Cost-efficient design and optimization of robotic assembly lines using a non-dominated sorting genetic algorithm framework

Manufacturing industries these days are more concerned about the energy consumption due to the increasing energy cost. It is observed that industries can achieve significant savings in the energy cost even when a small percentage of energy consumption is reduced. In a manufacturing industry, assembly lines are equipped with robots to perform the assembly tasks and there is a need for efficiently balancing the assembly line by reducing the energy consumption during the production phase. The authors could not find any research on optimizing energy consumption in U-shaped robotic assembly line systems to date. The objective of this paper is to propose an optimization models with an objective of minimizing the energy consumption in a U-shaped robotic assembly line when the number of workstations are fixed. Since the assembly line balancing problems falls under the category of NP-hard, an efficient bio inspired algorithm is implemented to solve the problem. The model proposed is very well applicable for automobile assembly line where robots are employed. The energy consumption is evaluated on the benchmark data and energy consumption obtained by the U-shaped robotic assembly line is compared with the energy consumption obtained with straight robotic assembly line. The cycle time of the layouts are also compared. The results obtained using this proposed model can be used for the decision making process in the industry. The paper also proposes an optimization model for minimizing the cycle time. The energy consumption of the assembly line when the cycle time is minimized is evaluated and presented.

PurposeThe extensive applications of the industrial robots have made the optimization of assembly lines more complicated. The purpose of this paper is to develop a balancing method of both workstation time and station area to improve the efficiency and productivity of the robotic assembly lines. A tradeoff was made between two conflicting objective functions, minimizing the number of workstations and minimizing the area of each workstation.Design/methodology/approachThis research proposes an optimal method for balancing robotic assembly lines with space consideration and reducing robot changeover and area for tools and fixtures to further minimize assembly line area and cycle time. Due to the NP-hard nature of the considered problem, an improved multi-objective immune clonal selection algorithm is proposed to solve this constrained multi-objective optimization problem, and a special coding scheme is designed for the problem. To enhance the performance of the algorithm, several strategies including elite strategy and global search are introduced.FindingsA set of instances of different problem scales are optimized and the results are compared with two other high-performing multi-objective algorithms to evaluate the efficiency and superiority of the proposed algorithm. It is found that the proposed method can efficiently solve the real-world size case of time and space robotic assembly line balancing problems.Originality/valueFor the first time in the robotic assembly line balancing problems, an assignment-based tool area and a sequence-based changeover time are took into consideration. Furthermore, a mathematical model with bi-objective functions of minimizing the number of workstations and area of each station was developed. To solve the proposed problem, an improved multi-objective immune clonal selection algorithm was proposed and a special coding scheme is designed.

Metaheuristic algorithms for balancing robotic assembly lines with sequence-dependent robot setup times

Solving the energy-efficient Robotic Mixed-Model Assembly Line balancing problem using a Memory-Based Cuckoo Search Algorithm

In this paper, type II robotic mixed-model two-sided assembly line balancing (RMTALB-II) problem is considered. In recent years, robots have been widely used in assembly systems as called robotic assembly lines where a set of tasks have to be assigned to stations and each station needs to select one of the different robots to process the assigned tasks. Two-sided assembly lines are especially used to produce large-sized high-volume products, such as automobiles, trucks, and buses. In this type of production line, both left side and right side of the line are used in parallel. However, little attention has been paid to solve RMTALB problems. Moreover, according to our best knowledge, there is no published work in the literature on RMTALB. This paper presents a new mixed-integer programming model for RMTALB-II to minimize the cycle time for a given number of mated stations. Since RMTALB problems are in NP-hard class of combinatorial optimization problems, a simulated annealing (SA) algorithm as metaheuristic method is proposed to solve the problem. Some problems are solved by SA and performance of the SA is evaluated by comparing their results with the optimal solution. The computational results show that SA is more efficient.

A beam search approach for solving type II robotic parallel assembly line balancing problem

Robotic assembly lines serve as a foundational element of modern manufacturing, facilitating the efficient production of high-quality goods. Reducing the energy consumption of robots in these assembly lines is essential to promoting greener manufacturing practices, lowering costs, and achieving global energy efficiency goals. This study seeks to create a model that optimizes robotic assembly line systems by minimizing cycle time and energy consumption, either independently or simultaneously. The research assumes an unlimited supply of various robot types, each with distinct variants, processing times, and energy demands for specific tasks. The problem is modeled using Integer Linear Programming (ILP) in the LINGO (21) solver. For multi-objective scenarios involving both cycle time and energy consumption, a weighted sum approach is applied to convert the problem into a single-objective format. To tackle large-scale problems more effectively, several concepts and rules are proposed to accelerate data processing. The results demonstrated improved performance compared to benchmark problems. The analysis indicated that reducing cycle time contributes to lower energy consumption, driven by an increase in the number of stations and robots. Additionally, the Pareto front analysis of cycle time and energy consumption revealed that energy usage remains nearly constant across a wide range of cycle times.

