Simple Assembly Line Balancing Problem Research Articles

Using the Taguchi method to optimize the differential evolution algorithm parameters for minimizing the workload smoothness index in simple assembly line balancing

An assembly line is a flow-oriented production system in which the productive units performing the operations, referred to as stations, are aligned in a serial manner. The simple assembly line balancing problem (SALBP) is a fundamental version of the general problem which has attracted attention of researchers and practitioners of operations research for almost half a century. With respect to the objective function, the SALBP is further classified into SALBP-1, SALBP-2, SALBP-E and finally SALBP-F. The types of SALBP may be complemented by a secondary objective which consists of smoothing station loads. This objective guarantees a better flow of material.Although there is a great deal of research addressing this problem, most of them consider smoothing station loads as a primary objective. In this paper, a differential evolution algorithm is developed to minimize workload smoothness index in SALBP-2. Also, the algorithm parameters are optimized using the Taguchi method. To validate the proposed algorithm, the results are compared with those of a published heuristic. This comparison indicates effectiveness of the proposed algorithm.

A solution procedure for type E simple assembly line balancing problem

This paper presents a type E simple assembly line balancing problem (SALBP-E) that combines models SALBP-1 and SALBP-2. Furthermore, this study develops a solution procedure for the proposed model. The proposed model provides a better understanding of management practice that optimizes assembly line efficiency while simultaneously minimizing total idle time. Computational results indicated that, under the given upper bound of cycle time ( ct max), the proposed model can solve problems optimally with minimal variables, constraints, and computing time.

Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization

Particle swarm optimization (PSO) one of the latest developed population heuristics has rarely been applied in production and operations management (POM) optimization problems. A possible reason for this absence is that, PSO was introduced as global optimizer over continuous spaces, while a large set of POM problems are of combinatorial nature with discrete decision variables. PSO evolves floating-point vectors (called particles) and thus, its application to POM problems whose solutions are usually presented by permutations of integers is not straightforward. This paper presents a novel method based on PSO for the simple assembly line balancing problem (SALBP), a well-known NP-hard POM problem. Two criteria are simultaneously considered for optimization: to maximize the production rate of the line (equivalently to minimize the cycle time), and to maximize the workload smoothing (i.e. to distribute the workload evenly as possible to the workstations of the assembly line). Emphasis is given on seeking a set of diverse Pareto optimal solutions for the bi-criteria SALBP. Extensive experiments carried out on multiple test-beds problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing multi-objective population heuristics show a quite promising higher performance for the proposed approach.

Simple assembly line balancing problem under the combinations of the effects of learning and deterioration

PurposeThe purpose of this paper is to describe the introduction of simple assembly line balancing problems (SALBPs) under four joint combinations of two learning effects and two deterioration effects.Design/methodology/approachIn this paper, effects of learning and deterioration are considered simultaneously. By the effects of learning and deterioration, it is meant that the task time of a task is defined by increasing function of its execution start time and position in the sequence.FindingsIt was shown that polynomial solutions can be obtained for SALBP under four joint combinations of two learning effects and two deterioration effects. Furthermore, this paper also discusses the adaptations of the proposed models by using known ALB problem.Originality/valueIn this paper, SALBPs were introduced under four joint combinations of two learning effects and two deterioration effects. Position dependent learning effect and time dependent learning effect were considered for learning effect when linear deterioration effect and non‐linear deterioration effect were taken up.

A note on “A multi-objective genetic algorithm for solving assembly line balancing problem”

Assembly line balancing has a considerable place in industrial importance. Hence, a lot of researchers are interested in this subject and several papers have been published so far. Many exact, heuristic, metaheuristic, and hybrid approaches have been used to solve this type of problems. Recently Ponnanbalam et al. (Int J Adv Manuf Technol 16:341–352, 2000) have considered a multi-objective genetic algorithm utilizing several simple heuristic rules for solving the simple assembly line balancing problems, one of these rules was “rank positional weight (RPW)” originally published in Helgeson and Birnie (J Ind Eng 12(6):394–398, 1961). Through providing two possible justifications, this note suggests that the mentioned rule can be mistakenly utilized and some revisions in Ponnanbalam et al. (Int J Adv Manuf Technol 16:341–352, 2000) seem to be necessary.

