多目标模糊机会约束规划的低碳多式联运路径优化

针对时间窗与需求量不确定性下的多式联运路径优化问题,运用梯形模糊数表示模糊需求量与模糊时间窗,并考虑碳排放成本、运输成本以及客户满意度,建立了多目标模糊机会约束模型。固定的交叉、变异概率会直接影响算法的收敛性,针对此问题,将自适应性与非支配排序遗传算法Ⅱ（NSGA-Ⅱ）结合,并通过与DOCPLEX和NSGA-Ⅱ的对比验证了所提模型与算法的有效性。最后,探究了碳税值与模糊需求量偏好值的变化对优化结果的影响。研究结果表明：碳税值的提出可有效促进“公转铁、公转水”,从而显著减少碳排放量,然而过高的碳税值并不一定意味着碳排放量的减少,还会对企业造成过高的成本;模糊需求量偏好值的提高会造成总成本的增加,意味着运输经济性与可靠性两者不可兼得。因此,合理设置碳税值与模糊需求量偏好值是提高多式联运环保效益与运输效益的有效方式。     

再制造/制造系统集成物流网络模糊机会约束规划模型

在再制造/制造(R/M)系统集成物流网络中,回收产品的数量具有不确定性.根据这一特点,将各消费区域废旧产品的回收数量看成是模糊参数,提出了该集成物流网络的模糊机会约束规划模型.通过把模型中模糊机会约束清晰化,将模型转化为确定性的混合整数规划模型.利用实例数据,针对不同的置信水平对模型进行分析,其结果为该集成物流网络的设计提供了依据.     

Designing emergency response networks for hazardous materials transportation

Undesirable consequences of dangerous goods incidents can be mitigated by quick arrival of specialized response teams at the accident site. We present a novel methodology to determine the optimal design of a specialized team network so as to maximize its ability to respond to such incidents in a region. We show that this problem can be represented via a maximal arc-covering model. We discuss two formulations for the maximal arc-covering problem, a known one and a new one. Through computational experiments, we establish that the known formulation has excessive computational requirements for large-scale problems, whereas the alternative model constitutes a basis for an efficient heuristic. The methodology is applied to assess the emergency response capability to transport incidents, that involve gasoline, in Quebec and Ontario. We point out the possibility of a significant improvement via relocation of the existing specialized teams, which are currently stationed at the shipment origins.     

解非线性约束规划问题的新型多目标遗传算法

给出非线性约束规划问题的一种新解法。把带约束的非线性规划问题转化成为两个目标的多目标优化问题,并为转化后的多目标优化模型设计了一种新型多目标遗传算法,数据实验表明该算法对带约束的非线性规划问题求解是非常有效的。     

Genetic algorithm-based fuzzy goal programming for class of chance-constrained programming problems

This paper presents a procedure for solving a multiobjective chance-constrained programming problem. Random variables appearing on both sides of the chance constraint are considered as discrete random variables with a known probability distribution. The literature does not contain any deterministic equivalent for solving this type of problem. Therefore, classical multiobjective programming techniques are not directly applicable. In this paper, we use a stochastic simulation technique to handle randomness in chance constraints. A fuzzy goal programming formulation is developed by using a stochastic simulation-based genetic algorithm. The most satisfactory solution is obtained from the highest membership value of each of the membership goals. Two numerical examples demonstrate the feasibility of the proposed approach.     

求解随机机会约束规划的混合智能算法

随机机会约束规划是一类有着广泛应用背景的随机规划问题,采用随机仿真产生样本训练BP网络以逼近随机函数,然后在微粒群算法中利用神经网络计算适应值和实现检验解的可行性,从而提出了一种求解随机机会约束规划的混合智能算法。最后通过两个实例的仿真结果说明了算法的正确性和有效性。     

Three-objective fuzzy chance-constrained programming model for multiproject and multi-item investment combination

This paper presents a new fuzzy chance-constrained programming model to find the solution for multiproject and multi-item investment combination in investment combination problems. The proposed 0-1 integer programming model has three objectives with fuzzy constraints, and NSGA-II is applied to solve the optimization model with a small modification of the constraint-handling rule. A simulation experiment illustrating the application of the proposed model is presented and Pareto-optimal solutions are obtained through a modified NSGA-II algorithm. A comparison among NSGA-II, PSO, and DE shows that modified NSGA-II has some advantages over PSO and DE.     

