Robotic Assembly Line Balancing Research Articles

A knowledge-guided Estimation of Distribution Algorithm for energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem

The assembly line serves as a fundamental system in discrete production. To address the challenges in balancing robotic assembly lines, timely part feeding, and the need for sustainable manufacturing, this paper studies an energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem (JRALB-FP) with the criteria of minimizing both cycle time and total fuel consumption cost. Considering the complexity of the multi-problem and multi-objective optimization, a knowledge-guided Estimation of Distribution Algorithm (KEDA) is proposed to solve energy-efficient JRALB-FP. First, a probability model of EDA for task-workstation allocation paired with a heuristic method-based sampling mechanism is created. Using this probability model, a specific encoding mechanism is designed for part-trailer allocation, and good initial solutions are produced. Second, several properties of the bi-objective problem are analyzed to guide the design of local search operators for both objectives optimization. Third, the updating mechanism of the probability model is designed to learn from the elite solutions. Fourth, two knowledge-guided local search operators are designed and implemented to exploit better non-dominated solutions sufficiently. A design of experiment is carried out to determine the parameters. Extensive computational tests and comparisons with the state-of-the-art multi-objective algorithms are carried out, which verify the effectiveness of the knowledge-guided local search operators, the problem-oriented heuristic-based sampling mechanism, and the special designs of the KEDA in solving the energy-efficient JRALB-FP.

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.

Robotic stochastic assembly line balancing

Robotic stochastic assembly line balancing

Mathematical model and a variable neighborhood search algorithm for mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times

Mathematical model and a variable neighborhood search algorithm for mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times

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.

Balancing and sequencing problem of mixed-model U-shaped robotic assembly line: Mathematical model and dragonfly algorithm based approach

Robotic assembly line outperforms labor assembly line in terms of efficiency and flexibility. This paper focuses on the mixed-model U-shaped robotic assembly line balancing and sequencing problem (MURALBSP). Different from most reported works, this paper includes the energy consideration. Two conflict objectives, i.e., energy consumption and makespan, are studied for energy saving and efficient production. To this end, a hybrid multi-objective dragonfly algorithm (HMODA) is proposed. First, the mathematical model of this bi-objective problem is formulated. Second, the basic dragonfly algorithm is improved to solve the problem. The specific encoding and decoding method is designed and the chaotic map is used to improve algorithm randomness. Besides, the solution update method is amended and multi-point crossover mechanism is employed. Finally, several multiple size benchmark problems are designed and comparisons are conducted, the sensitivity of decision variables is analyzed to provide managerial insights. The results suggest that HMODA is more efficient in solving the proposed problem than compared algorithms.

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.

Bound-guided hybrid estimation of distribution algorithm for energy-efficient robotic assembly line balancing

Under the pressure of climate change, energy-efficient manufacturing has attracted much attention. Robotic assembly lines are widely-used in automotive and electronic manufacturing. It is necessary to consider the energy saving and economic criteria simultaneously when robots are utilized to operate assembly tasks replacing human labor. This paper addresses an energy-efficient robotic assembly line balancing (EERALB) problem with the criteria to minimize both the cycle time and total energy consumption. We present a multi-objective mathematical model and propose a bound-guided hybrid estimation of distribution algorithm to solve the problem. When designing the optimization algorithm, we adopt estimation of distribution algorithm (EDA) to tackle the task assignment, and design a non-dominated robot allocation (NGRA) heuristic which is embedded into the EDA to allocate suitable robot to each workstation. Moreover, we propose a bound-guided sampling (BGS) method, which is able to reduce the search space of EDA and focus the search on the promising area. The computational complexity of the proposed algorithm is analyzed and the effectiveness of the proposed NGRA and BGS is tested. In addition, we compare the performances of the proposed mathematical model and the proposed algorithm with those of the existing model and algorithms on a set of widely-used benchmark instances. Comparative results demonstrate the effectiveness of the proposed model and 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.

