Line Balancing Problems Research Articles

A multiobjective hybrid imperialist competitive algorithm for multirobot cooperative assembly line balancing problems with energy awareness

The environmental and economic pressures caused by energy consumption arouse energy-saving consciousness of the manufacturing industry. In recent years, robots have been extensively used in assembly systems as called robotic assembly lines where the energy consumption is a major expense, particularly in the workstation with the multirobot cooperative assembly of multirobot. To deal with this problem, the paper presents a novel mathematical model with three objectives of minimizing the cycle time, the sum of energy consumption, and the total cost of robots of assembly lines. Due to the nondeterministic polynomial time nature of the considered problem, a multiobjective hybrid imperialist competitive algorithm with nondominated sorting strategy is developed, which uses a representation technique of three-level coding, i.e. the station level, the task level, and the robot level and proposes an original concept of workstation decision assignment matrix to identify the performed tasks by the same type of robots in a workstation. Furthermore, a late-acceptance hill-climbing algorithm is combined into the algorithm to improve the performance of the proposed algorithm. Finally, testing cases are designed to measure the performance of the proposed method by comparing with two other high-performing multiobjective methods. The computational and statistical results show that the proposed multiobjective hybrid imperialist competitive algorithm is conducive to improve the line efficiency, to reduce the sum of energy consumption, and to cut the total cost of robots in an assembly line effectively.

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.

Empirical working time distribution-based line balancing with integrated simulated annealing and dynamic programming

According to the Industry 4.0 paradigms, the balancing of stochastic production lines requires easily implementable, flexible and robust tools for task to workstations assignment. An algorithm that calculates the performance indicators of the production line based on the convolution of the empirical density distribution functions of the working times and applies dynamic programming to assign tasks to the workstations is proposed. The sequence of tasks is optimised by an outer simulated annealing loop that operates on the set of interchangeable task-pairs extracted from the precedence graph of the task-ordering constraints. Eight line-balancing problems were studied and the results by Monte Carlo simulations were validated to demonstrate the applicability of the algorithm. The results confirm that our methodology does not just provide optimal solutions, but it is an excellent tool in terms of the sensitivity analysis of stochastic production lines.

Mixed integer linear programming models for minimizing ergonomic risk dispersion in an assembly line at the Nissan Barcelona factory

We present a variant of the approach to the assembly line balancing problems, with the aim of reducing the ergonomic risk for operators of mixed-model assembly lines (MILP-3). Specifically, the MILP-3 model is focused on minimizing the average range between ergonomic risk values of workstations. Using a case study from Nissan’s plant in Barcelona, not only are the differences between levels of ergonomic risk of stations reduced, but we attempt to reduce the average maximum ergonomic risk of the assembly line. The new model is compared with two others, MILP-1 and MILP-2, which minimize the average maximum ergonomic risk and the average absolute deviation of the risks, respectively.

Resource-constrained assembly line balancing problems with multi-manned workstations

This study proposes a mixed integer programming model for the resource constrained assembly line balancing problem with multi-manned workstations. Resources refer to machines or tools (such as jigs and hand tools) in the production line. The objective is to minimize the number of workstations, operators, and resources to obtain the optimal allocation of tasks, operators, machines, and resources. To improve the solving efficiency, this study develops a hybrid heuristic approach that combines the procedure of building feasible balancing solutions and the genetic algorithm. In the practical case of an automobile factory, the traditional planning method was compared with the proposed approach. Results in terms of solution quality, production efficiency, and allocation configuration are analyzed and discussed. The findings of this study can be used as reference for decision making in the allocation of tasks, workstations, and operators in the manufacturing industry.

Enhanced migrating birds optimization algorithm for U-shaped assembly line balancing problems with workers assignment

U-shaped assembly lines have been popularly adopted in electronics and appliances to improve their flexibility and efficiency. However, most past studies assumed that the processing time of each task is fixed and hence just considered the task allocation but ignored worker assignment. In this paper, the processing time of each task depends on the workers and then the cooperative optimization of task allocation and workers assignment is considered in U-shaped assembly line balancing problems to optimize the cycle time. Later, an enhanced migrating birds optimization algorithm (EMBO) is proposed to solve it. In the EMBO algorithm, since this new problem has two subproblems: task allocation and worker assignment, the prevent work designs two neighborhood structures to improve the leader and following birds. Furthermore, the temperature acceptance criteria, to judge whether the neighbor replaces current following bird, are developed to ensure the diversity of population and avoid being trapped in the local optimum. And a competitive mechanism is introduced to increase the probability of the promising birds locating in the front of the line. The proposed algorithm is compared with other well-known algorithms in the literature, and the numerical results demonstrate that the proposed algorithm outperforms other algorithms.