求解模糊相关机会规划模型的混合差分进化算法

针对不确定规划领域中存在的模糊相关机会规划模型,基于群体智能的差分进化算法,设计一种新的求解模糊相关机会规划模型的混合智能算法．该算法基于粒子群优化算法对差分进化算法进行改进,并运用模糊模拟技术对模糊相关机会规划模型进行分析和数值求解,无需像传统的基于遗传算法的混合智能算法需要很长时间并经过复杂的计算才能得到合理的结果．最后,通过实例表明了所提混合智能算法的合理性和有效性．     

供应链库存的模糊机会约束规划模型

研究了不确定环境下的供应链库存优化问题。考虑需求为模糊量,且可能在一定条件下不满足约束条件的决策前提,用三角模糊数表示需求,结合可能性理论中的可信性测度,建立了多品种联合补充的模糊机会约束规划模型,目标函数为最小化供应链订货成本和库存成本的期望值。用遗传算法对优化模型求解,以目标函数值作为染色体适应度,给出了编码方案及选择、交叉、变异算子。用数值实例进行了仿真计算,证明了模型和算法的有效性和性能,并给出了不同置信水平下的计算结果。     

求解模糊机会约束规划的混合智能算法

模糊机会约束规划是一类重要的模糊规划,它广泛地存在于许多领域中,微粒群算法已实现了对其的有效求解,但求解速度仍不能满足大规模模糊机会约束规划问题的求解,为了寻找更为高效的求解模糊机会约束规划的算法,通过采用模糊模拟产生样本训练BP网络以逼近模糊函数,然后应用微粒群算法并以逼近模糊函数的神经网络作为适应值估计及检验解的可行性,从而提出了一种求解模糊机会约束规划的混合智能算法。最后通过仿真结果说明了算法的正确性和有效性。     

基于网络声誉的供应商选择模糊规划模型

根据网络声誉在供应商选择中的重要作用, 运用不确定理论, 对选择最有效的供应商组合问题进行研究。先运用模糊线性权重法确定供应商的声誉综合评价值,再结合产品成本、市场需求等内外环境因素,建立了模糊环境下的供应商组合选择模型,以确定最优的供应商组合及订货量。通过运用一种基于模糊模拟、遗传算法及神经网络的混合智能算法进行算例分析,验证了模型的可行性。     

Multi-objective solid transportation problems with budget constraint in uncertain environment

This paper investigates multi-objective solid transportation problems (MOSTP) under various uncertain environments. The unit transportation penalties/costs are taken as random, fuzzy and hybrid variables respectively, in three different uncertain multi-objective solid transportation models and in each case, the supplies, demands and conveyance capacities are fuzzy. Also, apart from source, demand and capacity constraints, an extra constraint on the total budget at each destination is imposed. Chance-constrained programming technique has been used for the first two models to obtain crisp equivalent forms, whereas expected value model is formulated for the last. We provide an another approach using the interval approximation of fuzzy numbers for the first model to obtain its crisp form and compare numerically two approaches for this model. Fuzzy programming technique and a gradient based optimisation - generalised reduced gradient (GRG) method are applied to beget the optimal solutions. Three numerical examples are provided to illustrate the models and programming.     

Application of fuzzy goal programming approach to multi-objective linear fractional inventory model

In this paper, we propose a model and solution approach for a multi-item inventory problem without shortages. The proposed model is formulated as a fractional multi-objective optimisation problem along with three constraints: budget constraint, space constraint and budgetary constraint on ordering cost of each item. The proposed inventory model becomes a multiple criteria decision-making (MCDM) problem in fuzzy environment. This model is solved by multi-objective fuzzy goal programming (MOFGP) approach. A numerical example is given to illustrate the proposed model.     

