基于CAIV 的机械产品设计方案费效权衡优化

针对民用机械产品设计方案选择时,既要考虑客户对产品的功能需求,又要符合企业费用目标约束的问题,采用基于以费用为独立变量(CAIV)的费效权衡优化方法,以混凝土泵车系列产品为研究对象,运用主成分回归和层次分析(AHP)方法分别构建生命周期费用和效能模型,并以费效比为优化目标权衡得到最优方案.研究表明,采用CAIV费效权衡方法选择的设计方案,可以实现产品全生命周期成本目标的有效控制.     

应用带补偿的机会约束规划策略优化精馏过程的研究

精馏过程优化存在许多不确定变量,其对优化结果的影响是不可忽略的,所以需要找到真实反映不确定变最的数学模型,找到最优的求解方法得到精确解,进而得到最优的优化结果.本研究通过分析随机规划策略和不确定因素的特性及其对精馏优化过程的影响,对不确定因素进行分类,应用带补偿的随机规划和机会约束规划实现不确定因素对优化目标和约束的影响.对于模型中的机会约束,由于不确定输入变量和不确定输出变量间存在单调关系,所以可以通过对不确定输入变量的多重积分将机会约束转化为等价地确定性非线性约束,此时模型转化为含有非线性约束的带补偿的随机规划模型,此模型通过改进的蒙特卡罗(Monte Carlo)积分和含有序列2次规划(SQP)的Benders分解相结合的混合算法有效的求解.所提出的求解方法借助于Matlab软件能够得到比较精确的结果.采用本文所提出的模型及求解方法对精馏塔操作的模拟实验过程进行优化研究,可以看出带补偿的机会约束规划模型能够很好地反映不确定变量对优化过程的影响.所以本文提出的模型和求解方法是有效可行的,且对于以后的不确定优化研究具有很好的指导意义.     

托盘共用系统调度两阶段随机机会约束规划模型研究

根据托盘共用系统调度的特点,建立了以托盘共用系统调度总成本最小为目标的两阶段随机机会约束规划模型．该模型综合考虑了需求随机、供给随机、运输能力随机、装卸能力随机等因素;采用机会约束规划方法对模型进行了确定性等价转换;通过算例进行了数值求解和数值分析,验证了模型的可行性和有效性．     

托盘共用系统托盘回收随机规划模型研究

介绍了托盘共用系统的研究现状,并在现有托盘共用系统的基础上,提出一个改进的托盘共用系统.基于改进的托盘共用系统,建立了托盘共用系统托盘回收随机规划模型,模型中考虑了需求和运输能力不确定等因素,采用机会约束规划方法对模型进行了确定性等价转换.通过算例进行了数值求解和数值分析,验证了模型的有效性,得出了一些有意义的结论.     

不确定条件下的质量交换网络综合

基于现有的质量交换网络综合方法，同时考虑不确定因素对于综合过程的影响，提出了不确定条件下质量交换网络综合的两步综合法。该方法首先确定网络结构，然后以年度总费用为综合目标，对设备尺寸和操作参数进行随机优化，并讨论了两种不同的随机模型的优化：期望值模型与机会约束模型。采用遗传算法和蒙特卡洛随机模拟相结合对上述随机优化模型进行求解。实例计算表明了这一综合方法的合理性以及算法的可行性。     

考虑混合型号托盘的托盘共用调度随机规划模型

如何将托盘科学地在系统内调度是目前托盘共用系统管理者们亟待解决的问题。面对各类参数的随机性和多种多样的托盘,管理者很难仅凭经验做出科学的决策。利用随机机会约束规划的方法,构建了考虑混合型号托盘的托盘共用系统调度随机规划模型,使用确定性等价转化的方法将机会约束转化为了其确定等价形式,通过算例进行了数值求解和分析,验证了模型的有效性,提出了决策策略建议。     

考虑不确定生产因素的表面组装生产线负载平衡优化模型

针对表面组装生产中的不确定因素造成企业订单完成时间滞后问题,本文设计并实现了一种考虑不确定生产因素的生产线负载平衡优化模型.首先,以不确定生产因素的历史样本数据作为随机模拟样本预估出不确定生产因素对订单完成造成的滞后时间;其次,优化元器件贴装工位分配方案并以任务完成作为触发事件模拟生产线实际运行得到动态生产计划;再次,根据动态生产计划计算出模型适应度值后,采用遗传算法对模型适应度值进行启发式寻优获得最优动态生产方案.最后,利用表面组装生产线试例对该模型进行验证,结果表明,该模型可准确预测产线各时段的生产任务、任务量及各器件贴装工位,有效提高了企业生产效率.     

