Multi-Objective Discrete Particle Swarm Optimisation Algorithm for Integrated Assembly Sequence Planning and Assembly Line Balancing

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set

Multi-Objective Optimisation of CNC Milling Process for Al 6061 using Modified NSGA-II

Computer numerical controlled (CNC) growth has revolutionised the manufacturing sectors by changing the way people work. In milling process, it has contributed to the higher productivity and better quality of the products. Although a lot of researches have been done on how to improve the process, the process improvement does not stop there because of evolving materials, methods and technologies. This paper presents a multi-objective optimisation of CNC milling process in order to achieve desired surface roughness and minimise machining time for Al 6061. A full factorial experiment has been conducted to model surface roughness by controlling three variables; spindle speed, feed rate and depth of cut. Multi-objective optimisation has been performed using modified Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) with two levels crossover. The optimisation result concluded that the modified NSGA-II was able to converge to Pareto-optimal, but having difficulties to spread solutions in wider range

Production Cost Modeling and Simulation in the Glove Manufacturing Industry

Product costing is an essential aspect of business strategy as it allows companies to forecast a product's future revenues and expenses and make informed decisions about its development and production. One of the main challenges in the manufacturing sector is the difficulty in selecting the optimal production setup. This can be due to changes in the product or component during the manufacturing process, leading to difficultiesin defining the best production quantity and forecasting production costs. Low productivity is another challenge faced by industrial organizations, which can affect their profitability. A case study was conducted in the glove manufacturing industry. The main objective of this research is to model the production cost for the selected case study. Cost modeling in the manufacturing industry involves creating a representation or simulation of a manufacturing process to estimate the costs associated with producing a product. Developing a cost model for the manufacturing industry involves collecting data from the industry and existing literature; developing a cost model with several function modules based on the data; validating the model by comparing its estimates to actual costs; and using the model for cost estimation, budgeting, and product pricing while keeping it updated and calibrated regularly to ensure its accuracy over time. Based on simulation analysis, productivity was improved by 4.0% compared to the original layout. In addition, the production cost per box was reduced by 4.2%. The results from this research can help companies to manage their resources and improve their profitability more effectively

Modeling of Optimizing Multi-hole Drilling Toolpath Distance with Multiple Tool Dimension

Multi-hole Drilling with Multiple Tool dimensions (MDMT) is a crucial technique in today's industry, allowing manufacturers to satisfy the increasing demand for precise and high-quality components while adopting the latest technological advancements and environmental standards. This paper introduces and validates a computational model for MDMT, offering numerous advantages over conventional drilling methods, including enhanced efficiency, accuracy, cost-effectiveness, and flexibility. The computational model was developed for the MDMT problem using the Travelling Salesman Problem (TSP) concept to measure the total toolpath distance. The Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) are applied to solving 12 cases of MDMT problems with varying numbers of holes, classified as small, medium, and large, using MATLAB software R2022b. Note that the algorithms were evaluated based on their solution quality, with lower fitness values indicating better performance. Overall, GA performed the best across most hole configurations, achieving the optimal fitness value in 5 out of 12 cases (small, medium, and large), ACO performed better in 4 out of 12 cases (small and medium) and PSO performed better in 3 out of 12 cases (medium and large). The research emphasizes the potential of multi-dimensional tools for accomplishing intricate drilling tasks. Other than that, this paper contributes to the existing literature on MDMT and highlights the importance of multi-dimensional tools in modern manufacturing. Future research could optimize the proposed computational model for various materials and drilling scenarios in MDMT

A new multiobjective tiki-taka algorithm for optimization of assembly line balancing

Purpose: This study aims to propose a new multiobjective optimization metaheuristic based on the tiki-taka algorithm (TTA). The proposed multiobjective TTA (MOTTA) was implemented for a simple assembly line balancing type E (SALB-E), which aimed to minimize the cycle time and workstation number simultaneously. Design/methodology/approach: TTA is a new metaheuristic inspired by the tiki-taka playing style in a football match. The TTA is previously designed for a single-objective optimization, but this study extends TTA into a multiobjective optimization. The MOTTA mimics the short passing and player movement in tiki-taka to control the game. The algorithm also utilizes unsuccessful ball pass and multiple key players to enhance the exploration. MOTTA was tested against popular CEC09 benchmark functions. Findings: The computational experiments indicated that MOTTA had better results in 82% of the cases from the CEC09 benchmark functions. In addition, MOTTA successfully found 83.3% of the Pareto optimal solution in the SALB-E optimization and showed tremendous performance in the spread and distribution indicators, which were associated with the multiple key players in the algorithm. Originality/value: MOTTA exploits the information from all players to move to a new position. The algorithm makes all solution candidates have contributions to the algorithm convergence

Analysis of measurement and calculation of MSD complaint of chassis assembly workers using OWAS, RULA and REBA method

