Coordination of set-ups between two stages of a supply chain using multi-objective genetic algorithms

A multi-objective genetic algorithm (MOGA) solution approach for a sequencing problem to coordinate set-ups between two successive stages of a supply chain is presented in this paper. The production batches are processed according to the same sequence in both stages. Each production batch has two distinct attributes and a set-up occurs in the upstream stage every time the first attribute of the new batch is different from the previous one. In the downstream stage, there is a set-up when the second attribute of the new batch is different from that of the previous one. Two objectives need to be considered in sequencing the production batches including minimizing total set-ups and minimizing the maximum number of set-ups between the two stages. Both problems are NP-hard so attainment of an optimal solution for large problems is prohibited. The solution approach starts with an initialization stage followed by evolution of the initial solution set over generations. The MOGA makes use of non-dominated sorting and a niche mechanism to rank individuals in the population. Selected individuals taken from a given population form the succeeding generation using four genetic operators as: reproduction, crossover, mutation and inversion. Experiments in a number of test problems show that the MOGA is capable of finding Pareto-optimal solutions for small problems and near Pareto-optimal solutions for large instances in a short CPU time.     

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.     

JIT mixed-model sequencing with batching and setup considerations via search heuristics

This research addresses the problem of sequencing items for production when it is desired that the production sequences result in minimal usage rates–surrogate measures for flexibility in a JIT environment. While seeking sequences with minimal usage rates, the number of required setups for the sequences is also considered, along with feasible batch-sizing combinations. The general intent is to find minimum usage-rate sequences for each associated number of setups and total batches. This multiple objective problem is addressed via a three-dimensional efficient frontier. 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 genetic algorithms are presented and used to find solutions for several problem sets from the literature. Experimentation shows that the simulated annealing approach outperforms the genetic algorithm approach in both objective function and CPU performance.     

Scheduling flow shops with blocking using a discrete self-organising migrating algorithm

A novel approach of a discrete self-organising migrating algorithm is introduced to solve the flowshop with blocking scheduling problem. New sampling routines have been developed that propagate the space between solutions in order to drive the algorithm. The two benchmark problem sets of Carlier, Heller, Reeves and Taillard are solved using the new algorithm. The algorithm compares favourably with the published algorithms Differential Evolution, Tabu Search, Genetic Algorithms and their hybrid variants. A number of new upper bounds are obtained for the Taillard problem sets.     

A hybrid approach for the capacitated lot sizing problem with setup carryover

The capacitated lot sizing problem with setup carryover deals with the issue of planning multiple products on a single machine. A setup can be carried over from one period to the next by incorporating the partial sequencing of the first and last product. This study proposes a novel hybrid approach by combining Genetic Algorithms (GAs) and a Fix-and-Optimise heuristic to solve the capacitated lot sizing problem with setup carryover. Besides this, a new initialisation scheme is suggested to reduce the solution space and to ensure a feasible solution. A comparative experimental study is carried out using some benchmark problem instances. The results indicate that the performance of the pure GAs improves when hybridised with the Fix-and-Optimise heuristic. Moreover, in terms of solution quality, promising results are obtained when compared with the recent results in the literature.     

A note on ‘A bit-wise mutation algorithm for mixed-model sequencing in JIT production systems’ (Nazar & Pillai, 2015, IJPR, 53:19, 5931-5947)

This note discusses three points about the problem of sequencing units on a mixed-model assembly line in ‘A bit-wise mutation algorithm for mixed-model sequencing in JIT production systems’ (Nazar & Pillai, 2015, IJPR, 53:19, 5931-5947). Specifically, the mixed-integer quadratic model, the bit-wise mutation algorithm and the bi-objective problem to optimise both product rate variation and makespan. The conclusion of the discussion is that the three alleged contributions are not valid or are outperformed by those presented in some previously published papers.     

