板式家具高速自动分拣系统出库及打包调度策略

为提升定制家具自动分拣系统出库及包装作业的整体效率,根据出库及包装作业的工作特点,将出库打包问题抽象为一类板件处理具有优先顺序约束及机器约束的三阶段柔性装配流水车间调度问题。在对各约束进行定义和数学描述的基础上,以最大出库完工时间、包装工位最大完工时间、板件平均等待时间三者加权和最小化为优化目标,建立了板件处理具有优先顺序约束和机器约束的三阶段柔性流水车间调度问题的数学模型;针对该模型,构造了一种启发式求解算法H~*。为验证算法的有效性,基于裂区试验设计的思想生成大量算例,并将启发式算法H~*与构造的9种组合规则算法、5种元启发式算法进行性能比较。结果表明,H~*算法可高效地获得高质量的解。     

设备共享环境下的并发预约问题研究

为解决设备并发预约下的作业调度问题,在分析预约流程和共享模式的基础上,给出了定量数学模型。根据问题的不确定特性和大量用户并发访问的实际情况,提出了基于问题分解的启发式算法。按照SF策略决定区间求解顺序,用改进Dantzig算法求解单区间问题,求解过程中进行局部回溯调整,最后再执行邻域搜索。仿真结果证明了上述步骤的有效性,该算法能在可忽略的时间内获得满意解。     

同顺序加工调度问题的一种精确求解方法

同顺序加工调度问题是NP问题,分析了这类问题的特点及求解的难点,结合广度优先搜索方法的特点,提出了启发式双侧广度优先搜索方法,混合使用动态规划方法、下界算法和近似求解方法求解同顺序加工调度问题.实验结果表明,启发式双侧广度优先搜索方法求解同顺序加工调度问题时,可以大大减少搜索次数,适合于求解工序较少的同顺序加工调度问题;如果下界算法较好,还能快速求解工序较多的同顺序加工调度问题.     

考虑工件释放时间和柔性维护的单机调度问题

针对晶圆制造过程中考虑清洗维护的生产调度联合优化问题，以最小化最大完工时间为求解目标，优化工件加工顺序及维护活动执行时间。证明了该问题为NP难的，建立了问题的整数规划模型并进行线性化。结合机器役龄约束下的成批调度问题特征，证明了解的性质，并设计ERD-LPT-BFLD启发式算法对问题进行求解。构建了考虑工件释放时间及清洁活动约束的下界算法。通过不同规模算例仿真实验，将所提启发式算法与CPLEX及下界算法求解结果进行比较，验证了所提算法的有效性。     

基于动态值启发式的约束满足求解算法

为提高约束满足问题的求解效率,提出了一种基于动态值启发式的约束满足问题求解算法.该算法在求解过程中吸收了以往启发式算法的优点,充分利用了预处理和弧相容检查阶段的信息.不但加入了变量启发式,而且在实例化变量时,对所有值的优先级进行动态的改变,从而实现了动态值启发式.比较了静态值启发式和动态值启发式的效率,分析了该算法的优缺点.通过随机问题标准库用例测试表明,该算法比经典主流算法具有更好的效率优势.     

含作业到达时间的单机调度问题的改进算法

研究了以作业完成时间之和最小化为目标函数的单机调度问题，该问题中各作业到达时间可能不同。在对传统启发式算法进行分析的基础上，提出一种改进算法，并给出了算例及其计算结果。大量的随机数据实验的结果表明，该改进算法的性能比传统启发式算法的性能更优。     

三维装载约束下带时间窗的车辆路径问题

为提高配送车辆的效率,集成研究了三维装载约束下带时间窗的车辆路径问题。提出了该问题的描述性模型,设计了一个混合禁忌搜索算法。该算法以空间装载算法、基础启发式算法和禁忌搜索算法为基础。针对测试数据集的计算结果表明,该算法有效地解决了三维装载约束下带时间窗的车辆路径问题。     

