Simple Assembly Line Balancing Research Articles

An artificial bee colony based-hyper heuristic algorithm with local search for the assembly line balancing problems

PurposeAssembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.Design/methodology/approachThis paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.FindingsThe experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.Originality/valueThis research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.

Assessing by Simulation the Effect of Process Variability in the SALB-1 Problem

The simple assembly line balancing (SALB) problem is a significant challenge faced by industries across various sectors aiming to optimise production line efficiency and resource allocation. One important issue when the decision-maker balances a line is how to keep the cycle time under a given time across all cells, even though there is variability in some parameters. When there are stochastic elements, some approaches use constraint relaxation, intervals for the stochastic parameters, and fuzzy numbers. In this paper, a three-part algorithm is proposed that first solves the balancing problem without considering stochastic parameters; then, using simulation, it measures the effect of some parameters (in this case, the inter-arrival time, processing times, speed of the material handling system which is manually performed by the workers in the cell, and the number of workers who perform the tasks on the machines); finally, the add-on OptQuest in SIMIO solves an optimisation problem to constrain the cycle time using the stochastic parameters as decision variables. A Gearbox instance from literature is solved with 15 tasks and 14 precedence rules to test the proposed approach. The deterministic balancing problem is solved optimally using the open solver GLPK and the Pyomo programming language, and, with simulation, the proposed algorithm keeps the cycle time less than or equal to 70 s in the presence of variability and deterministic inter-arrival time. Meanwhile, with stochastic inter-arrival time, the maximum cell cycle is 72.04 s. The reader can download the source code and the simulation models from the GitHub page of the authors.

Integrating real-time manufacturing data into a novel serial two-stage adaptive alternate genetic fireworks algorithm for solving stochastic type-II simple assembly line balancing problem

Due to various interference factors, a pre-planned assembly scheme and its cycle time can be disturbed, resulting in the failure of product delivery on schedule. However, when assembly data of the production line can be obtained in real time, the balance of the assembly line could be dynamically adjusted in case of experiencing serious interferences to optimize its cycle time in time and improve its production efficiency. Therefore, this paper proposes a dynamic rebalancing framework by integrating real time manufacturing data into a novel serial two-stage adaptive alternate genetic fireworks algorithm for solving a stochastic type-II simple assembly line balancing problem (SSALBP-II). MES (manufacturing execution system) is used to obtain some real time data such as the resource status, operation information and task information and to judge abnormal phenomenon and the overdue delivery caused by interferences. On this basis, the stochastic type-II simple assembly line balance model is constructed, with a new serial two-stage adaptive alternate genetic fireworks algorithm (STAGFA). This new algorithm can incorporate both genetic algorithm and fireworks algorithm to solve the model according to the transformation of population diversity discrimination index. Through the comparison between STAGFA and other algorithms such as fireworks algorithm, genetic algorithm, other improved intelligent algorithms, it is proved that the STAGFA is effective and superior in solving the assembly line (re)balance problem. Then, the rebalanced scheme verified by simulation is dispatched to control the physical assembly line and realize dynamic rebalancing cycle time effectively.

A new multiobjective tiki-taka algorithm for optimization of assembly line balancing

PurposeThis study aims to propose a new multiobjective optimization metaheuristic based on the tiki-taka algorithm (TTA). The proposed multiobjective TTA (MOTTA) was implemented for a simple assembly line balancing type E (SALB-E), which aimed to minimize the cycle time and workstation number simultaneously.Design/methodology/approachTTA is a new metaheuristic inspired by the tiki-taka playing style in a football match. The TTA is previously designed for a single-objective optimization, but this study extends TTA into a multiobjective optimization. The MOTTA mimics the short passing and player movement in tiki-taka to control the game. The algorithm also utilizes unsuccessful ball pass and multiple key players to enhance the exploration. MOTTA was tested against popular CEC09 benchmark functions.FindingsThe computational experiments indicated that MOTTA had better results in 82% of the cases from the CEC09 benchmark functions. In addition, MOTTA successfully found 83.3% of the Pareto optimal solution in the SALB-E optimization and showed tremendous performance in the spread and distribution indicators, which were associated with the multiple key players in the algorithm.Originality/valueMOTTA exploits the information from all players to move to a new position. The algorithm makes all solution candidates have contributions to the algorithm convergence.