The construction of man-made artifacts such as cell phones and computers relies on robotic assembly lines that place, package, and interconnect a variety of devices that have macroscopic (> 1 mm) dimensions. The key to the realization of these systems is our ability to integrate/assemble components in 2D/3D as well as link/interconnect the components to transport materials, energy, and information. The majority of these systems that are on the market today are heterogeneous in nature. Heterogeneous systems can be characterized as systems that contain at least two separate parts that prohibit monolithic integration. Such systems are typically fabricated using robotic pick and place. The size of the existing systems could be reduced by orders of magnitudes if microscopic building blocks could be assembled and interconnected effectively. The difficulty is not the fabrication of smaller parts but the assembly and formation of interconnects. For components with dimensions less than 100 lm, adhesive capillary forces often dominate gravitational forces, making it difficult to release the components from a robotic manipulator. Micromanipulator-based assembly and wafer-to-wafer transfer methods work poorly on non-planar surfaces, in cavities, and in the fabrication of 3D systems. Serial processes, in general, are slow. Conventional robotic methods and assembly lines are challenged because of the difficulty in building machines that can economically manipulate parts in three dimensions that are only micrometers in size. At another extreme, nature forms materials, structures, and living systems by self-assembly on a molecular length scale. As a result, self-assembly-based fabrication strategies are widely recognized as inevitable tools in nanotechnology and an increasing number of studies are being carried out to “scale up” these concepts to close the assembly gap between nanoscopic and macroscopic systems. Recent demonstrations of processes that can assemble micrometerto millimeter-sized components include: shape-directed fluidic methods that position electronic devices on planar surfaces using shape recognition and gravitational forces, liquid-solder-based self-assemblies that use the surface tension between pairs of molten solder drops to assemble functional systems, capillaryforce-directed self-assembly that uses hydrophilic/hydrophobic surface patterns and photocurable polymers to integrate micro-optical components, micromirrors, and semiconductor chips on silicon substrates, and shape-and-solder-directed self-assembly that combines geometrical shape recognition with site-specific binding involving liquid solder to assemble and package heterogeneous microsystems. [15–17] While all of these methods share the common feature of providing parallel assembly on a large scale, process parameters and design rules remain not well characterized. Another great challenge is the realization of heterogeneous systems that contain many different parts and enable contact-pad registration. While current methods allow the positioning of a large number of identical components in a massive parallel manner, systems that consist of more than one repeating unit are difficult to build. Recent studies to overcome this problem include the activation of selected receptors to enable batch transfer on desired locations, and a sequential self-assembly process that uses geometrical shape recognition for component registration and surface tension, involving liquid solder to form mechanical and electrical connections. In both cases, batches of components are added sequentially to build the system, as opposed to adding all components at the same time. In this communication we present a study on controlling angular orientation and contact-pad registration during the selfassembly process. The presented process can be seen as an extension of prior work in the area of surface-tension-directed self-assembly involving liquid solder to form interconnects and is compatible with these processes. Angular-orientation control is important because dies, packaging, or optical elements need to be placed on a substrate with correct angular orientation to enable contact-pad registration or device operation. Angular-orientation control has been challenging in self-assembly. For example, Srinivasan et al. assembled silicon components and micromirrors onto a goldcoated silicon substrate using hydrophilic/hydrophobic interactions and a non-conducting adhesive lubrication layer. The assembly of 98 parts, 500 lm in size, was accomplished with 0.3° rotational precision. However, due to the squareshaped binding and receptor sites four stable angular orientations, 0, 90, 180, and 270°, were observed. While Liang et al. C O M M U N IC A IO N S

Mixed-integer programming for robotic assembly line balancing considering cross-station task and carbon footprint

Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm

Modern assembly line systems utilize robotics to replace human resources to achieve higher level of automation and flexibility. This work studies the task assignment and robot allocation in a robotic U-shaped assembly line. Two new mixed-integer programming linear models are developed to minimize the cycle time when the number of workstations is fixed. Recently developed migrating birds optimization algorithm is employed and improved to solve large-sized problems. Problem-specific improvements are also developed to enhance the proposed algorithm including modified consecutive assignment procedure for robot allocation, iterative mechanism for cycle time update, new population update mechanism and diversity controlling mechanism. An extensive comparative study is carried out to test the performance of the proposed algorithm, where seven high-performing algorithms recently reported in the literature are re-implemented to tackle the considered problem. The computational results demonstrate that the developed models are capable to achieve the optimal solutions for small-sized problems, and the proposed algorithm with these proposed improvements achieves excellent performance and outperforms the compared ones.

PurposeCycle time fluctuations in assembly lines are one of the important reasons of re‐balancing. As a result of re‐balancing of assembly lines, it will be necessary to change task sequences or equipment locations. The purpose of this paper is to find the task sequence which enables assembly line balancing (ALB) with minimum number of stations (NS) for different cycle times such that tasks and equipment or fixture locations remain unchanged.Design/methodology/approachIn this paper a heuristic which consist of two stages is proposed to find a common task sequence for different cycle times in assembly lines.FindingsIt is shown that optimal NS for different cycle times can be achieved with a fixed task sequence.Research limitations/implicationsThe approach is limited to a single model case. Model variety together with cycle time variety can be investigated in further studies.Practical implicationsAssembly lines which require less time and cost for re‐balancing can be easily designed by the proposed approach.Originality/valueALB problem is handled with a new viewpoint. Also, it is observed that the proposed approach serves as a bridge between assembly line design and balancing. In this regard, it is thought to have an important place in the ALB literature.

Configuration design of reconfigurable single-product robotic assembly line for capacity scalability

Design of assembly lines is a knowledge intensive process relying significantly on experience and associated with high costs, long lead times and high probability of risks and reworks. This paper presents a methodology to support the early stage design of assembly lines through Knowledge Based Engineering (KBE). In this introductory paper the authors discuss the methodology to implement the KBE approach. A set of system engineering rules is extracted from direct interviews and domain best practices and knowledge about product and process is formalized. These rules will be implemented in a user-friendly platform allowing the design of first phase line layout by taking the defined system requirements (e.g. cycle time) as input. Then, the KBE approach is extended to a specific case study taken from the powertrain sector.

An efficient approach for type II robotic assembly line balancing problems