多目标等量分批柔性作业车间调度集成优化方法

针对多目标等量分批柔性作业车间调度问题,提出了一种集成优化方法。构建了一种以完工时间最短、生产成本最低为优化目标的多目标等量分批柔性调度集成优化模型。提出并设计了一种改进的非支配排序遗传算法对模型加以求解。算法中引入面向对象技术处理复杂的实体逻辑关系,采用三段式分段编码技术分别对分批方案、加工顺序、设备进行编码,采用三段式分段交叉和变异的混合遗传算子实现遗传进化,采用两种精细化调度技术进行解码以缩短流程时间。通过案例分析验证了所提方法的有效性。     

加工与装配车间集成调度的多目标优化模型

为解决一类具有多品种混流加工作业车间和流水装配车间的两阶段集成调度优化问题,以加工线最大完工时间和产品总生产完工时间最小为目标,并考虑通过对零部件加工提前完工和装配线等待施加惩罚系数,以保证缓冲区在制品库存和装配过程均匀连续生产,建立加工与装配车间集成调度的多目标优化模型,充分利用加工和装配工序之间存在的并行性,合理确定零部件加工顺序和装配排序,以缩短产品生产周期,降低生产成本,提高生产设备利用率;同时针对所建立的模型,设计遗传算法进行求解,采用零件加工和产品装配的两段实数编码,以稳态复制对群体进行选择,对交叉和变异算子进行设计,以保证新个体满足工序先后约束的可行性,避免了非可行解的混入影响优化结果;最后通过实例验证所建调度模型的可行性和算法的有效性。     

单阶段多产品批处理过程的短期调度1. 基本模型的建立

具有并行设备的多产品单阶段批处理过程短期 调度问题需考虑订单发布时间、交货期，订单生产的顺序相关建立时间、禁止生产子序列， 及设备的准备时间等生产约束．本文在考虑上述约束的基础的上，利用时间间隙的概念和连 续时间表达，将设备、订单分配给时间间隙分别表达为两类0-1变量，建立了具有并行生产 线的多产品单阶段批处理过程的短期调度数学模型．模型表达为一个混合整数规划（MILP） 问题．该模型不但比已有的基于时间间隙描述的调度模型0-1变量少，而且能优 化多种目标函数．本文的第二部分将引入一些适当的启发性规则，减小了模型的规模，并应 用大量的计算实例说明该模型的有效性和适用性．     

多机相关电网任务数据的动态分批优化调度算法

传统的动态分批优化调度算法对数据初始分析效果差,调度准确率较低,为解决上述问题,基于多机相关电网任务数据研究了一种新的动态分批优化调度算法,获取多机相关电网任务数据,对数据进行集中标定,保证数据处于系统可操作范围内,实现对数据的精准处理.实验结果表明,多机相关电网任务数据的动态分批优化调度算法能够很好地提高调度准确率,...     

商品混凝土生产与配送集成调度系统

讨论了商品混凝土企业在日常生产活动中关于生产计划和配送计划安排时所遇到的时间冲突和资源冲突等一系列问题的现象和产生的原因,详细分析了泵送设备和砼车在服务过程中的时间特点,并根据一种生产与配送集成的模式提出了解决问题的原则和方法,设计了一套商品混凝土生产与配送集成调度系统。这套系统应用到实际生产中,可以使企业管理者方便灵活地进行生产计划与配送计划的编排和调整,从而提高生产效率和设备利用率。     

QPSO算法在生产调度中的研究与应用

在对某印染企业的生产状况进行深入调研和分析的基础上,对微粒群算法及量子粒子群算法进行了对比研究,并根据实际情况对算法进行了部分改进,使之能适用于离散的生产调度问题。最后将量子粒子群算法应用到花布印染企业的生产调度中,对加工任务进行优化调度,并实现甘特图的动态生成。该结果可直接应用于企业车间调度中,具有一定的实际应用价值。     

带返工的定点装配车间生产计划与调度集成优化

针对定点装配车间不合格品返工导致生产计划及调度不可行的情况,提出一种基于返工延后处理的定点装配车间生产计划与调度集成优化方法.首先制定返工延后处理的粗生产计划;下达计划生产后,将每周期产生的不合格品放入缓冲区,在下周期初时重新调整生产计划并求解新计划下的最优调度,判断其是否满足装配班组负载率要求,不断交替迭代生产计划与调度直至达到计划与调度的均衡和优化.最后通过算例验证了所提出方法的可行性和有效性.     

