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Fulltext Maximum acceptable frequency of lift for combined manual material handling task in Malaysia

Widia M, Md Dawal SZ, Yusoff N

PLoS One, 2019;14(5):e0216918.
PMID: 31141545 DOI: 10.1371/journal.pone.0216918

BACKGROUND: Extensive studies have been carried out over the years to determine the maximum acceptable weight that a worker is capable of lifting in a given situation among Occidental populations across Europe and US. Nonetheless, studies that place emphasis on using lifting frequency as the quantifying task parameter, especially in developing countries such as Malaysia, appear to be in scarcity. Hence, this study determined the maximum acceptable frequency of lift (MAFL) for combined manual material handling (MMH) tasks amongst Malaysian males.
METHOD: Two lifting loads were considered in this study: 1 kg and 5 kg. Each subject adjusted his frequency of lifting using a psychophysical approach. The subjects were instructed to perform combined MMH task as fast as they could over a period of 45 minutes without exhausting themselves or becoming overheated. The physiological response energy expenditure was recorded during the experimental sessions. The ratings of perceived exertion (RPE) for four body parts (forearms, upper arm, lower back and entire body) were recorded after the subjects had completed the instructed task.
RESULTS: The mean frequencies of the MMH task had been 6.8 and 5.5 cycles/minute for lifting load of 1 and 5 kg, respectively, while the mean energy expenditure values were 4.16 and 5.62 kcal/min for 1 and 5 kg load, respectively. These displayed a significant difference in the Maximum Acceptable Frequency of Lift (MAFL) between the two loads, energy expenditure and RPE (p < 0.05) whereby the subjects appeared to work harder physiologically for heavier load.
CONCLUSION: It can be concluded that it is significant to assess physiological response and RPE in determining the maximum acceptable lifting frequency at varied levels of load weight. The findings retrieved in this study can aid in designing tasks that do not exceed the capacity of workers in order to minimise the risk of WRMSDs.
Actual safety performance of the Malaysian offshore oil platforms: Correlations between the leading and lagging indicators

Tang DKH, Md Dawal SZ, Olugu EU

J Safety Res, 2018 Sep;66:9-19.
PMID: 30121115 DOI: 10.1016/j.jsr.2018.05.003

INTRODUCTION: This study establishes the correlations between performance of a set of key safety factors and the actual lagging performance of oil platforms in Malaysia, hence the relevance of the key safety factors in evaluating and predicting the safety performance of oil and gas platforms. The key factors are crucial components of a safety performance evaluation framework and each key safety factor corresponds to a list of underlying safety indicators.
METHOD: In this study, participating industrial practitioners rated the compliance status of each indicator using a numbering system adapted from the traffic light system, based on the actual performance of 10 oil platforms in Malaysia. Safety scores of the platforms were calculated based on the ratings and compared with the actual lagging performance of the platforms. Safety scores of two platforms were compared with the facility status reports' findings of the respective platforms.
RESULTS: The platforms studied generally had good performance. Total recordable incident rates of the platforms were found to show significant negative correlations with management and work engagement on safety, compliance score for number of incident and near misses, personal safety, and management of change. Lost time injury rates, however, correlated negatively with hazard identification and risk assessment. The safety scores generally agreed with findings of the facility status reports with substandard process containment found as a contributor of hydrocarbon leaks.
CONCLUSIONS: This study proves the criterion validity of the safety performance evaluation framework and demonstrates its usability for benchmarking and continuous improvement of safety practices on the Malaysian offshore oil and gas platforms.
PRACTICAL APPLICATIONS: This study reveals the applicability of the framework and the potential of extending safety reporting beyond the few conventional lagging safety performance indicators used. The study also highlights the synergy between correlating safety factors to streamline safety management on offshore platforms.
Prediction of work productivity outcomes by identifying critical risk factors among garment industry workers

Javed I, Md Dawal SZ, Nukman Y, Ahmad A

Int J Occup Saf Ergon, 2022 Dec;28(4):2238-2249.
PMID: 34556003 DOI: 10.1080/10803548.2021.1984673

Work productivity is one of the most important economic measures in the manufacturing industry. However, the physical, psychosocial and individual risk factors of an industrial work environment affect workers' physical or mental health, resulting in work productivity loss, absenteeism and presenteeism. Therefore, this study aims to identify the most critical risk factors and develop statistical models for predicting work productivity loss, absenteeism and presenteeism of garment industry workers. A sample of 224 sewing machine operators was taken for data collection through observation and self-reported studies. The results indicated that the average work productivity loss, absenteeism and presenteeism was 38.21, 2.35 and 37.23%, respectively. Finally, the statistical models of work productivity loss, absenteeism and presenteeism was developed using multiple linear regression with precision of 69.9, 53.7 and 84.0%, respectively. Hence, this study will help garment industries to improve their work productivity by taking initiatives based on the developed models.
Fulltext An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation

Nguyen HT, Md Dawal SZ, Nukman Y, Aoyama H, Case K

PLoS One, 2015;10(9):e0133599.
PMID: 26368541 DOI: 10.1371/journal.pone.0133599

Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.
Fulltext Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment

Pai YS, Yap HJ, Md Dawal SZ, Ramesh S, Phoon SY

Sci Rep, 2016 06 07;6:27380.
PMID: 27271840 DOI: 10.1038/srep27380

This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively.
Fulltext Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

Mousavi M, Yap HJ, Musa SN, Tahriri F, Md Dawal SZ

PLoS One, 2017;12(3):e0169817.
PMID: 28263994 DOI: 10.1371/journal.pone.0169817

Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.