GRASP with path relinking for a multiple objective sequencing problem for a mixed-model assembly line

This research presents a new application of greedy randomised adaptive search procedure (GRASP) to address a production sequencing problem for mixed-model assembly line in a just-in-time (JIT) production system in two different cases. In the former case, small size sequencing problems are considered and two objectives are presented; minimisation of setups and optimisation of stability of material usage rates. These two objectives are inversely correlated with each other, so simultaneous optimisation of both is challenging. This type of problem is NP-hard. The GRASP, with path relinking, searches for efficient frontier where simultaneous optimisation of number of setups and usage rates is desired. Several test problems are solved via GRASP and its performance is compared to solutions obtained via complete enumeration and simulated annealing (SA), tabu search (TS) and genetic algorithms (GA) approaches from the literature. Experimental results reveal that the GRASP with path relinking provides near-optimal solutions in terms of the two objectives and its ‘average inferiority%’ and ‘average percentile’ performances are superior to that of other heuristics. In the latter case, the goal is to explore varying the emphasis of these two conflicting objectives. Larger sequencing problems are considered and solved via GRASP with path relinking. Its objective function values are compared to the solutions obtained via a SA approach from the literature. Experimental results show that GRASP also provides good performance on large size problems and its percentage improvement is better than that of SA. Overall results also show, however, that the GRASP performs poorly with regard to CPU time.     

A branch-and-bound approach for machine selection in just-in-time manufacturing systems

Equipment selection issues are very important in the early stages of implementation of just-in-time (JIT) manufacturing systems. This paper addresses the problem of determining the number of machines for each stage of a JIT system by minimizing production, imbalance and investment costs. The problem is modelled as a mixed-integer nonlinear optimization program and a branch-and-bound algorithm is developed for its solution. This algorithm guarantees the global optimum of the problem and is enhanced by simple, yet very effective, upper bounding heuristics. The solutions obtained by the developed branch-and-bound approach are compared to solutions that have appeared in the literature using heuristic approaches. The comparisons indicate that the proposed algorithm leads to significant economic savings, averaging 17% on a set of problems from the literature. The paper also considers the application of the algorithm to large-scale, industrially-relevant, problems with up to 10 stages and 200 products. Even for the largest of these problems, the search for the integer optimum requires modest computational times. This demonstrates the potential practical impact of the proposed methodology.     

Genetic Algorithms for architecture optimisation of Counter-Propagation Artificial Neural Networks

Counter-Propagation Artificial Neural Networks (CP-ANNs) require an optimisation step in order to choose the most suitable network architecture. In this paper, a new strategy for the selection of the optimal number of epochs and neurons of CP-ANNs was proposed. This strategy exploited the ability of Genetic Algorithms to optimise network parameters. Since both Genetic Algorithms and CP-ANNs can lead to overfitting, the proposed approach was developed taking into considerable account the validation of the multivariate models.Moreover, a new criterion for calculating the Genetic Algorithm fitness function was introduced. The percentage of correctly assigned samples for calibration and internal validation were both used in the optimisation procedure, in order to get simultaneously predictive and not overfitted models.The optimisation strategy was tested by the use of several chemical benchmark data sets for classification tasks and results were compared with those of the exhaustive searching of all the possible solutions.     

GA算子的代数模型

作为对生物进化的模拟,遗传算法的表述方法和用语与生物进化的基本相同。这种表述方法虽然形象,但往往容易掩盖遗传算法的数学本质,为进一步研究带来困难,试图揭示遗传算法的数学本质,建立它的代数表述形式,并为其应用提供一种更简明易读的模型。     

A probabilistic computational framework for bridge network optimal maintenance scheduling

This paper presents a probabilistic computational framework for the Pareto optimization of the preventive maintenance applications to bridges of a highway transportation network. The bridge characteristics are represented by their uncertain reliability index profiles. The in/out of service states of the bridges are simulated taking into account their correlation structure. Multi-objective Genetic Algorithms have been chosen as numerical tool for the solution of the optimization problem. The design variables of the optimization are the preventive maintenance schedules of all the bridges of the network. The two conflicting objectives are the minimization of the total present maintenance cost and the maximization of the network performance indicator. The final result is the Pareto front of optimal solutions among which the managers should chose, depending on engineering and economical factors. A numerical example illustrates the application of the proposed approach.     

