基于蚁群算法的多值属性系统测试序列优化

针对系统故障诊断中的多值属性系统测试序列优化问题,该文提出一种改进的蚁群算法,将成功运用在二值属性系统中的蚁群算法扩展到多值属性系统中,根据多值属性系统特点,设计相应的状态转移规则和信息素更新机制,并采用蚁群算法和遗传算法相融合的联合优化策略,解决了多值属性系统的序列优化问题,为多值属性系统的测试优化问题提供了一条新的解决途径。     

基于改进粒子群算法的Volterra模型参数辨识

针对非线性系统Volterra泛函级数模型,结合混沌优化策略和种群多样性控制思想,提出了一种改进粒子群算法,并应用于Volterra模型参数的辨识,将非线性系统的辨识问题转化为高维参数空间上的优化问题。利用混沌序列增加初始种群的多样性,通过构建动态子群以进行协作寻优,且各子群采用不同的参数自适应调整策略,并定义算法收敛性测度以对精英粒子进行合理的混沌变异,避免了算法早熟收敛,提高了算法的寻优速度和寻优精度。仿真实验中,将该方法与基于标准粒子群算法、遗传算法、量子粒子群算法的Volterra模型参数辨识方法相比较,验证了该辨识方法的有效性和鲁棒性。     

机车柴油机活塞连杆组的组合优化研究

对活塞连杆组的选配进行数学建模,并用遗传算法对活塞连杆组的组合优化进行了研究.在用遗传算法进行组合优化时,提出了采用惩罚函数法把约束条件转移到目标函数上,从而使整个问题转化为无约束问题.通过系统模拟,比较了基本遗传算法、进化规划法和精英保留遗传算法三种遗传算法,并最终确定进化规划算法为最适合活塞连杆组组合优化的算法.     

期权定价市场化情况下的供应链协调机制

通过对期权定价市场化情况下的供应链协调过程的研究,构建出期权定价市场化情况下的供应链决策机制模型.并对供应链期权契约双方的决策机制和决策过程进行深入分析,计算出使供应链整体利润优化化的供应商和零售商最优定价策略和最优采购决策,最后通过数例分析对结果进行验证.     

考虑次品率的零售商库存联盟订货量与稳定性分析

通过建立含有次品率的确定性需求EOQ库存控制模型,探讨了在多个零售商结成联盟从同一供应商处联合订货且产品进行统一检测的情形下,零售商联盟的订货策略和联盟稳定性.得到了联盟的最优订货策略,并且由联盟的次可加性得出了结成联盟可以降低运作成本的结论.根据合作博弈中核与总体单调分配机制的概念提出了按比例对零售商合作联盟成本进行分摊的规则,并证明了在此规则下,大联盟是零售商联盟的稳定结构.通过数值例子对成本分摊规则的合理性进行了说明.研究结果为现实中零售商如何进行库存联盟和联盟之后如何制定运作策略提供了一定的参考.     

忠诚奖励与统一定价的平台定价模型对比研究

平台定价的价格策略对于参与平台竞争的管理决策者们有重要的辅助作用,忠诚奖励与统一定价是两种各有侧重的定价策略．本文通过提出假设构建平台定价的两阶段动态模型,对比分析不同价格策略对平台定价及利润的影响,进而得出若干命题．研究结果表明,平台第二阶段的定价及顾客留购率与忠诚顾客奖励产生的内生性转移成本正相关．而统一定价模型的均衡结果显示,两阶段利润总和变化取决于外生转移成本和用户预期折现系数的大小．     

时效性商品最优定价模型及其粒子群优化解

对时效性商品的定价问题进行了研究.基于一种负二项分布的离散需求函数,并在利润最大化原则下,建立了时效商品最优定价模型.由于该模型涉及多个随机变量的概率分布,常规函数极值算法难以获得问题解析解,引入粒子群优化算法,对模型进行演化求解,并给出算例分析.结果表明:利用粒子群算法,可以快速有效得到不同库存量情况下应采取的最优定价.最后提出需要进一步解决的若干问题.     