Model and metaheuristics for robotic two-sided assembly line balancing problems with setup times

Two-sided robotic assembly lines are employed to assemble large-sized high-volume products, where robots are allocated to the workstations to perform the tasks and human workers are replaced for achieving lower cost and greater flexibility in production. In the two-sided robotic assembly lines, setup times are unavoidable and it has been ignored in most of the reported works. There has been limited attention on this till date. This paper focusses on the robotic two-sided assembly line with consideration of sequence-dependent setup times and robot setup times. A new mixed integer linear programming model is developed with the objective of optimizing the cycle time. Due to the NP-hard nature of the considered problem, this paper proposes a set of metaheuristics to solve this considered problem, where two main scenarios with low and high setup time’s variability are considered. Computational results verify that this new model is capable to achieve the optimal solutions for small-size instances whereas the simple adoption of the published mathematical model might produce wrong solutions for the considered problem. A comprehensive study with 13 algorithms demonstrates that the two variants of artificial bee colony algorithm and migrating bird optimization algorithm are capable to achieve the optimality for small-size instances and to obtain promising results for large-size instances.

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.

Multi-objective linear mathematical programming for solving U-shaped robotic assembly line balancing

In recent years, robots have been an eminent solution for manufacturers to facilitate their process and focus on a variety of their products. As the importance of robot usages, our paper focuses on the robotics assembly line. In this paper, we have considered the cycle time, robot operational costs, robot purchase costs, and robot energy consumptions. In the following, we add robot failure rates to have an efficient and high-quality assembly line. The presented model is a multi-objective problem, therefore, the linear programming methods as goal programming and augmented e-constraint method are applied to optimize the problem. In the end, we have considered a case study to examine and show the applicability of the proposed model on the real situation.

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.

A novel optimal method of robotic weld assembly line balancing problems with changeover times: a case study

PurposeThe balancing of robotic weld assembly lines has a significant influence on achievable production efficiency. This paper aims to investigate the most suitable way to assign both assembly tasks and type of robots to every workstation, and present an optimal method of robotic weld assembly line balancing (ALB) problems with the additional concern of changeover times. An industrial case of a robotic weld assembly line problem is investigated with an objective of minimizing cycle time of workstations.Design/methodology/approachThis research proposes an optimal method for balancing robotic weld assembly lines. To solve the problem, a low bound of cycle time of workstations is built, and on account of the non-deterministic polynomial-time (NP)-hard nature of ALB problem (ALBP), a genetic algorithm (GA) with the mechanism of simulated annealing (SA), as well as self-adaption procedure, was proposed to overcome the inferior capability of GA in aspect of local search.FindingsTheory analysis and simulation experiments on an industrial case of a car body welding assembly line are conducted in this paper. Satisfactory results show that the performance of GA is enhanced owing to the mechanism of SA, and the proposed method can efficiently solve the real-world size case of robotic weld ALBPs with changeover times.Research limitations/implicationsThe additional consideration of tool changing has very realistic significance in manufacturing. Furthermore, this research work could be modified and applied to other ALBPs, such as worker ALBPs considering tool-changeover times.Originality/valueFor the first time in the robotic weld ALBPs, the fixtures’ (tools’) changeover times are considered. Furthermore, a mathematical model with an objective function of minimizing cycle time of workstations was developed. To solve the proposed problem, a GA with the mechanism of SA was put forth to overcome the inferior capability of GA in the aspect of local search.

Mathematical models and simulated annealing algorithms for the robotic assembly line balancing problem