Assembly and Production Line Designing, Balancing and Scheduling with Inaccurate Data: A Survey and Perspectives

Assembly lines (conveyors) are traditional means of large-scale and mass-scale productions. An assembly line balancing problem is needed for optimizing the assembly process by configuring and designing an assembly line for the same or similar types of final products. This problem consists of designing the assembly line and distributing the total workload for manufacturing each unit of the fixed product to be assembled among the ordered workstations along the constructed assembly line. The assembly line balancing research is focused mainly on simple assembly line balancing problems, which are restricted by a set of conditions making a considered assembly line ideal for research. A lot of published research has been carried out in order to describe and solve (usually heuristically) more realistic generalized assembly line balancing problems. Assembly line designing, balancing and scheduling problems with not deterministic (stochastic, fuzzy or uncertain) parameters have been investigated in many published research works. This paper is about the design and optimization methods for assembly and disassembly lines. We survey the recent developments for designing, balancing and scheduling assembly (disassembly) lines. New formulations of simple assembly line balancing problems are presented in order to take into account modifications and uncertainties characterized by real assembly productions.

Stability factor for robust balancing of simple assembly lines under uncertainty

This paper deals with an optimization problem, which arises when a new simple assembly line has to be designed subject to a fixed number of available workstations, cycle time constraint, and precedence relations between necessary assembly tasks. The studied problem consists in assigning a given set of tasks to workstations so as to find the most robust line configuration, which can withstand processing time uncertainty as much as possible. The line robustness is measured by a new indicator, called stability factor . In this work, the studied problem is proven to be strongly NP-hard, upper bounds are proposed, and the relation of the stability factor with another robustness indicator, known as stability radius , is investigated. A mixed-integer linear program (MILP) is proposed for maximizing the stability factor in the general case, and an alternative formulation is also derived when uncertainty originates in workstations only. Computational results are reported on a collection of instances derived from classic benchmark data used in the literature for the Simple Assembly Line Balancing Problem (SALBP).

Variable-depth local search heuristic for assembly line balancing problems

ABSTRACT Assembly lines are production flow systems wherein activities are organised around a line consisting of various workstations through which the product flows. At each station, the product is assembled through a subset of operations. The assembly line balancing problem (ALBP) consists of allocating operations between stations to maximise the system efficiency. In this study, a variable-depth local search algorithm is proposed for solving simple assembly line balancing problems (SALBPs), which are the most widely studied versions of the ALBP. Although the state-of-the-art techniques for solving the SALBP consist of exact enumeration-based methods or heuristics, this paper proposes a local search-based heuristic using variable-length sequences that allow the solution space to be efficiently explored. The proposed algorithm improves the best solution known for multiple instances reported in the literature, indicating that its efficiency is comparable to those of the state-of-the-art method for solving the SALBP. Moreover, the characteristics of the instances for which the proposed procedure provides a better solution than previously reported construction procedures are investigated.

Approximation algorithms for simple assembly line balancing problems

Approximation algorithms for simple assembly line balancing problems

On the performance of task-oriented branch-and-bound algorithms for workload smoothing in simple assembly line balancing

Smoothing the workloads among the stations of an already installed assembly line is one of the major objectives in assembly line (re-)balancing. In order to find a feasible task-station assignment that distributes the total workload as equal as possible, two exact task-oriented branch-and-bound algorithms have recently been proposed. In this paper, we systematically analyse their effectiveness in solving the workload smoothing problem on simple assembly lines. In our experiments, we also examine the performance of a state-of-the-art mathematical programming solver and a ‘combined’ exact branch-and-bound procedure that integrates components of the two algorithms from the literature. In terms of theory, we show the equivalence of two recently developed local lower bounding arguments and suggest a slight improvement of the bound. We also propose an enhanced feasibility test.

SALSA: Combining branch-and-bound with dynamic programming to smoothen workloads in simple assembly line balancing

We consider a version of the well-known simple assembly line balancing problem (called SALBP-SX) where, given the cycle time and the number of stations, the workloads of the stations are to be leveled according to an adequately defined smoothness index SX. Our index SX involves for each station the quadratic deviation of its workload from the average (or ideal) workload and is therefore closely related to the variance, which is a common measure of dispersion in statistics. Contrary to the existing literature on workload smoothing in ALB, which often treats the optimization of a prespecified smoothness index as a secondary objective, we consider our SX-objective as the single one in order to account for the practical relevance of fair workload distributions and avoiding overloaded bottleneck stations.To optimally solve SALBP-SX, we develop a tailored branch-and-bound procedure. It contains a new station-oriented branching scheme, new lower bound arguments, logical tests and, in particular, a dynamic programming scheme for the pre-calculation of potential workloads, which accelerates the procedure greatly. In comprehensive computational experiments, we show that our method clearly outperforms a class of recently developed task-oriented branch-and-bound procedures and also the mathematical programming solver Gurobi.