基于约束理论的动态联盟业务流程再造分析

为了解决动态联盟管理与控制的困难和风险,提高动态联盟的效率以及核心竞争力,针对动态联盟进行企业流程再造是一种重要方式.基于约束理论并运用模糊评价方式,建立了动态联盟流程再造分析体系,提出了确定瓶颈的层次化的模糊综合评价算法.通过对瓶颈的追踪和确认,为进一步的流程再造提供方向,从而实现动态联盟绩效的改进.通过实例对研究进行了验证.     

基于改进粒子群算法的航空行李在线装载优化

目的 为解决航空行李自动装卸中关键装载算法问题,实现航空行李自动装卸,同时满足流水作业的实际需要.方法 基于关键点装载策略,提出一种以装载空间利用率为优化目标,考虑行李质量、体积及装载顺序等约束条件的改进粒子群算法.首先,通过关键点法输出流水线上待装载行李的全部可放点序列,然后根据约束条件重新定义粒子群算法的速度与位置,以空间利用率为适应度函数进行迭代寻优,输出全局最优解,实现对装载位置与姿态的优化.结果 实验部分采用真实行李数据对算法进行仿真验证表明,改进粒子群算法优化后可将箱体空间利用率提高了10.8％,平均规划布局效率提高了26.5％.结论 提出的装载算法能够有效地解决实际行李装载问题,为行李流水作业的货物装载提供理论依据及参考.     

基于关键指标控制的多目标绿色电力分时定价策略

针对现有研究中存在的时段划分方式单一、优化目标单一、缺乏对各时段电价和电量关系的量化控制等问题,提出了一种基于关键指标控制的多目标绿色电力分时定价策略。该策略同时考虑用户用电量和新能源发电量进行时段划分,以降低用电量波动性、提升新能源利用率、降低用户用电习惯的改变程度等为目标,考虑对关键指标的量化控制,建立绿色电力分时定价模型,并通过遗传算法求解。该策略能在满足上述优化目标的同时,使供电公司能对供电收入、总用电量、各时段电价比与电量比等指标进行更灵活地量化调控。通过数值实验结果可以证明,新定价策略下用电量波动性降低,绿色电力的利用率提高,对关键指标的控制效果较好。     

Stochastic optimization model for order acceptance with multiple demand classes and uncertain demand/supply

This article considers an order acceptance problem in a make-to-stock manufacturing system with multiple demand classes in a finite time horizon. Demands in different periods are random variables and are independent of one another, and replenishments of inventory deviate from the scheduled quantities. The objective of this work is to maximize the expected net profit over the planning horizon by deciding the fraction of the demand that is going to be fulfilled. This article presents a stochastic order acceptance optimization model and analyses the existence of the optimal promising policies. An example of a discrete problem is used to illustrate the policies by applying the dynamic programming method. In order to solve the continuous problems, a heuristic algorithm based on stochastic approximation (HASA) is developed. Finally, the computational results of a case example illustrate the effectiveness and efficiency of the HASA approach, and make the application of the proposed model readily acceptable.     

Pricing and location decisions in multi-objective facility location problem with M/M/m/k queuing systems

This article presents a new multi-objective model for a facility location problem with congestion and pricing policies. This model considers situations in which immobile service facilities are congested by a stochastic demand following M/M/m/k queues. The presented model belongs to the class of mixed-integer nonlinear programming models and NP-hard problems. To solve such a hard model, a new multi-objective optimization algorithm based on a vibration theory, namely multi-objective vibration damping optimization (MOVDO), is developed. In order to tune the algorithms parameters, the Taguchi approach using a response metric is implemented. The computational results are compared with those of the non-dominated ranking genetic algorithm and non-dominated sorting genetic algorithm. The outputs demonstrate the robustness of the proposed MOVDO in large-sized problems.     

