Balancing two-sided U-type assembly lines using modified particle swarm optimization algorithm

In this paper, a new two-sided U-type assembly line balancing (TUALB) procedure and a new algorithm based on the particle swarm optimization algorithm to solve the TUALB problem are proposed. The proposed approach minimizes the number of stations for a given cycle time as the primary objective and it minimizes the number of positions as a secondary objective. The proposed approach is illustrated with an example problem. In order to evaluate the efficiency of the proposed algorithm, the test problems available in the literature are used. The experimental results show that the proposed approach performs well.     

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.     

The stochastic U-line balancing problem: A heuristic procedure

Many heuristics have been proposed for the assembly line balancing problem due to its computational complexity and difficulty in identifying an optimal solution. Still, the basic line balancing model fails to consider a number of realistic elements. The implementation of a Just-In-Time manufacturing system generally entails the replacement of traditional straight assembly lines with U-shaped lines. An important issue in the U-line balancing problem is the consideration of task time variability due to human factors or various disruptions. In this paper, we consider the stochastic U-line balancing problem. A hybrid heuristic is presented consisting of an initial feasible solution module and a solution improvement module. To gain insight into its performance, we analyze the heuristic under different scenarios of task time variability. Computational results clearly demonstrate the efficiency and robustness of our algorithm.     

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.     

A shortest route formulation of simple U-type assembly line balancing problem

In this paper, a shortest route formulation of simple U-type assembly line balancing (SULB) problem is presented and illustrated on a numerical example. This model is based on the shortest route model developed in [Manage. Sci. 11 (2) (1964) 308.] for the traditional single model assembly line balancing problem.     

Simple and U-type assembly line balancing problems with a learning effect

In this paper, we introduced learning effect into assembly line balancing problems. In many realistic settings, the produced worker(s) (or machine(s)) develops continuously by repeated the same or similar activities. Therefore, the production time of product shortens if it is processed later. We show that polynomial solutions can be obtained for both simple assembly line balancing problem (SALBP) and U-type line balancing problem (ULBP) with learning effect.     

Robotic disassembly line balancing problem: A mathematical model and ant colony optimization approach

The use of robots is significantly increasing day by day in manufacturing systems, and especially improving the efficiency of the lines. Robots can be used to complete disassembly tasks, and each of the robots can need different operation times to perform the tasks. In this paper, the balancing of the robotic disassembly line problem has been studied to develop efficient solution techniques. Firstly, a mixed-integer linear mathematical model is proposed to determine and solve the problem optimally. A case study from literature is addressed to assess and show the efficiency and effectiveness of the model to minimize cycle time. Secondly, a heuristic algorithm based on ant colony optimization is also proposed to discover a solution for especially the large-size test problems due to the complexity of the problem. The performance of the proposed heuristic algorithm is verified and compared with the different heuristic on data sets. The computational results indicate that the proposed mathematical model and the algorithms are promising for the small and large-size test problems, respectively. Finally, it should be stated that robots have great potential to use in the area of disassembly line and useful solutions provide according to test results.     

Balancing stochastic two-sided assembly lines: A chance-constrained,piecewise-linear,mixed integer program and a simulated annealing algorithm

Two-sided assembly lines are often designed to produce large-sized products, such as automobiles, trucks and buses. In this type of a production line, both left-side and right-side of the line are used in parallel. In all studies on two-sided assembly lines, the task times are assumed to be deterministic. However, in real life applications, especially in manual assembly lines, the tasks may have varying execution times defined as a probability distribution. The task time variation may result from machine breakdowns, loss of motivation, lack of training, non-qualified operators, complex tasks, environment, etc. In this paper, the problem of balancing two-sided assembly lines with stochastic task times (STALBP) is considered. A chance-constrained, piecewise-linear, mixed integer program (CPMIP) is proposed to model and solve the problem. As a solution approach a simulated annealing (SA) algorithm is proposed. To assess the effectiveness of CPMIP and SA algorithm, a set of test problems are solved. Finally, computational results indicating the effectiveness of CPMIP and SA algorithm are reported.     