Heuristics based on Slope Indices for Simple Type I Assembly Line Balancing Problems and Analyzing for a Few Performance Measures

In any mechanical manufacturing industry, raw materials’ cost is the major cost component which constitutes up to 40% of the total cost. Other major cost is the processing cost to convert the raw materials in to a finished product that takes place in an assembly line. Optimizing the available resources including the number of work benches and operators is important to meet the timely delivery of finished goods with optimum cost. In an assembly line, the manufacturing processes on any material are sub-divided into many tasks which may include sub-assemblies and machining. These tasks are carried out in several work stations which are responsible for a single or a set of operations carried out on the materials. Assembly lines need to be balanced to have even distribution of work for both men and machines. Type 1 simple assembly line balancing problems (SALBP-1) refer to minimization of number of work stations by keeping the cycle time constant. If the number of work stations is fixed, minimizing the cycle time falls under Type II problems. To solve these types of NP-hard problems, heuristics are used by researchers. The heuristics use different parameters to assign a particular task to a work station. This paper proposes and analyses a few heuristics that can be used to solve SALBP-1. They use slope indices to order the jobs and assign them to different work stations. The slope indices are the ratios of any two parameters taken suitably

Simple assembly line balancing problem of Type 1 with grey demand and grey task durations

PurposeThe purpose of this paper is to present a mixed integer programming model for simple assembly line balancing problems (SALBP) with Type 1 when the annual demand and task durations are uncertain and encoded with grey numbers.Design/methodology/approachGrey theory and grey numbers are used for illustrating the uncertainty of parameters in an SALBP, where the objective is to minimize the total number of workstations. The paper proposes a 0-1 mathematical model for SALBP of Type 1 with grey demand and grey task durations.FindingsThe uncertainty of the demand and task durations are encoded with grey numbers and a well-known 0-1 mathematical model for SALBP of Type 1 is modified to find the minimum number of workstations in order to meet both the lower and upper bounds of the uncertain demand. The results obtained from the proposed mathematical model show a task-workstation assignment that does not distribute precedence relations among tasks and workstations and the sum of task durations in each single workstation is less than or equal to the grey cycle time.Originality/valueThe grey theory and grey numbers have not been previously used to identify uncertainties in assembly line balancing problems. Therefore, this study provides an important contribution to the literature.

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.

Combinatorial Benders cuts for assembly line balancing problems with setups

The classical assembly line balancing problem consists of assigning assembly work to workstations. In the presence of setup times that depend on the sequence of tasks assigned to each workstation, the problem becomes more complicated given that two interdependent problems, namely assignment and sequencing, must be solved simultaneously. The hierarchical nature of these two problems also suggest a natural decomposition of the problem. This paper adopts such an approach and describes an exact algorithm based on Benders decomposition to solve both simple and mixed-model assembly line balancing problems with setups. The algorithm is tested on a set of benchmark instances and numerically compared against a mixed-integer linear programming formulation of the problem solved using a commercial optimizer.

A review of two-sided assembly line balancing problem

Assembly line balancing (ALB) is concerned with assigning tasks within an assembly line to meet the required production rate for optimization purposes. On the other hand, two-sided ALB performs double-sided assembly operation on a single assembly line. In this paper, we have focused the survey on two-sided assembly line balancing (2S-ALB) research problems. The numerous factors mentioned in 2S-ALB literature were actually based on problem resolutions, and this paper will quote any preferred literature considering the frequent citation. In particular, this review explores in detail the ALB problems, optimization methods, objective functions, and specific constraints used in solving 2S-ALB problems. Among the purposes of ALB problems is that it traditionally focuses on simple ALB with various engaging approaches. General ALB comes second because of its complexity and nondeterministic polynomial (NP)-hard-classified problems. However, due to the current manufacturing issues, GALB problems, such as 2S-ALB, are forced to be examined and this comprehensive literature will specify anything necessary for the optimization purposes. Finally, future research direction has been discovered and put forward as the suggestion.

