Sequencing mixed-model flexible assembly lines with variable launching intervals

In real-life mixed-model assembly lines, multiple problems collectively affect the final production’s performance. In this study, mixed-model assembly lines integrated with balancing and sequencing problems are considered simultaneously solved. A comprehensive mathematical model is formulated to evaluate the current multi-objective problem. An intelligent hybrid genetic algorithm (IHGA) is proposed to solve the integrated mixed-model assembly line balancing and sequencing problem. The performance of the proposed algorithm is triggered by integrating heuristic rules through a generation gap mechanism which helps in reducing search space without succumbing to local optima. Additionally, parametric tuning of the algorithm is performed using Q-learning, enabling adaptive optimization through reinforcement learning. This helps to enhance computational efficiency and achieve robust performance of the proposed algorithm. The performance of the IHGA algorithm is rigorously compared with existing approaches, including a non-dominated sorting genetic algorithm, multi-objective artificial bee colony, multi-objective particle swarm optimization, multi-objective evolutionary algorithm based on Decomposition, and multi-objective grey wolf optimizer. Results demonstrate the superior performance of the proposed algorithm across various metrics, showcasing its efficacy in optimizing mixed-model assembly lines, where symmetry in task allocation and sequencing can significantly enhance operational efficiency in contemporary industrial settings. Additionally, a real-life case study is solved to validate the empirical applicability of the proposed IHGA. The extensive experimental analysis notably shows that the proposed IHGA outperforms the existing methods.

A mixed-model assembly line is a type of production line where different models of a product are assembled on. Mixed-model assembly lines can respond to unanticipated changes in product demands quickly without keeping so many inventories. Designing mixed-model assembly line involves solving the traditional problems of the assembly line design (consists of balancing problem, determining cycle time, and the number and sequence of stations) in addition of determining the sequence of products in assembly line. The main goal of this paper is presenting a method in order to determine the sequence of products in mixed-model assembly line by considering Just-in-Time systems. Moreover, supplying some required components from feeding lines is considered. A mathematical model is presented which is capable of specifying the sequence of products in the mixed-model assembly line by considering main criteria and keeping feeding lines balanced. Mathematical model can be used for solving small-size problems. Because the combinatorial nature of sequencing problems typically provides an intractable search space for problems of “real world” size, the search heuristics of simulated annealing and ant colony algorithms are presented and used to find solutions for several problem sets. Experimentations show that the simulated annealing approach outperforms the ant colony approach in objective function performance.

SUMMARYIn this paper, an integrated mathematical model for the balancing and sequencing problems of a mixed-model assembly line (MMAL) is developed. The proposed model minimizes the total overload and idleness times. For the sake of reality, the impact of operator’s learning and fatigue issues on the optimization of the assembly line balancing and sequencing problems is considered. Furthermore, it is assumed that the Japanese mechanism is used in this assembly line to deal with the overload issue. With respect to the complexity level of the proposed model, a genetic algorithm is developed to solve the model. In order to set the parameters of the developed genetic algorithm, the well-known Taguchi method is used and the efficiency of this solution method is compared with the GAMS software using several test problems with different sizes. Finally, the sensitivity of the balancing and sequencing problems to the parameters such as station length, learning rate, and fatigue rate are analyzed and the impact of changing these parameters on the model is studied.

An open-station assembly line that manufactures mixed models of sheet metal cabinets is considered in this research. The problem minimizes the total cost of the idle and utility times incurred in an assembly line with different line parameters (such as launch interval, station length, starting point of work, upstream walk, locus of the operator's movement, etc.) and operation sequences of the mixed models. An open-station system plays a significant controlling role in determining the optimal line parameters that minimize the total cost of idle and utility times in a mixed-model assembly line. Thus, a mixed-integer programming model for an open-station system is developed here to determine line parameters optimally. The model is tested on a three-station mixed-model line, which is a partial representation of a complete long assembly line. This research obtained a set of line parameters that minimize the total cost of idle and utility times optimally. Results indicate that the minimum total cost of idle and utility times in an open-station system decreases with line length. Other results pertinent to the line design are also demonstrated.

This study is concerned about how to optimize the input sequence of product models in Mixed-Model Assembly Lines (MMALs) with limited intermediate buffers.Two objectives are considered simultaneously: minimizing the variation in parts usage and minimizing the makespan. The mathematical model is presented by incorporating the two objectives according to their relative importance weights. A hybrid algorithm (GASA), based on a genetic algorithm (GA) and a simulated annealing algorithm (SA), is proposed for solving the model. The performance of the GASA is compared with the GA over various test problems. The results show that, in terms of solutions’ quality, both the GASA and GA can find the same best solution for small-sized problems and the GASA performs better than the GA for mediate and large-sized problems. Moreover, the impact of buffer size on MMAL’s performance is investigated.

This paper presents a fast sequencing algorithm for a mixed model assembly line with multiple workstations which minimize the total utility work and idle time. We compare the proposed algorithms with another heuristic, the Tsaibased heuristic, for a sequencing problem that minimizes the total utility works. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm. The Tsai-based heuristic performs best in terms of utility work, but the fast sequencing algorithm performs well for both utility work and idle time. However, the computational complexity of the fast sequencing algorithm is O (KN) while the Tsai-based algorithm is O (KNlogN). Actual computational time of the fast sequencing heuristic is 2-6 times faster than that of the Tsai-based heuristic.

