Model Assembly Line Balancing Research Articles

An integrated selection and scheduling for disjunctive network problems

In network optimization problems, the application of conventional integrated selection and scheduling solution methods becomes complicated when the size of the problems, such as real life project management, assembly and transportation problems, get bigger. These kinds of problems often consist of disjunctive networks with alternative subgraphs. Traditionally, in order to handle alternative subgraphs in a disjunctive network, researchers consider first selection and then solution (scheduling) of the problem sequentially. However, the use of traditional approaches result in the loss of the problem structural integrity. When the approach losses its integrated structure, the network problem also losses its integrity. Therefore, these two issues, i.e. selection and scheduling, have to be considered together. To provide a new approach to maintain the problem integrity, we proposed an integrated genetic algorithm for solving this selection and scheduling problems together using a multi-stage decision approach. In this study, two newly defined problems with different disjunctive networks and different characteristics, i.e. resource constrained multiple project scheduling (rc-mPSP) models with alternative projects and variable activity times, and U-shaped assembly line balancing (uALB) models with alternative subassemblies, have been solved using the proposed solution approach to highlight the applicability and performance of the proposed solution approach.

Formulation of simple workforce skill constraints in assembly line balancing models

The objective of assembly line balancing (ALB) is to minimize the number of workstations organized to perform tasks with precedence constraints. An important element of proper assignment of tasks to workstations is the consideration of skill requirement of tasks, and skill level of workers. Some tasks may require special skills, some workers might not be able to perform complicated tasks, and some workers might be specialized for specific tasks. This paper provides a general framework to model skill requirements and skill conditions for assembly line balancing models. Three types of skill constraints are defined. Low skill constraints determine workstations for workers who are able to perform only some simple tasks. High skill constraints consider tasks which require higher than average skills of workers. Finally, exclusive skill constraints consider situations where a group of workers is specialized in a subset of tasks. The paper summarizes the mathematical description of the different skill constraints, and shows how simple assembly line balancing models can be completed with skill considerations. The mathematical characteristics of the resulting models are discussed, and some sample problems are solved to illustrate the results of each specific skill situation.

A tabu search approach for mixed-model parallel assembly line balancing problem (type II)

There are two types of assembly line balancing problems (ALBPs), which have received a great attention in the literature during the past four decades: types I and II. Both versions of ALBP are NP-hard and cannot be solved by exact methods. This paper presents an efficient tabu search (TS) metaheuristic approach for allocating tasks to balance the mixed-model production through the parallel assembly lines. Firstly, an initial solution is determined by a heuristic algorithm. Secondly, an optimum cycle time for each line in the mixed-model parallel assembly line (MMPAL) is calculated through the TS. The developed methodology, tabu search mixed-model parallel assembly line balancing (TMMPALB) model, is tested on test problems producing three and four products. Computational experiments show that the TMMPALB produces good enough solutions in case of MMPALs. The results of the proposed procedure improve cycle times of 77 test problems out of 87.

Robust balancing of mixed model assembly line

PurposeThe purpose of this paper is to introduce robust optimization approaches to balance mixed model assembly lines with uncertain task times and daily model mix changes.Design/methodology/approachScenario planning approach is used to represent the input data uncertainty in the decision model. Two kinds of robust criteria are provided: one is min‐max related; and the other is α‐worst scenario based. Corresponding optimization models are formulated, respectively. A genetic algorithm‐based robust optimization framework is designed. Comprehensive computational experiments are done to study the effect of these robust approaches.FindingsWith min‐max related robust criteria, the solutions can provide an optimal worst‐case hedge against uncertainties without a significant sacrifice in the long‐run performance; α‐worst scenario‐based criteria can generate flexible robust solutions: through rationally tuning the value of α, the decision maker can obtain a balance between robustness and conservatism of an assembly line task elements assignment.Research limitations/implicationsThis paper is an attempt to robust mixed model assembly line balancing. Some more efficient and effective robust approaches – including robust criteria and optimization algorithms – may be designed in the future.Practical implicationsIn an assembly line with significant uncertainty, the robust approaches proposed in this paper can hedge against the risk of poor system performance in bad scenarios.Originality/valueUsing robust optimization approaches to balance mixed model assembly line.