Absalom: Balancing assembly lines with assignment restrictions

Abstract Assembly line balancing problems (ALBPs) arise whenever an assembly line is configured, redesigned or adjusted. An ALBP consists of distributing the total workload for manufacturing products among the work stations along the line. On the one hand, research has focussed on developing effective and fast solution methods for exactly solving the simple assembly line balancing problem (SALBP). On the other hand, a number of real-world extensions of SALBP have been introduced but solved with straight-forward and simple heuristics in many cases. Therefore, there is a lack of procedures for exactly solving such generalized ALBP. In this paper, we show how to extend the well-known solution procedure S alome [Scholl, A., Klein, R., 1997. S alome : A bidirectional branch-and-bound procedure for assembly line balancing. Informs J. Comput. 9 319–334], which is able to solve even large SALBP instances in a very effective manner, to a problem extension with different types of assignment restrictions (called ARALBP). The extended procedure, referred to as A bsalom , employs a favorable branching scheme, an arsenal of bounding rules and a variety of logical tests using ideas from constraint programming. Computational experiments show that A bsalom is a very promising exact solution approach although the additional assignment restrictions complicate the problem considerably and necessitate a relaxation of some components of S alome .

Soft Computing in Optimizing Assembly Lines Balancing

As part of manufacturing systems, the assembly line has become one of the most valuable researches to accomplish the real world problems related to them. Many efforts have been made to seek the best techniques in optimizing assembly lines. Problem statement: Since it was published by Salveson in 1955, some methods and techniques have been developed based on mathematical modeling. In recent years, some researches in Assembly Line Balancing (ALB) have been conducted using Soft Computing (SC) approaches. However, there is no comprehensive survey studies conducted regarding the use of SC in ALB problems, which is became the aim of this study. Approach: This study reviewed published literatures and previous related works that applied SC in solving ALB problems. Main outcomes: This study looks into the suitability of SC approaches in several types of ALB problems. Furthermore, this study provides the classification of ALB problems that can facilitate distinguishing those problems as fields of research. Result: This study found that Genetic Algorithms (GAs) are predominantly applied to solve ALB problems compared to other SC approaches. This high suitability in ALB refers to GAs' main characteristics that include its robustness and flexibility. These SC approaches have mostly been applied to simple ALB problems, which are not problems that are covered in a real complex manufacturing environment. Conclusion/Recommendations: This study recommends that future researches in ALB should be conducted with regard to other issues, beyond the simple ALB problems and more practical to the industries. Besides the advantages of GAs, there are still opportunities to use other SC approaches and the hybrid-systems among them that could increase the suitability of these approaches, especially for multi-objective ALB problems. This study also recommends that human involvement in ALB needs to be considered as a problem factor in ALB.

Scheduling and balancing assembly lines with the task deterioration effect

Assembly lines are special flowline production systems that are of great importance in industrial mass production. This paper introduces task deterioration into the traditional simple assembly line balancing problem (SALBP). Task deterioration means that a task processed later consumes more time than the same task processed earlier. The aim is to find an optimal assignment and schedule of tasks at workstations so as to minimize the cycle time for a given number of stations, which in the literature is known as SALBP-2. For this purpose, a mathematical model is proposed. As SALBP-2 is classified as NP hard, and as the considered problem becomes more complicated with the additional deterioration parameter, a genetic algorithm is proposed. Finally, several well-known examples are solved to illustrate the approach proposed.

An improved mathematical program to solve the simple assembly line balancing problem

The simple assembly line balancing problem (SALBP) has been extensively examined in the literature. Various mathematical programs have been developed to solve SALBP type-1 (minimising the number of workstations, m, for a given cycle time, ct) and SALBP type-2 (minimising ct given m). Usually, an initial pre-process is carried out to calculate the range of workstations to which a task i may be assigned, in order to reduce the number of variables of task–workstation assignment. This paper presents a more effective mathematical program than those released to date to solve SALBP-1 and SALBP-2. The key idea is to introduce additional constraints in the mathematical program, based on the fact that the range of workstations to which a task i may be assigned depends either on the upper bound on the number of workstations or on the upper bound on the cycle time (for SALBP-1 and SALBP-2, respectively). A computational experiment was carried out and the results reveal the superiority of the mathematical program proposed.

Simple and U-type Assembly Line Balancing by Using an Ant Colony Based Algorithm

In this paper, an Ant Colony Optimization (ACO) based heuristic algorithm is proposed for solving simple (straight line) and U-shaped assembly line balancing problems (ALBP). The paper makes one of the first attempts to show how ACO heuristic can be used to solve U-shaped ALBP. A new algorithm is proposed in this paper that seamlessly integrates COMSOAL algorithm, Ranked Positional Weight Heuristic (RPWH) and an ACO based heuristic in order to obtain good solutions to simple and U-shaped ALBPs. The result of the computational study has shown that the proposed algorithm is effective in solving simple/U-shaped line balancing problems.

