A novel tiki-taka algorithm for engineering optimisation

Metaheuristic algorithm inspired by football playing style, tiki-tak

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

An integrated representation scheme for assembly sequence planning and assembly line balancing

In a typical assembly optimisation, Assembly Sequence Planning and Assembly Line Balancing are performed independently. However, competition has compelled the manufacturer to innovate by integrating the optimisation of both problems. To incorporate ASP and ALB optimisations into a single integrated optimisation, a clear prerequisite is the availability of integrated ASP and ALB representation. Although many assembly representation works has been proposed, none of them fully meet the requirements of integrated optimisation because they were developed independently from various needs. In this paper, an integrated representation scheme for ASP and ALB that incorporate essential optimisation information is developed. The proposed representation scheme is built based on assembly tasks and represented using precedence graph and data matrix. The outcome from presented example showed that the information for ASP and ALB optimisation can be integrated and represented using task based precedence graph and data matrix, without discarding important attributes

Comparison of sequential and integrated optimisation approaches for ASP and ALB

Combining Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) is now of increasing interest. The customary approach is the sequential approach, where ASP is optimised before ALB. Recently, interest in the integrated approach has begun to pick up. In an integrated approach, both ASP and ALB are optimised at the same time. Various claims have been made regarding the benefits of integrated optimisation compared with sequential optimisation, such as access to a larger search space that leads to better solution quality, reduced error rate in planning and expedited product time-to-market. These benefits are often cited but no existing work has substantiated the claimed benefits by publishing a quantitative comparison between sequential and integrated approaches. This paper therefore compares the sequential and integrated optimisation approaches for ASP and ALB using 51 test problems. This is done so that the behaviour of each approach in optimising ASP and ALB problems at different difficulty levels can be properly understood. An algorithm named Multi-Objective Discrete Particle Swarm Optimisation (MODPSO) is applied in both approaches. For ASP, the optimisation results indicate that the integrated approach is suitable to be used in small and medium-sized problems, according to the number of non-dominated solution and error ratio indicators. Meanwhile, the sequential approach converges more quickly in large-sized problems. For pure ALB, the integrated approach is preferable in all cases. When both ASP and ALB are considered, the integrated approach is superior to the sequential approach

A review of assembly line balancing optimisation with energy consideration using meta-heuristic algorithms

Energy utilisation is one of the global trending issues. Various approaches have been introduced to minimise energy utilisation especially in the manufacturing sector, which is the largest consumer sector. One of the approaches includes the consideration of energy utilisation in the Assembly Line Balancing (ALB) optimisation. This paper reviews the ALB with energy consideration from 2012 to 2020. The selected articles were limited to problems solved using meta-heuristic algorithms. The review mainly focusses on the soft computing aspect such as problem variant, optimisation objectives, energy modelling and optimisation algorithm for ALB with energy consideration. Based on the review, the ALB with energy consideration was able to reduce energy utilisation up to 11.9%. It was found that the contribution in future ALB with energy research will be human-oriented, either social factor consideration in optimisation or energy utilisation modelling for workers. In addition, the effort to introduce an algorithm with efficient performance must be pursued because ALB problems have become more complicated. The findings from this review could assist future researchers to align their research direction with the observed trend. This paper also provides the research gap and research opportunities in the future

Modelling and Optimization of Energy Efficient Assembly Line Balancing Using Modified Moth Flame Optimizer

Energy utilization is a global issue due to the reduction of fossil resources and also negative environmental effect. The assembly process in the manufacturing sector needs to move to a new dimension by taking into account energy utilization when designing the assembly line. Recently, researchers studied assembly line balancing (ALB) by considering energy utilization. However, the current works were limited to robotic assembly line problem. This work has proposed a model of energy efficient ALB (EE-ALB) and optimize the problem using a new modified moth flame optimizer (MMFO). The MMFO introduces the best flame concept to guide the global search direction. The proposed MMFO is tested by using 34 cases from benchmark problems. The numerical experiment results showed that the proposed MMFO, in general, is able to optimize the EE-ALB problem better compared to five comparison algorithms within reasonable computational time.  Statistical test indicated that the MMFO has a significant performance in 75% of the cases. The proposed model can be a guideline for manufacturer to set up a green assembly line in future

A novel Tiki-Taka algorithm to optimize hybrid flow shop scheduling with energy consumption

Hybrid flow shop scheduling (HFS) has been thoroughly studied due to its significant impact on productivity. Besides the impact on productivity, the abovementioned problem has attracted researchers from different background because of its difficulty in obtaining the most optimum solution. HFS complexity provides good opportunity for researcher to propose an efficient optimization method for the said problem. Recently, research in HFS has moved towards sustainability by considering energy utilization in the study. Consequently, the problem becomes more difficult to be solved via existing approach. This paper modeled and optimized HFS with energy consumption using Tiki-Taka Algorithm (TTA). TTA is a novel algorithm inspired by football playing style that focuses on short passing and player positioning. In different with existing metaheuristics, the TTA collected information from nearby solution and utilized multiple leaders’ concept in the algorithm. The research began with problem modeling, followed by TTA algorithm formulation. A computational experiment is then conducted using benchmark problems. Then, a case study problem is presented to assess the applicability of model and algorithm in real-life problems. The results indicated that the TTA consistently was in the first and second ranks in all benchmark problems. In addition, the case study results confirmed that TTA is able to search the best fitness solution by compromising the makespan and total energy utilization in the production schedule. In future, the potential of TTA will be further investigated for flexible hybrid flow shop scheduling problems

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 the 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

Modeling and optimization of cost-based hybrid flow shop scheduling problem using metaheuristics

The cost-based hybrid flow shop (CHFS) scheduling has been immensely studied due to its huge impact on productivity. For any profit-oriented organization, it is important to optimize total production costs. However, few researchers have studied hybrid flow shops (HFS) with total production cost utilization. This paper aims to develop a computational model and test the exploration capability of metaheuristics algorithms while optimizing the CHFS problem. Carlier and Neron defined three hypothetical benchmark problems for computational experiments. The popular optimization algorithms PSO, GA, and ACO were implemented on the CHFS model with ten optimization runs. The experimental results proven that ACO performed well regarding mean fitness value for all benchmark problems. Besides this, CPU time for PSO was very high compared to other algorithms. In the future, other optimization algorithms will be tested for the CHFS model, such as Teaching Learning Based Optimization (TLBO) and the Crayfish Optimization Algorithm (COA)

Modelling and optimization of energy efficient assemblyline balancing using modified moth flame optimizer

Energy utilization is a global issue due to the reduction of fossil resources and also negative environmental effect. The assembly process in the manufacturing sector needs to move to a new dimension by taking into account energy utilization when designing the assembly line. Recently, researchers studied assembly line balancing (ALB) by considering energy utilization. However, the current works were limited to robotic assembly line problem. This work has proposed a model of energy efficient ALB (EE-ALB) and optimize the problem using a new modified moth flame optimizer (MMFO). The MMFO introduces the best flame concept to guide the global search direction. The proposed MMFO is tested by using 34 cases from benchmark problems. The numerical experiment results showed that the proposed MMFO, in general, is able to optimize the EE-ALB problem better compared to five comparison algorithms within reasonable computational time. Statistical test indicated that the MMFO has a significant performance in 75% of the cases. The proposed model can be a guideline for manufacturer to set up a green assembly line in future