An integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production

The determination of completion time to produce a new product is one of the most important indicators for manufacturers in delivering goods to customers. Failure to fulfil delivery on-time or known as tardiness contributes to a high cost of air shipment and production line down at other entities within the supply chain. The uncertainty of completion time has created a big problem for manufacturers of audio speakers which involved semiautomatic production lines. Therefore, the main objective of this research is to develop an integrated model that enhances the artificial neural networks (ANN) and system dynamics (SD) methods in estimating completion time focusing on the cycle time. Three ANN models based on multilayer perceptron (MLP) were developed with different network architectures to estimate cycle time. Furthermore, a proposed momentum rate equation was formulated for each model to improve learning process, where the 3-2-1 network emerged as the best network with the smallest mean square error. Subsequently, the estimated cycle time of the 3-2-1 network was simulated through the development of an SD model to evaluate the performance of completion time in terms of product quantity, manpower fatigue and production workload scores. The success of the proposed integrated ANNSD model also relied on a proposed coefficient correlation of causal loop diagram (CLD) to identify the most influential factor of completion time. As a result, the proposed integrated ANNSD model provided a beneficial guide to the company in determining the most influential factor on completion time so that the time to complete a new audio product can be estimated accurately. Consequently, product delivery was smooth for on-time shipment while successfully fulfilling customers’ demand

Designing the re-layout of the production floor using integrated systematic layout planning (SLP) and simulation methods

In the manufacturing company, an efficient production floor affects the productivity of the company. Thus, a well design production floor layout assists the company to achieve its objectives. In this regard, this study aims to design a new alternative production floor layout for the XYZ manufacturing company. The company facing the facility layout problem (FLP) where their workstation on the production floor was not located based on the activity-relationship. Thus, the company struggles to reduce the distance travel of their workers from one station to another by re-layout their production floor. The Systematic Layout Planning (SLP) method was used to determine the best new alternative layout for the company. Subsequently, the AnyLogic simulation software was utilized to test the effectiveness of the layout by using the number of steps as the parameter. As a result, it is found that the total number of steps of workers in the production floor can be reduced from 16,554 steps (in existing layout) to 15,956 steps (in new alternative layout)

Predicting Completion Time for Production Line in a Supply Chain System through Artificial Neural Networks

Completion time in manufacturing sector is the time needed to produce a product through production processes in sequence and it reflects the delivery performance of such company in supply chain system to meet customer demands on time. However, actual completion time always deviated from the standard completion time due to unavoidable factors and consequently affect delivery due date and ultimately lead to customer dissatisfaction. Therefore, this paper predicts completion time based on historical data of production line activities and discovers the most influential factor that contributes to the tardiness or a late jobs due date from its completion time. A well-known company in producing audio speaker is selected as a case company. Based on the review of previous works, it is found that Artificial Neural Networks (ANN) has superior capability in prediction of future occurrence by capturing the underlying relationship among variables through historical data. Besides, ANN is also capable to provide final weight for each of related variable. Variable with the highest value of final weight indicates the most influential variable and should be concerned more to solve completion time issue which has persisted among entities in supply chain system. The obtained result is expected to become an advantageous guidance for every entity in supply chain system to fulfil completion time requirement as requested by customer in order to survive in this turbulent market place

Effect of Manpower Factor on Semiautomatic Production Line Completion Time: A System Dynamics Approach

Completion time in a manufacturing sector is the time required to complete a product in sequence process during production operation. In a semiautomatic production line, manpower factors such as fatigue and pressure are two significant influences on completion time. However, it is found that previous studies lack the concern to include manpower factor in completion time. Hence, this paper develops a causal loop diagram and stock flow diagram to simulate the influence of manpower factor on the completion time in a semiautomatic production line. In this research, a well-known audio speaker manufacturer is selected as a case company. As a result, it is found that the preparation time for materials has a great impact on fatigue and pressure as it contributes the highest percentage of deviation from the completion time base run with 72.22%. Finally, a policy regarding completion time improvement is recommended to the management to enhance their production performanc

Predicting Completion Time for Production Line in a Supply Chain System through Artificial Neural Networks

Completion time in manufacturing sector is the time needed to produce a product through production processes in sequence and it reflects the delivery performance of such company in supply chain system to meet customer demands on time. However, actual completion time always deviated from the standard completion time due to unavoidable factors and consequently affect delivery due date and ultimately lead to customer dissatisfaction. Besides, it is found that little attention has been given in analysing completion time at production line from previous literatures. Therefore, this paper fill the knowledge gap by predicting completion time based on historical data of production line activities and discovers the most influential factor that contributes to the tardiness or a late job’s due date from its completion time. A wellknown company in producing audio speaker is selected as a case company. Based on the review of previous works, it is found that Artificial Neural Networks (ANN) has superior capability in prediction of future occurrence by capturing the underlying relationship among variables through historical data. Besides, ANN is also capable to provide final weight for each of related variable. Variable with the highest value of final weight indicates the most influential variable and should be concerned more to solve completion time issue which has persisted among entities in supply chain system. The obtained result is expected to become an advantageous guidance for every entity in supply chain system to fulfil completion time requirement as requested by customer in order to survive in this turbulent market plac