具有柔性加工时间的机器人制造单元调度问题改进遗传算法

为克服传统遗传算法在求解具有柔性加工时间的机器人制造单元调度问题时易出现早熟收敛、冗余迭代等缺陷,提出了改进遗传算法。该算法采用基于工件搬运顺序的染色体编码,并根据调度问题特征,设计构造型启发式算法来生成初始种群,避免了大量不可行染色体的产生,提高了后续操作的优化质量。同时,在交叉变异操作中引入局部邻域搜索,通过对子代邻域的局部寻优提高了算法的收敛速度。最后,分别应用该算法和传统遗传算法求解六个基准案例,实验结果验证了该算法的有效性。     

求解矩形件排样问题的十进制狼群算法

为了使矩形件排样问题在可接受的时间内获得精确解,以在一定时间内获得高利用率的排样布局方案为研究目标,提出一种适合求解矩形件排样问题的十进制狼群算法。该算法结合基于复合评价因子的最低水平线搜索算法,对人工狼的位置进行十进制整数编码,重新设计游走和奔袭等智能行为,具有狼群算法的职责分工协作式搜索特性,能够较好地平衡算法的全局优化和局部搜索能力。采用多组算例对所提算法进行测试,并与其他元启发式和启发式算法进行对比,结果表明所提算法具有实用性和有效性。     

[车间调度]并行多机开放车间调度问题的模型与算法

发光二极管制造过程中,晶粒分类拣选工序的调度问题是典型的并行多机开放车间调度问题,属于NP-hard问题。研究了该调度问题以最小化总加权完工时间为目标的求解模型与算法。根据问题特性构建了可获得最优解的混合整数规划模型,并设计了同时考虑质量与求解效率的启发式算法和改进粒子群优化算法。仿真结果显示,启发式算法和改进粒子群优化算法都能在合理的时间内迅速有效地获得较佳的调度解。     

MODIFIED BOTTLENECK-BASED PROCEDURE FOR LARGE-SCALE FLOW-SHOP SCHEDULING PROBLEMS WITH BOTTLENECK

A new bottleneck-based heuristic for large-scale flow-shop scheduling problems with a bottleneck is proposed, which is simpler but more tailored than the shifting bottleneck (SB) procedure. In this algorithm, a schedule for the bottleneck machine is first constructed optimally and then the non-bottleneck machines are scheduled around the bottleneck schedule by some effective dispatching rules. Computational results show that the modified bottleneck-based procedure can achieve a tradeoff between solution quality and computational time comparing with SB procedure for medium-size problems. Furthermore it can obtain a good solution in quite short time for large-scale scheduling problems.     

可焊接的一维排样问题的一种启发式算法

根据某大型钢结构生产企业提出的型材下料时既要切割又要焊接的生产工艺问题，构造出一种可焊接的一雏排样问题的启发式算法。该算法以矩阵来表述问题的数学模型，可以通过直接在矩阵上进行操作来求问题的可行解以及优化该可行解。通过对企业实际数据的实算，从型材利用率和剩余型材的零散程度对这一算法进行了评价。     

Sequencing of an M machine flow shop with setup, processing and removal times separated

In this research, a flow shop scheduling problem in which setup, processing and removal times are separated, with the objective of minimizing makespan, is considered. A tabu search based heuristic is presented for solving the addressed problem. The proposed heuristic begins with the construction of artificial processing times for each operation; then a modified NEH algorithm is used to generate an initial solution, followed by a designed tabu search procedure applied for further improvement of the solution. The proposed heuristic, as well as the existing one, is evaluated in a large number of randomly generated problems. The results of the experimental investigations of the proposed heuristic algorithm and the existing heuristic in meeting the objective of makespan are also reported. It is found that the solution quality of the proposed tabu search heuristic is better than that of the existing heuristic, but for large size problems more computational efforts are needed; however, the CPU time is still acceptable.     

