带折扣因子的单机干扰管理研究

针对可预见的干扰管理问题,考虑单机环境下,加权折扣最短加工时间优先(WDSPT)序为原目标的最优加工次序,研究了如何对初始加工时间表进行修改。在干扰事件影响下,初始加工时间表将不再是最优,甚至不再可行。和大多数重排序研究不同,构建了同时考虑原目标和由干扰事件造成的扰动目标的重排序模型,并基于理想点法提出一种动态规划算法来求解所建模型中的双目标排序问题。最后通过一个数值算例来说明该重调度模型对于求解带折扣因子的单机干扰管理问题是有效的。     

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。     

面向多目标优化的一种混合进化算法

针对多目标优化问题,设计一种基于量子计算和非支配排序遗传算法相结合的智能算法进行求解,综合量子算法和非支配排序遗传算法的优点,在局部搜索和全局搜索之间进行权衡。混合算法采用量子比特对问题的解进行编码,基于量子旋转门算子、分散交叉算子以及高斯变异算子对种群进行更新。进行局部深入搜索时,用一个解在目标空间中跟理想点的距离来评价该解的优劣;进行全局搜索时,基于非支配排序遗传算法中的有效前沿的划分和解之间的拥挤距离来评价某个解。最后,在经典的测试函数ZDT5上对所提混合算法进行了测试。通过对比分析若干项针对有效解集的评价指标,该混合算法在跟最优有效前沿的逼近程度以及有效解集分布的均匀程度上均优于目前得到广泛应用的非支配排序遗传算法。     

具有学习和退化效应的单机干扰管理问题

针对工件同时具有学习和退化效应、机器具有可用性限制这一问题,建立可预见性单机干扰管理模型。在这一模型中,工件的加工时间是既与工件所排的加工位置又与工件开始加工的时间有关的函数。同时,在生产过程中由于机器发生故障或定期维修等扰动事件导致机器在某段时间内不能加工工件。目标是在同时考虑原目标函数和由扰动造成的偏离函数的情况下,构建一个新的最优时间表序列。根据干扰度量函数的不同研究了两个问题,第一个问题的目标函数是极小化总完工时间与总误工时间的加权和;第二个问题的目标函数是极小化总完工时间与总提前时间的加权和。对于所研究的问题,首先证明了最优排序具有的性质,然后建立了相应的拟多项式时间动态规划算法。     

加工时间依赖于机器的自由作业排序问题

1992年M．Dror提出工件的加工时间依赖于机器的排序问题（schedulingwithmachinedependentprocessingtimes），并研究以最大完工时间（makespan）和以总的完工时间为优化目标的两种这类排序问题．然而，M．Dror对以总的完工时间为优化目标提出的“最优算法”是错误的．本文用算例表明他提出的算法不是最优的，并在机器连续加工的条件下，把这个排序问题转化成指派问题（assignmentproblem），从而可以用匈牙利算法得到最优解．最后，我们提出几个尚未解决的问题，以期引起国内外同行进一步研究．     

各机器具有相同加工时间的Flow Shop 成组排序问题

本文讨论了m台机器的Folw Shop成组排序问题，工件在不同机器上的加工时间相同，目标函数为极小化完工时间和。给出了一个多项式时间可解的最优算法。     

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。     

具有优先约束的单机随机排序问题

讨论工件的加工时间为常数,机器发生随机故障的单机随机排序问题,目标函数极小化工件的加权完工时间和的数学期望最小.考虑两类优先约束模型.在第一类模型中,设工件间的约束为串并有向图.证明了模块M的ρ因子最大初始集合I中的工件优先于模块中的其它工件加工,并且被连续加工所得的排序为最优排序,从而将Lawler用来求解约束为串并有向图的单机加权总完工时间问题的方法推广到机器发生随机故障的情况.在第二类模型中,设工件间的约束为出树优先约束.证明了最大家庭树中的工件优先于家庭树中其它的工件加工,并且其工件连续加工所得到的排序为最优排序并给出了最优算法.     

带机器故障的两台机带权误工数排序问题

讨论机器带故障中断的两台平行机排序问题,工件加工时间均为单位时间,目标是极小化带权误工工件数.当转移时间t=0时给出了最优的算法.当t≠0时,给出了一个多项式时间的近似算法,并证明算法解与最优解至多相差一个带权误工数.     

具有退化工件和工期窗口安排的排序问题

针对具有退化工件的排序模型,考虑了单机排序和两台机器流水作业的工期窗口安排问题,在这一模型中,工件的加工时间是与其开工时间和退化率有关的一个线性函数。目标是找到一个最优排序和确定工期窗口的开始时间及大小以便最小化所有工件的费用函数,费用函数由四部分组成:提前、延误、工期窗口开始时间和工期窗口大小。对所研究的单机问题,详细地讨论了符合现实情况的几种类型问题,并得到了问题的最优解;对两台机器流水作业问题,给出了多项式算法。     

On the Biobjective Adjacent Only Quadratic Spanning Tree Problem

The adjacent only quadratic minimum spanning tree problem is an NP-hard version of the minimum spanning tree where the costs of interaction effects between every pair of adjacent edges are included in the objective function. This paper addresses the biobjective version of this problem. A Pareto local search algorithm is proposed. The algorithm is applied to a set of 108 benchmark instances. The results are compared to the optimal Pareto front generated by a branch and bound algorithm, which is a multiobjective adaptation of a well known algorithm for the mono-objective case.     