基于全文索引知识图谱的危化品运输地址匹配研究

地址匹配是危化品运输交通起止点调查分析的关键技术之一。为解决复杂非标危化品道路运输地址匹配精度较低的问题,通过构建自扩展中文分词及自扩展的地址数据知识图谱,基于全文索引知识图谱进行危化品运输中文地址的匹配,纳入加权拼音全文搜索机制以提高拼写错误地址的匹配准确率,结合在线地理解析接口构建危化品运输地址多重匹配机制,并对少量疑难地址执行半监督匹配,形成了完整的危化品运输地址匹配方法体系。针对危化品运输电子运单地址数据的计算表明,算法能实现复杂中文危险化学品运输地址匹配的高准确率及高精度。在随机地址测试集中准确率达94.6%,在较难分类地址测试集中准确率达67.5%,在较难分类地址匹配的准确率及精度上均相比于通用匹配方法及地理搜索引擎有大幅度的提升。     

A mixed-integer programming model for global logistics transportation problems

In today's highly competitive global environment, companies are forced to compete on price and delivery speed. Global logistics transportation presents some special challenges and issues for business organizations, and these issues differ from those posed by domestic logistics transportation. This study considers road transportation problems between two countries. A mixed-integer programming model is formulated to determine the optimal fleet components, route plans, and warehouse control in two countries. A series of experiments is designed to test the effectiveness of the proposed model. To enhance the practical implications of the model, different logistics plans are evaluated according to future changes.     

多配送中心危险货物配送路径鲁棒优化

针对危险货物配送路径对不确定因素敏感度较高的问题,提出了鲁棒性可调的多配送中心危险货物配送路径鲁棒优化方法。首先,以最小化运输风险和最小化运输成本为目标,根据Bertsimas鲁棒离散优化理论,建立鲁棒优化模型;然后,在改进型强度Pareto进化算法（SPEA2）的基础上设计一种三段式编码的多目标遗传算法进行求解,在遗传操作中对不同染色体段分别采用不同的交叉和变异操作,有效避免了种群进化过程中不可行解的产生;最后,以庆阳市西峰区部分路网为例进行实证研究,并将配送方案落实到运输过程的路段中,形成具体的运输路径。研究结果表明：在多配送中心下,运用该鲁棒优化模型及算法,能快速得到具有较好鲁棒性的危险货物配送路径。     

Credibility-based chance-constrained integer programming models for capital budgeting with fuzzy parameters

In this paper, we discuss a problem of capital budgeting in a fuzzy environment. Two types of models are proposed using credibility to measure confidence level. Since the proposed optimization problems are difficult to solve by traditional methods, a fuzzy simulation-based genetic algorithm is applied. Two numerical experiments demonstrate the effectiveness of the proposed algorithm.     

求多目标优化问题的粒子群优化算法

将粒子群优化算法应用于求解多目标优化问题,提出一种双向搜索机制,指导粒子向着搜索空间中非劣目标区域以及粒子分布最为稀疏的区域这两个方向进行寻优,进而提出了求解多目标优化问题的基于粒子群优化算法的双向搜索法,该算法对粒子全局最优经验的选择策略以及粒子群的状态更新机制进行了改进。实验研究表明,该算法不仅能快速有效地获得多目标优化问题的非劣最优解集,而且求出的解集具有良好的分布性。     

Fuzzy parametric programming model for multi-objective integrated solid waste management under uncertainty

Solid waste management is increasingly becoming a challenging task for the municipal authorities due to increasing waste quantities, changing waste composition, decreasing land availability for waste disposal sites and increasing awareness about the environmental risk associated with the waste management facilities. The present study focuses on the optimum selection of the treatment and disposal facilities, their capacity planning and waste allocation under uncertainty associated with the long-term planning for solid waste management. The fuzzy parametric programming model is based on a multi-objective, multi-period system for integrated planning for solid waste management. The model dynamically locates the facilities and allocates the waste considering fuzzy waste quantity and capacity of waste management facility. The model addresses uncertainty in waste quantity as well as uncertainties in the operating capacities of waste management facilities simultaneously. It was observed that uncertainty in waste quantity is likely to affect the planning for waste treatment/disposal facilities more as compared with the uncertainty in the capacities of the waste management facilities. The relationship between increase in waste quantity and increase in the total cost/risk involved in waste management is found to be nonlinear. Therefore, it is possible that a marginal change in waste quantity could increase the total cost/risk substantially. The information obtained from the analysis of modeling results can be effectively used for understanding the effect of changing the priorities and objectives of planning decisions on facility selections and waste diversions.     

非线性约束规划的极大熵多目标进化算法

解非线性约束规划的困难在于如何处理问题的约束,从问题的约束条件出发构造了一个新的极大熵函数,利用此函数将原非线性约束规划问题转化成了两个目标的多目标优化问题。通过对搜索操作和参数的合理设计给出了一种新的极大熵多目标进化算法。计算机仿真表明该算法对带约束的非线性优化问题求解是非常有效的。