Robotic Assembly Line Balancing Problem Research Articles

Robotic assembly line balancing considering the carbon footprint objective with cross-station design

Robotic assembly lines are widely applied in the manufacturing sector to produce a wide range of products because of their efficiency and multifunctionality. The robotic assembly line balancing problem (RALBP) is a combinatorial optimization problem where the decision variables are task assignment and robot allocation. However, RALBP considering carbon footprint, which is a very significant environmental concern, has scarcely been studied in the literature and a practical “cross-station” design is never mathematically formulated. In this paper, a mixed-integer programming model is proposed to optimize the two objectives according to the Pareto principle. A particle swarm algorithm (PSO) with some improvement rules is designed to solve the problem. To examine the efficiency of the algorithm, computational experiments including five medium-sized and five large-sized datasets are conducted. The results show that the efficiency of PSO is better than that of four other classic algorithms in terms of three evaluation metrics. Further, the production manager and assembly line designer can choose the appropriate production plan and upgrade the line configuration.

Towards scalability for resource reconfiguration in robotic assembly line balancing problems using a modified genetic algorithm

AbstractAssembly lines are still one of the most used manufacturing systems in modern-day production. Most research affects the building of new lines and, less frequently, the reconfiguration of existing lines. However, the first is insufficient to meet the reconfigurable production paradigm required by volatile market demands. Consequent reconfiguration of resources by production requests affects companies’ competitiveness. This paper introduces a problem-specific genetic algorithm for optimizing the reconfiguration of a Robotic Assembly Line Balancing Problem with Task Types, including additional company constraints. First, we present the greenfield and brownfield optimization objectives, then a mathematical problem formulation and the composition of the genetic algorithm. We evaluate our model against an Integer Programming baseline on a reconfiguration dataset with multiple equipment alternatives. The results demonstrate the capabilities of the genetic algorithm for the greenfield case and showcase the possibilities in the brownfield case. With a scalability improvement through computation time decrease of up to $$\sim $$ ∼ 2.75$$\times $$ × , reduced number of equipment and workstations, but worse objective values, the genetic algorithm holds the potential for reconfiguring assembly lines. However, the genetic algorithm has to be further optimized for the reconfiguration to leverage its full potential.

Hybrid particle swarm optimization algorithms for cost-oriented robotic assembly line balancing problems

Purpose The cost-oriented robotic assembly line balancing problem (cRALBP) has practical importance in real-life manufacturing scenarios. However, only a few studies tackle the cRALBP using exact methods or metaheuristics. This paper aims to propose a hybrid particle swarm optimization (PSO) combined with dynamic programming (DPPSO) to solve cRALBP type-I. Design/methodology/approach Two different encoding schemes are presented for comparison. In the frequently used Scheme 1, a full encoding of task permutations and robot allocations is adopted, and a relatively large search space is generated. DPSO1 and DPSO2 with the full encoding scheme are developed. To reduce the search space and concern promising solution regions, in Scheme 2, only task permutations are encoded, and DP is used to obtain the optimal robot sequence for a given task permutation in a polynomial time. DPPSO is proposed. Findings A set of instances is generated, and the numerical experiments indicate that DPPSO achieves a tradeoff between solution quality and computation time and outperforms existing algorithms in solution quality. Originality/value The contributions of this paper are three aspects. First, two different schemes of encoding are presented, and three PSO algorithms are developed for the purpose of comparison. Second, a novel updating mechanism of discrete PSO is adjusted to generate feasible task permutations for cRALBP. Finally, a set of instances is generated based on two cost parameters, then the performances of algorithms are systematically compared.

