A real-time sequence control system for the level production of the automobile assembly line

In the automobile industry, mixed-model assembly lines are used to produce many different vehicles without carrying large inventory. For achieving high productivity, it is required to keep a constant rate of usage of parts used by the assembly line. We have developed a new dynamic sequencing method for level production of assembly line and built up an automated sequence-control system which had been operated manually. In this paper, we present i) the nature of sequence control problems, ii) a dynamic sequence control algorithm, and iii) the experience of automating a buffer control system.     

混合装配线平衡排产协同优化新算法

装配线平衡和产品排序是紧密相关而且对目标值存在交互影响作用的两个NP—hard问题。文中基于这两个问题的交互影响以及贪婪随机自适应算法（GI己AsP）比较好的收敛速度和全局满意度,设计了协同优化贪婪随机自适应算法（（X）GRASP）,并行协同地优化混合装配线,并用实例对此算法进行了仿真研究。此外,文中还考虑了可能存在的瓶颈工序对协同优化效果的影响,将一种基于OPT思想的关键资源调度方法融入原来的COGRASP中,通过相应实例验证,取得的效果也非常好。     

A Pareto biogeography-based optimisation for multi-objective two-sided assembly line sequencing problems with a learning effect

This research presents a Pareto biogeography-based optimisation (BBO) approach to mixed-model sequencing problems on a two-sided assembly line where a learning effect is also taken into consideration. Three objectives which typically conflict with each other are optimised simultaneously comprising minimising the variance of production rate, minimising the total utility work and minimising the total sequence-dependent setup time. In order to enhance the exploration and exploitation capabilities of the algorithm, an adaptive mechanism is embedded into the structure of the original BBO, called the adaptive BBO algorithm (A-BBO). A-BBO monitors a progressive convergence metric in every certain generation and then based on this data it will decide whether to adjust its adaptive parameters to be used in the next certain generations or not. The results demonstrate that A-BBO outperforms all comparative algorithms in terms of solution quality with indifferent solution diversification.     

A comparative analysis of sequencing heuristics for solving the Toyota Goal Chasing problem

In this paper, the sequencing of a mixed model paced assembly line is investigated assuming the component parts usage smoothing as the goal of the sequence selection. This sequencing problem, commonly known as Toyota Goal Chasing method, is studied here taking into account not only the traditional Goal Chasing approaches, which assume zero-length assembly lines, but also models which consider the effective length of the assembly line. This means that the number of workstations and their extensions become critical parameters in the selection of the optimal sequence of models to be assembled: in fact, the epochs corresponding to the requirement of different components vary in accordance to the values of the line parameters. The sequencing of the parts is carried out here through a set of heuristic procedures, the commonly adopted Goal Chasing algorithms and a simulated annealing, whose performances are compared with respect to different line scenarios. In particular, the numbers of workstations, parts to be worked and components to be assembled are varied to statistically test their influence on the efficiency of the optimizing procedures and on the differences between zero and finite length approaches.     

A sequencing problem for a mixed-model assembly line in a JIT production system

In an assembly line of a just-in-time (JIT) production system, workers have the power and the responsibility to stop the line when they fail to complete their operations within their work zones. This paper deals with a sequencing problem for the mixed-model assembly conveyor line in the JIT production system. In some environment, the most important criterion is the line stoppage rather than the variation of production rates. The problem is to find an optimal sequence of units that minimizes the total line stoppage time. Lower and upper bounds of the total line stoppage time are derived and the branch-and-bound method is applied to the problem. A numerical example is given.     

A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers’ demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems.     

A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers’ demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems.     

Mixed-model assembly line balancing in the make-to-order and stochastic environment using multi-objective evolutionary algorithms

The present study introduces a multi-objective genetic algorithm (MOGA) to solve a mixed-model assembly line problem (MMALBP), considering cycle time (CT) and the number of stations simultaneously. A mixed-model assembly line is one capable of producing different types of products to respond to different market demands, while minimizing on capital costs of designing multiple assembly lines. In this research, according to the stochastic environment of production systems, a mixed-model assembly line has been put forth in a make-to-order (MTO) environment. Furthermore, a MOGA approach is presented to solve the corresponding balancing problem and the decision maker is provided with the subsequent answers to pick one based on the specific situation. Finally, a comparison is carried out between six multi-objective evolutionary algorithms (MOEA) so as to determine the best method to solve this specific problem.     

