Assembly Line production is one of the widely used basic principles in production system. The problem of Assembly Line Balancing deals with the distribution of activities among the workstations so that there will be maximum utilization of human resources and facilities without disturbing the work sequence. Research works reported in the literature mainly deals with minimization of idle time i.e. balancing, subject to precedence constraints. Lack of uniqueness in their optimum solutions has led to the present work where minimization of both balancing loss and system loss has been envisaged under the usual precedence constraints. The researchers suggest a generic approach for designing of an assembly line where, with a given number of workstations, one can efficiently arrive at the desired solution under different methods of search like simulation, heuristic etc. Thus, the main aim of this paper is to redefine the objective of the Assembly Line Balancing Problem and sequentially handle Balancing Loss and System Loss. A numerical example has been added to demonstrate the generic nature of the researcher's approach and improvement in the solution set when compared with different standard line balancing methods available so far in the literature.

The aim of this study is to investigate the assembly line balancing problems (ALBP) in the garment industry. For many years, manual methods of balancing production lines have been used, while computer-based methods can determine the number of workstations and activities of each work station with high efficiency. This research investigates the application of Flexible Line Balancing (FLB) software in balancing of work pants sewing process. For this purpose, the work pants sewing line and the sequence of activities in the production process were identified, and then the balanced sewing line was designed and suggested using FLB software. First, the results show five workstations with relatively the same workload, so that the idle time for each station is at least 10 seconds. In this way the line balancing efficiency is equal to 84.8%. On the other hand, the examination of the number and variety of machines required in each work station leads to at least 13 sewing machines for the pants sewing process. In this regard, the ability of activities separation at the FLB was investigated in order to reduce the number of machines and the results showed that it is possible to plan the workstations in such a way that three less machines are required. In this case, the line balancing efficiency shows a 2% increase compared to the previous case. In general, the production cycle time and the limitations of sequence and prerequisite activities are well considered in this software so that the workload of each station is almost the same. The results of this paper could be used in determining the number of work stations and assigning activities in the production planning for garment manufacturing.

This paper presents a multi-objective simulated annealing algorithm for the mixed-model assembly line balancing with stochastic processing times. Since, the stochastic task times may have effects on the bottlenecks of a system, maximizing the weighted line efficiency (equivalent to the minimizing the number of station), minimizing the weighted smoothness index and maximizing the system reliability are considered. After solving an example in detail, the performance of the proposed algorithm is examined on a set of test problems. The experimental results show the new approach performs well.

There are some machineries and equipments as well as relatively fix amount of human resources involved in any production process. In many cases, by observing operational method of a process, one may see that some of these machineries are not working, but on the other hand, other equipments are continuously working, and many parts are set in front of them, waiting to be set up over machines to continue production procedure. Existence of non-working time as well as overload tasks, that means imbalance in production process, are among factors creating some problems for system’s management. One guideline that a manager may use to encounter imbalance problem of production process is assembly line balancing. In this thesis, genetic algorithm method is used to solve assembly line balance problem. This algorithm presents a new method during execution of crossover operation and combination of parent chromosomes in order to produce child and mutation building in chromosomes. Finally, efficiency of obtained answers by genetic algorithm is compared with numeric method. 

With the growth in customers demand diversification, mixed-model U-lines (MMUL) have acquired increasing importance in the area of assembly systems. There are generally two different approaches in the literature for balancing such systems. Some researchers believe that since the types of models can be very diverse, a balancing approach without simultaneously sequencing of models will not yield an optimum con guration. On the other hand, another group of researchers point to the high cost of balancing systems and prefer to do it only one time regardless of the models sequences. In this paper, we aim to compare these two approaches by introducing an economic indicator. To do so, two models as representatives of the two different viewpoints are taken from the literature. To check the validity of this methodology, it is implemented by Lingo 11.0, for small scale, and GA, for a large scale. The obtained results indicate that, from the proposed economic indicator point of view, mixed-model U-lines balancing and sequencing (MMUL/BS) is preferred to its counterpart, mixed-model U-lines balancing (MMULB).This paper offers economic guidelines for managers to choose between only balancing and implementing it by sequencing at the same time.

In this research, a new model for cost-oriented assembly line balancing problem has been presented that consists of labor and equipment cost. The approach of this model for these costs is coincided with real condition of assembly lines and yield possibility of using common equipment among tasks. The objective function and constrains of this model has been shown by mathematical relations and also some relations for lower and upper bounds of objective function have been presented. Genetic algorithm has been used for solving the problems of this model and the best parameters of this algorithm for different size of problems have been found. Also the performance of suggested genetic algorithm has been compared by a famous method named RTA (Random Task Assignment) and lower bound of objective function. The results of these experiments indicate better performance of the genetic algorithm in quality of solution and computing time.