2-型供应链网络设计优化

基于模糊可能性理论,研究了2-型模糊供应链网络设计问题。考虑到运输费用和顾客需求的不确定性,我们用2-型模糊变量来刻画,建立了2-型模糊环境下的期望值供应链网络设计模型。当2-型模糊变量相互独立且服从三角分布时,我们将原模型转化为等价的确定模型。等价模型是一个0-1混合整数参数规划,因此可采用Lingo软件求解。最后,我们通过数值例子演示所提建模思想。实验结果证明了所建模型的有效性。     

基于在险价值的物流网络规划模糊两阶段模型与精确求解方法

针对物流网络规划问题中顾客需求和运输成本的不确定性,使用在险价值量化投资风险,建立了以投资损失的在险价值最小化为目标的模糊两阶段物流网络规划模型。对于模型中不确定参数均为规则模糊数的这一类模糊两阶段规划模型,本文通过理论分析和证明将其转化为等价的确定一阶段规划模型进行求解,从而将无穷维的优化问题转化为有限维的经典优化问题,降低了计算难度且得到了模型的精确解。不同规模的数值实验证实了所提出模型及其求解方法的有效性。     

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。     

考虑港口物流能力的多目标海运煤炭供应链网络优化模型

海运煤炭供应链网络优化旨在提高供应链运作效率并降低成本.以煤矿、铁路装载点、港口和煤炭消费客户构成的海运煤炭供应链为研究对象,从供应链集成服务商的视角,提出了考虑港口物流能力的供应链网络优化问题.在最小化固定成本和运输成本这一单目标的基础上,考虑港口物流能力,增加了最大化港口加权煤炭流量的目标,以各节点的能力限制和流量平衡为约束,建立了海运煤炭供应链网络多目标混合整数规划模型.模型求解时,引入"关系成本系数"将多目标问题转化为单目标问题.算例研究表明,所建立的模型能够真实地反映海运煤炭供应链网络优化的决策环境,同时发现,港口物流能力评分和"关系成本系数"均会影响网络优化结果.     

基于物理规划的工程项目综合均衡优化

质量、工期和成本是工程项目三大主要控制目标,对于工程项目中质量-工期-成本综合均衡优化问题,传统的基于权重的决策方法存在各目标权重难以合理确定的问题.为此引入物理规划方法建立工程项目质量-工期-成本综合均衡优化模型.决策者只需设定各目标的偏好,即可通过该均衡优化模型获得符合决策者偏好的优化方案,使决策过程更加符合工程实际,避免了确定无实际物理意义的各目标权重的问题.通过桥梁工程实例验证了该方法的有效性和实用性.     

基于模糊需求的多产品供应商选择的多目标规划模型

由决策于环境的不确定性,供应商选择问题存在大量的模糊信息,传统的确定性规划模型已经不能够很好地处理此类问题。本文基于模糊需求量信息,对于多产品供应商问题建立了模糊多目标规划模型。同时考虑到各目标及约束的重要性程度不同的影响,通过引进适当的权重对多目标规划模型进行求解。文中结合实际算例验证模型的可行性和有效性。     

不确定条件下工程项目的多目标模糊均衡优化

在工程项目多目标优化问题研究基础上,研究不确定环境下工程项目多目标均衡优化问题.利用模糊数表示费用变化率和质量变化率,考虑模糊集的不同可能性水平,建立工程项目多目标模糊均衡优化模型,给出模型的求解方法和步骤,得到不同可能性水平下多目标优化问题的最优折衷解变化范围.优化方法使决策者能够根据决策风险的大小进行最优目标值的确定.     

供应链多层规划问题及其合作对策协商求解

针对由制造商、分销中心和零售商组成的多级分布式供应链协同计划问题,建立了三层规划生产-分销计划谈判模型,设计了基于合作对策的两步协商方法。首先,基于模糊隶属度原理,将三层规划问题转化为单层规划问题。接着,设计了基于合作对策的两步协商过程,描述了两步协商模型和交互协商算法步骤,并对算法进行了性能分析。最后,给出了基于多层规划的协同生产-分销计划模型,并得到了基于合作对策的协商算法的解决方案。并通过与其它优化策略的对比,验证该解决方案的优越性。     

模糊遗传规划方法在预测深部开采岩体移动中的应用

运用模糊(Fuzzy)系统理论,给出了地下深部开采岩体移动变形预测分析的Fuzzy模型,对岩体移动参数采用遗传规划(GP)方法进行确定,进而形成了模糊遗传规划方法.用工程实测数据对遗传规划网络进行了训练,并用测试样本对GP模型进行了测试,证明了模型的预测性能是令人满意的.通过工程实例计算分析表明,采用本文提出的模糊遗传规划方法所获结果符合工程实际.     