Algorithms for the car sequencing and the level scheduling problem

This paper deals with two most important problems arising in sequencing mixed-model assembly lines. One problem is to keep the line's workstations loads as constant as possible (the 'car sequencing problem') while the other is to keep the usage rate of all parts fed into the final assembly as constant as possible (the 'level scheduling problem'). The first problem is a difficult constraint-satisfaction problem while the second requires to optimize a nonlinear objective function. The contribution of this paper is twofold: First, we describe a branching scheme and bounding algorithms for the computation of feasible sequences for the car sequencing problem. Second, we present an algorithm which can optimize a level scheduling objective while taking care of the car sequencing constraints. Computational results are presented which show that feasible sequences can be obtained quickly for large problem instances.     

A note on Toyota's goal of sequencing mixed models on an assembly line

Toyota's goal of sequencing mixed models on an assembly line is to keep the constant usage of every part used in the assembly line. This goal is a good way of fitting the just-in-time concept in Toyota’s production system. In all of Toyota's goal oriented studies a consideration which has not been explained explicitly in the literature is that all the parts of a given product are assumed to be used at the epoch of just this unit into the assembly line. This treatment is equivalent to an assumption of a single workstation assembly line with zero length. For an assembly line with multiple workstations, however, it is clear that the parts of a given product are used at different epochs subsequent to originally feeding this unit into it. This note discusses Toyota's goal of sequencing mixed models on an assembly line with multiple workstations. The sequencing problem is formulated based on defining the ideal usage rate of a part as the requirement for the part per time period. A modified goal chasing algorithm is proposed for solving this sequencing problem. An example is given to illustrate the methodology.     

A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II

Mixed-model assembly lines allow for the simultaneous assembly of a set of similar models of a product, which may be launched in the assembly line in any order and mix. As current markets are characterized by a growing trend for higher product variability, mixed-model assembly lines are preferred over the traditional single-model assembly lines.

This paper presents a mathematical programming model and an iterative genetic algorithm-based procedure for the mixed-model assembly line balancing problem (MALBP) with parallel workstations, in which the goal is to maximise the production rate of the line for a pre-determined number of operators.

The addressed problem accounts for some relevant issues that reflect the operating conditions of real-world assembly lines, like zoning constraints and workload balancing and also allows the decision maker to control the generation of parallel workstations.     

A Lagrangean relaxation approach for the mixed-model flow line sequencing problem

In this study, a mixed-model flow line sequencing problem is considered. A mixed-model flow line is a special case of production line where products are transported on a conveyor belt, and different models of the same product are intermixed on the same line. We have focused on product-fixed, rate-synchronous lines with variable launching. Our objective function is minimizing makespan. A heuristic algorithm based on Lagrangean relaxation is developed for the problem, and tested in terms of solution quality and computational efficiency.     

Fuzzy activity time-based model predictive control of open-station assembly lines

The sequencing and line balancing of manual mixed-model assembly lines are challenging tasks due to the complexity and uncertainty of operator activities. The control of cycle time and the sequencing of production can mitigate the losses due to non-optimal line balancing in the case of open-station production where the operators can work ahead of schedule and try to reduce their backlog. The objective of this paper is to provide a cycle time control algorithm that can improve the efficiency of assembly lines in such situations based on a specially mixed sequencing strategy. To handle the uncertainty of activity times, a fuzzy model-based solution has been developed. As the production process is modular, the fuzzy sets represent the uncertainty of the elementary activity times related to the processing of the modules. The optimistic and pessimistic estimates of the completion of activity times extracted from the fuzzy model are incorporated into a model predictive control algorithm to ensure the constrained optimization of the cycle time. The applicability of the proposed method is demonstrated based on a wire-harness manufacturing process with a paced conveyor, but the proposed algorithm can handle continuous conveyors as well. The results confirm that the application of the proposed algorithm is widely applicable in cases where a production line of a supply chain is not well balanced and the activity times are uncertain.     

On the part inventory model sequencing problem: complexity and Beam Search heuristic

In many industries mixed-model assembly systems are increasingly supplied out of third-party consignment stock. This novel trend gives rise to a new short-term sequencing problem which decides on the succession of models launched down the line and aims at minimizing the cost of in-process inventory held by the manufacturer. In this work, we investigate the mathematical structure of this part oriented mixed-model sequencing problem and prove that general instances of the problem are NP-hard in the strong sense. Moreover, we develop a new Beam Search heuristic, which clearly outperforms existing solution procedures.     