供应链中二级分销网络优化设计的模糊机会约束规划模型

提出了供应链中二级分销网络优化设计的模糊机会约束规划模型. 模型中将各个需求地对产品的需求量以及各分厂的生产能力等难于确定的参数看成是模糊参数, 并进一步讨论了如何将模型中的机会约束清晰化. 文中还讨论了采用启发式算法同分枝定界法相结合以提高问题的求解速度.     

随机需求车辆路径问题及混合变邻域分散搜索算法求解

随机需求车辆路径问题(capacitated vehicle routing problem with stochastic demand,CVRPSD)是对带容量约束车辆路径问题(capacitated vehicle routing problem,CVRP)的扩展,需求不确定的特点使其较CVRP更复杂,对求解方法要求更高.基于先预优化后重调度思想,提出两阶段的混合变邻域分散搜索算法(variable neighborhood scatter search,VNSS)对该问题进行求解:预优化阶段构建随机机会约束规划模型,对客户点随机需求作机会约束确定型等价处理,生成最优预优化方案;重调度阶段采用新的点重优化策略进行线路调整,降低因失败点而产生的额外成本,减少对人工和车辆的占用.算例验证表明,随机机会约束模型和两阶段变邻域分散搜索算法在求解CVRPSD时较为有效,点重优化策略调整效果较佳.     

集装箱海运空箱分派随机规划模型研究*

通过对海运集装箱空箱分派过程的仔细分析,在考虑需求不确定性的基础上引入了空箱运输能力的不确定性这一重要因素,建立了同时考虑供需平衡约束以及需求和空箱运输能力不确定性的空箱分派随机规划模型,并应用机会约束规划方法对模型求解.最后通过数值仿真,揭示了运输成本、租箱成本和存储成本等参数和空箱运输能力的不确定性对集装箱空箱分派策略的影响机制.     

Stochastic Constraint Programming: A Scenario-Based Approach

To model combinatorial decision problems involving uncertainty and probability, we introduce scenario based stochastic constraint programming. Stochastic constraint programs contain both decision variables, which we can set, and stochastic variables, which follow a discrete probability distribution. We provide a semantics for stochastic constraint programs based on scenario trees. Using this semantics, we can compile stochastic constraint programs down into conventional (non-stochastic) constraint programs. This allows us to exploit the full power of existing constraint solvers. We have implemented this framework for decision making under uncertainty in stochastic OPL, a language which is based on the OPL constraint modelling language [Van Hentenryck et al., 1999]. To illustrate the potential of this framework, we model a wide range of problems in areas as diverse as portfolio diversification, agricultural planning and production/inventory management.     

Capacitated closed-loop supply chain network design under uncertainty

This study optimizes the design of a closed-loop supply chain network, which contains forward and reverse directions and is subject to uncertainty in demands for new & returned products. To address uncertainty in decision-making, we formulate a two-stage stochastic mixed-integer non-linear programming model to determine the distribution center locations and their corresponding capacity, and new & returned product flows in the supply chain network to minimize total design and expected operating costs. We convert our model to a conic quadratic programming model given the complexity of our problem. Then, the conic model is added with certain valid inequalities, such as polymatroid inequalities, and extended with respect to its cover cuts so as to improve computational efficiency. Furthermore, a tabu search algorithm is developed for large-scale problem instances. We also study the impact of inventory weight, transportation weight, and marginal value of time of returned products by the sensitivity analysis. Several computational experiments are conducted to validate the effectiveness of the proposed model and valid inequalities.     

需求不确定的设施选址与多阶段生产鲁棒模型研究

选址决策是长期的战略性问题,在选址问题中考虑不确定性因素至关重要。假设需求取值于有界的对称区间,在设施选址与多阶段生产问题中提出一种新的鲁棒性方法,通过调节不确定预算,来权衡解的鲁棒性与系统成本之间的关系。该鲁棒性问题不仅能够转化成线性规划,而且可以计算出设施的最低服务水平。最后,通过随机生成数值算例,得出不同鲁棒性水平下拓扑结构截然不同的设施网络,并分析了服务水平与成本之间的权衡关系,同时对需求的不确定水平作了敏感性分析。     

Fuzzy logic-based decision-making for fault diagnosis in a DC motor

The uncertainty of system models, the presence of noise and the stochastic behaviour of several variables reduce the reliability and robustness of the fault diagnosis methods. To tackle these kinds of problems, this paper presents a decision-making module based on fuzzy logic for model-based fault diagnosis applications. Fuzzy rules use the concept of fault possibility and knowledge of the sensitivities of the residual equations. A fault detection and isolation system, based on the input–output linear model parity equations approach, and including this decision-making module, has been successfully applied in laboratory equipment, resulting in a reduction of the uncertainty due to disturbances and modelling errors. Furthermore, the experimental sensitivity values of the residual equations allow the fault size to be estimated with sufficient accuracy.     