基于多智能体系统的集成电路制造生产调度的分布异构数据集成

集成电路制造体系是典型的分布异构数据动态计算环境,分布异构数据集成是集成电路制造企业进行生产调度首要解决的关键问题之一。针对集成电路制造企业分布异构数据的特点,以及生产调度系统对数据及时性、实时性的特点,提出一种基于多智能体系统的面向集成电路制造生产调度的分布异构数据集成技术。在国内某集成电路制造企业实施结果表明,该技术方案在分布异构环境下,具有很强的数据集成与服务能力。     

基于改进多目标遗传算法求解混合流水车间调度问题

为解决混合流水车间调度问题(HFSP),基于多目标遗传算法和粒子群算法的优点,提出一种多目标混合算法。该算法引入一种扩展的基于工序的编码,将两种算法产生的最优解分别作为彼此的初始因子,增强了遗传算法的进化速度,有效避免了粒子群算法陷入局部最优,并实现了不同加工路线的生产车间的灵活性调度。最后通过实例的数值仿真验证了算法的有效性。     

汽车装配车间生产计划与调度的同时优化方法

文中提出三种新方法来解决汽车装配车间生产计划与调度的同时优化问题.首先将汽车装配线简化为一个Flow shop问题,并建立其混合整数规划模型,以求得使各装配工位的准备成本和空闲时间尽可能少并尽可能满足产品需求的粗生产计划.然后在粗生产计划的基础上考虑装配线的细节,用Tabu搜索法与快速调度仿真相结合的三种不同启发式算法使生产计划与调度同时得到优化,并给出了三种算法的复杂性.大量算例的比较研究表明了这些算法的有效性和适用性.     

A hybrid VNS approach for the short-term production planning and scheduling: A case study in the pulp and paper industry

Mathematical formulations for production planning are increasing complexity, in order to improve their realism. In short-term planning, the desirable level of detail is particularly high. Exact solvers fail to generate good quality solutions for those complex models on medium- and large-sized instances within feasible time. Motivated by a real-world case study in the pulp and paper industry, this paper provides an efficient solution method to tackle the short-term production planning and scheduling in an integrated mill. Decisions on the paper machine setup pattern and on the production rate of the pulp digester (which is constrained to a maximum variation) complicate the problem. The approach is built on top of a mixed integer programming (MIP) formulation derived from the multi-stage general lotsizing and scheduling problem. It combines a Variable Neighbourhood Search procedure which manages the setup-related variables, a specific heuristic to determine the digester's production speeds and an exact method to optimize the production and flow movement decisions. Different strategies are explored to speed-up the solution procedure and alternative variants of the algorithm are tested on instances based on real data from the case study. The algorithm is benchmarked against exact procedures.     

An ILS heuristic for the ship scheduling problem: application in the oil industry

This paper addresses a real‐life rescheduling problem of a pipe‐laying support vessel (PLSV) fleet in charge of subsea oil well connections. The short‐term schedule of these vessels is subject to uncertainties inherent to its operations, resulting in ships idleness or delays in oil production. The objective of this study is to develop methods to support a Brazilian oil and gas company in overcoming impacts caused by operational disruptions, while reaching its planned production level. The PLSV rescheduling problem was treated as an identical parallel machine scheduling problem, where the machines represent the vessels and the jobs are the activities for the subsea well connections. We propose a mathematical programming model and a method based on the iterated local search (ILS) metaheuristic to solve the problem. This paper contributes to this by considering simultaneously setup times, machine eligibility, release dates, due dates, and machine availability. Both methods were applied on 10 instances based on real PLSV data. Taking into account an objective function that measures the operational impact on schedules, the ILS provided an average improvement above 91% in schedules when compared to the initial solution provided by the studied company. The ILS outperformed a mathematical programming model for the problem, in eight instances, within a 30‐minute execution time limit, fitting to the company process.     

基于改进型批决策与调度建模的生产计划调度系统的研究

热轧型钢生产工艺复杂,其生产中极易出现由于计划调度安排不当而产生的交货期延误、库存超负荷等问题。针对以上问题研究设计了MES生产计划调度系统,改进了批决策调度策略用于数学建模,利用自适应遗传算法求解生产调度计划。以此为基础,为某热轧企业设计实现了生产计划调度系统,并通过真实的热轧型钢订单、原料、设备等数据,对模型改进前后的计划编制方法进行模拟与比较,验证了利用该改进型批决策与调度模型编制的热轧型钢生产调度计划可节省生产时间、降低设备调度时间,以此来指导热轧型钢的生产可切实减少交货延误和减少库存占用率,并提高企业利润率。     

间歇过程生产调度建模的研究

生产调度是为实现某一目的而对共同使用的资源进行时间上的分配.调度中存在大量的模糊因素,将模糊的思想运用到调度领域可以帮助决策者进行有效决策.本文提出一种基于模糊规划的间歇过程生产调度建模方法及其模糊优化的新算法.应用模糊集合论的方法,在已有模糊规划模型的基础上,针对间歇生产过程,分析调度中存在的模糊信息以及出现的形式和方式,将确定型生产调度模型的约束条件和目标方程中的参数模糊化,采用非精确的量化形式,以隶属函数来表示,建立基于模糊参数的生产调度模糊线性规划模型MIFCLP.通过对一个调度问题实例进行了仿真,仿真结果证明,采用模糊模型更容易得到可行解,采用模糊线性规划解决间歇生产调度问题是一种有效方法.     