An enhanced NSGA-II algorithm for fuzzy bi-objective assembly line balancing problems

Abstract Due to immense role of an efficient assembly line in today’s manufacturing systems, the consideration of this paper is devoted to address the straight and U-shaped assembly line balancing problems. Assembly Line Balancing Problem (ALBP) considers conflicting objective functions that should be optimized simultaneously subject to set of constraints. To this end, this paper spends endeavor to develop a new fuzzy linear programming model. Having dealt with uncertain nature of the real production system, triangular fuzzy numbers (TFNs) are employed in order to represent uncertainty and vagueness associated with the task processing times. The proposed model can be regarded as a basis of fuzzy programming for further practical development in assembly line problems. To solve the problem, an efficient Multi Objective Genetic Algorithm (MOGA) is proposed. For this purpose, having respected several special characteristics of both fuzzy straight and U-shaped assembly line balancing problems for algorithm segments, including initial generation, encoding and decoding schemes, and GA’s operations, an new approach is proposed to promote population diversity and search efficiency. Finally, the proposed algorithm is evaluated though several benchmarks as well as that of exact method. The obtained results proved high efficiency of our method over other approaches suggested in the literature.

A novel competitive hybrid approach based on grouping evolution strategy algorithm for solving U-shaped assembly line balancing problems

Assembly line balancing problems (ALBPs) are among the well-known problems in manufacturing systems that belong to NP-hard class of problems. In the literature, there are various metaheuristic methods proposed to solve different models of such a problem under various assumptions. This research considers the U-shaped ALBP and proposes a hybrid solution method based on grouping evolution strategy algorithm. To develop a competitive approach, two most popular constructive methods of solving ALBP including the ranked positional weight method, and COMSOAL algorithm are modified and improved. We investigate the effectiveness of the proposed improvements and evaluate the performance of the proposed approach via solving a number of existing problems in the literature and compare the results with some current methods in the literature. Computational results indicate that the proposed approach for solving U-shaped ALBP test problems performs efficiently and is able to obtain the global optimal solution of the most of high dimensional problems.

Solving the accessibility windows assembly line problem level 1 and variant 1 (AWALBP-L1-1) with precedence constraints

Assembly line balancing problems (ALBPs) are among the most studied combinatorial optimization problems due to their relevance in many production systems. In particular, the accessibility windows ALBP (AWALBP) may arise when the workpieces are larger than the workstations, which implies that at a given instant the workstations have access to only a portion of the workpieces. Thus, the cycle is split into forward steps and stationary stages. The workpieces advance during the forward steps and the tasks are processed during the stationary stages. Several studies have dealt with the AWALBP assuming that there are no precedence relationships between tasks. However, this assumption is not always appropriate. In this work we solve the first level of AWALBP (AWALBP-L1) considering the existence of precedence relationships. Specifically, this work deals with variant 1 (AWALBP-L1-1), in which each task can be performed at only one workstation and, therefore, only the stationary stages and the starting instants in which the tasks are performed have to be decided. We design a solution procedure that includes pre-processing procedures, a matheuristic and a mixed integer linear programming model. An extensive computational experiment is carried out to evaluate its performance.

A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation

The safety hazards existing in the process of disassembling waste products pose potential harms to the physical and mental health of the workers. In this article, these hazards involved in the disassembly operations are evaluated and taken into consideration in a disassembly line balancing problem. A multi-objective mathematical model is constructed to minimise the number of workstations, maximise the smoothing rate and minimise the average maximum hazard involved in the disassembly line. Subsequently, a Pareto firefly algorithm is proposed to solve the problem. The random key encoding method based on the smallest position rule is used to adapt the firefly algorithm to tackle the discrete optimisation problem of the disassembly line balancing. To avoid the search being trapped in a local optimum, a random perturbation strategy based on a swap operation is performed on the non-inferior solutions. The validity of the proposed algorithm is tested by comparing with two other algorithms in the existing literature using a 25-task phone disassembly case. Finally, the proposed algorithm is applied to solve a refrigerator disassembly line problem based on the field investigation and a comparison of the proposed Pareto firefly algorithm with another multi-objective firefly algorithm in the existing literature is performed to further identify the superior performance of the proposed Pareto firefly algorithm, and eight Pareto optimal solutions are obtained for decision makers to make a decision.