Sequencing heuristic for scheduling jobs with dependent setups in a manufacturing system

In this paper we discuss a single-machine scheduling problem with machine maintenance. In many production systems, the sequence-dependent setup time of a job cannot be ignored when a switch between two different jobs occurs. In our research, we develop a heuristic to minimize the completion time, or equivalently the total setup time subject to maintenance and due dates. The heuristic consists of three procedures: the initialization procedure, the Stinson heuristic procedure and the iterative procedure. Computational performance of the heuristic is evaluated by comparing its solution with the solution of the branch-and-bound algorithm. The performance of the heuristic on various sizes problems is provided.     

A GRASP heuristic for the multi-objective permutation flowshop scheduling problem

This paper presents a multi-objective greedy randomized adaptive search procedure (GRASP)-based heuristic for solving the permutation flowshop scheduling problem in order to minimize two and three objectives simultaneously: (1) makespan and maximum tardiness; (2) makespan, maximum tardiness, and total flowtime. GRASP is a competitive metaheuristic for solving combinatorial optimization problems. We have customized the basic concepts of GRASP algorithm to solve a multi-objective problem and a new algorithm named multi-objective GRASP algorithm is proposed. In order to find a variety of non-dominated solutions, the heuristic blends two typical approaches used in multi-objective optimization: scalarizing functions and Pareto dominance. For instances involving two machines, the heuristic is compared with a bi-objective branch-and-bound algorithm proposed in the literature. For instances involving up to 80 jobs and 20 machines, the non-dominated solutions obtained by the heuristic are compared with solutions obtained by multi-objective genetic algorithms from the literature. Computational results indicate that GRASP is a promising approach for multi-objective optimization.     

Scheduling on identical parallel machines to minimize total completion time with deadline and machine eligibility constraints

This study addresses the identical parallel machine scheduling problem with job deadlines and machine eligibility constraints to minimize total job completion time. Jobs must be completed before or at a deadline and preemptions are not allowed. Every job is allowed to be processed on a specified subset of machines. This problem is NP-hard. A heuristic and a branch and bound algorithm are developed to solve the problem. For the branch and bound algorithm, a lower bound based on the dual solution of the assignment problem is proposed and the heuristic serves as the initial upper bound. Many dominance rules are developed to curtail the branching nodes during the search procedure. Computational results indicate that the lower bound improves the performance of those in the literature in terms of execution time, and heuristic consistently generates a good quality schedule.     

一种求解Job-shop调度问题的遗传局部搜索算法

利用遗传局部搜索算法求解了作业车间调度问题,遗传算法中的染色体编码采用基于工序的编码,并用插入式贪婪解码机制将染色体解码至主动调度。为了克服传统遗传算法易于早熟收敛的缺点,设计了一种改进的优先操作交叉IPOX操作和子代产生模式的遗传算法。对于遗传算法每个染色体个体,使用基于N6邻域结构的局部搜索进一步使它们得到改善。利用所提出的混合遗传算法求解基准问题,验证了算法的有效性。     

A heuristic algorithm for solving flexible job shop scheduling problem

This paper deals with the flexible job shop scheduling problem with the objective of minimizing the makespan. An efficient heuristic based on a constructive procedure is developed to obtain high-quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic can obtain effective solutions in very short and nearly zero time and is comparable with even metaheuristic algorithms and promising for practical problems.     

A heuristic algorithm for the loading problem in flexible manufacturing systems

The paper considers the loading problem in flexible manufacturing systems (FMSs). This problem involves the assignment to the machine tools of all operations and associated cutting tools required for part types that have been selected to be produced simultaneously. The loading problem is first formulated as a linear mixed 0–1 program with the objective to minimize the greatest workload assigned to each machine. A heuristic procedure is presented in which an assignment of operations to machine tools is obtained by solving a parameterized generalized assignment problem with an objective function that approximates the use of tool slots required by the operations assigned to the machines. The algorithm is coded in FORTRAN and tested on an IBM-compatible personal computer. Computational results are presented for different test problems to demonstrate the efficiency and effectiveness of the suggested procedure.