Simple heuristics for the assembly line worker assignment and balancing problem

We propose simple heuristics for the assembly line worker assignment and balancing problem. This problem typically occurs in assembly lines in sheltered work centers for the disabled. Different from the classical simple assembly line balancing problem, the task execution times vary according to the assigned worker. We develop a constructive heuristic framework based on task and worker priority rules defining the order in which the tasks and workers should be assigned to the workstations. We present a number of such rules and compare their performance across three possible uses: as a stand-alone method, as an initial solution generator for meta-heuristics, and as a decoder for a hybrid genetic algorithm. Our results show that the heuristics are fast, they obtain good results as a stand-alone method and are efficient when used as a initial solution generator or as a solution decoder within more elaborate approaches.Comment: 18 pages, 1 figur

임시 작업자를 활용한 혼합모델 조립라인의 통합적 균형화 연구

학위논문 (석사)-- 서울대학교 대학원 : 산업공학과, 2015. 2. 문일경.이 논문은 단일 제품을 조립하는 일반적인 조립라인 균형화 문제를 복수 제품들을 동시에 조립할 수 있는 혼합 모델 조립라인으로 확장하였으며, 임시 작업자를 고용하여 조립라인을 효율화할 수 있도록 하였다. 이를 고려한 세 가지 버전의 수학적 모형들을 개발하였다. 각 모형의 목표는 모든 직원의 임금과 작업장 비용을 합친 총 비용을 최소화하는 것, 작업장 수가 주어진 상황에서 사이클 시간을 최소화하는 것, 그리고 정해진 작업장 안에서 업무 과부하를 최소화하는 것이다. 제안된 모형들은 숙련된 작업자와 임시 작업자를 동시에 할당하는 사안과 작업들 사이의 선행관계 등 실제 현장에서 적용되는 실용적 특성들을 고려하고 있다. 뿐만 아니라, 총 비용을 최소화할 수 있는 복합유전알고리즘도 개발되었다. 해의 타당성을 보장하고 복합유전알고리즘의 우수성을 높이기 위해 특별한 유전연산자들과 발견적 기법이 사용되었다. 수치실험들을 통해서 복합유전알고리즘의 우수성을 입증하기 위하여 수학적 모형과 비교하였다.This study extends a single-model assembly line balancing problem to an integrated mixed-model assembly line balancing problem by incorporating temporary unskilled workers, who enhance productivity. Three mathematical models are developed to minimize the sum of total workstation costs, salaries of all workers, and cycle times and potential work overload of a predetermined number of workstations. The proposed models are based on particular features of the real-world problem, such as simultaneous assignments of skilled and temporary unskilled workers as well as precedent restrictions among the tasks. Furthermore, a hybrid genetic algorithm that minimizes total operation costs is developed. Special genetic operators and heuristic algorithms are used to ensure feasibility of solutions and make the hybrid genetic algorithm efficient. Computational experiments demonstrate the superiority of the hybrid genetic algorithm over the mathematical models.Chapter 1. Introduction 1 1.1 The assembly line 1 1.1.1 Characteristics of assembly line problem 1 1.1.2 Assembly line balancing problem 2 1.2 The mixed-model assembly line 3 1.2.1 Characteristics of mixed model assembly line problem 3 1.2.2 Mixed model assembly line balancing problem 4 1.3 Literature review 5 1.4 Contributions 9 Chapter 2. Mathematical Models 11 2.1 General features of mathematical models 11 2.2 Problem description 11 2.3 Model Ⅰ 14 2.4 Model Ⅱ 18 2.5 Model Ⅲ 20 Chapter 3. A Hybrid Genetic Algorithm 23 3.1 Chromosome representation 24 3.2 Objective and fitness function 26 3.3 Genetic operator 27 3.3.1 Selection 27 3.3.2 Crossover 28 3.3.3 Mutation 29 3.4 Terminating conditions and parameters 30 Chapter 4. Computational Experiments 31 4.1 Experiments for Model Ⅰ 39 4.2 Experiments for Model Ⅱ 42 4.3 Experiments for Model Ⅲ 46 4.4 Validation of a hybrid genetic algorithm 48 Chapter 5. Conclusions 50 Bibliography 51 Abstract 54Maste

Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines

Copyright © 2015 Springer. This is a PDF file of an unedited manuscript that has been accepted for publication in The International Journal of Advanced Manufacturing Technology. The final publication is available at: http://link.springer.com/article/10.1007/s00170-015-7320-y. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.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

Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set

A note on “A multi-objective genetic algorithm for solving assembly line balancing problem”

Assembly line balancing has a considerable place in industrial importance. Hence, a lot of researchers are interested in this subject and several papers have been published so far. Many exact, heuristic, metaheuristic, and hybrid approaches have been used to solve this type of problems. Recently Ponnanbalam et al. (Int J Adv Manuf Technol 16:341–352, 2000) have considered a multi-objective genetic algorithm utilizing several simple heuristic rules for solving the simple assembly line balancing problems, one of these rules was “rank positional weight (RPW)” originally published in Helgeson and Birnie (J Ind Eng 12(6):394–398, 1961). Through providing two possible justifications, this note suggests that the mentioned rule can be mistakenly utilized and some revisions in Ponnanbalam et al. (Int J Adv Manuf Technol 16:341–352, 2000) seem to be necessary

Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms

The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of theoptimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies

An integrated representation scheme for assembly sequence planning and assembly line balancing

In a typical assembly optimisation, Assembly Sequence Planning and Assembly Line Balancing are performed independently. However, competition has compelled the manufacturer to innovate by integrating the optimisation of both problems. To incorporate ASP and ALB optimisations into a single integrated optimisation, a clear prerequisite is the availability of integrated ASP and ALB representation. Although many assembly representation works has been proposed, none of them fully meet the requirements of integrated optimisation because they were developed independently from various needs. In this paper, an integrated representation scheme for ASP and ALB that incorporate essential optimisation information is developed. The proposed representation scheme is built based on assembly tasks and represented using precedence graph and data matrix. The outcome from presented example showed that the information for ASP and ALB optimisation can be integrated and represented using task based precedence graph and data matrix, without discarding important attributes