Simultaneous solving of balancing and sequencing problems in mixed-model assembly line systems

Recently, the mixed-model assembly line (MMAL) has been widely studied by many researchers. In fact, there are two basic problems, namely balancing and sequencing problems, which have been investigated in a lot of studies separately, but few researchers have solved both problems simultaneously. Regarding this, the best results in minimising total utility work have been gained by developing a co-evolutionary genetic algorithm (Co-GA) so far. This paper provides a mixed-integer linear programming (MILP) model to jointly solve the problems. Because of NP-hardness, an evolution strategies (ES) algorithm is presented and evaluated by the same test problems in the literature. Two main hypotheses, namely simultaneous search and feasible search, are tested in the proposed algorithm to improve the quality of solutions. To calibrate the algorithm, a Taguchi design of experiments is employed. The proposed ES is compared with the modified version of Co-GA and the MILP model results. According to numerical experiments and statistical proving, the proposed ES outperformed the modified Co-GA from two points of view: the objective function and the computational time. Additionally, the meta-heuristic algorithms are examined in terms of other well-known criteria in MMAL. Finally, the contribution of each hypothesis in accounting for this superiority is analysed.     

Hybrid evolutionary multi-objective algorithms for integrating assembly sequence planning and assembly line balancing

Assembly sequence planning (ASP) and assembly line balancing (ALB) play critical roles in designing product assembly systems. In view of the trend of concurrent engineering, pondering simultaneously over these two problems in the development of assembly systems is significant for establishing a manufacturing system. This paper contemplates the assembly tool change and the assembly direction as measurements in ASP; and further, Equal Piles assembly line strategy is adopted and the imbalanced status of the system employed as criteria for the evaluation concerning ALB. Focus of the paper is principally on proposing hybrid evolutionary multiple-objective algorithms (HEMOAs) for solutions with regard to integrate the evolutionary multi-objective optimization and grouping genetic algorithms. The results provide a set of objectives and amend Pareto-optimal solutions to benefit decision makers in the assembly plan. In addition, an implemented decision analytic model supports the preference selection from the Pareto-optimal ones. Finally, the exemplifications demonstrate the effectiveness and performance of the proposed algorithm. The consequences definitely illustrate that HEMOAs search out Pareto-optimal solutions effectively and contribute to references for the flexible change of assembly system design.     

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.     

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.     

Mathematical models for parallel two-sided assembly line balancing problems and extensions

In this study, a mixed integer programming model for the parallel two-sided assembly line balancing problem is developed. Several extensions such as a cost-oriented model, a model with time and space constraints and a model with assignment restrictions which considers characteristics of parallel lines are also presented. The model has been tested on a number of test problems from the literature. The results for different objective functions are analysed on the test problems.     

Modelling and solving a cost-oriented resource-constrained multi-model assembly line balancing problem

A line balancing problem considers the assignment of operations to workstations in an assembly line. While assembly lines are usually associated to mass production of standardised goods, their advantages have led to their widespread use whenever a product-oriented production system is applicable and the benefits of the labour division and specialisation are significant, even when some of its characteristics may deviate from classical assembly lines. In this work, we study a line balancing problem found in the textile industry in which the line must be balanced for multiple types of goods taking into account resource requirements. In order to solve the problem, a hybrid method that combines classical methods for line balancing with an Estimation of Distribution Algorithm is proposed. Computational experiments show that the new procedure improves upon the state of the art when compared using a benchmark set derived from the literature, as well as when compared using data from the manufacturer that originated this research work.     

Development of a knowledge-based expert system for solving metal cutting problems

In metal cutting, the problems need to be well analyzed in order to take precautions before any unexpected results are encountered. This process plays a significant role in achieving consistent quality and in controlling the overall cost of manufacturing. However, it is a difficult task that needs an expert who has a great deal of information and experience in metal cutting. In the present paper, a knowledge-based expert system (COROSolve) that investigates problems that are encountered in three main metal cutting areas: turning, milling and drilling is developed. A great deal of metal cutting operations such as external/internal turning with negative/positive inserts, aluminum turning, parting bars/tubes, grooving, profiling, recessing and threading operations in turning; face milling, square shoulder milling, end milling, multi-purpose milling and side and face milling operations in milling; and drilling operations that use solid drills or drills with indexable inserts in drilling are taken into consideration. COROSolve gives recommendations for the cutting data (i.e., cutting speed, depth of cut, and feed) and updates the problem, cause and remedy database, thus the number of problems that the system can handle is increased.     