The objective of car sequencing problem is to find an optimal permutation for a collection of cars sharing the same basic product model to be built in the same production line of car plants. Competence in car sequencing is of great significance for efficient use of mixed-model assembly lines in that the launching sequence of cars to the lines imposes a heavy effect on the performance of logistics as well as the workload assigned to a sequence of workstations of the line. The main effort of this paper is to find an optimal permutation of cars from the perspective of logistics optimisation, considering the ratio constraint of respective workstations. However, the uncertainty caused by the stochastic arrival of customer orders in a built-to-order environment increases the complexity of car sequencing problems, and the logistics activities in particular. Confronted with the uncertainty and complexity, we put the car sequencing into a more vivid and intuitive scenario by simulating the behaviour of materials. To begin with, the logistics activities occurring in a specific automobile assembly line is investigated. Secondly, the ratio constraint of car sequencing problem is examined and a logistics model is built. Using the genetic algorithm toolbox embedded in Matlab7.0 package, we obtain subsequently an optimised permutation satisfying the proposed objective. Finally, the proposed logistics model is validated by the simulation results.

Facing the increasingly competitive market environment, assembly lines are gained attention because of the assembly process expenditure consumes more than 20 % of the manufacturing cost, and also improve its products to meet demand. In this paper, an attempt has been made to develop worker-task assigned to workstation heuristic (W-TAWH) approach for solving mixed-model assembly line balancing problem (M-MALBP), in which the assembly tasks have to be assigned to workstations, and each workstation needs to select only one of the available operators to perform the assigned tasks corresponding to the straight and U-shaped assembly lines with the objective minimum cycle time. The performance of the developed approach was evaluated and validated through applying the practical case in the Dayla State Company for Electrical Industries (DSCEI) particularly in the electric transformer factory (ETF). Taguchi method was used to evaluate the contribution of each one of the considerations in the assembly line are solution approach, layout, sequence vector (SV), and workstation numbers. All results proved the stated considerations have impact significant in any trying to re-balance the assembly line.

The assembly line balancing problem deals with the assignment of tasks to work stations. Mixed-model assembly line problem is a type of assembly line balancing problem at which two or more models of the same product are assembled sequentially at the same line. To achieve optimality and efficiency of solving this problem, tasks at each work station have to be well balanced satisfying all constraints. This paper deals with the mixed-model assembly line balancing problem (MALBP) in which the objective is to minimize the cycle time for a given number of work stations. The problem is solved by using a hybrid of an ant colony optimization and a greedy algorithm (Ant-Greedy). MATLAB Software is used to perform the proposed method. Then, the proposed method is applied to a real case problem found in the literature for the assembly line of automatic changeover in the Electronic Industries Company in Iraq. The results of the proposed method are compared with the performance of the Merging Shortest and Longest Operation (MMSLO) method. The comparison shows that the Ant-Greedy optimization method is more efficient, where the efficiency increased from 93.53% for MMSLO method to 97.26% for the Ant-Greedy method.

Dynamic takt time decisions for paced assembly lines balancing and sequencing considering highly mixed-model production: An improved artificial bee colony optimization approach

Different from a large number of existing studies in the literature, this paper addresses two important issues in managing production lines, the problems of line balancing and model sequencing, concurrently. A novel hybrid agent-based ant colony optimization–genetic algorithm approach is developed for the solution of mixed model parallel two-sided assembly line balancing and sequencing problem. The existing agent-based ant colony optimization algorithm is enhanced with the integration of a new genetic algorithm-based model sequencing mechanism. The algorithm provides ants the opportunity of selecting a random behavior among ten heuristics commonly used in the line balancing domain. A numerical example is given to illustrate the solution building procedure of the algorithm and the evolution of the chromosomes. The performance of the developed algorithm is also assessed through test problems and analysis of their solutions through a statistical test, namely paired sample t test. In accordance with the test results, it is statistically proven that the integrated genetic algorithm-based model sequencing engine helps agent-based ant colony optimization algorithm robustly find significantly better quality solutions.

Mixed-model assembly lines are widely used in just-in-time (JIT) production systems. One of the commonly used goals in sequencing mixed-model assembly lines in JIT production systems is to maintain...

In a mixed-model assembly line, different models of a common base product can be manufactured in intermixed production sequences. A famous solution approach for the resulting short-term sequencing problem is the so-called level scheduling problem, which aims at evenly smoothing the material requirements over time in order to facilitate a just-in-time supply. However, if materials are delivered in discrete quantities, the resulting spread of material usages implies that issued cargo carriers of a respective material remain at a station for a longer period of time. In practical applications with many materials required per station, this procedure might lead to bottlenecks with respect to the scarce storage space at stations. This paper investigates level scheduling under the constraint that the induced part usage patterns may not violate given storage constraints. The resulting sequencing problem is formalised and solved by suitable exact and heuristic solution approaches.

Model-dependent task assignment in multi-manned mixed-model assembly lines with walking workers

The increasing market demand for product variety forces the manufacturers to design a mixed-model assembly line on which different model products can be switched quickly to satisfy diversified demand. Scheduling is critical to generate a sequence of different products to meet due dates and to avoid possible component shortage or starvation. Because of the combinatorial nature of integer programming and large sizes of real problems, the scheduling for mixed-model assembly line has been recognized to be extremely difficult. In this paper, an optimization-base scheduling method for mixed-model assembly line is presented. The study is motivated by the design and implementation of a scheduling system for the compressor assembly lines of Toshiba. In the shop, an assembly line can be simplified as a single machine and the number of components can be supplied is limited within fixed length windows. A separable integer programming formulation is developed and a solution methodology based on a combined Lagrangian relaxation and heuristic can be applied to get near-optimal schedule. The method has been implemented using the object-oriented programming language C++, and numerical testing shows that the generated sequence will help on-time product delivery, low finished goods inventory and smooth usage of components.