基于重新排序时间错位限制下的最小化总完工时间的平行分批排序（英文）

In the rescheduling on a single machine, a set of the original jobs has already been scheduled, in order to make a given objective function is optimal. The decision maker needs to insert the new jobs into the existing schedule without excessively disrupting it. A batching machine is a machine that can handle up to some jobs simultaneously. In this paper,we consider the total completion time under a limit on the sequence disruptions for parallel batching based on rescheduling. For the parallel batching problem based on rescheduling, we research the properties of feasible schedules and optimal schedules on the total completion time under a limit on the maximum time disruptions or total time disruptions, in which the jobs are sequenced in SPT order, and give out the pseudo-polynomial time algorithms on the number of jobs and the processing time of jobs by applying the dynamic programming method.     

Rescheduling unrelated parallel machines with total flow time and total disruption cost criteria

In this paper, we consider a rescheduling problem where a set of jobs has already been assigned to unrelated parallel machines. When a disruption occurs on one of the machines, the affected jobs are rescheduled, considering the efficiency and the schedule deviation measures. The efficiency measure is the total flow time, and the schedule deviation measure is the total disruption cost caused by the differences between the initial and current schedules. We provide polynomial-time solution methods to the following hierarchical optimization problems: minimizing total disruption cost among the minimum total flow time schedules and minimizing total flow time among the minimum total disruption cost schedules. We propose exponential-time algorithms to generate all efficient solutions and to minimize a specified function of the measures. Our extensive computational tests on large size problem instances have revealed that our optimization algorithm finds the best solution by generating only a small portion of all efficient solutions.     

A new multi objective optimization approach in distribution systems

This paper presents a multi objective optimal location of AVRs in distribution systems at the presence of distributed generators based on modified teaching-learning-based optimization (MTLBO) algorithm. In the proposed MTLBO algorithm, teacher and learner phases are modified. The proposed objective functions are energy generation costs, electrical energy losses and the voltage deviations. The proposed algorithm utilizes several teachers and considers the teachers as an external repository to save found Pareto optimal solutions during the search process. Since the objective functions are not the same, a fuzzy clustering method is used to control the size of the repository. The proposed technique allows the decision maker to select one of the Pareto optimal solutions (by trade-off) for different applications. The performance of the suggested algorithm on a 70-bus distribution network in comparison with other evolutionary methods such as GA, PSO and TLBO, is extraordinary.     

A capacity constrained single-facility dynamic lot-size model

A computationally efficient algorithm for a multi-period single commodity production planning problem with capacity constraints is developed. The model differs from earlier well-known studies involving concave cost functions in the introduction of production capacity constraints which need not be equal in every period. The objective is to find an optimal production schedule that minimizes the total production and inventory costs. Backlogging is not allowed. The structure of the optimal solution is characterized and then used in an efficient algorithm.     

工件延误和可拒绝下的单机重新排序问题的近似方案

研究工件延误产生干扰且延误工件可拒绝下的单机重新排序问题.在该问题中,给定计划在零时刻到达的一个工件集需在一台机器上加工,工件集中的每个工件有它的加工时间和权重,在工件正式开始加工前,按照最短赋权加工时间优先的初始排序已经给定,目标函数是极小化赋权完工时间和,据此每个工件的承诺交付截止时间也给定.然而,在工件正式开始加...     

Solving a bicriteria scheduling problem on unrelated parallel machines occurring in the glass bottle industry

This paper deals with the resolution of a bicriteria scheduling problem connected with the glass bottles production. The shop is made up of unrelated parallel machines and the aim is to compute a schedule of orders that maximizes the total margin and that minimizes the difference in machines workload. An algorithm to compute the set of all strict Pareto optima is offered and later extended into an interactive algorithm.     

Two-Sided Pareto Front Approximations

A new approach to derive Pareto front approximations with evolutionary computations is proposed here. At present, evolutionary multiobjective optimization algorithms derive a discrete approximation of the Pareto front (the set of objective maps of efficient solutions) by selecting feasible solutions such that their objective maps are close to the Pareto front. However, accuracy of such approximations is known only if the Pareto front is known, which makes their usefulness questionable. Here we propose to exploit also elements outside feasible sets to derive pairs of such Pareto front approximations that for each approximation pair the corresponding Pareto front lies, in a certain sense, in-between. Accuracies of Pareto front approximations by such pairs can be measured and controlled with respect to distance between elements of a pair. A rudimentary algorithm to derive pairs of Pareto front approximations is presented and the viability of the idea is verified on a limited number of test problems.     

Computing multiobjective Markov chains handled by the extraproximal method

This paper suggests a new method for generating the Pareto front in multi-objective Markov chains, which overcomes some existing drawbacks in multi-objective methods: a fundamental issue is to find strong Pareto policies which are policies whose cost-function value is the closest in Euclidean norm to the utopian point. Each strong Pareto policy is reached when each cost-function, constrained by the strategy of others, cannot improve further its own criterion. Constraints associated to the objective function are implemented formulating the problem as a bi-level optimization approach. We convert the problem into a single level optimization approach by introducing a generalized Lagrangian function to represent the original multi-objective problem in terms of a related nonlinear programming problem. Then, we apply the Tikhonov regularization method to the objective function. The regularization method ensures that all the possible Pareto policies to be generated along the Pareto front are strong Pareto policies. For solving the problem we employ the extra-proximal method. The method effectively approximates to every optimal Pareto point, which in this case is a strong Pareto point, in the Pareto front. The experimental result, applied to the route selection for counter-kidnapping problem, validates the effectiveness and usefulness of the method.     

基于退化效应的两台机器流水作业可拒绝排序

考虑了工件具有退化效应的两台机器流水作业可拒绝排序问题,其中工件的加工时间是其开工时间的简单线性增加函数.每个工件或者被接收,依次在两台流水作业机器上被加工,或者被拒绝但需要支付一个确定的费用.考虑的目标是被接收工件的最大完工时间加上被拒绝工件的总拒绝费用之和.证明了问题是NP-难的,并提出了一个动态规划算法.最后对一种特殊情况设计了多项式时间最优算法.