Profit-oriented partial disassembly line design: dealing with hazardous parts and task processing times uncertainty

This paper addresses the problem of profit-oriented disassembly line design and balancing considering partial disassembly, presence of hazardous parts and uncertainty of task processing times. Few papers have studied the stochastic disassembly line balancing problem and existing approaches have focused on heuristic and metaheuristic methods. Most existing work has concentrated on complete disassembly where task times are assumed to be normal random variables and where AND/OR graphs are not considered. The objective of this paper is the design of a serial line that obtains the maximum revenue and then balances the workload under uncertainty. The processing time of a disassembly task is assumed to be a random variable with any known probability distribution. An AND/OR graph is used to model the precedence relationships among tasks. Stochastic programming models and exact-based solution approaches combining the L-shaped algorithm and Monte Carlo sampling techniques are proposed. The relevance and applicability of the proposed models and solution methods are shown by solving efficiently a set of disassembly problem instances from the literature.

A differential evolution algorithm for simple straight and U-type assembly line balancing problems

A differential evolution algorithm for simple straight and U-type assembly line balancing problems

A human-machine learning curve for stochastic assembly line balancing problems

The Assembly Line Balancing Problem (ALBP) represents one of the most explored research topics in manufacturing. However, only a few contributions have investigated the effect of the combined abilities of humans and machines in order to reach a balancing solution. It is well-recognized that human beings learn to perform assembly tasks over time, with the effect of reducing the time needed for unitary tasks. This implies a need to re-balance assembly lines periodically, in accordance with the increased level of human experience. However, given an assembly task that is partially performed by automatic equipment, it could be argued that some subtasks are not subject to learning effects. Breaking up assembly tasks into human and automatic subtasks represents the first step towards more sophisticated approaches for ALBP. In this paper, a learning curve is introduced that captures this disaggregation, which is then applied to a stochastic ALBP. Finally, a numerical example is proposed to show how this learning curve affects balancing solutions.

MILP model for integrated line balancing and model sequencing problems for Mixed-Model Two-Sided Assembly Line

This research explores two interrelated problems in Mixed-Model Two-Sided Assembly Line (MMTSAL), which are line balancing and model sequencing. These two problems are solved simultaneously using Mixed Integer Linear Programming (MILP) with the objectives of minimizing total utility work and idle time by considering various practical constraints. The problem is analyzed using small-size to large-sized test cases using General Algebraic Modelling System (GAMS) with the solver CPLEX. Experimental results indicate that integrating the problems help to minimize the proposed objective function. Also, it is found that the feasible solution for model sequence with the assignment of tasks to assembly line is optimal.

Mixed model line balancing with parallel stations, zoning constraints, and ergonomics

Assembly lines are cost efficient production systems that mass produce identical products. Due to customer demand, manufacturers use mixed model assembly lines to produce customized products that are not identical. To stay efficient, management decisions for the line such as number of workers and assembly task assignment to stations need to be optimized to increase throughput and decrease cost. In each station, the work to be done depends on the exact product configuration, and is not consistent across all products. In this paper, we propose a mixed model line balancing integer program (IP) that considers parallel workers, zoning, task assignment, and ergonomic constraints with the objective of minimizing the number of workers. Upon observing the limitation of the IP, a Constraint Programming (CP) model is developed to solve larger assembly line balancing problems. Data from an automotive OEM are used to assess the performance of both the MIP and CP models, including sensitivity analysis to measure the computational cost of enabling the different constraints. To the best of our knowledge, we are the first paper to incorporate the different realistic mixed model assembly line constraints and develop a CP model based on the scheduling module of the IBM ILOG Optimizations Studio. Using the OEM data, we show that the CP model outperforms the IP model for bigger problems.

One- and two-sided assembly line balancing problems with real-world constraints

In this study, we consider balancing problems of one- and two-sided assembly lines with real-world constraints like task or machine incompatibilities. First, we study the one-sided assembly line balancing problem (ALBP) with a limited number of machine types per workstation. Using a genetic algorithm (GA), we find optimal results for real-world instances. A set of larger test cases is used to compare two well-established solution approaches, namely GA and tabu search (TS). Additionally, we apply a specific differential evolution algorithm (DE), which has recently been proposed for the considered ALBP. Our computational results show that DE is clearly dominated by GA. Furthermore, we show that GA outperforms TS in terms of computational time, if capacity constraints are tight. Given the algorithm’s computational performance as well as the fact that it can easily be adapted to additional constraints, we then use it to solve two-sided ALBP. Three types of constraints and two different objectives are considered. We outperform all previously published methods in terms of solution quality and computational time. Finally, we are the first to provide feasible test instances as well as benchmark results for fully constrained two-sided ALB.