Car assembly is a combination of all components that form one completed vehicle unit. The work process is manually done and repeatedly, which contributes to a risk of musculoskeletal disorders (MSD). Chassis assembly is a job with a high level of complexity and associated with MSD risk for its employees. This study consists of 30 assembly activities divided into six groups based on posture and working methods used during the work process. Group A consists of 7 assemblies, Group B consists of 8 assemblies, Group C consists of 5 assemblies, Group D consists of 2 assemblies, Group E consists of 5 assemblies, and Group F consists of 3 assemblies. This study aims to compare the measurement and calculation of the risk level of MSD workers by using the RULA, REBA, and OWAS methods. The results of the measurements and computations acquired using these three approaches yielded the same risk category: 83.33 % medium risk/dangerous in working groups A, C, D, E, and F, and 16.67 % very high risk/highly hazardous in working group B. These six groups, particularly group B, requires immediate attention to reduce worker complaints of MSD

Optimisation of vehicle routing problem with time windows using Harris Hawks optimiser

Vehicle routing problem is one of the combinatorial optimisation problems that have gained attraction for studies because of its complexity and significant impact to service providers and passengers. Vehicle routing problem with time windows (VRPTW) is a variant where vehicles need to visit the predetermined stop points within the given time frame. This problem has been widely studied and optimised using different methods. Since the performance of algorithms in different problems is dissimilar, the study to optimise the VRPTW is ongoing. This paper presents a VRPTW study for a public transportation network in Kuantan and Pekan districts, located in East Pahang, Malaysia. There were 52 stop points to be visited within two hours. The main objective of the study is to minimise the number of vehicles to be assigned for the routing problem subjected to the given time windows. The problem was optimised using a new algorithm known as Harris Hawks Optimiser (HHO). To the best of authors’ knowledge, this is the first attempt to build HHO algorithm for VRPTW problem. Computational experiment indicated that the HHO came up with the best average fitness compared with other comparison algorithms in this study including Artificial Bee Colony (ABC), Particle Swarm Optimisation (PSO), Moth Flame Optimiser (MFO), and Whale Optimisation Algorithm (WOA). The optimisation results also indicated that all the stop points can be visited within the given time frames by using three vehicles

Hybrid flow shop scheduling with energy consumption in machine shop using moth flame optimization

Hybrid flow shop with energy consumption (HFS-EC) combine the flow shop scheduling and parallel machine scheduling problem with the aim to optimize energy utilization, besides regular makespan in the production scheduling. This paper optimizes an HFS-EC case study using Moth Flame Optimization (MFO). The case study has been conducted in a machine shop concentrating on three machining types; lathe, milling and deburring. The objectives were to optimize makespan and total energy consumption in the machine schedule. Optimization using MFO has been conducted and the results was compared with well-established algorithm like Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization. The results were also compared with relatively recent algorithm such as Whale Optimization Algorithm and Harris Haws Optimization. Based on the optimization results, the MFO outperformed other comparison algorithms for the mean fitness and also the best fitness. Although there were other solutions with better individual optimization objectives, but results obtained by MFO compromised between minimum makespan and energy consumption. The proposed HFS-EC model and MFO algorithm has a great potential to be implemented in other scheduling case study due to benefit of reducing carbon emission and at the same time maintain the production output

Ergonomics study in Quick Response Manufacturing (QRM) automotive workstation environment to overcome employee complaints

The assembly line is an important process in producing a complete car unit before the product is checked and delivered to the consumer. Assembly workers during working hours are affected by work position, workload, placement of components and aids during the process and equipment used in assisting heavy work processes. Work positions with non-ergonomic workloads impact the disability and musculoskeletal complaints (MSD) of workers. The purpose of this study is to identify the ergonomic risks of assembly workers. Analytical methods using the Nordic Body Map (NBM) and QRM principles were used in this study. The results of the analysis of the level of complaints of workers’ MSD during the work were obtained for the categories of not sick (NS) 27.94%, slightly sick (SS) 36.76%, sick (S) 29.69% and very sick (VS) 5.6%. The most dominant complaints about S and VS complaints were shoulders, arms, back, waist, buttocks, wrists and hands. MSD complaints that employees feel are in the middle category with an average score of 64 points which means immediate remedial action is needed. Using the time-focused QRM principle, improvements in work procedures and designing ergonomic tools are needed to minimize MSD complaints that impact working hours faster, and there is no overtim

Integration of QRM and ergonomics in the design of a framework in identification complaints among automotive assembly line workers

The assembly line is the most critical area of automotive manufacturing. The smoothness of the production process depends on the situation and conditions of the environment and its workers. The assembly process is done manually by using humans to install all the related components in the production line. Complaints felt by workers during the manufacturing process can hinder the smooth running of production in meeting capacity, thus affecting the company's performance. Therefore, the purpose of this study is to design a framework for identifying workers' complaints by using a combination of Quick Response Manufacturing (QRM) and ergonomics. This framework is expected to identify grievances felt by workers from all aspects of the assembly environment that could potentially impact employment grievances. Framework design is created using the main concept of QRM which consists of time is money, tailoring your dynamics, focusing on the target market segment and thinking gold. Each of these concepts contains ergonomic elements such as workload variables and complaints of musculoskeletal disorders related to production schedules, production time, overtime, facility layout and equipment used. It is hoped that this framework can achieve the desired goal of minimizing work risk in optimizing the production process of the assembly line