Optimal assignment and scheduling of workers and tasks on an assembly line

In this paper the assignment of cross-trained and temporary workers to tasks on an assembly line is investigated. Cross-trained workers are skilled to perform more than one task on the assembly line in the production process. Temporary workers are viewed as either trained or untrained and may be hired or laid off as required. The solution procedure may be divided into three parts. During the first part a model is formulated to determine an optimal assignment of the workers to the production tasks. During the second part the model is extended to determine the effect of the assignment of both trained and untrained temporary workers to the tasks on the assembly line. During the final part of the model an optimal sequence of tasks in the assembly line is determined that minimises the resulting execution times of these tasks. During the first part the objective is to maximise the total production utility. This is achieved by implementing a two-phase model. The first phase maximises the utility of pro-duction by minimising labour shortage in the assembly line. During the second phase the improvement of the workers’ levels of skill is maximised while the effect of the learning and forgetting of skills is taken into consideration. A learn-forget-curve model (LFCM) is implemented to model the effect of this human characteristic on the master model. This approach ensures that the advantageous cross-trained nature of the workers is maintained and optimized, without a large deviation from the solution determined by the first phase. The objective of the second part is to minimise the labour cost of production by determin-ing the best type of workers for a certain task as well as the manner in which they should be hired or laid off. A worker is classified as either permanently or temporarily employed. Tem-porarily employed workers are further classified as either untrained or cross-trained workers. The assignment of workers to tasks on the assembly line is achieved by means of a Master Production Scheduling (MPS) model. The MPS has as its objective the minimisation of the total labour cost of performing all the tasks. The labour cost is defined as the sum of the temporary workers’ daily wages, the overtime cost of permanent workers, the overtime cost of temporary workers and the cost of employing and laying off temporary workers. Finally, during the third part an optimal sequence of tasks is determined in the production process in order to minimise the total production time. This is achieved by means of a two-phase dynamic assembly line balancing model, which is adjusted to incorporate the critical path method. During the first phase, an optimal task sequence is determined, while during the second phase, an optimal assignment of tasks to workstations and the timing thereof, is determined. The practical applicability of the model is demonstrated by means of a real life case study. The production of various styles of shoes in a leatherworks factory is considered. The production of each style requires a different set of tasks and each task requires a different level of skill. The factory under consideration employs both cross-trained and temporary workers and data sets were obtained empirically by observation, interviews and questionnaires. Upon execution of the first phase of the assignment model, an optimal utility is found and the second phase is able to maximise the increase of the workers’ skill level without deviation from this optimum. Upon execution of the employment model, it is found that labour costs are minimized by increasing the use of temporary workers and by assigning the maximum allowable number of overtime hours to them. Upon application of the scheduling model, an improved time is obtained compared to the standard execution time of each style. The results obtained from the case study indicate that the application of the model presented in this paper shows a substantial improvement in production, while reducing the cost of labour as well as improving the overall level of workers’ skills. A multi-objective model is thus developed which successfully maximises production utility, maximises skill development of workers, minimises labour costs and the occurrence of idle workers as well as minimises total execution time.

Balancing mixed-model assembly lines: a computational evaluation of objectives to smoothen workload

Mixed-model assembly lines are widely used in a range of production settings, such as the final assembly of the automotive and electronics industries, where they are applied to mass-produce standardised commodities. One of the greatest challenges when installing and reconfiguring these lines is the vast product variety modern mixed-model assembly lines have to cope with. Traditionally, product variety is bypassed during mid-term assembly line balancing by applying a joint precedence graph, which represents an (artificial) average model and serves as the input data for a single model assembly line balancing procedure. However, this procedure might lead to considerable variations in the station times, so that serious sequencing problems emerge and work overload threatens. To avoid these difficulties, different extensions of assembly line balancing for workload smoothing, i.e. horizontal balancing, have been introduced in the literature. This paper presents a multitude of known and yet unknown objectives for workload smoothing and systematically tests these measures in a comprehensive computational study. The results suggest that workload smoothing is an essential task in mixed-model assembly lines and that some (of the newly introduced) objectives are superior to others.

