顺序相依拆卸线平衡多目标优化模型及算法研究

拆卸是产品回收过程最关键环节之一,拆卸效率直接影响再制造成本。本文在分析现有模型不足基础上,考虑最小化总拆卸时间,建立多目标顺序相依拆卸线平衡问题优化模型,并提出了一种自适应进化变邻域搜索算法。所提算法引入种群进化机制,并采用一种组合策略构建初始种群,通过锦标赛法选择个体进化;在局部搜索时,设计了邻域结构自适应选择策略,并采用基于交叉的全局学习机制加速跳出局部最优,以提高算法寻优能力。对比实验结果,证实了所提模型的合理性以及算法的高效性。     

A balancing method and genetic algorithm for disassembly line balancing

Disassembly activities take place in various recovery operations including remanufacturing, recycling and disposal. The disassembly line is the best choice for automated disassembly of returned products. It is therefore important that the disassembly line be designed and balanced so that it works as efficiently as possible. The disassembly line balancing problem seeks a sequence which: is feasible, minimizes workstations, and ensures similar idle times, as well as other end-of-life specific concerns. However finding the optimal balance is computationally intensive with exhaustive search quickly becoming prohibitively large even for relatively small products. In this paper the problem is mathematically defined and proven NP-complete. Additionally, a new formula for quantifying the level of balancing is proposed. A first-ever set of a priori instances to be used in the evaluation of any disassembly line balancing solution technique is then developed. Finally, a genetic algorithm is presented for obtaining optimal or near-optimal solutions for disassembly line balancing problems and examples are presented to illustrate implementation of the methodology.     

第II类双边拆卸线平衡问题建模与优化

报废汽车体积大,废弃物污染大,拆卸过程复杂,将作业任务均衡分配难。为此,本文对工作站数量确定的双边拆卸线平衡问题进行研究,建立问题数学模型,设计一种变邻域蛙跳算法。所提算法在寻优过程中采用变邻域搜索提高族群局部搜索效率;引入个体学习机制加快精英个体进化速度;提出基于二分法的节拍时间调整策略加快对最优节拍的搜索。最后,通过算例对算法性能进行验证并通过实例说明任务在拆卸线上平衡分配的重要性。     

一种自适应人工蜂群算法求解U型顺序相依拆卸线平衡问题

产品拆卸过程中零部件之间会相互干扰影响任务作业时间,基于该情形构建了多目标U型SDDLBP优化模型,并提出一种自适应ABC算法。所提算法设计了自适应动态邻域搜索方法,以提高局部开发能力;采用了轮盘赌与锦标赛法结合的分段选择法,以有效评价并选择蜜源进行深度开发;建立了基于当前最优解的变异操作,以提高全局探索能力快速跳出局部最优。最后,通过算例测试和实例分析验证算法的高效性。     

An efficient grouping genetic algorithm for U-shaped assembly line balancing problems with maximizing production rate

U-type assembly line is one of the important tools that may increase companies’ production efficiency. In this study, two different modeling approaches proposed for the assembly line balancing problems have been used in modeling type-II U-line balancing problems, and the performances of these models have been compared with each other. It has been shown that using mathematical formulations to solve medium and large size problem instances is impractical since the problem is NP-hard. Therefore, a grouping genetic and simulated annealing algorithms have been developed, and a particle swarm optimization algorithm is adapted to compare with the proposed methods. A special crossover operator that always obtains feasible offspring has been suggested for the proposed grouping genetic algorithm. Furthermore, a local search procedure based on problem-specific knowledge was applied to increase the intensification of the algorithm. A set of well-known benchmark instances was solved to evaluate the effectiveness of the proposed and existing methods. Results showed that while the mathematical formulations can only be used to solve small size instances, metaheuristics can obtain high quality solutions for all size problem instances within acceptable CPU times. Moreover, grouping genetic algorithm has been found to be superior to the other methods according to the number of optimal solutions, or deviations from the lower bound values.     