Modeling and analysis for multi-period, multi-product and multi-resource production scheduling

This study presents a framework for solving the multi-period, multi-product and multi-resource production-scheduling (M³PS) problem. Practically, the main concern for an M³PS problem is how to satisfy two management policies: (1) each product is manufactured in a continuous manner so that once the product is on a production line, it will complete its production procedure without interruption, and (2) the number of the product's types is limited during one period. By defining the decision variables and taking into account the machine's capacity and the customers' demand, a mixed integer programming (MIP) Model is formulated. To solve this MIP problem, a two-phase approach is proposed. In phase 1, the search space of the MIP Model is transformed into a preliminary pattern by a heuristic mining algorithm so that a hyper assignment problem can be formed as a reference model to be solved. In phase 2, a stochastic global optimization procedure that incorporates a genetic algorithm with neighborhood search techniques is designed to obtain the optimal solution. A numerical experiment is presented with an illustration, and it shows that the proposed model is adequate to cope with complicate scheduling problems.     

基于群智能算法的自动化码头协同调度研究

为了提高码头作业效率和服务水平,保障港口在激烈竞争中的生存和发展,研究自动化码头自动引导车、岸桥和自动化轨道吊的协同调度问题,根据边装边卸作业模式,建立混合整数规划模型,以完成船舶装卸时间最小化为目标,利用群智能算法中多种算法进行求解,通过数值实验证明了该模型的有效性,获得优化的调度方案,并对不同算法的性能进行比较,结果表明启发式的混合遗传粒子群算法能够在最短的时间内获得最优解,其在求解的质量和速度方面都表现得更为优秀,可以应用于码头的实际作业中。     

冷轧合同批量调度的模糊Job Shop模型及算法

针对冷轧薄板生产线机组设备多、产品种类多的特点,提出一种合同批量调度方法．该方法将生产合同按产品种类和交货期组批处理,建立了具有模糊处理时间的Ｊｏｂｓｈｏｐ调度模型来描述整个生产物流情况,并定义了分情况的合同批量加工时间推算规则．针对调度模型的求解,给出一种多子种群并行粒子群算法．利用宝钢冷轧薄板厂的实际生产数据进行了试运行,结果表明,该方法可大幅度提高合同交货期满意度,并能有效减少合同批量调度的工作时间。     

An optimization approach for the lot sizing and scheduling problem in the brewery industry

This study considers a production lot sizing and scheduling problem in the brewery industry. The underlying manufacturing process can be basically divided into two main production stages: preparing the liquids including fermentation and maturation inside the fermentation tanks; and bottling the liquids on the filling lines, making products of different liquids and sizes. This problem differs from other problems in beverage industries due to the relatively long lead times required for the fermentation and maturation processes and because the “ready” liquid can remain in the tanks for some time before being bottled. The main planning challenge is to synchronize the two stages (considering the possibility of a “ready” liquid staying in the tank until bottling), as the production bottlenecks may alternate between these stages during the planning horizon. This study presents a novel mixed integer programming model that represents the problem appropriately and integrates both stages. In order to solve real-world problem instances, MIP-based heuristics are developed, which explore the model structure. The results show that the model is able to comprise the problem requirements and the heuristics produce relatively good-quality solutions.     

Simultaneous scheduling of football games and referees using Turkish league data

Assignment decisions of referees to football (soccer) games are highly debated in sports media. Referee assignments are typically done on a weekly basis as the league progresses. However, this practice ignores important workload constraints on referees. Moreover, referees' skill levels should also be considered in determining their assignments. In this article, we first give a mixed integer linear program formulation for the problem of simultaneously generating a game schedule and assigning main referees to games by incorporating specific rules in the Turkish league. We also approach this problem using a genetic algorithm (GA) because of the computational difficulties in solving the problem. In the GA solution pool, we suggest using templates for referee assignments that follow several referee‐related workload constraints. We explain how these templates can be obtained by solving a mixed integer linear model prior to running the GA. The usage of these templates for referee assignments is conceptually similar to using a basic match schedule for game scheduling such as the one used in the Turkish Football League. We use the Turkish Football League fixtures for 2010–2013 as a case study. Experiments with the GA using real‐world data show a rather modest performance in terms of computation time and objective function value. Our numerical results indicate that the problem is extremely hard to solve.     

Modeling and solving the project selection and scheduling

This paper considers the integrated bi-objective problem of projects selection and scheduling to optimize both total expected benefit and resource usage variation. The benefit is time-dependent. Although this integrated problem has become a very active field of research, the available model and algorithms suffer from serious shortcomings. This paper analyzes the available methods and develops a novel mathematical model, in form of a mixed integer linear program, for the problem. Then, it proposes an ant colony optimization algorithm employing four features of ant generation, colonial, Pareto front updating, and pheromone updating mechanisms. To evaluate the proposed algorithm, it is compared with two available genetic algorithm and scatter search. Using comprehensive numerical experiments and statistical tools, it is shown that the proposed ant colony optimization outperforms the two available algorithms.