A mathematical model for assembly line balancing model to consider disordering sequence of workstations

Purpose – A binary integer programming model for the simple assembly line balancing problem (SALBP), which is well known as SALBP-1, was formulated more than 30 years ago. Since then, a number of researchers have extended the model for the variants of assembly line balancing problem.The model is still prevalent nowadays mainly because of the lower and upper bounds on task assignment. These properties avoid significant increase of decision variables. The purpose of this paper is to use an example to show that the model may lead to a confusing solution. Design/methodology/approach – The paper provides a remedial constraint set for the model to rectify the disordered sequence problem. Findings – The paper presents proof that the assembly line balancing model formulated by Patterson and Albracht may lead to a confusing solution. Originality/value – No one previously has found that the commonly used model is incorrect.

Heuristic procedures for solving the general assembly line balancing problem with setups

The general assembly line balancing problem with setups (GALBPS) was recently defined in the literature. It adds sequence-dependent setup time considerations to the classical simple assembly line balancing problem (SALBP) as follows: whenever a task is assigned next to another at the same workstation, a setup time must be added to compute the global workstation time, thereby providing the task sequence inside each workstation. This paper proposes heuristic procedures, based on priority rules, for solving GALBPS, many of which are an improvement upon heuristic procedures published to date.

Balancing and scheduling tasks in assembly lines with sequence-dependent setup times

The classical Simple Assembly Line Balancing Problem (SALBP) has been widely enriched over the past few years with many realistic approaches and much effort has been made to reduce the distance between the academic theory and the industrial reality. Despite this effort, the scheduling of the execution of tasks assigned to every workstation following the balancing of the assembly line has been scarcely reported in the scientific literature. This is supposed to be an operational concern that the worker should solve himself, but in several real environments, setups between tasks exist and optimal or near-optimal tasks schedules should be provided inside each workstation. The problem presented in this paper adds sequence-dependent setup time considerations to the classical SALBP in the following way: whenever a task is assigned next to another at the same workstation, a setup time must be added to compute the global workstation time. After formulating a mathematical model for this innovative problem and showing the high combinatorial nature of the problem, eight different heuristic rules and a GRASP algorithm are designed and tested for solving the problem in reasonable computational time.

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.

A Petri net approach for simple assembly line balancing problems

A simple assembly line balancing problem of type-1 (SALBP-1) aims to minimize the number of workstations for a given cycle time. In the relevant literature, several heuristics based on a branch-and-bound procedure, tabu search, and genetic algorithms (GAs) were proposed to solve SALBP-1. In this paper, an algorithm based on the reachability analysis of Petri nets is developed for SALBP-1. The proposed algorithm searches enabled transitions (or assignable tasks) in the Petri net model of precedence relations between tasks, and then the task minimizing the idle time is assigned to the station under consideration. The algorithm is coded in MATLAB, and its efficiency is tested on Talbot’s and Hoffmann’s benchmark datasets according to some performance measures and classifications. A computational study validates its effectiveness on Tonge’s 70-task problem by comparison with optimal solutions of traditional heuristics and a GA.

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.

A DIFFERENTIAL EVOLUTION ALGORITHM FOR SIMPLE ASSEMBLY LINE BALANCING

This paper describes the application of differential evolution algorithm (DEA) to the simple assembly line balancing problem (SALBP). DEA is an evolutionary algorithm similar to a real-coded genetic algorithm for global optimization over continues spaces. The paper is concerned with SALBP type-1 whose objective is to minimize the number of workstations required to manufacture a product in an assembly line within a given fixed cycle time. Extensive experimental work over public benchmarks test problems show the effectiveness of the proposed approach.

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.

Finding the best possible solutions to simple assembly line balancing problems

Assembly line balancing (ALB) is an important aspect in the design of many flow line type manufacturing systems which may or may not be assembly oriented. It involves essentially assigning individual tasks into stations such that certain constraints are satisfied and some specified objectives achieved. Although this problem has attracted a great deal of research effort over the past forty years, it still remains unsolved. This paper presents a computer-based method for the simple assembly line balancing problem (SALBP). For a given problem, the method makes use of some efficient and systematic search and identification techniques to establish task trees for individual stations and to develop a station tree for multiple solutions. Different from all known existing assembly line balancing methods, this method aims at finding the best possible solutions that lead to the maximum production rate and the minimum station workload variation. The essential and distinct concepts adopted by the method are described in this paper, along with its step-by-step execution procedure and an illustrative example. Since the method does not involve difficult mathematics, many industrial practitioners can easily understand it and appreciate its effectiveness.

Simple assembly line balancing?Heuristic approaches

In this paper heuristics for Type 1 and Type 2 of the Simple Assembly Line Balancing Problem (SALBP) are described. Type 1 of SALBP (SALBP-1) consists of assigning tasks to work stations such that the number of stations is minimized for a given production rate whereas Type 2 (SALBP-2) is to maximize the production rate, or equivalently, to minimize the sum of idle times for a given number of stations. In both problem types, precedence constraints between the tasks have to be considered.