PurposeRobots are used in assembly lines because of their higher flexibility and lower costs. The purpose of this paper is to develop mathematical models and simulated annealing algorithms to solve the robotic assembly line balancing (RALB-II) to minimize the cycle time.Design/methodology/approachFour mixed-integer linear programming models are developed and encoded in CPLEX solver to find optimal solutions for small-sized problem instances. Two simulated annealing algorithms, original simulated annealing algorithm and restarted simulated annealing (RSA) algorithm, are proposed to tackle large-sized problems. The restart mechanism in the RSA methodology replaces the incumbent temperature with a new temperature. In addition, the proposed methods use iterative mechanisms for updating cycle time and a new objective to select the solution with fewer critical workstations.FindingsThe comparative study among the tested algorithms and other methods adapted verifies the effectiveness of the proposed methods. The results obtained by these algorithms on the benchmark instances show that 23 new upper bounds out of 32 tested cases are achieved. The RSA algorithm ranks first among the algorithms in the number of updated upper bounds.Originality/valueFour models are developed for RALBP-II and their performance is evaluated for the first time. An RSA algorithm is developed to solve RALBP-II, where the restart mechanism is developed to replace the incumbent temperature with a new temperature. The proposed methods also use iterative mechanisms and a new objective to select the solution with fewer critical workstations.

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

• A single product robotic assembly line balancing problem is addressed. • Sequence-dependent robot setup times are considered. • The objective is the minimization of the cycle time. • Metaheuristic algorithms involving migrating birds optimization are proposed. • Comparisons indicate the superiority of the migrating birds optimization. Industries are incorporating robots into assembly lines due to their greater flexibility and reduced costs. Most of the reported studies did not consider scheduling of tasks or the sequence-dependent setup times in an assembly line, which cannot be neglected in a real-world scenario. This paper presents a study on robotic assembly line balancing, with the aim of minimizing cycle time by considering sequence-dependent setup times. A mathematical model for the problem is formulated and CPLEX solver is utilized to solve small-sized problems. A recently developed metaheuristic Migrating Birds Optimization (MBO) algorithm and set of metaheuristics have been implemented to solve the problem. Three different scenarios are tested (with no setup time, and low and high setup times). The comparative experimental study demonstrates that the performance of the MBO algorithm is superior for the tested datasets. The outcomes of this study can help production managers improve their production system in order to perform the assembly tasks with high levels of efficiency and quality.

Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem

Robotics are extensively utilized in modern industry to replace human labor and achieve high automation and flexibility. In order to produce large-size products, two-sided assembly lines are widely applied, where robotics can be employed to operate tasks on workstations. Since the applied traditional optimization methods are limited, the current work presented a new discrete cuckoo search algorithm to solve the two-sided robotic assembly line balancing problem. The original cuckoo search algorithm was modified by employing neighbor operations. Furthermore, a new procedure to generate individuals to replace the abandoned nests was developed to enhance the intensification. Since the considered problem has two subproblems, namely the robot allocation and assembly line balancing, the present work extended the cuckoo search algorithm to cooperative coevolutionary paradigm by dividing the cuckoos into two sub-swarms, each addressing a subproblem. In order to emphasize the exploration, a restart mechanism was employed. The proposed discrete algorithm’s evolution process and convergence were compared with another two popular optimization algorithms, namely the genetic algorithm and particle swarm optimization algorithm. Computational study on the proposed algorithms and other five recent algorithms along with statistical analysis demonstrated that the proposed methods yielded promising results.

Heuristics for solving a multi-model robotic assembly line balancing problem

Topic of balancing assembly lines is of great interest for researchers and industry practitioners due to the significant impact it has on increasing productivity and efficiency of manufacturing systems. Robots are widely applied in manufacturing industries for assembly processes. Wide literature has been reported on balancing of robotic assembly lines with single and mixed models. Researchers have extensively used heuristics and metaheuristics to solve these problems due to their NP-hard nature. However, no work has been reported on how to balance a robotic assembly line with multiple models (MuRALB) with batch production. This problem is highly relevant for large-scale assembly of products found, e.g. the automotive industry. To authors’ knowledge, this is the first attempt to solve this problem. This research proposes a novel heuristic to solve type II MuRALB problem. Type II problem deals with minimizing the cycle time for a fixed set of robots. Heuristic is implemented, and method for scheduling batched production with related setup times for a robotic assembly line is presented, and based on the analysis conducted, advantage of batching is presented. Proposed heuristic is tested on a set of new five datasets, and performance of this heuristic and batching is presented in detail.

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.