基于SEC准则的油田开发规划不确定优化模型及算法

在油价低迷的国际背景下,上市石油公司进行油田开发规划时越来越重视经营效益。美国证券交易委员会(SEC)要求上市石油公司采用产量法计提折耗,极大地影响了石油公司开发规划方案的制定。油田开发受到自然、技术等多种不确定因素的影响,在制定开发规划时需充分考虑这些不确定性。本文基于不确定理论,考虑措施增油效果和新增投资两类不确定参数,以经营效益最大化和新增投资最小化为目标,构建了基于SEC准则的油田开发规划不确定优化模型,并利用差分进化算法求解,给出措施工作量的帕累托解集。本文以D油田年度规划为例,通过构建模型并求解,给出开发规划方案集,并进一步分析SEC储量的下降对上市石油公司经营效益、新增投资回报率、油气总产量、油气完全成本和措施工作量的影响,为企业制定开发规划方案提供参考。     

A facility location model for global closed-loop supply chain network design

Forward and reverse supply chains form a closed-loop supply chain. In this paper, a mathematical model is proposed for a closed-loop supply chain network by considering global factors, including exchange rates and customs duties. The model is a multi-objective mixed-integer linear programming model under uncertain demand. A solution approach based on fuzzy programming is developed for solving the optimization problem. The model is then applied in a network, which is located in Southwestern Ontario, Canada. A sensitivity analysis is provided to validate the model. This model considers global factors, multi-objectives, and uncertainty simultaneously in a closed-loop supply chain network.     

Environmental supply chain network design using multi-objective fuzzy mathematical programming

The concern about environmental impact of business activities has spurred an interest in designing environmentally conscious supply chains. This paper proposes a multi-objective fuzzy mathematical programming model for designing an environmental supply chain under inherent uncertainty of input data in such problem. The proposed model is able to consider the minimization of multiple environmental impacts beside the traditional cost minimization objective to make a fair balance between them. A life cycle assessment-based (LCA-based) method is applied to assess and quantify the environmental impact of different options for supply chain network configuration. Also, to solve the proposed multi-objective fuzzy optimization model, an interactive fuzzy solution approach is developed. A real industrial case is used to demonstrate the significance and applicability of the developed fuzzy optimization model as well as the usefulness of the proposed solution approach.     

A robust optimization approach to closed-loop supply chain network design under uncertainty

The concern about significant changes in the business environment (such as customer demands and transportation costs) has spurred an interest in designing scalable and robust supply chains. This paper proposes a robust optimization model for handling the inherent uncertainty of input data in a closed-loop supply chain network design problem. First, a deterministic mixed-integer linear programming model is developed for designing a closed-loop supply chain network. Then, the robust counterpart of the proposed mixed-integer linear programming model is presented by using the recent extensions in robust optimization theory. Finally, to assess the robustness of the solutions obtained by the novel robust optimization model, they are compared to those generated by the deterministic mixed-integer linear programming model in a number of realizations under different test problems.     

Robust environmental closed-loop supply chain design under uncertainty

With the fast developments in product remanufacturing to improve economic and environmental performance, an environmental closed-loop supply (ECLSC) chain is important for enterprises' competitiveness. In this paper, a robust ECLSC network is investigated which includes multiple plants, collection centers, demand zones, and products, and consists of both forward and reverse supply chains. First, a robust multi-objective mixed integer nonlinear programming model is proposed to deal with ECLSC considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. The first objective function aims to minimize the economical cost and the second objective function is to minimize the environmental influence. Then, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. Finally, numerical example has been presented to test the model. The results indicate that the proposed model is applicable in practice.     