A new diversity induction mechanism for a multi-objective ant colony algorithm to solve a real-world time and space assembly line balancing problem

Time and space assembly line balancing considers realistic multi-objective versions of the classical assembly line balancing industrial problems. It involves the joint optimisation of conflicting criteria such as the cycle time, the number of stations, and/or the area of these stations. The different problems included in this area also inherit the precedence constraints and the cycle time limitations from assembly line balancing problems. The presence of these hard constraints and their multi-criteria nature make these problems very hard to solve. Multi-objective constructive metaheuristics (in particular, multi-objective ant colony optimisation) have demonstrated to be suitable approaches to solve time and space assembly line balancing problems. The aim of this contribution is to present a new mechanism to induce diversity in an existing multi-objective ant colony optimisation algorithm for the 1/3 variant of the time and space assembly line balancing problem. This variant is quite realistic in the automative industry as it involves the joint minimisation of the number and the area of the stations given a fixed cycle time limit. The performance of our proposal is validated considering ten real-like problem instances. Moreover, the diversity induction mechanism is also tested on a real-world instance from the Nissan plant in Barcelona (Spain).     

具有工作站数量约束的多人工作站混合装配线平衡问题研究

针对装配线设计或改造过程中存在的因场地或成本原因导致的工作站数量不易变更的问题,研究了节拍已知情况下,具有工作站数量约束的多人工作站混合装配线平衡问题,建立以装配线总人数最小、工人负荷量标准差最小、各产品在各工作站装配时间与节拍之间的标准差最小为目标的数学模型,设计了一种结合差分进化的多目标混合遗传算法对该问题求解。通过案例计算以及与其他算法的对比分析表明,本文算法在收敛性和综合性能方面优于NSGAII和DEMO,在装配线人数和工人负荷标准差方面优于Roshani和Nezami提出的算法。     

Decomposition of unreliable assembly/disassembly networks with limited buffer capacity and random processing times

An assembly/disassembly (A/D) network is a manufacturing system in which machines perform assembly and/or disassembly operations. We consider tree-structured systems of unreliable machines that produce discrete parts. Processing times, times to failure and times to repair in the inhomogeneous system are assumed to be stochastic and machine-dependent. Machines are separated by buffers of limited capacity. We develop Markov process models for discrete time and continuous time systems and derive approximate decomposition equations to determine performance measures such as production rate and average buffer levels in an iterative algorithm. An improved parameter updating procedure leads to a dramatic improvement with respect to convergence reliability. Numerical results demonstrate that the methods are quite accurate.     

随机混流U型拆卸线平衡排序问题多目标进化算法优化

针对混流U型拆卸线平衡排序问题,考虑拆卸时间不确定,建立了该问题最小拆卸线平均闲置率、尽早拆卸危害和高需求零部件、最小化平均方向改变次数的多目标优化模型,并提出一种基于分解和动态邻域搜索的混合多目标进化算法(Hybrid Multi-objective Evolutionary Algorithm Based on Decomposition, HMOEA/D)。该算法通过采用弹性任务分配策略、动态邻域结构和动态调整权重以保证解的可行性并搜索得到分布较好的非劣解集。最后,仿真求解实验设计技术(DOE)生成的测试算例,结果表明HMOEA/D较其它算法能得到更接近Pareto最优、分布更好的近似解集。     

An linear programming based lower bound for the simple assembly line balancing problem

The simple assembly line balancing problem is a classical integer programming problem in operations research. A set of tasks, each one being an indivisible amount of work requiring a number of time units, must be assigned to workstations without exceeding the cycle time. We present a new lower bound, namely the LP relaxation of an integer programming formulation based on Dantzig–Wolfe decomposition. We propose a column generation algorithm to solve the formulation. Therefore, we develop a branch-and-bound algorithm to exactly solve the pricing problem. We assess the quality of the lower bound by comparing it with other lower bounds and the best-known solution of the various instances from the literature. Computational results show that the lower bound is equal to the best-known objective function value for the majority of the instances. Moreover, the new LP based lower bound is able to prove optimality for an open problem.     