Ergonomics in assembly line balancing based on energy expenditure: a multi-objective model

In many assembly systems, ergonomics can have great impact on productivity and human safety. Traditional assembly systems optimisation approaches consider only time and cost variables, while few studies include also ergonomics aspects. In this study, a new multi-objective model for solving assembly line balancing problem is developed and discussed in order to include also the ergonomics aspect. First, based on main features of assembly workstations, the energy expenditure concept is used in order to estimate the ergonomics level, thanks to a new technique, called Predetermined Motion Energy System, which helps rapidly estimate the energy expenditure values. Then, a multi-objective approach, based on four different objective functions, is introduced in order to define the efficient frontiers of optimal solutions. To complete the study, a simple numerical example for a real case is presented to analyse the behaviour of Pareto frontiers varying several parameters linked to the energy and time value.     

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.     

Aspects of assembly line balancing

A particular form of assembly line is examined by computer assisted simulation for conditions of perfect and imperfect balance between manual work stations. Some account has been taken of a possible response by operators to the pacing inherent in this form of working. Conclusions are drawn in regard to the effects of initial feed rate, variable operator work cycle times and tolerance time (i.e. the time that a part is available for servicing before passing unprocessed) on output, unprocessed work and operator utilization.     

Modelling and optimisation of energy-efficient U-shaped robotic assembly line balancing problems

Within U-shaped assembly lines, the increase of labour costs and subsequent utilisation of robots has led to growing energy consumption, which is the current main expense of auto and electronics industries. However, there are limited researches concerning both energy consumption reduction and productivity improvement on U-shaped robotic assembly lines. This paper first develops a nonlinear multi-objective mixed-integer programming model, reformulates it into a linear form by linearising the multiplication of two binary variables, and then refines the weight of multiple objectives so as to achieve a better approximation of true Pareto frontiers. In addition, Pareto artificial bee colony algorithm (PABC) is extended to tackle this new complex problem. This algorithm stores all the non-dominated solutions into a permanent archive set to keep all the good genes, and selects one solution from this set to overcome the strong local minima. Comparative experiments based on a set of newly generated benchmarks verify the superiority of the proposed PABC over four multi-objective algorithms in terms of generation distance, maximum spread, hypervolume ratio and the ratio of non-dominated solution.     

A multi-objective distribution-free model and method for stochastic disassembly line balancing problem

End-of-life product recycling is a hot research topic in recent years, which can reduce the waste and protect the environment. To disassemble products, the disassembly line balancing is a principal problem that selects tasks and assigns them to a number of workstations under stochastic task processing times. In existing works, stochastic task processing times are usually estimated by probability distributions or fuzzy numbers. However, in real-life applications, only their partial information is accessible. This paper studies a bi-objective stochastic disassembly line balancing problem to minimise the line design cost and the cycle time, with only the knowledge of the mean, standard deviation and upper bound of stochastic task processing times. For the problem, a bi-objective chance-constrained model is developed, which is further approximated into a bi-objective distribution-free one. Based on the problem analysis, two versions of the ?-constraint method are proposed to solve the transformed model. Finally, a fuzzy-logic technique is adapted to propose a preferable solution for decision makers according to their preferences. A case study is presented to illustrate the validity of the proposed models and algorithms. Experimental results on 277 benchmark-based and randomly generated instances show the efficiency of the proposed methods.     

The sequence-dependent assembly line balancing problem

Assembly line balancing problems (ALBP) arise whenever an assembly line is configured, redesigned or adjusted. An ALBP consists of distributing the total workload for manufacturing any unit of the products to be assembled among the work stations along the line. The sequence-dependent assembly line balancing problem (SDALBP) is an extension of the standard simple assembly line balancing problem (SALBP) which has significant relevance in real-world assembly line settings. SDALBP extends the basic problem by considering sequence-dependent task times. In this paper, we define this new problem, formulate several versions of a mixed-integer program, adapt solution approaches for SALBP to SDALBP, generate test data and perform some preliminary computational experiments. As a main result, we find that applying SALBP-based search procedures is very effective, whereas modelling and solving the problem with MIP standard software is not recommendable.     

Two-sided U-type assembly line balancing problem

U-type and two-sided assembly lines are two types of design having advantages over traditional straight assembly lines. In this paper, a new line design hybrid of U-type and two-sided lines is presented. A bi-objective 0-1 integer programming model is developed to solve the line balancing problem of the proposed design. Zoning constraints are also considered for the proposed design. A number of test problems from the literature with up to 65 tasks are solved. Benefits of two-sided U-type lines are discussed.     

A Petri net-based heuristic for mixed-model assembly line balancing problem of Type-E

To effectively respond to the changing market demands, a manufacturer should produce variety of products with small lots. Thus, multiple products (models) are assembled simultaneously on a same line. However, it is very challenging to balance such an assembly line. This paper conducts a study on balancing a mixed-model assembly line of Type E. To solve this problem, a coloured-timed Petri net model is developed to describe the task precedence relationship. Also, the optimisation problem is formulated as a mathematical programming model. Then, with the models, a two-stage heuristic algorithm is proposed to solve the problem. At the first stage, based on the Petri net model, a P-invariant algorithm (PA) is presented to minimise the number of workstations. At the second stage, a heuristic is proposed to further minimise the cycle time by combining the PA with a binary search algorithm (BSA). Performance of the proposed method is evaluated by an illustrative example and numerical experiments. It is shown that it works well in terms of both solution accuracy and computational efficiency for large size problems.     