A novel discrete particle swarm algorithm for assembly line balancing problems

PurposeThe purpose of this paper is to develop a feasible sequence-oriented new discrete particle swarm optimization (NDPSO) algorithm with novel particles’ updating mechanism for solving simple assembly line balancing problems (SALBPs).Design/methodology/approachIn the NDPSO, a task-oriented representation is adopted to solve type I and type II SALBPs, and a particle directly represents a feasible task sequence (FTS) as a permutation. Then, the particle (permutation) is updated as a whole using the geometric crossover based on the edit distance with swaps for two permutations. Furthermore, the fragment mutation with adaptive mutation probability is incorporated into the NDPSO to improve exploration ability.FindingsCase study illustrates the effectiveness of the NDPSO. Comparative results between the NDPSO and existing real-encoded PSO (CPSO) and direct discrete PSO (DDPSO) against benchmark instances of type I SALBP and type II SALBP show promising higher performance of the proposed NDPSO.Originality/valueA novel particles’ updating mechanism for FTS-encoded particle is proposed to solve the SALBPs. The comparative results indicate that updating of FTS as a whole seems superior to existing updating of FTS by fragment with respect to exploration ability for solving SALBPs. The novel particles’ updating mechanism is also applicable to generalized assembly line balancing problems.

A simulation-based approach for solving assembly line balancing problem

Assembly line balancing problem is directly related to the production efficiency, since the last century, the problem of assembly line balancing was discussed and still a lot of people are studying on this topic. In this paper, the problem of assembly line is studied by establishing the mathematical model and simulation. Firstly, the model of determing the smallest production beat under certain work station number is anysized. Based on this model, the exponential smoothing approach is applied to improve the the algorithm efficiency. After the above basic work, the gas stirling engine assembly line balancing problem is discussed as a case study. Both two algorithms are implemented using the Lingo programming environment and the simulation results demonstrate the validity of the new methods.

An efficient grouping genetic algorithm for U-shaped assembly line balancing problems with maximizing production rate

U-type assembly line is one of the important tools that may increase companies’ production efficiency. In this study, two different modeling approaches proposed for the assembly line balancing problems have been used in modeling type-II U-line balancing problems, and the performances of these models have been compared with each other. It has been shown that using mathematical formulations to solve medium and large size problem instances is impractical since the problem is NP-hard. Therefore, a grouping genetic and simulated annealing algorithms have been developed, and a particle swarm optimization algorithm is adapted to compare with the proposed methods. A special crossover operator that always obtains feasible offspring has been suggested for the proposed grouping genetic algorithm. Furthermore, a local search procedure based on problem-specific knowledge was applied to increase the intensification of the algorithm. A set of well-known benchmark instances was solved to evaluate the effectiveness of the proposed and existing methods. Results showed that while the mathematical formulations can only be used to solve small size instances, metaheuristics can obtain high quality solutions for all size problem instances within acceptable CPU times. Moreover, grouping genetic algorithm has been found to be superior to the other methods according to the number of optimal solutions, or deviations from the lower bound values.

Constraint programming for solving various assembly line balancing problems

In this paper, the constraint programming (CP) approach is applied for the simple assembly line balancing problem (SALBP) as well as some of its generalizations. CP is a rich modeling language that enables the formulation of general combinatorial problems and is coupled with a strong set of solution methods that are available through general purpose solvers. The proposed formulations are conversions of well-known mixed integer programming (MILP) formulations to CP, along with a new set of constraints that helps the CP solver to converge faster. As a generic solution method, we compare its performance with the best known generic MILP formulations and show that it consistently outperforms MILP for medium to large problem instances. A comparison with SALOME, a well-known custom-made algorithm for solving the SALBP-1, shows that both approaches are capable of efficiently solving problems with up to 100 tasks. When 1000-task problems are concerned, SALOME provides better performance; however, CP can provide relatively good close to optimal solutions for multiple combinations of problem parameters. Finally, the generality of the CP approach is demonstrated by some adaptations of the proposed formulation to other variants of the assembly line balancing problem including the U-shaped assembly line balancing problem and the task assignment and equipment selection problem.