基于细菌觅食和蚁群算法的工艺路线优化

针对工艺路线规划中满足多重约束的最优方案选择问题,提出一种细菌觅食和蚁群优化（bacteria foraging ant colony optimization,BFACO）算法。首先,将工艺路线规划转化为对加工元顺序的优化问题,构造满足多种工艺准则的加工元拓扑优先顺序图,并构建了在缩短加工周期、提高加工质量和降低加工成本目标下的最低加工资源更换成本的目标函数;其次,设计加工元序列与加工资源两个搜索阶段的蚁群搜索,拓扑优先顺序图可弥补加工元序列搜索阶段信息素匮乏的缺点,而在加工资源搜索阶段引入细菌觅食优化算法的复制与趋向操作,可使加工元在多个可选加工资源的情况下获得加工资源更换成本最低的加工序列;最后,基于细菌觅食与蚁群算法的融合优化,完成多个加工元序列的信息素积累并输出最优解,解决蚁群算法局部收敛且计算速度慢的问题。将BFACO算法应用于实例并与其他优化算法的优化结果进行对比,结果显示BFACO算法在工艺路线优化方面较其他优化算法具有较高的计算效率,验证了BFACO算法的可行性与有效性。研究表明,BFACO算法可有效应用于同时考虑工艺约束与加工资源更换成本的工艺规划,为实际生产提供高效且灵活的工艺路线的优化选择。     

基于DMPSO的单元集成设计方法

为避免单元设计中出现独立分步求解造成的解空间丢失,建立一个多目标生产单元集成布局优化模型,对单元构建与单元布局同时进行描述求解.模型考虑了设施的摆放方向,设施与单元之间的安全距离和设施空间尺寸.针对该多目标复杂问题,设计了基于结构化编码的动态多种群粒子群优化算法,避免粒子较快收敛陷入局部极值,提高了解的质量.同时采用个体密度值的外部档案维护策略,保持了解的多样性与均匀性.最后通过求解单元设计问题实例,验证模型和算法的有效性和可行性.     

集中控制VMI&TPL模式下的库存运输集成问题

以“多供应商,单TPL,单制造商”供应链网络为研究环境,研究了集中控制VMI&TPL模式下的库存运输集成优化问题,旨在为供应商和TPL提供补货决策和车辆路线计划.基于滚动计划期,构建了库存运输集成计划模型.鉴于该模型问题是一个NPC问题,提出了基于模拟退火遗传算法的四步骤启发式算法.算例分析结果表明该启发式算法是有效可行的.     

基于混合遗传算法的生产工艺规划与决策问题

零件生产加工过程中,由于各加工特征有多个加工工艺而不同工艺方法又有不同的机器选择,以及受工艺约束的工序特征排序问题,使得柔性工艺规划问题具有NP难特性.通过对可选工序和机器进行分段编码;并用约束调整算法解决受工艺约束的工序排序问题;对于问题的多目标特性,采用随机权重来设置适应度函数,用外部精英保留策略并引入k-means聚类算法裁剪精英集来保持群体多样性,该方法通过该混合遗传算法的交差,变异等操作,能有效解决受工序约束的多工艺路线的优化与决策问题.以实例的形式论证了该算法在求解柔性工艺规划问题的有效可行性.     

城市物流配送系统的多目标优化LRP模型研究

建立了准时到达、总成本最低的多目标定位--运输路线安排问题（LRP）的数学模型;此LRP问题包括配送中心的分派和运输路线优化两方面的决策,属于NP难题.在构建模型算法过程中,采用了两阶段法：首先采用启发式算法确定配送中心分派问题;其次,给出各个目标的优先级,根据优先因子建立以准时到达为最高优先级、总成本最低为次优先级的新的数学模型,并运用遗传算法求解优化运输路线问题.     