A multiple criteria decision making approach for the assembly line balancing problem

In practice, measuring total profit for a given assembly line balancing (ALB) problem is an involved process that is sometimes impossible because of much uncertainty and unavailability of data. In this paper, ALB is formulated as a multiple criteria problem where several easily quantifiable criteria (objectives) and constraints are defined. Objective functions include number of stations, cycle time, and operations cost, all to be minimized. After a discussion of applications and an overview of multiple criteria decision making (MCDM) approaches for ALB, the MCDM-ALB problem is formulated. Basic definitions and properties of MCDM for ALB are outlined and then an interactive MCDM approach is developed for solving the MCDM-ALB problem. To solve the problem, the decision maker (DM) interactively responds to paired comparisons of multicriteria alternatives. Through a limited number of interactions with the DM, the most preferred alternative is obtained. Many unexplored alternatives are eliminated by using a one-dimensional multiple criteria search. To present the DM's preference, we use the most flexible and general class of utility functions; namely, either quasi-concave or quasi-convex utility functions. An example is solved and computational experiments are reported. It is demonstrated that the bicriteria ALB, cycle time versus number of stations, can be easily solved by using the developed procedure. For the case that there are different criteria, an improved goal programming is developed to solve the MCDM-ALB problem. The motivation for development of the method, based on a case study of a lamp-making plant of the General Electric Company, is discussed.     

Supply chain optimisation with assembly line balancing

Supply chain management operates at three levels, strategic, tactical and operational. While the strategic approach generally pertains to the optimisation of network resources such as designing networks, location and determination of the number of facilities, etc., tactical decisions deal with the mid-term, including production levels at all plants, assembly policy, inventory levels and lot sizes, and operational decisions are related to how to make the tactical decisions happen in the short term, such as production planning and scheduling. This paper mainly discusses and explores how to realise the optimisation of strategic and tactical decisions together in the supply chain. Thus, a supply chain network (SCN) design problem is considered as a strategic decision and the assembly line balancing problem is handled as a tactical decision. The aim of this study is to optimise and design the SCN, including manufacturers, assemblers and customers, that minimises the transportation costs for determined periods while balancing the assembly lines in assemblers, which minimises the total fixed costs of stations, simultaneously. A nonlinear mixed-integer model is developed to minimise the total costs and the number of assembly stations while minimising the total fixed costs. For illustrative purposes, a numerical example is given, the results and the scenarios that are obtained under various conditions are discussed, and a sensitivity analysis is performed based on performance measures of the system, such as total cost, number of stations, cycle times and distribution amounts.     

Integrated assembly line balancing with resource restrictions

This paper documents a study carried out on the problem of designing an integrated assembly line when many workers with a variety of skills are employed. This study addresses the problem of selecting multi-functional workers with different salaries to match their skills and of assigning tasks to work stations when there are precedence restrictions among the tasks. The objective of this study is to minimise the total annual work station costs and the annual salary of the assigned workers within a predetermined cycle time. A mixed integer linear program is developed with a genetic algorithm in order to address the problem of resource restrictions related to integrated assembly line balancing. Numerical examples demonstrate the efficiency of the developed genetic algorithm.     

Stochastic assembly line balancing using beam search

This paper presents a beam search-based method for the stochastic assembly line balancing problem in U-lines. The proposed method minimizes total expected cost comprised of total labour cost and total expected incompletion cost. A beam search is an approximate branch and bound method that operates on a search tree. Even though beam search has been used in various problem domains, this is the first application to the assembly line balancing problem. The performance of the proposed method is measured on various test problems. The results of the computational experiments indicate that the average performance of the proposed method is better than the best-known heuristic in the literature for the traditional straight-line problem. Since the proposed method is the first heuristic for the stochastic U-type problem with the total expected cost criterion, we only report its results on the benchmark problems. Future research directions and the related bibliography are also provided in the paper.     

Hybridizing evolutionary strategies with continuation methods for solving multi-objective problems

Two techniques for the numerical treatment of multi-objective optimization problems—a continuation method and a particle swarm optimizer—are combined in order to unite their particular advantages. Continuation methods can be applied very efficiently to perform the search along the Pareto set, even for high-dimensional models, but are of local nature. In contrast, many multi-objective particle swarm optimizers tend to have slow convergence, but instead accomplish the ‘global task’ well. An algorithm which combines these two techniques is proposed, some convergence results for continuous models are provided, possible realizations are discussed, and finally some numerical results are presented indicating the strength of this novel approach.