模糊置换流水车间调度的多目标模型

为了求解同时考虑模糊加工时间和模糊交货期的多目标置换流水车间调度问题,提出一种模糊多目标调度模型。针对目标之一的最大化满意度,考虑决策者偏好,建立基于悲观准则的偏好满意度模型,并在此基础上,兼顾考虑可信度,对满意度模型进行改进;针对Pareto最优解的选取,引入模糊集理论和概率论,运用面积补偿法将最大模糊完工时间去模糊化,便于可行解之间进行比较。最后,采用随机系列算例以及典型算例进行优化计算,计算结果验证了模型的有效性。     

具有非对称模糊加工时间工期指派调度优化问题的一类加权模型及其求解算法

根据模糊变量截集所表达的信息的重要程度,建立了模糊环境下工期指派调度优化问题的一类加权模型,该模型中工件加工时间为非对称三角模糊数,目标函数为极小化提前完工惩罚和拖期完工惩罚和的加权可能性均值.证明了当工件加工时间具有相同宽度比时,模型是多项式可解的,并给出了求解的多项式算法.数值实验表明加权模型与现有的非加权模型相比能有效的降低总费用.     

考虑供应链风险和绩效的供应商选择的模糊动态非线性多目标规划模型

基于供应链风险和供应链绩效的模糊性和供应商选择问题的动态性,本文考虑供应链风险和供应链绩效作为模糊变量,讨论如何给生产商一个满意的动态多目标供应商选择方案,确定供应链风险和总成本最小,以及供应链绩效最大。然后对该问题提出了一个动态多目标多产品供应商选择模型,该模型是首次同时考虑供应商选择,订单分配,供应链风险和供应链绩效的一个模糊动态非线性多目标规划模型。为了去模糊化和求解该模型,给出了一个风险和绩效的模糊评估法。最后给出一个数值算例验证了该模型的可行性,为决策者选择供应商提供了理论依据。     

不确定彩排时长下节目调度的鲁棒优化

实际节目彩排调度中,节目的表演时长受内外因素影响,具有不确定性。为了合理调度所有节目,控制演员的空闲时间,使得演员的总等待成本最小,采用了鲁棒优化方法进行研究。首先,建立了节目彩排调度的确定型模型;进一步,考虑节目表演时长的不确定性,采用有界区间描述节目表演时长并考虑决策者风险偏好,在确定型模型的基础上构建区间型两阶段鲁棒优化模型;接着,将鲁棒优化模型转化为0-1混合线性规划模型;最后,采用Matlab进行数值实验,结果表明决策者越偏好规避风险,演员的总等待成本越大。     

不确定彩排时长下节目调度的鲁棒优化

实际节目彩排调度中,节目的表演时长受内外因素影响,具有不确定性。为了合理调度所有节目,控制演员的空闲时间,使得演员的总等待成本最小,采用了鲁棒优化方法进行研究。首先,建立了节目彩排调度的确定型模型;进一步,考虑节目表演时长的不确定性,采用有界区间描述节目表演时长并考虑决策者风险偏好,在确定型模型的基础上构建区间型两阶段鲁棒优化模型;接着,将鲁棒优化模型转化为0-1混合线性规划模型;最后,采用Matlab进行数值实验,结果表明决策者越偏好规避风险,演员的总等待成本越大。     

网格环境下制造资源在线优化调度的区间规划模型

针对网格环境下影响制造资源优化调度的关键参数具有区间性的特点,本文基于区间数建立了在线处理模式下的资源优化调度模型,分别以任务完工的总成本最低与资源的利益最大为目标,将任务的成本限制及资源的价格转换为区间数,充分考虑了任务与资源工作时间限制,给出区间规划模型及其解法。最后通过算例分析得出该模型在反映市场需求以及快速应对市场变化基础上,可得出合理的优化调度方案,验证该方法的可行性与有效性。     

基于决策者风险偏好的模糊鲁棒性项目调度优化

不确定条件下模糊鲁棒性项目调度计划的生成受决策者风险偏好影响。本文研究模糊活动工期下考虑决策者风险偏好的鲁棒性项目调度优化问题，目标是合理安排活动开始时间，生成特定风险偏好下鲁棒性最大的进度计划。首先界定问题，构建优化模型；随后针对问题NP-hard属性和模型特点设计交替禁忌搜索启发式算法，求解得到不同风险偏好下满意的进度计划；最后用实例验证说明，并分析关键参数影响。结论如下：决策者风险偏好由规避转乐观时，项目冲突区间总和增多；截止日期、资源可用量较紧张时，风险偏好变化对冲突区间总和变化影响更大；风险偏好乐观时，截止日期变化对冲突区间总和变化影响更大。研究成果可为不同风险偏好决策者在不具历史数据的高不确定环境中制定合理前摄性计划提供决策支持。     