Reducing the Number of Workstations and Increasing the Overall Efficiency of an Existing Assembly Line Using New Heuristic Method Based On CPM

In mass production systems, line balancing plays a great role but this is not easy even for simple straight line. Therefore utilizing heuristic strategies for these issues is completely desirable. Moreover, in line balancing, heuristic methods are the base of the metaheuristic methods, so it's necessary to use more efficient heuristic methods. This research work presents a new heuristic method based on (CPM) critical path method for simple assembly line balancing. This research is concerned with objectives of minimizing the number of workstations, improving smoothness index, increasing line efficiency and improving mean absolute deviation (MAD) .A comparison is done between this new heuristic method and three popular heuristic methods. FLB software based on COMSOAL is also used to compare the results

BALANCING THE LINE BY USING HEURISTIC METHOD BASED ON CPM IN SALBP –A CASE STUDY

In mass production systems, line balancing plays a great role, but this is not easy even if it is a simple straight line. So, in order to solve these problems Heuristic methods are very much desirable. It is also found that Heuristic methods play a great role in the formation of metaheuristic methods.Therefore it is very much important to use more efficient heuristic methods. In this research paper we presents a heuristic method that is based on critical path method for simple assembly line balancing. This research is mainly concerned with objectives of minimizing the number of workstations, improvement of smoothness index, mean absolute deviation (MAD) and increasing line efficiency. Keywords-Heuristic methods,Assembly line balancing problem, Critical path method, Simple assembly line balancing. --------------------------------------------------------------------***--------------------------------------------------------------------

A review on simple assembly line balancing type-e problem

Simple assembly line balancing (SALB) is an attempt to assign the tasks to the various workstations along the line so that the precedence relations are satisfied and some performance measure are optimised. Advanced approach of algorithm is necessary to solve large-scale problems as SALB is a class of NP-hard. Only a few studies are focusing on simple assembly line balancing of Type-E problem (SALB-E) since it is a general and complex problem. SALB-E problem is one of SALB problem which consider the number of workstation and the cycle time simultaneously for the purpose of maximising the line efficiency. This paper review previous works that has been done in order to optimise SALB -E problem. Besides that, this paper also reviewed the Genetic Algorithm approach that has been used to optimise SALB-E. From the reviewed that has been done, it was found that none of the existing works are concern on the resource constraint in the SALB-E problem especially on machine and tool constraints. The research on SALB-E will contribute to the improvement of productivity in real industrial application.

Improved integer programming models for simple assembly line balancing and related problems

AbstractWe propose a stronger formulation of the precedence constraints and the station limits for the simple assembly line balancing problem. The linear relaxation of the improved integer program theoretically dominates all previous formulations using impulse variables, and produces solutions of significantly better quality in practice. The improved formulation can be used to strengthen related problems with similar restrictions. We demonstrate their effectiveness on the U‐shaped assembly line balancing problem and on the bin packing problem with precedence constraints.

A hybrid genetic algorithm approach for solving an extension of assembly line balancing problem

The lively field of assembly line configuration and adjustment often have a significant impact on the performance of manufacturing systems. In this context, assembly line balancing problems (ALBPs) are widely cited in the literature. An ALBP consists of distributing the total product manufacturing workload among the stations along the manufacturing line. Previous research has focused on developing effective and fast solution methods for solving simple assembly line balancing problems (SALBP) and their various extensions. Each extension is motivated by several real-life applications and the need for solving precise practical problems. In this article, another interesting extension of SALBP (named in this work ‘Task Restrictions Assembly Line Balancing Problem’ of type 2 (TRALBP-2)) is focused on. In this situation, the number of stations is known and the objective is to minimise cycle time where both precedence and zoning constraints between tasks must be satisfied. For the resolution of such problem, an innovative hybrid genetic algorithm (HGA) scheme hybridised with a local search procedure is implemented. This genetic algorithm consists of a new representation scheme and a special genetic operator. The effectiveness of the proposed HGA is evaluated through various sets of instances which are (1) theoretically and randomly generated, (2) collected from the literature and (3) based on a real case study of an automotive cable manufacturer. Comparison of the proposed HGA results with CPLEX software for the TRALBP-2 demonstrates that, in a reasonable time, the proposed HGA generates consistent solutions that are very close to their optimal ones. Therefore, the proposed HGA approach is very effective and competitive.