物流服务供应链订单分配优化及其遗传算法

针对物流服务供应链订单分配问题中,物流服务集成商通常会按照所分配的订单价值向分包商收取一定比例交易费用的特点,设定交易费用为交易额的线性函数,构建了新的物流服务供应链订单分配优化混合整数规划模型,其优化目标为最小化交易费用、采购费用、短缺服务与延迟供给的物流能力数量。鉴于问题的NP-hard特性,设计了相应的遗传算法,并结合基于优先权的启发式规则避免了大量非法初始解的出现。实验算例表明所建立的模型能够反映物流服务供应链订单分配过程中的线性交易费用因素,其所设计的算法能够在可接受的时间内获得质量较高的满意解,并且对于大规模订单分配优化问题,遗传算法的求解时间与求解结果要优于LINGO软件。     

带准备时间的单机指数时间学习效应排序问题

研究带有准备时间的单机学习效应模型,其中工件加工时间具有指数时间学习效应,即工件的实际加工时间是已经排好的工件加工时间的指数函数。学习效应模型考虑工件的实际加工时间同时依赖于工件本身的加工时间和已加工工件的累计加工时间,目标函数为最小化总完工时间。这个问题是NP-难的,提出了一个数学规划模型来求解该问题的最优解。通过分析几个优势性质和下界,提出分支定界算法来求解此问题,并设计启发式算法改进分支定界算法的上界值。通过仿真实验验证了分支定界算法在求解质量和时间方面的有效性。     

置换流水车间调度问题的中心引力优化算法求解

目前求解置换流水车间调度问题的智能优化算法都是随机型优化方法,存在的一个问题是解的稳定性较差。针对该问题,本文给出一种确定型智能优化算法——中心引力优化算法的求解方法。为处理基本中心引力优化算法对初始解选择要求高的问题,利用低偏差序列生成初始解,提高初始解质量;利用加速度和位置迭代方程更新解的状态;利用两位置交换排序法进行局部搜索,提高算法的优化性能。采用置换流水车间调度问题标准测试算例进行数值实验,并和基本中心引力优化算法、NEH启发式算法、微粒群优化算法和萤火虫算法进行比较。结果表明该算法不仅具有更好的解的稳定性,而且具有更高的计算精度,为置换流水车间调度问题的求解提供了一种可行有效的方法。     

一种求解延迟工件数最小的混合流水车间调度问题的模拟退火算法

针对延迟工件数最小的混合流水车间调度问题,给出了一种改进的模拟退火求解算法. 该算法首先给出一个启发式算法来获得初始解,然后用模拟退火算法对初始解改进. 通过交换工件在第一阶段的排序来获得一个新的解,采用最先空闲设备分配规则和先到先被加工规则,对工件在剩余各级的工序进行调度. 实验仿真表明算法是可行有效的.     

混合无等待流水线干扰管理调度方法研究

针对带分批约束的混合无等待流水加工环境中干扰事件的出现导致初始调度计划发生偏离的问题,研究如何运用干扰管理理论来应对工件变更扰动情况,建立了兼顾最小化工件完工时间加权和指标(初始调度目标)和最小化工件完工滞后时间加权和指标(偏离校正目标)的干扰管理调度模型,提出了双层微粒群优化策略与随机多邻域搜索机制相结合的混合求解算法。数值算例仿真实验结果表明,包含“插入-交换”大概率邻域搜索算子的混合微粒群优化算法求解本文所构建的干扰管理调度模型是有效的。     

可转包两台流水作业机排序的近似算法

研究可转包的两台流水作业机排序问题, 目标是极小化最大完工时间和总外包费用之和. 首先给出最坏情况界为2的近似算法, 接着对工件满足有序化约束的情形给出最坏情况界为\frac{3}{2}的改进算法, 以上算法界均为紧界.     

