Sustainable operator assignment in an assembly line using genetic algorithm

This paper addresses the operator assignment in predefined workstations of an assembly line to get a sustainable result of fitness function of cycle time, total idle time and output where genetic algorithm is used as a solving tool. A proper operator assignment is important to get a sustainable balanced line. To improve the efficiency and meet the desired target output within the time limit, a balanced assembly line is a must. Real world lines consist of a large number of tasks and it is very time consuming and crucial to choose the most suitable operator for a particular workstation. In addition, it is very important to assign the suitable operator at the right place as his skill of operating machines finally reflects in productivity or in the cost of production. To verify better assignments of workers, a genetic algorithm is adopted here. A heuristic is proposed to find out the sustainable assignment of operators in the predefined workstations.     

遗传禁忌搜索算法在混流装配线排序中的应用

针对混流装配线排序问题,提出了一种混合遗传禁忌搜索算法,在每一代遗传演化之后,按一定比例随机选择部分解进行禁总搜索,以提高算法的全局搜索能力和收敛性。通过一个混流装配线排序实验,分别利用遗传算法和遗传禁忌搜索算法进行求解,结果表明遗传禁忌搜索算法具有更好的全局搜索能力和收敛性能。     

An adaptive genetic algorithm approach for the mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line that is capable of producing a variety of different product models simultaneously and continuously. The design and planning of such lines involve several long- and short-term problems. Among these problems, determining the sequence of products to be produced has received considerable attention from researchers. This problem is known as the Mixed-Model Assembly Line Sequencing Problem (MMALSP). This paper proposes an adaptive genetic algorithm approach to solve MMALSP where multiple objectives such as variation in part consumption rates, total utility work and setup costs are considered simultaneously. The proposed approach integrates an adaptive parameter control (APC) mechanism into a multi-objective genetic algorithm in order to improve the exploration and exploitation capabilities of the algorithm. The APC mechanism decides the probability of mutation and the elites that will be preserved for succeeding generations, all based on the feedback obtained during the run of the algorithm. Experimental results show that the proposed adaptive GA-based approach outperforms the non-adaptive algorithm in both solution quantity and quality.     

Stochastic two-sided U-type assembly line balancing: a genetic algorithm approach

In this paper, a novel stochastic two-sided U-type assembly line balancing (STUALB) procedure, an algorithm based on the genetic algorithm and a heuristic priority rule-based procedure to solve STUALB problem are proposed. With this new proposed assembly line design, all advantages of both two-sided assembly lines and U-type assembly lines are combined. Due to the variability of the real-life conditions, stochastic task times are also considered in the study. The proposed approach aims to minimise the number of positions (i.e. the U-type assembly line length) as the primary objective and to minimise the number of stations (i.e. the number of operators) as a secondary objective for a given cycle time. An example problem is solved to illustrate the proposed approach. In order to evaluate the efficiency of the proposed algorithm, test problems taken from the literature are used. The experimental results show that the proposed approach performs well.     

多类约束下U型装配线平衡建模研究

现有针对U型装配线平衡问题的研究假设除了节拍约束和任务间的优先顺序关系外,不存在其它约束,实际上受生产环境、产品设计、工艺要求以及人因等多种因素的制约,在对U型装配线进行优化设计时还需满足其它约束,将各种表象不同的约束抽象为相连、相斥、相关以及工作站属性约束四类,针对多类约束下U型装配线平衡问题建立了整数规划模型,通过...     

An improved multi-objective genetic algorithm for heterogeneous coverage RFID network planning

Recent research has demonstrated the potential benefits of radio frequency identification (RFID) technology in the supply chain and production management via its item-level visibility. However, the RFID coverage performance is largely impacted by the surrounding environment and potential collisions between the RFID devices. Thus, through RFID network planning (RNP) to achieve the desired coverage within the budget becomes a key factor for success. In this study, we establish a novel and generic multi-objective RNP model by simultaneously optimising two conflicted objectives with satisfying the heterogeneous coverage requirements. Then, we design an improved multi-objective genetic algorithm (IMOGA) integrating a divide-and-conquer greedy heuristic algorithm to solve the model. We further construct a number of computational cases abstracted from an automobile mixed-model assembly line to illustrate how the proposed model and algorithm are applied in a real RNP application. The results show that the proposed IMOGA achieves highly competitive solutions compared with Pareto optimal solutions and the solutions given by four recently developed well-known multi-objective evolutionary and swarm-based optimisers (SPEA2, NSGA-II, MOPSO and MOPS²O) in terms of solution quality and computational robustness.     