Investigating two variants of the sequence-dependent robotic assembly line balancing problem by means of a split-based approach

The Robotic Assembly Line Balancing Problem (RALBP) is a joint optimisation problem that is concerned with assigning both assembly operations and robots to workstations that are placed within a straight line. RALBP-2 is the particular problem where the cycle time, which is the maximum time spent on a workstation by the product being assembled, is minimised while the number of workstations is fixed. Sequence-dependent setup times are considered which raises the problem of sequencing the operations assigned to each workstation. Both the durations of the operations and the setup times depend on the robot. Two different variants are identified from literature. The first variant assumes that, given a set of types of robots, each type of robot can be assigned to multiple workstations without any limitation. Given a set of robots, the second variant forces each robot to be assigned to at most one workstation. Both assumptions are studied in this paper. The particular case of a given giant sequence of operations is solved thanks to a polynomial optimal algorithm. The latter algorithm, called split, is then embedded in a metaheuristic framework that explores the space of giant sequences. Benchmark data sets from literature are considered in the experimental section. A comparative study with other methods from literature shows the competitiveness of the suggested approach.

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

Robotic assembly lines are widely applied to process workflow, increase capacity, and easily produce a wide range of products because of their flexibility and multifunctionality. Robotic assembly line balancing problem (RALBP) refers to allocating the tasks and robots to workstations in order to maximize the line efficiency, which has become a popular research direction recently. In this paper, we propose a mixed-integer programming model to optimize the carbon footprint of the RALBP. Minimizing carbon footprint can reduce global greenhouse effect and atmospheric dust pollution, which is a very significant environmental topic in the world. We also investigate a realistic production process design in our model—cross-station task. In this design, a task can be operated by the station to which it is assigned as well as the adjacent stations. The model is solved by a commercial solver (Gurobi) and some results are presented.

A comprehensive review of robotic assembly line balancing problem

The research on the robotic assembly line balancing problem (RALBP) was originated for the first time nearly three decades ago. This problem is under the umbrella of the assembly line balancing problem in which robots and automated equipment are employed to take on human workers’ roles to form a flexible assembly line. In this review paper, the development and generalisation throughout the time of the RALBP are addressed. To make the review easy to comprehend and effective, the RALBP is first classified based on the types of layouts and then further dividing up according to the 4 M (Man, Machine, Material and Method) concept. The main contributions of different articles are chronologically summarised in the form of a table. Besides, the research contribution precedence diagram is used to illustrate the sequential order and linkage relationship among researches. Finally, from the findings of the review, future research directions are pinpointed and discussed.

An estimation of distribution algorithm with branch-and-bound based knowledge for robotic assembly line balancing

Robotic assembly lines are widely used in manufacturing industries. The robotic assembly line balancing (RALB) problem aims to balance the workloads among different workstations and optimize the assembly line efficiency. This paper addresses a particular type of RALB problem, which minimizes the assembly line cycle time by determining the task and robot assignment in each workstation under precedence constraints. To solve the problem, we present an effective hybrid algorithm fusing the estimation of distribution algorithm and branch-and-bound (B&B) based knowledge. A problem-specific probability model is designed to describe the probabilities of each task being assigned to different workstations. Based on the probability model, an incremental learning method is developed and a sampling mechanism with B&B based knowledge is proposed to generate new feasible solutions. The fuse of B&B based knowledge is able to reduce the search space of EDA while focusing the search on the promising area. To enhance the exploitation ability, a problem-specific local search is developed based on the critical workstation to further improve the quality of elite solutions. The computational complexity of the proposed algorithm is analyzed, and the effectiveness of the B&B based knowledge and the problem-specific local search is demonstrated through numerical experiments. Moreover, the performance of the proposed algorithm is compared with existing algorithms on a set of widely-used benchmark instances. Comparative results demonstrate the effectiveness and efficiency of the proposed algorithm.

Decomposition-based bi-objective optimization for sustainable robotic assembly line balancing problems

Due to the increasing greenhouse gas emissions and the energy crisis, the manufacturing industry which is one of the most energy intensive sector is paying close attention to the improvement of environmental performance efficiency. Therefore, in this paper the automated assembly line is balanced in a sustainable way which aims to optimize a green manufacturing objective (the total energy consumption) and a productivity-related objective (similar working load) simultaneously. A comprehensive total energy consumption of each processing stage was analyzed and modeled. To make the model more practical, a sequence-based changeover time and robots with different efficiencies and energy consuming rates are considered and optimized. To properly solve the problem, the proposed novel optimal solution takes the well-known MOEA/D as a base and incorporates a well-designed coding scheme and a problem-specific local search mechanism. Computational experiments are conducted to evaluated each improving strategies of the algorithm and its superiority over two other high-performing multi-objective optimization methods. The model allows decision makers to select more sustainable assembly operations based on their decision impacts in both productivity and energy-saving.