具有学习效应的半导体晶圆制造绿色车间调度问题研究

针对最小化最大完工时间、总碳排放以及总拖期时间的具有学习效应的半导体晶圆制造绿色车间调度问题,构建了双影响因素的新型学习效应模型,提出了改进的多元宇宙优化算法,并对其收敛性进行证明。通过对初始种群进行反向学习、宇宙个体进行莱维飞行扰动和对外部档案中的个体进行邻域搜索变异更新,产生新的父代个体,扩大了种群的多样性,避免算法陷入局部最优。通过对小规模和大规模测试算例的仿真实验,以及利用改进算法求解具有异质性机器的学习型半导体晶圆制造绿色车间调度问题,验证了本文所提出的算法对于求解具有学习效应的半导体晶圆制造绿色车间调度问题的有效性和可行性。     

双机成比例无等待流水线重调度干扰管理研究

针对由异速机构成的双机成比例无等待流水线的加工特点,研究了机器扰动工况下的生产重调度问题,提出了兼顾初始调度目标(最小化制造期)和扰动修复目标(最小化工件滞后时间和)的干扰管理方法。在最短加工时间优先(SPT)排序规则的最优解特性分析基础上,证明了右移初始加工时间表是事后干扰管理的最优调度方案,建立了基于SPT规则的事前干扰管理模型,设计了基于理想点趋近的多目标处理策略,提出了离散量子微粒群优化与局部搜索机制相结合的启发式模型求解算法。算例实验结果表明,本文提出的干扰管理模型和算法是有效的。     

转包费用有限的两机流水作业排序问题（英文）

考虑工件可以转包的两机流水作业排序问题,假设转包费用有一个上限,极小化未转包工件最大完工时间或未转包工件总完工时间与转包费用之和,分析问题的复杂性并设计拟多项式时间算法.     

求解旅行商问题的基于类Kruskal的混合粒子群算法

本文针对求解旅行商问题的标准粒子群算法所存在的早熟和低效的问题,提出一种基于Greedy Heuristic的初始解与粒子群相结合的混合粒子群算法(SKHPSO)。该算法通过本文给出的类Kruskal算法作为Greedy Heuristic的具体实现手段,产生一个较优的初始可行解,作为粒子群中的一员,然后再用改进的混合粒子群算法进行启发式搜索。SKHPSO的局部搜索借鉴了Lin-Kernighan邻域搜索,而全局搜索结合了遗传算法中的交叉及置换操作。应用该算法对TSPLIB中的典型算例进行了算法测试分析,结果表明:SKHPSO可明显提高求解的质量和效率。     

Numerical Methods for Single Machine Scheduling with Non-linear Cost Functions to Minimize Total Cost

In this paper we are concerned with the problem of sequencing a given set of jobs without preemption on a single machine so as to minimize total cost, where associated with each job is a processing time and a differentiable cost function defined on the completion time of the job. The problem, in general, is NP-complete and, therefore, there is unlikely to be an algorithm to solve the problem in reasonable time, thus a heuristic algorithm is desirable. We present two heuristic algorithms to solve the problem. The first algorithm is based on the differential of the cost functions, and the second algorithm is based on the least square approximation of the cost functions. Computational experiences for the case of quadratic, cubic, and exponential cost functions are presented.     

A hybrid two-stage transportation and batch scheduling problem

We study the coordinated scheduling problem of hybrid batch production on a single batching machine and two-stage transportation connecting the production, where there is a crane available in the first-stage transportation that transports jobs from the warehouse to the machine and there is a vehicle available in the second-stage transportation to deliver jobs from the machine to the customer. As the job to be carried out is big and heavy in the steel industry, it is reasonable assumed that both the crane and the vehicle have unit capacity. The batching machine processes a batch of jobs simultaneously. Each batch occur a setup cost. The objective is to minimize the sum of the makespan and the total setup cost. We prove that this problem is strongly NP-hard. A polynomial time algorithm is proposed for a case where the job transportation times are identical on the crane or the vehicle. An efficient heuristic algorithm for the general problem is constructed and its tight worst-case bound is analyzed. In order to further verify the performance of the proposed heuristics, we develop a lower bound on the optimal objective function. Computational experiments show that the heuristic algorithm performs well on randomly generated problem instances.     

Minimizing total tardiness in parallel machine scheduling with setup times: An adaptive memory-based GRASP approach

This paper deals with the problem of scheduling jobs in uniform parallel machines with sequence-dependent setup times in order to minimize the total tardiness relative to job due dates. We propose GRASP versions that incorporate adaptive memory principles for solving this problem. Long-term memory is used in the construction of an initial solution and in a post-optimization procedure which connects high quality local optima by means of path relinking. Computational tests are carried out on a set of benchmark instances and the proposed GRASP versions are compared with heuristic methods from the literature.     