基于CAD/CAAP集成的焊接结构装配顺序设计

基于综合拆散法和装配法整合的思路 ,按照问题分层归约求解方法的思想 ,对产品CAD信息建模并进行了二次开发 ,运用图论的带权优先搜索理论、组件选择规则等算法 ,实现了基于CAD/CAAP集成的焊接结构装配路线的最优工艺路线生成 ,并应用于液压机焊接结构件的装配顺序设计     

基于改进粗糙集的典型结构件工艺路线决策方法研究

工艺路线设计是工艺设计的关键环节,而对于零件加工而言关键加工余量参数的确定是工艺路线设计的核心。针对典型航天结构件工艺路线的特点,将典型框环类零件核心工艺路线的确定问题转化为了切削和铣削两种工艺的加工余量序列确定的问题。提出了基于改进粗糙集的典型结构件工艺路线决策方法和流程,在获得影响工艺制定的零件特征属性集合的基础上,提出了面向多分类决策属性的条件属性约简方法和匹配度计算方法,并以火箭框环类零件的工艺实例数据为基础,进行了实例计算和应用,验证的方法的可行性和有效性。     

半色调技术在印花中的应用

为了在印花图案中充分表现云纹的效果,在计算机辅助印花分色系统中借鉴了彩色印刷的挂网技术．由于印花工艺的特殊性,一般采用调幅的技术,也就是采用基于网模的半色调方法来进行挂网．规则的网模容易在实际的印刷过程中出现龟纹,为从根本上避免龟纹的产生,提出了印染CAD系统的适合印花行业的半色调算法,将基于六角形的7个圆作为一个基本挂网单元．由于基准挂网单元中7个挂网单元圆的圆心位置排列的特殊性,用该算法产生的网点在任何一个方向上都不会存在有规律的直线,从而克服了规则网点对挂网图案本身的依赖．     

混流汽车液压电子制动控制单元装配线平衡问题设计与研究

为了有效解决混流汽车液压电子制动控制单元装配线平衡问题,提出了基于优化遗传算法的求解方法。首先对于复杂的液压电子制动控制单元装配线的工艺装配过程进行设计与计算,分析液压电子制动控制单元的生产工艺步序、工作站数、生产节拍、以最小化生产节拍、最小化工作站数以及均衡各工作站的加权平均负荷进行多目标优化。同时构建液压制动控制单元装配工艺平衡问题的测量模型,并进行案例分析,利用模型求解。最后,以某独资汽车零部件工厂的汽车液压电子制动控制单元装配线为例,对工艺过程进行数学建模,采用优化遗传算法进行求解。结果表明,所提方法与测量模型可为汽车液压电子制动控制单元装配线平衡问题提供可行的解决方案。     

基于自适应遗传算法的模具生产调度研究

研究了一种基于自适应遗传算法的模具企业车间作业调度算法,建立了调度问题的数学模型,给出了具体的遗传操作算子设计方法.针对离散型模具生产的工艺特征,设计了生产单元分配方法,将生产单元作为调度对象.该算法改进了传统的基于工序的编码方法,给出了一种动态调整交叉概率和变异概率的计算方法.对实际模具企业的生产调度求解结果表明,该...     

Tabu search‐based approach to multi‐objective machine‐part cell formation

The machine‐part cell formation with respect to multiple objectives has been an attractive search topic since 1990 and many methodologies have been applied to consider simultaneously more than one objective. However, the majority of these works unify the various objectives into a single objective. The final result of such an approach is a compromise solution, whose non‐dominance is not guaranteed. A Pareto‐optimality‐based multi‐objective tabu search (MOTS) algorithm is presented for the machine‐part grouping problems with multiple objectives: it minimizes the total cost, which includes intra‐ and inter‐cell transportation cost and machine investment cost, minimizing the intra‐cell loading unbalance and minimizing the inter‐cell loading unbalance. A new approach is developed to maintain the archive storing non‐dominated solutions produced by the tabu search. The comparisons and analysis show that the proposed algorithm has considerable promise in multi‐objective cell design.     