An improved immune clonal selection algorithm for bi-objective robotic assemble line balancing problems considering time and space constraints

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.

Modelling and optimisation of energy-efficient U-shaped robotic assembly line balancing problems

Within U-shaped assembly lines, the increase of labour costs and subsequent utilisation of robots has led to growing energy consumption, which is the current main expense of auto and electronics industries. However, there are limited researches concerning both energy consumption reduction and productivity improvement on U-shaped robotic assembly lines. This paper first develops a nonlinear multi-objective mixed-integer programming model, reformulates it into a linear form by linearising the multiplication of two binary variables, and then refines the weight of multiple objectives so as to achieve a better approximation of true Pareto frontiers. In addition, Pareto artificial bee colony algorithm (PABC) is extended to tackle this new complex problem. This algorithm stores all the non-dominated solutions into a permanent archive set to keep all the good genes, and selects one solution from this set to overcome the strong local minima. Comparative experiments based on a set of newly generated benchmarks verify the superiority of the proposed PABC over four multi-objective algorithms in terms of generation distance, maximum spread, hypervolume ratio and the ratio of non-dominated solution.

Exact and heuristic methods for solving the Robotic Assembly Line Balancing Problem

In robotic assembly lines, the task times depend on the robots assigned to each station. Robots are considered an unlimited resource and multiple robots of the same type can be assigned to different stations. Thus, the Robotic Assembly Line Balancing Problem (RALBP) consists of assigning a set of tasks and a type of robot to each station, subject to precedence constraints between the tasks. This paper proposes a lower bound, and exact and heuristic algorithms for the RALBP. The lower bound uses chain decomposition to explore the graph dependencies. The exact approaches include a novel linear mixed-integer programming model and a branch-bound-and-remember algorithm with problem-specific dominance rules. The heuristic solution is an iterative beam search with the same rules. To fully explore the different characteristics of the problem, we also propose a new set of instances. The methods and algorithms are extensively tested in computational experiments showing that they are competitive with the current state of the art.

Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem

Industries utilize two-sided assembly lines for producing large-sized volume products such as cars and trucks. By employing robots, industries achieve a high level of automation in the assembly process. Robots help to replace human labor and execute tasks efficiently at each workstation in the assembly line. From the literature, it is concluded that not much work has been conducted on two two-sided robotic assembly line balancing problems. This article addresses the two-sided robotic assembly line balancing problem with the objective of minimizing the cycle time. A mixed-integer programming model of the proposed problem is developed which is solved by the CPLEX solver for small-sized problems. Due to the problems in non-polynomial--hard nature, a co-evolutionary particle swarm optimization algorithm is developed to solve it. The co-evolutionary particle swarm optimization utilizes local search on the global best individual to enhance intensification, modification of global best to emphasize exploration, and restart mechanism to escape from local optima. The performances of the proposed co-evolutionary particle swarm optimization are evaluated on the modified seven well-known two-sided assembly line balancing problems available in the literature. The proposed algorithm is compared with five other well-known metaheuristics, and computational and statistical results demonstrate that the proposed co-evolutionary particle swarm optimization outperforms most of the other metaheuristics for majority of the problems considered in the study.

Differential evolution algorithm for solving RALB problem using cost- and time-based models

Assembly process is one of the important aspects in manufacturing industries. Industries are extensively using advanced technologies in assembly lines recently such as robots instead of human labor. Cost associated with human labor such as wages, training, safety, and employee management are eliminated with the help of robots. Investments on assembly lines are cost intensive, and industries continuously need to maximize their utilization. In this paper, a cost-based robotic assembly line balancing (RALB) problem with an objective of minimizing assembly line cost and cycle time is addressed. Moreover, there is no research reported on concurrently optimizing cycle time and assembly line cost for a robotic assembly line system to date. The objective of this paper is to propose models with dual focus on time and cost to minimize the cycle time and total assembly line cost simultaneously. Time-based model with the primary focus to optimize cycle time and the cost-based model with the primary focus to optimize total assembly line cost are developed. Due to NP-hard nature, differential evolution (DE) is the algorithm used to solve the RALB problem. Straight and U-shaped robotic assembly line problems are solved using the proposed algorithm, and the detailed comparisons of the results obtained are presented. While comparing straight and U-shaped RALB problems, assembly line cost and cycle time obtained by U-shaped RALB problems are better than the straight RALB problems. The proposed models have significant managerial implications, and these have been discussed in detail.