Increasing production rate in U-type assembly lines with sequence-dependent set-up times

U-shaped assembly lines are commonly used in just-in-time production systems as they have some advantages over straight lines. Although maximizing production rates on these lines by assigning tasks to stations is common practice in industrial environments, studies on the stated assembly line balancing problem are limited. This article deals with maximizing the production rate on U-shaped assembly lines under sequence-dependent set-up times. Sequence-dependent set-up times mean that after a task is performed, a set-up time, the duration of which depends on adjacent tasks, is required to start the next task operation. These set-ups are considered by dividing them into two groups, named forward and backward set-ups, to make the problem more practical. Two heuristics based on simulated annealing and genetic algorithms are improved beside the mathematical model. Experimental results show that solving the stated problem using the mathematical model is nearly impossible, while heuristics may obtain solutions that have acceptable deviations from the lower bounds.     

一种基于的遣传算法的微波测量分析方法

论述了遗传算法在微波测量分析中的应用,首先简要介绍了应用遗传算法处理微波数据信号．然后在微波电路的分析设计中,设计了一种较好的遗传算法多目标优化方法对电路进行全局优化．使整个系统在电路设计等关键的衡量指标同时达到最优．文章对双波段、六单元接换阵列智能天线运用遗传算法进行优化设计,在900MHz和1900MHz两个频段上-10dB带宽分别达到和,天线辐射方向上的最大增益为．最小增益为,并且得到了天线阵列中每个变量的灵敏度分析．通过与文献的比较,结果证明遗传算法的应用可以显著的提高天线的性能．     

Self-adaptation procedures in genetic algorithms applied to the optimal design of composite structures

It is recognized that the efficiency of Genetic Algorithms improves if some adaptive rules are included. In this work, adaptive properties in Genetic Algorithms applied to structural optimization are studied. The adaptive rules work by using additional information related to the behavior of state and design variables of the structural problem. At each generation, the self-adaptation of the genetic parameters to evolutionary conditions attempts to improve the efficiency of the genetic search. The introduction of adaptive rules occurs at three levels: (i) when defining the search domain in each generation; (ii) considering a crossover operator based on commonality and local improvements; and (iii) by controlling mutation, including behavioral data. Self-adaptation has proved to be highly beneficial in automatically and dynamically adjusting evolutionary parameters. Numerical examples showing these benefits are presented.     

Computer-aided set-up planning for machining centres configuration

One of the key issues in defining the optimal configuration of a machining centre is the problem of determining the minimal number of set-ups for the part types to be machined. This paper proposes a method to define near-optimal set-up plans for prismatic workpieces when multiple parts can be mounted on the same pallet. Set-ups are determined taking into account the accessibility of the machining directions of the workpiece and the technological constraints among the required operations. The technological constraints are divided into three types: constraints that force the operations to share the same set-up, precedence constraints that cannot be violated in the sequence of set-ups, and constraints that translate technological preferences and that might be sacrificed to optimize the set-up plan. The technological constraints are analysed with a graph-based approach. The method proposes a solution for three-, four- and five-axis machines. The set-up plan for three axes is the starting point to determine the solutions for four- and five-axis machines: the set-up plan for four and five axes results from the combination of set-ups of the three-axis machine. Alternative solutions with the minimal number of set-ups are determined. Each solution specifies the orientation of the workpiece on the pallet fixture in each set-up, the operations to be executed in each set-up and the precedence relations among set-ups. Starting from the results of the set-up planning, the configuration of the pallet can be defined and taking into account the pallet configuration, the optimal machining centre for specific manufacturing needs is selected.     