A polymorphic uncertain equilibrium model and its deterministic equivalent formulation for decentralized supply chain management

Supply chain management is a multidisciplinary engineering problem. In this paper, a polymorphic uncertain equilibrium model (PUEM) is constructed to capture the joint maximization of the profits for the manufacturers and retailers in a supply chain network. To ensure applicability of the model in practice, the demand of the consumers is regarded as a continuous random variable, the holding cost of the retailer and the transaction cost between the manufacturer and retailer are described by fuzzy sets. For the PUEM, a deterministic equivalent formulation (DEF) is first derived by compromise programming approach such that the existing powerful algorithms in the standard smooth optimization are employed to find an approximate equilibrium point for the uncertain problem. Actually, the DEF turns out to be a nonlinear complementarity problem (NCP), a special variational inequality. Thus, a modified partially Jacobian smoothing algorithm is developed to solve the corresponding NCP, where the gradient information of the model is used to efficiently generate search direction. Sensitivity analysis offers a number of useful managerial implications based on practical applications of the model.     

A robust optimization model for agile and build-to-order supply chain planning under uncertainties

Supply chain planning as one of the most important processes within the supply chain management concept, has a great impact on firms’ success or failure. This paper considers a supply chain planning problem of an agile manufacturing company operating in a build-to-order environment under various kinds of uncertainty. An integrated optimization approach of procurement, production and distribution costs associated with the supply chain members has been taken into account. A robust optimization scenario-based approach is used to absorb the influence of uncertain parameters and variables. The formulation is a robust optimization model with the objective of minimizing the expected total supply chain cost while maintaining customer service level. The developed multi-product, multi-period, multi-echelon robust mixed-integer linear programming model is then solved using the CPLEX optimization studio and guidance related to future areas of research is given.     

二层供应链网络均衡模型的研究

利用均衡理论和二层规划理论来研究供应链网络均衡问题。针对供应链网络中上下层成员之间具有的Stackelberg博弈特征以及同层成员之间具有的非合作博弈特征,构建了二层供应链网络的均衡模型,该模型实际上一个均衡约束的二层规划问题。此外,为了使得供应链网络在整体上实现最优,本文还在模型中引入回收契约以协调供应链网络。最后,利用罚函数法对模型进行了求解,算例分析说明了模型的合理性和有效性。     

非对称信息和模糊需求下的闭环供应链协调决策研究

在模糊需求和非对称销售价格信息下,研究了由单一制造商和零售商组成的两级闭环供应链系统的协调问题。通过建立不确定环境下的集中决策和收益共享-费用共担契约决策模型,分别得到了不同模型下闭环供应链系统的最优策略。并进一步通过算例分析了契约下供应链各方收益随模糊需求和价格的变化情况,以及该契约对整个系统决策效率的影响。结果表明:收益共享-费用共担契约能有效实现模糊闭环供应链系统的协调,实现闭环供应链系统决策的最优化;其次,需求和零售价格的不确定性程度越高,对零售商的最优订购量、供应链成员及系统利润的影响也越大。     

Time-dependent optimization of a multi-item uncertain supply chain network: A hybrid approximation algorithm

We consider the uncertain least cost shipping problem. The input is a multi-item supply chain network with time-evolving uncertain costs and capacities. Exploiting the operational law of uncertainty theory, a mathematical model of the problem is established and the indeterminacy factors are tackled. We use the scaling idea together with transformation approach and uncertainty programming to develop a hybrid algorithm to optimize and obtain the uncertainty distribution of the total shipping cost. We analyze the practical performance of the algorithm and present an illustrative example.     

Environmentally conscious optimization of supply chain networks

The optimization of supply chain structures considering both economic and environmental performances is nowadays an important research topic. However, enterprises are commonly faced with the competing issues of reduced cost, improved customer service and increased environmental factors as a multi-faceted trade-off problem when designing supply chains. Hence, this paper proposes an environmentally conscious optimization model of a supply chain network with a broader and more comprehensive objective function that considers not just the transportation costs, but also the costs for the amount of greenhouse gas emissions, fuel consumption, transportation times, noise and road roughness. The paper sheds light on the trade-offs between various parameters such as vehicle speed, fuel, time, emissions, noise and their total cost, and offers managerial insights on economies of environmentally conscious supply chain optimization. An integer non-linear programming model is developed to help decision makers find the optimal solution under mentioned considerations. The proposed model is validated through the solution of an example, where its applicability to supply chain problems is demonstrated for managerial insights.