Integrated Set Parts Supply system in a mixed-model assembly line

The mixed-model assembly line (MMAL) is a type of assembly line in which a variety of product models are assembled on the same line. The use of highly variant parts on the assembly line need to be considered carefully to enable satisfactory material flow control and allow for smooth production. To increase the quality of parts supply and parts assembly in MMAL, Toyota has introduced an innovation system known as Set Parts Supply (SPS). In this paper, we investigate the parts supply issues in SPS implementation using a case study in the automotive industry. The linkage of parts supply strategies with Manufacturing Execution System (MES) is introduced to improve the SPS implementation which are (i) synchronized parts supply, (ii) e-kanban system and (iii) Synchronized Supply Sheet. From the research findings, the integration with MES has contributed to the Just In Time in parts supply at the supermarket area and assembly line.     

BINARY INTEGER FORMULATION FOR MIXED-MODEL ASSEMBLY LINE BALANCING PROBLEM

The assembly line balancing problem has been a focus of interest to the academicians of production/operations management for the last 40 years. Although there are numerous studies published on the various aspects of the problem, the number of studies on mixed-model assembly lines are relatively small. In this paper, a binary integer programming model for the mixed-model assembly line balancing problem is developed and some computational properties of the model are given.     

A simulated annealing algorithm based approach for balancing and sequencing of mixed-model U-lines

It is known that two interrelated problems called as line balancing and model sequencing should be solved simultaneously for an efficient implementation of a mixed-model U-shape assembly line in a JIT (Just in Time) environment. On the other hand, three versions of assembly line balancing problem can be identified: Type I, Type II, and Type E. There are only two articles ( Kara, Ozcan, & Peker, 2007a and Hamzadayi & Yildiz, 2012) related to simultaneous balancing and sequencing of mixed-model U-lines for minimizing the number of stations (Type 1 problem) by ignoring the fixed model sequence in the current literature. In this paper, a simulated annealing algorithm is proposed for solving a problem of type 1 by ignoring the fixed model sequence. Accordingly, simulated annealing based fitness evaluation approach proposed by Hamzadayi and Yildiz (2012) is enhanced by adding the tabu list, and inserted into the proposed algorithm. Implementation difficulties experienced in meta-heuristics based on solution modification for solving these types of problems are demonstrated. ‘Absolute deviation of workloads’ (ADW) is quite frequently used as performance criteria in the literature. It is found that ADW is an insufficient performance criterion for evaluating the performance of the solutions, and this is showed by means of an illustrative example. The parameters of the proposed algorithm are reviewed for calibrating the algorithm by means of Taguchi design of experiments. Performance of the proposed approach is tested through a set of test problems. The results of computational experiments indicate that the proposed approach is an effective method in solving simultaneous line balancing/model sequencing problems for mixed-model U-lines for minimizing the number of stations.     

A multi-objective scatter search for a mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line where a variety of product models similar to product characteristics are assembled. There is a set of criteria on which to judge sequences of product models in terms of the effective utilization of this line. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Since this type of problem is NP-hard, a new multi-objective scatter search (MOSS) is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three prominent multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSS outperforms the existing genetic algorithms, especially for the large-sized problems.     

Modeling and solving mixed-model assembly line balancing problem with setups. Part I: A mixed integer linear programming model

This paper is the first one of the two papers entitled “modeling and solving mixed-model assembly line balancing problem with setups”, which has the aim of developing the mathematical programming formulation of the problem and solving it with a hybrid meta-heuristic approach. In this current part, a mixed-integer linear mathematical programming (MILP) model for mixed-model assembly line balancing problem with setups is developed. The proposed MILP model considers some particular features of the real world problems such as parallel workstations, zoning constraints, and sequence dependent setup times between tasks, which is an actual framework in assembly line balancing problems. The main endeavor of Part-I is to formulate the sequence dependent setup times between tasks in type-I mixed-model assembly line balancing problem. The proposed model considers the setups between the tasks of the same model and the setups because of the model switches in any workstation. The capability of our MILP is tested through a set of computational experiments. Part-II tackles the problem with a multiple colony hybrid bees algorithm. A set of computational experiments is also carried out for the proposed approach in Part-II.     

A decomposition approach for the car resequencing problem with selectivity banks

An important decision problem when mass-producing customized product to order is the sequencing problem, which decides on the succession of models launched down a mixed-model assembly line. To avoid work overload of workforce the car sequencing problem restricts the maximum occurrence of labor-intensive options, e.g., a sunroof, in a subsequence of a certain length by applying sequencing rules. In the real-world, frequently perturbations occur stirring up an initially planed sequence, so that a resequencing is required. This paper treats the car resequencing problem where a selectivity bank, which is a special form of buffer organization consisting of parallel line segments without assembly operations, is applied to reshuffle a given initial sequence and rule violations are to be minimized. The problem is formalized and suited heuristic solution procedures are presented and tested. Furthermore, the impact of differently sized mix-banks on resequencing flexibility is investigated.