A dynamic programming based heuristic for the assembly line balancing problem

The simple assembly line balancing problem is the simplification of a real problem associated to the assignment of the elementary tasks required for assembly of a product in an assembly line. This problem has been extensively studied in the literature for more than half a century. The present work proposes a new procedure to solve the problem we call Bounded Dynamic Programming. This use of the term Bounded is associated not only with the use of bounds to reduce the state space but also to the reduction of such space based on heuristics. This procedure is capable of obtaining an optimal solution rate of 267 out of 269 instances, which have been used in previous works, thus obtaining the best-known performance for the problem. These results are an improvement from any previous procedure found in the literature even when using smaller computing times.     

A branch-and-bound algorithm to minimize the line length of a two-sided assembly line

A new branch-and-bound algorithm is presented to solve the two-sided assembly line balancing problem of type 1 (TALB-1). First, a pair of two directly facing station is defined as a position, and then the two-sided assembly line (TAL) is relaxed to a one-sided assembly line (OAL). Some new lower bound on positions are computed, and dominance rules and reduction rules for the one-sided assembly line balancing problem of type 1 (OALB-1) are extended and incorporated into a station-oriented assignment procedure for the TALB-1 problem. Finally, the tests are carried out on a well-known benchmark set of problem instances, and experimental results demonstrate that the proposed procedure is efficient.     

A two-stage heuristic for disassembly scheduling with assembly product structure

Disassembly scheduling, one of the important operational problems in disassembly systems, is the problem of determining the ordering and disassembly schedules of used or end-of-life products while satisfying the demand of their parts or components over a certain planning horizon. This paper considers products with assembly structure for the objective of minimizing the sum of purchase, set up, inventory holding, and disassembly operation costs, and suggests a two-stage heuristic, in which an initial solution is obtained in the form of the minimal latest ordering and disassembly schedule, and then improved iteratively considering trade-offs among different cost factors. To show the performance of the heuristic, computational experiments were done on the example obtained from the literature and a number of randomly generated test problems, and the results show that the heuristic can give optimal or very near-optimal solutions within very short computation times.     

An imperialist competitive algorithm for multi-objective U-type assembly line design

Many assembly lines are now being designed as U-type assembly lines rather than straight lines because of the pressure of the just-in-time (JIT) manufacturing concept. Since any type of an assembly line balancing problem is known to be NP-hard, there has been a growing tendency toward using evolutionary algorithms to solve such a hard problem. This paper proposes a new population-based evolutionary algorithm, namely imperialist competitive algorithm (ICA) inspired by the process of socio-political evolution, to address the multi-objective U-type assembly line balancing problem (UALBP). Two considered objectives are to minimize the line efficiency and minimize the variation of workload. Furthermore, the Taguchi design is applied to tune the effective parameters of the proposed ICA. To demonstrate the efficiency of the proposed algorithm, the associated results are compared against an efficient genetic algorithm (GA) in the literature over a large group of benchmarks taken from the literature. The computational results show that the proposed ICA outperforms GA.     

Ant algorithms for a time and space constrained assembly line balancing problem

The present article focuses on the application of a procedure based on ant colonies to solve an assembly line balancing problem. After an introduction to assembly line problems, the problem under study is presented: the Time and Space constrained Assembly Line Balancing Problem (TSALBP); and a basic model of one of its variants is put forward for study. Subsequently, an ant algorithm is presented that incorporates some ideas that have offered good results with simple balancing problems. Finally, the validity of the proposed algorithms is tested by means of a computational experience with reference instances, and the conclusions of the study are presented.     