考虑决策者风险态度的三角直觉模糊双矩阵博弈均衡及应用

在现实博弈问题中,行动方案的选择不可避免的需要对预期支付值(收益值)进行估计和排序,且选择结果往往受到决策者风险态度有的影响。针对决策者具有不同风险态度的博弈环境,基于模糊值指标和模糊度指标确定的三角直觉模糊数排序关系,研究了具有风险态度的三角直觉模糊双矩阵博弈模型,并利用双线性规划方法,给出了该环境下的均衡策略确定方法,为现实博弈均衡的确定提供了有效的途径。最后通过企业营销策略选择的应用研究,对方法的现实有效性予以说明。     

基于两阶段混合整数规划模型的洪涝灾害应急管理研究

本文以洪涝自然灾害为现实背景, 考虑多种应急物资、灾情的不确定性和应急救灾的多目标性, 集成优化灾前准备和灾后响应两阶段, 建立了一定最大救援时间下的两阶段多目标混合整数规划模型。模型的目标一是使得不同灾害情景下灾后响应阶段总物资不足惩罚和延误损失的期望最小, 目标二是使得灾前准备阶段应急物资存储点建造成本、物资存储成本及灾后响应阶段物资分配成本之和最小。该模型保证了应急救灾的及时有效以及物资的公平分配。本文设计了一种多目标遗传算法用于模型求解, 结合具体算例, 得到了模型在最大救援时间为4到9区间内任意数值下的pareto最优解, 很好地适应了决策者不同的决策需求, 并根据pareto应急方案的数目, 灾后响应阶段成本期望和两阶段总成本等模型的三个关键产出随最大救援时间的变化趋势, 得出最优的最大救援时间为5.7。     

恶化率与工件无关的线性加工时间调度问题

讨论恶化率与工件无关的线性加工时间调度问题 .对于工件间具有平行链约束 ,目标函数为极小化最大完工时间的单机问题 ,分别就链不允许中断和链允许中断两种情况给出了最优算法 .对于工件间没有优先约束 ,目标函数为极小化完工时间和的平行机问题 ,证明了工件按基本加工时间不减排列可以得到最优调度 .     

Optimisation of integrated reverse logistics networks with different product recovery routes

The awareness of importance of product recovery has grown swiftly in the past few decades. This paper focuses on a problem of inventory control and production planning optimisation of a generic type of an integrated Reverse Logistics (RL) network which consists of a traditional forward production route, two alternative recovery routes, including repair and remanufacturing and a disposal route. It is assumed that demand and return quantities are uncertain. A quality level is assigned to each of the returned products. Due to uncertainty in the return quantity, quantity of returned products of a certain quality level is uncertain too. The uncertainties are modelled using fuzzy trapezoidal numbers. Quality thresholds are used to segregate the returned products into repair, remanufacturing or disposal routes. A two phase fuzzy mixed integer optimisation algorithm is developed to provide a solution to the inventory control and production planning problem. In Phase 1, uncertainties in quantity of product returns and quality of returns are considered to calculate the quantities to be sent to different recovery routes. These outputs are inputs into Phase 2 which generates decisions on component procurement, production, repair and disassembly. Finally, numerical experiments and sensitivity analysis are carried out to better understand the effects of quality of returns and RL network parameters on the network performance. These parameters include quantity of returned products, unit repair costs, unit production cost, setup costs and unit disposal cost.     

Resolution approach for multi-objective problems with uncertain demands

The job-shop scheduling problem (JSP) is one of the hardest problems (NP-complete problem). In a lot of cases, the combination of goals and resource exponentially increases search space. The objective of resolution of such a problem is generally, to maximize the production with a lower cost and makespan. In this paper, we explain how to modify the objective function of genetic algorithms to treat the multi-objective problem and to generate a set of diversified “optimal” solutions in order to help decision maker. We are interested in one of the problems occurring in the production workshops where the list of demands is split into firm (certain) jobs and predicted jobs. One wishes to maximize the produced quantity, while minimizing as well as possible the makespan and the production costs. Genetic algorithms are used to find the scheduling solution of the firm jobs because they are well adapted to the treatment of the multi-objective optimization problems. The predicted jobs will be inserted in the real solutions (given by genetic algorithms). The solutions proposed by our approach are compared to the lower bound of the cost and makespan in order to prove the quality and robustness of our proposed approach.     

需求不确定下供应链间古诺博弈决策分析

通过引入区间数和心态指标将需求函数模糊化,研究了需求不确定下供应链间竞争的决策问题。首先,分别构建了分散-分散决策、集中-分散决策和集中-集中决策三种模式下的供应链间价格竞争模型;其次,分析了不同竞争模式下市场规模等变量对零售价格、顾客需求量和供应链利润的影响;最后,以两条供应链为例,进行算例分析,并在此基础上,进一步分析了心态指标、市场规模等变量对最终决策结果的影响。     

Production planning for hybrid remanufacturing and manufacturing system with component recovery

In this paper, we address component recovery under the condition of limited resources from the OEM's (Original Equipment Manufacturer's) standpoint. We develop a linear programming model for a hybrid remanufacturing and manufacturing system for production planning problems with deterministic returns. In this paper, a data set from an OEM that both remanufactures and manufactures the products is used to demonstrate the performance of the proposed model. Subsequently, an analysis of the impact of the remanufactured product’s price and the quantity of returns on revenue and total cost will be discussed. We have found that uncertain factors of manufacturing influence the profit and uncertain factors of remanufacturing influence the production planning, such as the rate of the yield on component remanufacturing and the quantity of returns.     