A theoretical review on the preventive measures to landslide disaster occurrences in Penang State, Malaysia

Based on the frequently unanticipated occurrences of natural landslide disaster across Malaysia, it can be seen that Malaysia is still not fully prepared for occurrences of natural landslide disaster.The lack of predictive and warning systems for the disaster in the country is creating panic and apprehension among citizens alongside with both economic and property losses. The general objectives of this research are: to identify the meteorological factors that cause landslide natural disaster occurrences in Malaysia and to suggest a predictive model for landslide disaster occurrence in Malaysia. This research therefore explored modelling disasters occurrences in order to predict, warn, and prevent huge impact of landslide disasters in Penang, Malaysia. This research shall make use of past literatures and data from Malaysian Meteorological department considering climatic parameters such as daily mean temperature and daily rainfall only. Data mining and Artificial Neural Networks (ANN) shall be suggested to predict landslide disaster occurrences in Malaysia. Thus, the need for a predictive model for occurrence of landslide natural disaster is imperative to the safety of lives and protection of both environmental and economy of the region

Cycle time minimization in production line using robust hybrid optimization algorithm

Bio-inspired algorithms that have been introduced by mimicking the biological phenomenon of nature have widely implemented to cater various real-world problems. As example, memetic algorithm, EGSJAABC3 is applied for economic environmental dispatch (EED) optimization, Hybrid Pareto Grey Wolf Optimization to minimize emission of noise and carbon in U-shaped robotic assembly line and Polar Bear Optimization to optimize heat production. The results obtained from their research have clearly portrayed the robustness of bio-inspired algorithms to cater complex problems. Assembly line, which is normally the last step of production that involves final assembly of the products. An assembly line generally consists of several workstations placed in sequential order. Each of the workstation is in charge to complete certain specific jobs. Hence, it is a must to make the best use of the efficiency of the assembly line. Cycle time minimization is part of the assembly line balancing problem due to its uncertainty that dependent on the number of manpower, material preparation and machine capacity. Cycle time basically means time needed to process a product using a specific task in a production line. This project proposes the application of new hybrid optimization algorithm named JAABC5-RRO to minimize cycle time to produce a new audio product on a production line in a production company

Variations in the number of hidden nodes through multilayer perceptron networks to predict cycle time

Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure of the MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no rule of thumb in determining the number of hidden nodes within the MLP structure. Researchers normally test with various numbers of hidden nodes to obtain the lowest square error value for optimal prediction results since none of the approaches has yet to be claimed as the best practice. Thus, the aim of this study is to determine the best MLP network by varying the number of hidden nodes of developed networks to predict cycle time for producing a new audio product on a production line. The networks were trained and validated through 100 sets of production lots from a selected audio manufacturer. As a result, the 3-2-1 MLP network was the best network based on the lowest square error value compared to the 3-1-1 and 3-3-1 networks. The 3-2-1 predicted the best cycle time of 5 seconds to produce a new audio product. Hence, the prediction result could facilitate production planners in managing assembly processes on the production line

Packaging waste generation by households: A mixed method study

The paper reports a study to determine the challenges faced by households in managing packaging waste, to compute the theoretical recovery rate, actual recovery rate and the total recovery potential of packaging waste generated, and to forecast the amount of waste generated by the households in Kota Samarahan, Sarawak for the next ten years. This study applies semi-structured interview, mathematical formulation and simulation modelling. The results reveal that the theoretical recovery rate among the majority of respondents is higher and the actual recovery rate among respondents is lower than it should be. There is an upward trend in the production of waste in the future

Linking integrity with road pricing cause-and-effect model: A system dynamics simulation approach

With Malaysia's rapid urbanisation and continuous improvement of living standards, vehicle ownership and trip volume continue to grow. Increases in motor traffic in large cities and their environs result in a number of social, environmental, and economic issues, which are frequently attributable to the widespread use of automobiles as the primary mode of urban transportation. This exacerbates traffic congestion on the country's highways, particularly in urban areas such as Kuala Lumpur. This traffic congestion poses an ongoing threat to the sustainability of transport development. Thus, by using the system dynamics, this study establishes a cause-and-effect relationship regarding the implementation of road pricing as a tool for reducing congestion and a stepping stone for enhancing sustainability. Road pricing is a direct charge assessed to drivers who use the road network with the goal of reducing the number of private vehicles on the road during peak hours. The developed Causal Loop Diagram (CLD) composed of five subsystems: road congestion, road attractiveness, new road construction, public transportation, and road pricing. The road congestion, new road construction, and road pricing all encounter mutual reinforcement as a result of a variety of negative polarities. As a result, authorities should place a greater emphasis on these loopholes, as they will inevitably result in unexpected changes. Additionally, by incorporating holistic perspectives from previous works and experts in the field, CLD can aid in identifying the primary factors underlying the problem being studied. In future work, the developed CLD should be extended to the next stage of the SD model, dubbed stock-flow-diagram (SFD)