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.

A team-oriented design methodology for mixed model assembly systems

Team-oriented approaches are widely being used in modern real life assembly systems, as are other modern systems. In this paper, first the literature of single and mixed model assembly line balancing, which plays an important role for the design of assembly systems, is reviewed. The associated literature matrixes reveal that team-oriented approaches do not have an intensive research area. Second, a team-oriented mathematical programming model for creating assembly teams (physical stations) in mixed model assembly lines is devised. Owing to the fact that this model is NP hard, a scheduling based heuristic algorithm is developed. The mixed model assembly line design methodology, which includes a team-oriented algorithm as a step, is proposed. Both model sequencing and worker transfer systems are included in the methodology. The algorithms for each step of the methodology are also coded by using MATLAB and MS Excel is used as the user interface. Furthermore, the presented methodology was applied in a chosen segment of a real life mixed model tractor assembly system.

A goal programming approach to simple U-line balancing problem

In this paper, a goal programming model for the simple U-line balancing (ULB) problem is developed. The model is based on the integer programming formulation developed by Urban [Urban, Note: Optimal balancing of U-shaped assembly lines, Management Science 44(5) (1998) 738–741] for the ULB problem and the goal model of Deckro and Rangachari [Deckro, Rangachari, A goal approach to assembly line balancing, Computers and Operations Research 17 (1990) 509–521] developed for the traditional single model assembly line balancing (ALB) problem. The proposed model which is the first multi-criteria decision making approach to the U-line version provides increased flexibility to the decision maker since several conflicting goals can be simultaneously considered.

Performance of Bucket Brigades When Work Is Stochastic

“Bucket brigades” are a way of sharing work on a flow line that results in the spontaneous emergence of balance and consequent high throughput. All this happens without a work-content model or traditional assembly line balancing technology. Here we show that bucket brigades can be effective even in the presence of variability in the work content. In addition, we report confirmation at the national distribution center of a major chain retailer, which experienced a 34% increase in productivity after the workers began picking orders by bucket brigade.

Balancing flexible lines for car components assembly

The design and balancing of a line which assembles car heaters is examined. To accomplish a balance, the general assembly line balancing model is extended to cope with some restrictions due to the physical structure of the production line: namely, incompatibilities among operations and operations that must be necessarily performed by certain work stations. A polynomial time dynamic programming algorithm is provided to balance the workloads among the work stations taking into account these additional constraints, exploiting the special structure of the operation graph. The algorithm was applied to two designs: a single line on which all the heater components are assembled, and also to several segmented lines, which collectively assemble heaters. Demand volume variations are also addressed.

Complexity of single model assembly line balancing problems

This paper describes a method for generating assembly line balancing problems randomly and measuring their complexities. In this present study, a measure for complexity of assembly line balancing problem, TF-ratio (ratio of the sum of total number of followers of each element and maximum possible number of ordering relations among them) is used. A complex problem will need more CPU time for its solution. Some of the factors, such as, work content (W), cycle time (C), standard deviation (SD) of elemental times, TF-ratio, etc., which are responsible for the complexity of the line balancing problem, are identified and their effect on the complexity of the problem is discussed.

Branch and bound and heuristic procedures for assembly line balancing with paralleling of stations

Abstract A critical assumption made in formulating the classical assembly line balancing model is that the line is ‘serial’ with no ‘paralleling’ of stations allowed. A model is proposed here which allows the paralleling of stations, thereby achieving a higher production rate and lower labour costs. The mathematical formulation of the proposed model results in a mixed integer program. A branch and bound and a heuristic solution procedure, derived from the branch and bound method, are presented. An example problem is used for illustration. Some computational experience is also provided.

Mixed Model Line Balancing with Smoothed Station Assignments

The general practice in mixed model assembly line balancing is to assign work to stations in a manner that each station has an equal amount of work on a daily or shift basis. This paper shows how a modification to mixed model line balancing algorithms can be used so that the stations are loaded more consistently on a model by model basis as well. In addition, the modification now allows mixed model line balancing to become a plausible tool for batched assembly line processes. The procedure is general in nature and is applicable to most line balancing algorithms.