基于混沌量子算法和MAGTD的多目标FJSP求解策略

针对多目标环境下柔性作业车间的调度问题,以最小化最大完工时间和惩罚值为目标,建立调度问题的数学模型,提出了基于混沌理论的量子粒子群算法。针对实际生产交货期不确定的特点,在量子粒子群算法基础上,提出引入混沌机制建立初始群的方法;利用混沌机制的遍历性,提出混沌局部优化策略;为获取最优调度方案提出了引入多指标加权灰靶选择策略。通过典型基准算例和对比测试,验证了所提出的算法获得最满意调度方案的可行性和求解多目标柔性作业车间调度问题的有效性。     

不确定环境下制造再制造供应链定价与协调问题研究

研究模糊不确定环境下制造/再制造供应链系统的定价与协调问题。通过考虑制造产品与再制造产品的差异性,利用模糊理论和博弈论理论等知识,在集中式和分散式决策方式下,分别给出了制造/再制造商和零售商关于制造产品和再制造产品的最优价格决策,以及分散决策方式下的系统协调策略,并且利用数值算例对所得结果进行了分析。     

Master disassembly scheduling in a remanufacturing system with stochastic routings

Remanufacturing is an important source of sustainable development. Remanufactured products have proven to be high quality and low cost. Due to their unique characteristics, remanufacturing processes have many differences compared to manufacturing processes. These characteristics, which make remanufacturing complex, require good performance from Production Planning and Control (PPC) activities. The goal of the paper is to propose a mathematical model for disassembly master production scheduling considering stochastic routings in the remanufacturing environment. The proposed model is based on stochastic dynamic programming and it is applied to a real case of automotive clutch remanufacturing. The results contribute to the development of theory and practice by filling a gap in knowledge of the use of PPC systems, developing a mathematical method that can be easily implemented in a spreadsheet. The findings also show some decisions that are counterintuitive. For example, in some situations disassemble more products than necessary to meet the demand can result in a lower expected total cost.     

Intuitionistic fuzzy optimization technique for Pareto optimal solution of manufacturing inventory models with shortages

This paper discusses a manufacturing inventory model with shortages where carrying cost, shortage cost, setup cost and demand quantity are considered as fuzzy numbers. The fuzzy parameters are transformed into corresponding interval numbers and then the interval objective function has been transformed into a classical multi-objective EPQ (economic production quantity) problem. To minimize the interval objective function, the order relation that represents the decision maker’s preference between interval objective functions has been defined by the right limit, left limit, center and half width of an interval. Finally, the transformed problem has been solved by intuitionistic fuzzy programming technique. The proposed method is illustrated with a numerical example and Pareto optimality test has been applied as well.     

A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown

As the research interest in distributed scheduling is growing, distributed permutation flowshop scheduling problems (DPFSPs) have recently attracted an increasing attention. This paper presents a fuzzy logic-based hybrid estimation of distribution algorithm (FL-HEDA) to address DPFSPs under machine breakdown with makespan criterion. In order to explore more promising search space, FL-HEDA hybridises the probabilistic model of estimation of distribution algorithm with crossover and mutation operators of genetic algorithm to produce new offspring. In the FL-HEDA, a novel fuzzy logic-based adaptive evolution strategy (FL-AES) is adopted to preserve the population diversity by dynamically adjusting the ratio of offspring generated by the probabilistic model. Moreover, a discrete-event simulator that models the production process under machine breakdown is applied to evaluate expected makespan of offspring individuals. The simulation results show the effectiveness of FL-HEDA in solving DPFSPs under machine breakdown.     

Sequencing with uncertain numerical data for makespan minimisation

We consider a scheduling problem with the objective of minimising the makespan under uncertain numerical input data (for example, the processing time of an operation, the job release time and due date) and fixed structural input data (for example the precedence and capacity constraints). We assume that at (before) the scheduling stage the structural input data are known and fixed but all we know about the numerical input data are their upper and lower bounds, where the uncertain numerical data become realised at the control stage as the scheduled process evolves. After improving the mixed graph model, we present an approach for dealing with our scheduling problem under uncertain numerical data based on a stability analysis of an optimal makespan schedule. In particular, we investigate the candidate set of the critical paths in a circuit-free digraph, characterise a minimal set of the optimal schedules, and develop an optimal and a heuristic algorithm. We also report computational results for randomly generated as well as well-known test problems.