基于多体系统传递矩阵法和遗传算法的一种物理参数识别方法

将多体系统传递矩阵法与遗传优化算法相结合,形成了一种新的基于多体系统传递矩阵法和遗传算法的物理参数识别方法(MS-TMM&GA).应用多体系统传递矩阵法进行动力学建模以及固有振动特性分析.将参数识别问题转化为优化问题,结合遗传算法,对由系统固有频率和增广特征矢量构造的目标函数全局最小值优化求解.给出了通过系统模态参数识别物理参数的计算步骤以及流程图.通过两个数值算例,表明了该方法的可行性及有效性.该方法对多体系统传递矩阵法和遗传算法进行了结合与拓展,无需建立复杂多体系统的总体动力学方程,涉及矩阵阶次低,即可快速获得高精度的优化计算结果.     

混流装配线调度问题的离散粒子群优化解

混流装配线调度问题是JIT生产中的一个重要问题。借鉴二进制遗传算法中的交叉操作过程，对传统的连续型粒子群算法进行改进，使其适用于离散问题的优化处理。然后以丰田公司的汽车组装调度函数作为目标函数，利用改进的离散粒子群算法进行求解。对比分析表明：新算法所得结果优于常用的目标追随法、遗传算法、模拟退火等方法。     

Robot arc welding task sequencing using genetic algorithms

This paper addresses welding task sequencing for robot arc welding process planning. Although welding task sequencing is an essential step in welding process planning, it has been considered through empirical knowledge, rather than a systematic approach. Thus, an effective task sequencing method for robot arc welding is required. Welding operations can be classified by the number of weldlines and layers. Genetic algorithms are applied to tackle those welding task sequencing problems in productivity and welding quality aspects. A genetic algorithm for the Traveling Salesman Problem (TSP) is utilized to determine welding task sequencing for a multiweldline-singlepass problem. Further, welding task sequencing for multiweldline-multipass welding is investigated and appropriate genetic algorithms are introduced. A random key genetic algorithm is presented to solve multi-robot welding task sequencing: mutliweldline with multiple robots. Finally, the genetic algorithms are implemented for the welding task sequencing of three-dimensional weld plate assemblies. Various simulation tests for a welded structure are performed to find the combination of genetic algorithm parameters suitable to weld sequencing problems and to verify the quality of genetic algorithm solutions. Robot operations for weld sequences are simulated graphically using the robot simulation software IGRIP.     

A comparative study of genetic algorithms for the multi-objective optimization of composite stringers under compression loads

Optimization methods are close to become a common task in the design process of many mechanical engineering fields, specially those related with the use of composite materials which offer the flexibility in the design of both the shape and the material properties and so, are very suitable to any optimization process. While nowadays there exist a large number of solution methods for optimization problems there is not much information about which method may be most reliable for a specific problem. Genetic algorithms have been presented as a family of methods which can handle most of engineering problems. However, starting from a common basic set of rules many algorithms which differ slightly from each other have been implemented even in commercial software packages. This work presents a comparative study of three common Genetic Algorithms: Archive-based Micro Genetic Algorithm (AMGA), Neighborhood Cultivation Genetic Algorithm (NCGA) and Non-dominate Sorting Genetic Algorithm II (NSGA-II) considering three different strategies for the initial population. Their performance in terms of solution, computational time and number of generations was compared. The benchmark problem was the optimization of a T-shaped stringer commonly used in CFRP stiffened panels. The objectives of the optimization were to minimize the mass and to maximize the critical buckling load. The comparative study reveals that NSGA-II and AMGA seem the most suitable algorithms for this kind of problem.     

A simulation analysis of sequencing rules for ASRS in a pull-based assembly facility

Just-in-time (JIT) systems operate on a pull-based production control. The material needed is expected to be at its production site when and only when it is needed. When an automated storage retrieval system (ASRS) is used as a valve warehouse to support JIT manufacturing, the delivery time becomes critical. This paper presents an investigation on the effect of job sequencing rule on delivery performance of an ASRS, which is used as a valve warehouse to support a pull-based Kanban-driven assembly line. The analysis was based on computer simulation. The interaction of the sequencing rules with other control variables was also examined