Formation of independent manufacturing cells with the consideration of multiple identical machines

Cellular manufacturing is a manufacturing philosophy with the goal to produce low-medium volume products with high variety, while maintaining the high productivity of large-scale production. It is recognised as one of the most powerful management innovations in job-shop and batch production. Among the problems of designing a cellular manufacturing system, cell formation is the central and foremost issue. In the present paper, we investigate the formation of independent manufacturing cells with the consideration of multiple identical machines, in which inter-cell movements are completely eliminated by allocating identical machines in different manufacturing cells. Incorporating many real-life production factors including processing time, set-up time, alternative processing routes, machine capacity, batch size and cell size, we formulate a bi-objective mathematical model to minimise workload imbalance among manufacturing cells. Then, a genetic algorithm based on non-dominated sorting genetic algorithm II is developed to solve it. The computational results of numerical examples and the comparison analysis validated the performance of the proposed algorithm.     

AN ASSIGNMENT METHOD FOR THE PART-MACHINE CELL FORMATION PROBLEM IN THE PRESENCE OF MULTIPLE PROCESS ROUTES

This paper considers the part-machine cell formation decision of the generalized Group Technology (GT) problem in which multiple process routes can be generated for each part. The existing p-median model and similarity coefficient algorithm can solve only small-sized or well-structured cases. An assignment method for the cell formation problem is suggested. This method uses an assignment model which is a simple linear programming. Numerical examples show that the assignment method provides good separable cell formation even for large-sized and ill-structured problems.     

A parallel genetic algorithm for dynamic cell formation in cellular manufacturing systems

Instead of using expensive multiprocessor supercomputers, parallel computing can be implemented on a cluster of inexpensive personal computers. Commercial accesses to high performance parallel computing are also available on the pay-per-use basis. However, literature on the use of parallel computing in production research is limited. In this paper, we present a dynamic cell formation problem in manufacturing systems solved by a parallel genetic algorithm approach. This method improves our previous work on the use of sequential genetic algorithm (GA). Six parallel GAs for the dynamic cell formation problem were developed and tested. The parallel GAs are all based on the island model using migration of individuals but are different in their connection topologies. The performance of the parallel GA approach was evaluated against a sequential GA as well as the off-shelf optimization software. The results are very encouraging. The considered dynamic manufacturing cell formation problem incorporates several design factors. They include dynamic cell configuration, alternative routings, sequence of operations, multiple units of identical machines, machine capacity, workload balancing, production cost and other practical constraints.     

Dynamic cellular manufacturing system design considering alternative routing and part operation tradeoff using simulated annealing based genetic algorithm

In this paper, an integrated mathematical model of multi-period cell formation and part operation tradeoff in a dynamic cellular manufacturing system is proposed in consideration with multiple part process route. This paper puts emphasize on the production flexibility (production/subcontracting part operation) to satisfy the product demand requirement in different period segments of planning horizon considering production capacity shortage and/or sudden machine breakdown. The proposed model simultaneously generates machine cells and part families and selects the optimum process route instead of the user specifying predetermined routes. Conventional optimization method for the optimal cell formation problem requires substantial amount of time and memory space. Hence a simulated annealing based genetic algorithm is proposed to explore the solution regions efficiently and to expedite the solution search space. To evaluate the computability of the proposed algorithm, different problem scenarios are adopted from literature. The results approve the effectiveness of the proposed approach in designing the manufacturing cell and minimization of the overall cost, considering various manufacturing aspects such as production volume, multiple process route, production capacity, machine duplication, system reconfiguration, material handling and subcontracting part operation.     

Multiple routeings and capacity considerations in group technology applications

This paper addresses the problem of manufacturing cell formation, given multiple part routeings, and multiple functionally similar workcentres. Cellular manufacturing is intended to facilitate production, and thus should be based on projected production requirements. The originality of the approach lies in considering both the manufacturing system as well as projected production, and distributing the demand among alternate routeings in order to obtain a better manufacturing cell design. The suggested choice of part routeings favours the decomposition of the manufacturing system into manufacturing cells in a way that minimizes part traffic, along with satisfying the part demand and workcentre capacity constraints. We show that the problem can be formulated as a linear programming type problem which simultaneously addresses two problems: (i) routeing selection, and (ii) cell formation. The common objective is to minimize the inter-cell traffic in the system. The proposed algorithm iteratively solves two problems. The first problem is formulated as a linear-programming problem, while the latter is approached by an existing heuristic bottom-up aggregation procedure, known as Inter-Cell Traffic Minimization Method (ICTMM), enhanced appropriately.     