Heuristics for a project management problem with incompatibility and assignment costs

Consider a project which consists of a set of jobs to be performed, assuming each job has a duration of at most one time period. We assume that the project manager provides a set of possible durations (in time periods) for the whole project. When a job is assigned to a specific time period, an assignment cost is encountered. In addition, for some pairs of jobs, an incompatibility cost is encountered if they are performed at the same time period. Both types of cost depend on the duration of the whole project, which also has to be determined. The goal is to assign a time period to each job while minimizing the costs. We propose a tabu search heuristic, as well as an adaptive memory algorithm, and compare them with other heuristics on large instances, and with an exact method on small instances. Variations of the problems are also discussed     

工件具有子工件工期的排序问题

研究了工件具有子工件工期的排序问题.需要在一台单机上加工若干个给定的工件.每个工件由若干个子工件组成,每个子工件都有各自的工期.只有当工件的每个子工件都按时完成,才能称该工件是按时完工工件,否则,称该工件产生延误.目标是最大化按时完工的工件个数.证明当每个工件都被分成两个子工件时,该问题是NP-难的,而且不存在完全多项式时间近似方案(fully polynomial time approximation scheme,简记为FPTAS).提出两个启发式算法,利用数值模拟比较它们的性能,并且将这两个启发式算法的解与最优解的上界进行比较.     

转包且具有不同费用时间段的排序问题

本文研究制造商可以将工件转包给承包商加工的排序模型,承包商仅有一台机器,转包费用由分配给转包工件的不同时间段费用确定.本文分别研究制造商有一台单机及两台自由作业机器环境情形,需要确定被转包工件集及全部工件的加工顺序,使得工件最大完工时间与转包费用和最小.本文利用归约方法对制造商每个机器环境,证明问题NP困难性,并提出动态规划算法.     

Reduction of job-shop problems to flow-shop problems with precedence constraints

We study the problem of scheduling N independent jobs in a job-shop environment. Each job must be processed on M machines according to individual routes. The objective is to minimize the maximum completion time of the jobs. First, the job-shop problem is reduced to a flow-shop problem with job precedence constraints. Then, a set of flow-shop algorithms are modified to solve it. To evaluate the quality of these heuristics, several lower bounds on the optimal solution have been computed and compared with the heuristic solutions for 3040 problems. The heuristics appear especially promising for job-shop problems with ‘flow-like’ properties.     

需要安装时间的平行多功能机排序问题的启发式算法

考虑需要安装时间的平行多功能机排序问题。在该模型中,每个工件对应机器集合的一个子集,其只能在这个子集中的任一台机器上加工,称这个子集为该工件的加工集合;工件分组,同组工件具有相同的加工时间和加工集合,不同组中的工件在同一台机器上连续加工需要安装时间,目标函数为极小化最大完工时间。对该问题NP-难的一般情况设计启发式算法:首先按照特定的规则将所有工件组都整组地安排到各台机器上,然后通过在各机器间转移工件不断改进当前最大完工时间。通过与下界的比较检验算法的性能,大量的计算实验表明,算法是实用而有效的。     

An iterative approach for the serial batching problem with parallel machines and job families

In this paper, we consider a parallel machine scheduling problem to minimize the total completion time. Each job belongs to a certain family. All jobs of one family have identical processing times. Major setups occur between jobs of different families, and we include sequence dependencies. Batches of jobs belonging to the same family can be formed to avoid these setups. Furthermore, we assume serial batching and batch availability. Therefore, the processing time of a batch is the sum of the processing times of all jobs grouped into the corresponding batch. An iterative method is developed for solving this specific problem. This approach alternates between varying batch sizes using an efficient heuristic and sequencing batches based on variable neighborhood search (VNS). Computational results demonstrate that the iterative heuristic outperforms heuristics based on a fixed batch size and list scheduling.     

The coordination of transportation and batching scheduling

We study a coordinated scheduling problem of production and transportation in which each job is transported to a single batching machine for further processing. There are m vehicles that transport jobs from the holding area to the batching machine. Each vehicle can transport only one job at a time. The batching machine can process a batch of jobs simultaneously where there is an upper limit on the batch size. Each batch to be processed occurs a processing cost. The problem is to find a joint schedule of production and transportation such that the sum of the total completion time and the total processing cost is optimized. For a special case of the problem where the job assignment to the vehicles is predetermined, we provide a polynomial time algorithm. For the general problem, we prove that it is NP-hard (in the ordinary sense) and present a pseudo-polynomial time algorithm. A fully polynomial time approximation scheme for the general problem is obtained by converting an especially designed pseudo-polynomial dynamic programming algorithm.