Bio-inspired search algorithms to solve robotic assembly line balancing problems

Robots are employed in assembly lines to increase the productivity. The objective of robotic assembly line balancing (rALB) problem is to balance the assembly line, by allocating equal amount of tasks to the workstations on the line while assigning the most efficient robot to perform the assembly task at the workstation. In this paper, bio-inspired search algorithms, viz. particle swarm optimization (PSO) algorithm and a hybrid cuckoo search and particle swarm optimization (CS-PSO), are proposed to balance the robotic assembly line with the objective of minimizing the cycle time. The performance of the proposed PSO and hybrid CS-PSO is evaluated using the 32 benchmark problems available in the literature. The simulation results show that both PSO and hybrid CS-PSO are capable of providing solutions within the upper bound obtained by hybrid GA, the only metaheuristic reported so far for rALB in the literature and comparable to the solutions obtained by IBM CPLEX Optimization solver. It is also observed that hybrid CS-PSO is performing better than PSO in terms of cycle time.

An efficient approach for type II robotic assembly line balancing problems

In the past decades, robots have been extensively applied in assembly systems as called robotic assembly lines. When changes in the production process of a product take place, the line needs to be reconfigured in order to improve its productivity. This study presents a type II robotic assembly line balancing (rALB-II) problem, in which the assembly tasks have to be assigned to workstations, and each workstation needs to select one of the available robots to process the assigned tasks with the objective of minimum cycle time. An innovative genetic algorithm (GA) hybridized with local search is proposed for the problem. The genetic algorithm uses a partial representation technique, where only part of the decision information about a candidate solution is expressed in the chromosome and the rest is computed via a heuristic method. Based on different neighborhood structures, five local search procedures are developed to enhance the search ability of GA. The coordination between these procedures is well considered in order to escape from local optima and to reduce computation time. The performance of the hybrid genetic algorithm (hGA) is tested on 32 rALB-II problems and the obtained results are compared with those by other methods.

A genetic algorithm for robotic assembly line balancing

Flexibility and automation in assembly lines can be achieved by the use of robots. The robotic assembly line balancing (RALB) problem is defined for robotic assembly line, where different robots may be assigned to the assembly tasks, and each robot needs different assembly times to perform a given task, because of its capabilities and specialization. The solution to the RALB problem includes an attempt for optimal assignment of robots to line stations and a balanced distribution of work between different stations. It aims at maximizing the production rate of the line. A genetic algorithm (GA) is used to find a solution to this problem. Two different procedures for adapting the GA to the RALB problem, by assigning robots with different capabilities to workstations are introduced: a recursive assignment procedure and a consecutive assignment procedure. The results of the GA are improved by a local optimization (hill climbing) work-piece exchange procedure. Tests conducted on a set of randomly generated problems, show that the Consecutive Assignment procedure achieves, in general, better solution quality (measured by average cycle time). Further tests are conducted to determine the best combination of parameters for the GA procedure. Comparison of the GA algorithm results with a truncated Branch and Bound algorithm for the RALB problem, demonstrates that the GA gives consistently better results.

A GENETIC ALGORITHM FOR ROBOTIC ASSEMBLY LINE BALANCING

Robotic assembly line balancing (RALB) is specific for robotic assembly, where different robots require different assembly times to perform the same task because of their specialization. The problem includes optimal assignment of robots to line stations and balanced distribution of work between different stations. It aims at maximizing the production rate of the line. This problem is solved by a genetic algorithm (GA). Different procedures for adapting the GA to the RALB problem are introduced, including a local optimization (hill climbing) work-piece exchange procedure. The best combination of these procedures is reached by testing on a set of randomly generated problems.