Multi-objective Assembly Line Balancing Problems Research Articles

Multi-Objective Assembly Line Balancing Problem with Setup Times Using Fuzzy Goal Programming and Genetic Algorithm

Assembly lines are often indispensable in factories, and in order to attain a certain level of assembly line productivity, multiple goals must be considered at the same time. However, these multiple goals may conflict with each other, and this is a multi-objective assembly line balancing problem. This study considers four objectives, namely minimizing the cycle time, minimizing the number of workstations, minimizing the workload variance, and minimizing the workstation idle time. Since the objectives conflict with each other, for example, minimizing the cycle time may increase the number of workstations, the fuzzy multi-objective linear programming model is used to maximize the satisfaction level. When the problem becomes too complicated, it may not be solved by the fuzzy multi-objective linear programming model using a mathematical software package. Therefore, a genetic algorithm model is proposed to solve the problem efficiently. By studying practical cases of an automobile manufacturer, the results show that the proposed fuzzy multi-objective linear programming model and the genetic algorithm model can solve small-scale multi-objective assembly line balancing problems efficiently, and the genetic algorithm model can obtain good solutions for large-scale problems in a short computational time. Datasets from previous works are adopted to examine the applicability of the proposed models. The results show that both the fuzzy multi-objective linear programming model and the genetic algorithm model can solve the smaller problem cases and that the genetic algorithm model can solve larger problems. The proposed models can be applied by practitioners in managing a multi-objective assembly line balancing problem.

Multi-objective assembly line balancing problem under uncertainty using genetic algorithm

This paper addresses the single-model multi-objective assembly line balancing (ALB) problem with fuzzy processing time. A fuzzy optimisation model is formulated for the problem. In the classical mathematical formulation of SALBP, the relevant data are considered deterministic. But, the data of the real world problems are imprecise, vague or uncertain. The input data should be estimated within uncertainty represented by a fuzzy number. The concept of fuzzy numbers is introduced to treat imprecise data, such as the processing time of each task. Due to the NP-hard nature of the ALB problem, heuristics are used to solve real life cases. In this paper, an efficient heuristic is proposed to solve the fuzzy multi-objective ALB problem. The proposed heuristic is based on genetic algorithm (GA) that is efficient to handle fuzzy job time through the evolution process. As the proposed algorithm can find out the optimum solution as a trade-off among several objectives, it outperforms the existing heuristics on several test cases. The proposed algorithm can be effectively used in the design phase of an assembly line.

Multi-objective assembly line balancing problem under uncertainty using genetic algorithm

This paper addresses the single-model multi-objective assembly line balancing (ALB) problem with fuzzy processing time. A fuzzy optimisation model is formulated for the problem. In the classical mathematical formulation of SALBP, the relevant data are considered deterministic. But, the data of the real world problems are imprecise, vague or uncertain. The input data should be estimated within uncertainty represented by a fuzzy number. The concept of fuzzy numbers is introduced to treat imprecise data, such as the processing time of each task. Due to the NP-hard nature of the ALB problem, heuristics are used to solve real life cases. In this paper, an efficient heuristic is proposed to solve the fuzzy multi-objective ALB problem. The proposed heuristic is based on genetic algorithm (GA) that is efficient to handle fuzzy job time through the evolution process. As the proposed algorithm can find out the optimum solution as a trade-off among several objectives, it outperforms the existing heuristics on several test cases. The proposed algorithm can be effectively used in the design phase of an assembly line.

Balancing Lexicographic Multi-Objective Assembly Lines with Multi-Manned Stations

In a multi-manned assembly line, tasks of the same workpiece can be executed simultaneously by different workers working in the same station. This line has significant advantages over a simple assembly line such as shorter line length, less work-in-process, smaller installation space, and less product flow time. In many realistic line balancing situations, there are usually more than one objective conflicting with each other. This paper presents a preemptive goal programming model and some heuristic methods based on variable neighborhood search approach for multi-objective assembly line balancing problems with multi-manned stations. Three different objectives are considered, minimizing the total number of multi-manned stations as the primary objective, minimizing the total number of workers as the secondary objective, and smoothing the number of workers at stations as the tertiary objective. A set of test instances taken from the literature is solved to compare the performance of all methods, and results are presented.

Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems

This paper aims at multi-objective straight and U-shaped assembly line balancing problems with the fuzzy task processing times. The problems are referred to herein as f-SALBP and f-SULBP and the objectives that are considered to be satisfied are: (a) minimizing the numbers of stations, (b) maximizing the fuzzy line efficiency, (c) minimizing the fuzzy idleness percentage, and (d) minimizing the fuzzy smoothness index. In fact, the f-SALBP and f-SULBP are SALBP and SULBP generalization in fuzzy circumstance, respectively. Initially, the two problems are formulated and due to the uncertainty, variability and imprecision that often occurred in real-world production systems, the processing time of tasks are supposed as triangular fuzzy numbers. Then, to solve the problem, a hybrid multi-objective genetic algorithm is proposed. A One-Fifth Success Rule (OFSR) is deployed for the selection and mutation operators to improve the genetic algorithm's performance. The results in the genetic algorithm are being controlled in convergence and diversity simultaneously by means of controlling the selective pressure (SP) and mutation rate. Likewise, a fuzzy controller to SP is employed for the OFSR toward a better implementation of the genetic algorithm. In addition, the Taguchi design of experiments is used for parameter control and calibration. Finally, the numerical examples are presented to compare the performance of the proposed method with the existing ones. The results show significantly better performance for the proposed algorithm.

Soft Computing in Optimizing Assembly Lines Balancing

As part of manufacturing systems, the assembly line has become one of the most valuable researches to accomplish the real world problems related to them. Many efforts have been made to seek the best techniques in optimizing assembly lines. Problem statement: Since it was published by Salveson in 1955, some methods and techniques have been developed based on mathematical modeling. In recent years, some researches in Assembly Line Balancing (ALB) have been conducted using Soft Computing (SC) approaches. However, there is no comprehensive survey studies conducted regarding the use of SC in ALB problems, which is became the aim of this study. Approach: This study reviewed published literatures and previous related works that applied SC in solving ALB problems. Main outcomes: This study looks into the suitability of SC approaches in several types of ALB problems. Furthermore, this study provides the classification of ALB problems that can facilitate distinguishing those problems as fields of research. Result: This study found that Genetic Algorithms (GAs) are predominantly applied to solve ALB problems compared to other SC approaches. This high suitability in ALB refers to GAs' main characteristics that include its robustness and flexibility. These SC approaches have mostly been applied to simple ALB problems, which are not problems that are covered in a real complex manufacturing environment. Conclusion/Recommendations: This study recommends that future researches in ALB should be conducted with regard to other issues, beyond the simple ALB problems and more practical to the industries. Besides the advantages of GAs, there are still opportunities to use other SC approaches and the hybrid-systems among them that could increase the suitability of these approaches, especially for multi-objective ALB problems. This study also recommends that human involvement in ALB needs to be considered as a problem factor in ALB.

A knowledge-based system for solving multi-objective assembly line balancing problems

This paper presents design-and-development details of a knowledge-based system that solve multi-objective assembly line balancing problems to obtain an optimal assignment of a set of assembly tasks to a sequence of workstations. Assembly line balancing problems arise in high-volume production systems with a significant regularity. The formulation and solutions currently employed by managers and practitioners usually aims at optimizing one objective (e.g., number of work stations or cycle time), thus ignoring the multi-dimensional nature of the overall objectives of the manager. Furthermore, in practice ALBPs are ill-defined and ill-structured, making it difficult to formulate and solve them by mere mathematical approaches. The knowledge-based system multi-objective assembly line balancing approach, presented in this paper, addresses these needs. This paper presents a knowledge-base multi-objective approach to assembly line balancing problems. It demonstrates how such a system can be constructed and how a ...