A PSO-based approach to cell formation problems with alternative process routings

Group technology (GT) has been extensively applied to cellular manufacturing system (CMS) design for decades due to many benefits such as decreased number of part movements among cells and increased machine utilisation in cells. This paper considers cell formation problems with alternative process routings and proposes a discrete particle swarm optimisation (PSO) approach to minimise the number of exceptional parts outside machine cells. The approach contains two main steps: machine partition and part-routing assignment. Through inheritance and random search, the proposed algorithm can effectively partition machines into different cells with consideration of multiple part process routings. The computational results are compared with those obtained by using simulated annealing (SA)-based and tabu search (TS)-based algorithms. Experimental results demonstrate that the proposed algorithm can find equal or fewer exceptional elements than existing algorithms for most of the test problems selected from the literature. Moreover, the proposed algorithm is further tailed to incorporate various production factors in order to extend its applicability. Four sample cases are tested and the results suggest that the algorithm is capable of solving more practical cell formation problems.     

A two-phase heuristic algorithm for cell formation problems considering alternative part routes and machine sequences

In this paper, we deal with the multi-objective machine cell formation problem. This problem is characterized as determining part route families and machine cells such that the total sum of inter-cell part movements and maximum machine workload imbalance are simultaneously minimized. Together with the objective function, alternative part routes and the machine sequences of part routes are considered in grouping part route families. Also, it is assumed that the number of machine cells is not pre-defined. Owing to the complexity of the problem, a two-phase heuristic algorithm is proposed. Computational experiments were conducted to verify the performance of the algorithm. Throughout the computational experiments, we verified that the two-phase heuristic algorithm is effective for large-scale machine cell formation problems.     

Manufacturing cells' design in consideration of various production factors

A cell formation problem is introduced that incorporates various real-life production factors such as the alternative process routing, operation sequence, operation time, production volume of parts, machine capacity, machine investment cost, machine overload, multiple machines available for machine types and part process routing redesigning cost. None of the cell formation models in the literature has considered these factors simultaneously. A similarity coefficient is developed that incorporates alternative process routing, operation sequence, operation time and production volume factors. Although very few studies have considered the machine capacity violated issue under the alternative process routing environment, owing to the difficulties of the issue discussed here, these studies fail to deal with the issue because they depend on some unrealistic assumptions. Five solutions have been proposed here and are used to cope with this difficulty. A heuristic algorithm that consists of two stages is developed. The developed similarity coefficient is used in stage 1 to obtain basic machine cells. Stage 2 solves the machine-capacity violated issue, assigns parts to cells, selects process routing for each part and refines the final cell formation solution. Some numerical examples are used to compare with other related approaches in the literature and two large size problems are also solved to test the computational performance of the developed algorithm. The computational results suggest that the approach is reliable and efficient in either the quality or the speed for solving cell formation problems.     

Tabu search approach based on a similarity coefficient for cell formation in generalized group technology

Both a similarity coefficient method (SCM)-based algorithm and meta-heuristics have been widely applied to various cell formation problems; however, few studies have explored the combination of the two methods. This paper addresses a hybrid algorithm, in which, based on the initial solution produced by a new SCM-based hierarchical clustering method, a fast and effective tabu search approach is presented to solve cell formation in group technology (GT). The proposed algorithm is applied to several problems from literature and a group of the randomly generated instances with alternative process plans and compared with simulated annealing (SA) and other TS; the results demonstrate that the proposed algorithm is available and efficient for cell formation in generalized GT.