Balancing assembly lines with tabu search

Balancing assembly lines is a crucial task for manufacturing companies in order to improve productivity and minimize production costs. Despite some progress in exact methods to solve large scale problems, softwares implementing simple heuristics are still the most commonly used tools in industry. Some metaheuristics have also been proposed and shown to improve on classical heuristics but, to our knowledge, no computational experiments have been performed on real industrial applications to clearly assess their performance as well as their flexibility. Here we present a new tabu search algorithm and discuss its differences with respect to those in the literature. We then evaluate its performance on the Type I assembly line balancing problem. Finally, we test our algorithm on a real industrial data set involving 162 tasks, 264 precedence constraints, and where the assembly is carried out on a sequential line with workstations located on both sides of the conveyor, with two possible conveyor heights and no re-positioning of the product. We discuss the flexibility of the metaheuristic and its ability to solve real industrial cases.     

An endosymbiotic evolutionary algorithm for the integration of balancing and sequencing in mixed-model U-lines

This paper proposes a new evolutionary approach to deal with both balancing and sequencing problems in mixed-model U-shaped lines. The use of U-shaped lines is an important element in Just-In-Time production. For an efficient operation of the lines, it is important to have a proper line balancing and model sequencing. A new genetic approach, called endosymbiotic evolutionary algorithm, is proposed to solve the two problems of line balancing and model sequencing at the same time. The algorithm imitates the natural evolution process of endosymbionts that is an extension of existing cooperative or symbiotic evolutionary algorithm. The distinguishing feature of the proposed algorithm is that it maintains endosymbionts that are a combination of an individual and its symbiotic partner. The existence of endosymbionts can accelerate the speed that individuals converge to good solutions. This enhanced capability of exploitation together with the parallel search capability of traditional symbiotic algorithms results in finding better quality solutions than existing hierarchical approaches and symbiotic algorithms. A set of experiments are carried out, and the results are reported.     

A genetic algorithm of determining cycle time for printed circuit board assembly lines

The problem of allocating components to a printed circuit board (PCB) assembly line, which has several non-identical placement machines in series, is formulated as a minimax-type integer programming (IP) model in this paper. In order to achieve the best production throughput, the objective of the model is to minimize the cycle time of the assembly line. The IP model is proven to be NP-complete (non-deterministic polynomial), so a genetic algorithm (GA) is developed for the cycle time determination problem in this paper. Finally, the efficiency of the genetic algorithm is illustrated by a numerical example.     

A branch-and-bound algorithm to minimize the line length of a two-sided assembly line

A new branch-and-bound algorithm is presented to solve the two-sided assembly line balancing problem of type 1 (TALB-1). First, a pair of two directly facing station is defined as a position, and then the two-sided assembly line (TAL) is relaxed to a one-sided assembly line (OAL). Some new lower bound on positions are computed, and dominance rules and reduction rules for the one-sided assembly line balancing problem of type 1 (OALB-1) are extended and incorporated into a station-oriented assignment procedure for the TALB-1 problem. Finally, the tests are carried out on a well-known benchmark set of problem instances, and experimental results demonstrate that the proposed procedure is efficient.     

A survey on problems and methods in generalized assembly line balancing

Assembly lines are traditional and still attractive means of mass and large-scale series production. Since the early times of Henry Ford several developments took place which changed assembly lines from strictly paced and straight single-model lines to more flexible systems including, among others, lines with parallel work stations or tasks, customer-oriented mixed-model and multi-model lines, U-shaped lines as well as unpaced lines with intermediate buffers.In any case, an important decision problem, called assembly line balancing problem, arises and has to be solved when (re-) configuring an assembly line. It consists of distributing the total workload for manufacturing any unit of the product to be assembled among the work stations along the line.Assembly line balancing research has traditionally focused on the simple assembly line balancing problem (SALBP) which has some restricting assumptions. Recently, a lot of research work has been done in order to describe and solve more realistic generalized problems (GALBP). In this paper, we survey the developments in GALBP research.     

