Conceptual Framework

This paper aims to examine the practical applications of Total Productive Maintenance (TPM) framework .Attempt was made to discuss the available literature related to TPM framework. The outcomes from these reviews would serve as useful guidelines for the future TPM research. This review justifies on the need of a comprehensive TPM framework aimed at integrating the theoretical and practical applications.

Maintenance Management: Rationale of TPM as the Research Focus

This paper aims to examine the Total Productive Maintenance (TPM) as the significant technique to improve the maintenance management of production equipment. Attempt was made to discuss the available literature related to existing techniques in maintenance management, particularly on breakdown maintenance (BM), preventive maintenance (PM), predictive maintenance (PdM) or condition-based maintenance (CBM), reliability-centered maintenance (RCM), computerized maintenance management system (CMMS) and TPM. The outcomes from these reviews would serve as useful guidelines for the future research in maintenance management. This review justifies TPM as a comprehensive technique to improve the flaw in maintenance management. Notably, TPM encompasses of all elements applied by other maintenance methodology, from tools and techniques to involvement of all operational hierarchical in the organization as what is very much required in manufacturing organization is to integrate different functional areas in a coherent manner.

Wireless System for Monitoring Environmental Variables of Rain Shelter House (RSH)

Wireless system is one of the technologies currently applied in agriculture to improve quality, save labour costs, increase yields, and conserve water. A real-time wireless monitoring system for measuring climatic environment parameters of rain shelter house (RSH) for chili plantation was developed. The wireless monitoring system integrates the hardware and software components for the measurement of environment parameters that can affect the growth of chili, such as temperature, humidity, solar radiation and etc. The system described here offers real potential for monitoring spatially environment variables. This paper describes the component for the developed wireless monitoring system of chili (capsicum) plantation discusses the experimental results acquired and the relationships between those parameters. The experimental results showed our proposed system is very feasible for future applications towards precision agriculture and it has potential to offer great technologies in modernizing the agricultural sector.