An integrated supply chain model for new products with imprecise production and supply under scenario dependent fuzzy random demand

In the present day business scenario, instant changes in market demand, different source of materials and manufacturing technologies force many companies to change their supply chain planning in order to tackle the real-world uncertainty. The purpose of this paper is to develop a multi-objective two-stage stochastic programming supply chain model that incorporates imprecise production rate and supplier capacity under scenario dependent fuzzy random demand associated with new product supply chains. The objectives are to maximise the supply chain profit, achieve desired service level and minimise financial risk. The proposed model allows simultaneous determination of optimum supply chain design, procurement and production quantities across the different plants, and trade-offs between inventory and transportation modes for both inbound and outbound logistics. Analogous to chance constraints, we have used the possibility measure to quantify the demand uncertainties and the model is solved using fuzzy linear programming approach. An illustration is presented to demonstrate the effectiveness of the proposed model. Sensitivity analysis is performed for maximisation of the supply chain profit with respect to different confidence level of service, risk and possibility measure. It is found that when one considers the service level and risk as robustness measure the variability in profit reduces.     

A solution procedure for mixed-integer nonlinear programming formulation of supply chain planning with quantity discounts under demand uncertainty

Quantity discount policy is decision-making for trade-off prices between suppliers and manufacturers while production is changeable due to demand fluctuations in a real market. In this paper, quantity discount models which consider selection of contract suppliers, production quantity and inventory simultaneously are addressed. The supply chain planning problem with quantity discounts under demand uncertainty is formulated as a mixed-integer nonlinear programming problem (MINLP) with integral terms. We apply an outer-approximation method to solve MINLP problems. In order to improve the efficiency of the proposed method, the problem is reformulated as a stochastic model replacing the integral terms by using a normalisation technique. We present numerical examples to demonstrate the efficiency of the proposed method.     

An output oriented super-efficiency measure in stochastic data envelopment analysis: Considering Iranian electricity distribution companies

In this paper, the concept of chance constrained programming approaches is used to develop output oriented super-efficiency model in stochastic data envelopment analysis. Output oriented super-efficiency model is one of the classic models in data envelopment analysis widely used by DEA people and practitioners. However, in many real applications, data is often imprecise. A successful method to address uncertainty in data is replacing deterministic data by random variables, leading to stochastic DEA. Therefore, in this paper, output oriented super-efficiency model is developed in stochastic data envelopment analysis, and its deterministic equivalent which is a nonlinear program is derived. Moreover, it is shown that the deterministic equivalent of the stochastic super-efficiency model can be converted to a quadratic program. Furthermore, sensitivity analysis of the proposed super-efficiency model is also discussed with respect to changes on parameter variables. Finally, data related to seventeen Iranian electricity distribution companies is used to illustrate the methods developed in this article.     

A stochastic optimization model for integrated forward/reverse logistics network design

In this paper, we develop a stochastic programming model for an integrated forward/reverse logistics network design under uncertainty. First, an efficient deterministic mixed integer linear programming model is developed for integrated logistics network design to avoid the sub-optimality caused by the separate design of the forward and reverse networks. Then the stochastic counterpart of the proposed MILP model is developed by using scenario-based stochastic approach. Numerical results show the power of the proposed stochastic model in handling data uncertainty.     

A two-stage stochastic programming model for transportation network protection

Network protection against natural and human-caused hazards has become a topical research theme in engineering and social sciences. This paper focuses on the problem of allocating limited retrofit resources over multiple highway bridges to improve the resilience and robustness of the entire transportation system in question. The main modeling challenges in network retrofit problems are to capture the interdependencies among individual transportation facilities and to cope with the extremely high uncertainty in the decision environment. In this paper, we model the network retrofit problem as a two-stage stochastic programming problem that optimizes a mean-risk objective of the system loss. This formulation hedges well against uncertainty, but also imposes computational challenges due to involvement of integer decision variables and increased dimension of the problem. An efficient algorithm is developed, via extending the well-known L-shaped method using generalized benders decomposition, to efficiently handle the binary integer variables in the first stage and the nonlinear recourse in the second stage of the model formulation. The proposed modeling and solution methods are general and can be applied to other network design problems as well.