Balancing modular transfer lines with serial-parallel activation of spindle heads at stations

The paper deals with an as yet unexplored combinatorial optimization problem concerning balancing complex transfer lines in the machining/process environment. In contrast to similar problems for assembly lines, in transfer line balancing, tasks are grouped into blocks. All tasks of each block are executed simultaneously (in parallel) by one piece of equipment (spindle head). For the transfer lines considered in this paper, spindle heads at each station are activated in serial-parallel order. The set of all available spindle heads is known beforehand. Precedence, cycle time, compatibility, and parallelism constraints for the blocks and tasks are given. The line investment cost is estimated by the sum of block and station costs. The problem is to assign all tasks (using the available blocks) such that all constraints are respected and line investment cost is at a minimum. This paper focuses on solving the problem via a branch-and-bound algorithm. An approach for obtaining an efficient lower bound is offered, based on a reduction of the initial problem to a set partitioning problem. Computational experiments reveal that the proposed approach is efficient mathematically and can be used to solve practical transfer line design problems of a medium size.     

A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop

In this paper a new mixed-integer linear programming (MILP) model is proposed for the multi-processor open shop scheduling (MPOS) problems to minimize the makespan with considering independent setup time and sequence dependent removal time. A hybrid imperialist competitive algorithm (ICA) with genetic algorithm (GA) is presented to solve this problem. The parameters of the proposed algorithm are tuned by response surface methodology (RSM). The performance of the algorithm to solve small, medium and large sized instances of the problem is evaluated by introducing two performance metrics. The quality of obtained solutions is compared with that of the optimal solutions for small sized instances and with the lower bounds for medium sized instances. Also some computational results are presented for large sized instances.     

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

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.     

Ant algorithms for a time and space constrained assembly line balancing problem

The present article focuses on the application of a procedure based on ant colonies to solve an assembly line balancing problem. After an introduction to assembly line problems, the problem under study is presented: the Time and Space constrained Assembly Line Balancing Problem (TSALBP); and a basic model of one of its variants is put forward for study. Subsequently, an ant algorithm is presented that incorporates some ideas that have offered good results with simple balancing problems. Finally, the validity of the proposed algorithms is tested by means of a computational experience with reference instances, and the conclusions of the study are presented.     

Simple heuristics for the assembly line worker assignment and balancing problem

We propose simple heuristics for the assembly line worker assignment and balancing problem. This problem typically occurs in assembly lines in sheltered work centers for the disabled. Different from the well-known simple assembly line balancing problem, the task execution times vary according to the assigned worker. We develop a constructive heuristic framework based on task and worker priority rules defining the order in which the tasks and workers should be assigned to the workstations. We present a number of such rules and compare their performance across three possible uses: as a stand-alone method, as an initial solution generator for meta-heuristics, and as a decoder for a hybrid genetic algorithm. Our results show that the heuristics are fast, they obtain good results as a stand-alone method and are efficient when used as a initial solution generator or as a solution decoder within more elaborate approaches.     

Rules-based heuristic approach for the U-shaped assembly line balancing problem

The type-2 U-shaped assembly line balancing problem is important for many just-in-time manufactures, but an efficient algorithm is not available at present. Thus, in this study, a novel heuristic approach based on multiple rules and an integer programming model is proposed to address this problem. In the proposed approach, three rules are systematically grouped together, i.e., task selection, task assignment, and task exchange rules. The sufficient conditions for implementing the exchange rules are proposed and proved. Thirteen small or medium scale benchmark issues comprising 63 instances were solved, where the computational results demonstrate the efficiency and effectiveness of the proposed method compared with integer programming. The computational results obtained for 18 examples comprising 121 instances demonstrate that the task exchange rules significantly improve the computational accuracy compared with the traditional heuristic. Finally, 30 new standard instances produced by a systematic data generation process were also solved effectively by the proposed approach. The proposed heuristic approach with multiple rules can provide a theoretical basis for other local search algorithms, especially for addressing issues such as the U-Shaped assembly line balancing problem.     

A chance-constrained approach to stochastic line balancing problem

In this paper, chance-constrained 0–1 integer programming models for the stochastic traditional and U-type line balancing (ULB) problem are developed. These models are solved for several test problems that are well known in the literature and the computational results are given. In addition, a goal programming approach is presented in order to increase the system reliability, which is arising from the stochastic case.