Modelling and optimisation of energy-efficient U-shaped robotic assembly line balancing problems

Within U-shaped assembly lines, the increase of labour costs and subsequent utilisation of robots has led to growing energy consumption, which is the current main expense of auto and electronics industries. However, there are limited researches concerning both energy consumption reduction and productivity improvement on U-shaped robotic assembly lines. This paper first develops a nonlinear multi-objective mixed-integer programming model, reformulates it into a linear form by linearising the multiplication of two binary variables, and then refines the weight of multiple objectives so as to achieve a better approximation of true Pareto frontiers. In addition, Pareto artificial bee colony algorithm (PABC) is extended to tackle this new complex problem. This algorithm stores all the non-dominated solutions into a permanent archive set to keep all the good genes, and selects one solution from this set to overcome the strong local minima. Comparative experiments based on a set of newly generated benchmarks verify the superiority of the proposed PABC over four multi-objective algorithms in terms of generation distance, maximum spread, hypervolume ratio and the ratio of non-dominated solution.     

Flexible assembly line design problem with fixed number of workstations

In the paper, we study a flexible assembly line design problem with equipment decisions. We assume the task times and equipment costs are correlated in the sense that for all tasks the cheaper equipment gives no smaller task time. Given the cycle time and number of workstations we aim to find the assignment of tasks and equipment to the workstations so as to minimise the total equipment cost. We develop a branch and bound algorithm that uses powerful lower bounds and reduction mechanisms. Our computational experiments have revealed that our algorithm can solve large-sized problem instances in reasonable solution times.     

基于分段遗传编码的配送路径优化

为减小物流配送中车辆路径优化这一NP难题的组合规模，增大其搜索空间及寻优效率，提出了一种分段编码方法，编码中的各段表示相应车辆路径的需求城市集合。以非完全连通配送网络为研究对象，基于分段遗传编码，构造了车辆路径优化问题的遗传算法，通过对编码各段超载、包容等的判断，得出一个关于需求城市的最优划分。最后利用改进的迪杰斯特拉算法，根据最优化分中的需求城市，求取最优的配送路径集。计算示例证明了该方法的有效性。     

Non-dominated ranked genetic algorithm for a multi-objective mixed-model assembly line sequencing problem

The increasing market demand for product variety forces manufacturers to design mixed-model assembly lines (MMAL) on which a variety of product models similar to product characteristics are assembled. This paper presents a method combining the new ranked based roulette wheel selection algorithm with Pareto-based population ranking algorithm, named non-dominated ranking genetic algorithm (NRGA) to a just-in-time (JIT) sequencing problem when two objectives are considered simultaneously. The two objectives are minimisation the number of setups and variation of production rates. This type of problem is NP-hard. Various operators and parameters of the proposed algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. The solutions obtained via NRGA are compared against solutions obtained via total enumeration (TE) scheme in small problems and also against four other search heuristics in small, medium and large problems. Experimental results show that the proposed algorithm is competitive with these other algorithms in terms of quality and diversity of solutions.     

U型生产单元可重构设施布局方法研究

针对制造企业生产单元重新布局的决策问题,以U型生产单元为研究对象,以物料搬运成本、单元重构成本和生产时间损失成本之和最低为优化目标,考虑了重新布局的预算约束以及各类惩罚约束,建立了单元生产可重构设施布局决策的集成模型。设计了决策问题的遗传算法,以实验测试方式确定遗传算法的参数设置,避免陷入局部最优,并通过实例求解,验证了模型和算法的可行性。根据U型单元现有布局、下一周期订单数量和产品工艺要求等信息,利用上述模型和算法进行分析和优化,为企业进行新一轮的生产组织提供决策支持。