Approximate solutions to the turbine balancing problem

The turbine balancing problem (TBP) is an NP-Hard combinatorial optimization problem arising in the manufacturing and maintenance of turbine engines. Exact solution methods for solving the TBP are not appropriate since the problem has to be solved in real time and the input data is itself inaccurate. In this paper the TBP is formulated as a quadratic assignment problem (QAP) and we propose a heuristic algorithm for solving the resulting problem. Computational results on a set of instances provided by Pratt & Whitney (P&W) and from the literature, indicate that the proposed algorithm outperforms the current methods used for solving the TBP, and has the best overall performance with respect to other heuristic algorithms in the literature.     

Robotic disassembly line balancing problem: A mathematical model and ant colony optimization approach

The use of robots is significantly increasing day by day in manufacturing systems, and especially improving the efficiency of the lines. Robots can be used to complete disassembly tasks, and each of the robots can need different operation times to perform the tasks. In this paper, the balancing of the robotic disassembly line problem has been studied to develop efficient solution techniques. Firstly, a mixed-integer linear mathematical model is proposed to determine and solve the problem optimally. A case study from literature is addressed to assess and show the efficiency and effectiveness of the model to minimize cycle time. Secondly, a heuristic algorithm based on ant colony optimization is also proposed to discover a solution for especially the large-size test problems due to the complexity of the problem. The performance of the proposed heuristic algorithm is verified and compared with the different heuristic on data sets. The computational results indicate that the proposed mathematical model and the algorithms are promising for the small and large-size test problems, respectively. Finally, it should be stated that robots have great potential to use in the area of disassembly line and useful solutions provide according to test results.     

Balancing assembly lines with tabu search

Balancing assembly lines is a crucial task for manufacturing companies in order to improve productivity and minimize production costs. Despite some progress in exact methods to solve large scale problems, softwares implementing simple heuristics are still the most commonly used tools in industry. Some metaheuristics have also been proposed and shown to improve on classical heuristics but, to our knowledge, no computational experiments have been performed on real industrial applications to clearly assess their performance as well as their flexibility. Here we present a new tabu search algorithm and discuss its differences with respect to those in the literature. We then evaluate its performance on the Type I assembly line balancing problem. Finally, we test our algorithm on a real industrial data set involving 162 tasks, 264 precedence constraints, and where the assembly is carried out on a sequential line with workstations located on both sides of the conveyor, with two possible conveyor heights and no re-positioning of the product. We discuss the flexibility of the metaheuristic and its ability to solve real industrial cases.     

A Multiple-rule Heuristic for Assembly Line Balancing

This paper introduces a four-rule heuristic method for production/assembly line balancing which seeks to minimize the number of workstations for a given cycle time. To evaluate its performance, the proposed method was compared with 15 other heuristic methods ranging in complexity from random assignment of work elements to Hoffmann's enumeration procedure. The results, based on both randomly generated problems and problems taken from the literature, indicate that the proposed heuristic outperformed other procedures. Further, the suggested method was able to find the optimal solution for 57 (85%) of the 67 literature problems.     

The product rate variation problem and its relevance in real world mixed-model assembly lines

Production processes in a wide range of industries rely on modern mixed-model assembly systems, which allow an efficient manufacture of various models of a common base product on the same assembly line. In order to facilitate a just-in-time supply of materials, the literature proposes various sequencing problems under the term “level scheduling”, which all aim at evenly smoothing the part consumption induced by the production sequence over time. Among these approaches, the popular product rate variation (PRV) problem is considered to be an appropriate approximate model, if either (i) all products require approximately the same number and mix of parts or (ii) part usages of all products are (almost completely) distinct. These statements are (iii) further specified by analytical findings, which prove the equivalence of product and material oriented level scheduling under certain conditions. These three prerequisites commonly cited in the literature when justifying the practical relevance of the PRV are evaluated by means of three simple computational experiments and are then discussed with regard to their relevance in practical settings. It is concluded that the PRV is in fact inappropriate for use in today’s real world mixed-model assembly systems.