基于批发价定价权转移的制造商渠道选择

制造商的批发价定价权发生转移会带来供应链绩效增加和制造商利润降低。利用这一供应链特征,给出了制造商渠道选择的数理方法。制造商在零售商具有/不具有批发价定价权的渠道选择中,潜在的市场规模是关键的选择因素。研究表明:市场规模相同时,制造商会选择拥有批发价定价权的渠道;当拥有批发价定价权的零售商拥有较大市场规模时,制造商会选择该供应链渠道,而此时对供应链双方都具有价值。     

考虑供方组内强外部性的双边服务平台定价策略

针对双边平台连接的服务消费者与提供者两群体之间存在组内与组间外部性,重点研究了在服务供方存在组内强外部性时,双边平台最优定价策略,以及不同策略对用户参与和利润结构的影响。研究表明,平台对消费者和服务供方的最优定价策略均有收费、免费和补贴等3种,且能使平台利润总为正;组内外部性对平台收益具有损益影响;组间外部性之间的大小关系和组内组间外部性之间的数量关系共同决定了服务的供需双方对平台利润的贡献占比。     

Active demand management for substitute products through price optimisation

This paper presents a systematic mathematical programming approach for active demand management in process industries. The proposed methodology aims to determine optimal pricing policies as well as output levels for substitute products, while taking into consideration manufacturing costs, resource availability, customer demand elasticity, outsourcing and market competition. First, profit maximisation analytical formulae are derived for determining Nash equilibrium in prices for a duopolistic market environment where each company produces only one product. An iterative algorithm is then proposed so as to determine the decision-making process by solving a series of non-linear mathematical programming (NLP) models before determining the Nash equilibrium in prices for the competing companies. The proposed algorithm is extended in order to accommodate the case of multi-product companies, each one selling a set of substitute products at different prices. The applicability of the proposed methodology is demonstrated by a number of illustrative examples.     

Pricing and ordering strategies in manufacturing and distribution alliances

This paper studies the effect of coordination in a manufacturing and distribution system consisting of one manufacturer and one distributor. The system operates to meet price-sensitive random demand with the objective of maximizing expected profits of both the manufacturer and the distributor. The coordinated pricing and production/ordering policies that maximize the expected profits of the manufacturer and the distributor, as well as the distributor's optimal pricing and ordering policies without coordination, are developed. The focus of this study is to address the following managerial questions: What factors make coordination an effective strategy for both the manufacturer and the distributor? What are the coordination strategies and the coordinated policies that maximize both parties' expected profits and the joint expected profit? These and other related managerial issues are explored in this paper.     

Parallel importation and price competition in a duopoly supply chain

Nowadays, diversion of products distribution from authorised channels to the gray markets is one of the main challenges of manufacturers. Suppose an international supplier distributes the products in several countries with different prices. In parallel importation, there are unauthorised distributers who supply products with a lower price and import them to a higher price market. The problem of parallel importation considering a manufacturer and a competitor is analysed using the game theory approach in this article. We investigate the pricing strategy for manufacturers and the effect of unauthorised distributer on price, market share and profit. We also investigate the performance of different policies in a numerical study and show managerial insights.     

Application of the TOC thinking processes to challenging assumptions of profit and cost centre performance measurement

The interaction of the measurement criteria of cost centres and profit centres, and the resultant effect on organizational profit is examined by applying the Theory of Constraints thinking processes. Three cases are described based on the assumptions that a profit centre should maximize profit and a cost centre should at least recover its costs. These cases illustrate that there will exist conflict between profit centres and cost centres due to disagreement about transfer pricing and hourly tariffs; that some outsourcing decisions taken in isolation can lead to a spiral of declining competitiveness; and that the attempts by a cost centre to maximize cost recovery leads to a lower profit for the organization. The thinking process logic trees show that these negative effects are caused by the application of wrong measurement criteria.     

Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

Due to global financial crisis, risk management has received significant attention to avoid loss and maximize profit in any business. Since the financial crisis prediction (FCP) process is mainly based on data driven decision making and intelligent models, artificial intelligence (AI) and machine learning (ML) models are widely utilized. This article introduces an intelligent feature selection with deep learning based financial risk assessment model (IFSDL-FRA). The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise. In addition, the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection (WSOA-FS) manner to an optimum selection of feature subsets. Moreover, Deep Random Vector Functional Link network (DRVFLN) classification technique was applied to properly allot the class labels to the financial data. Furthermore, improved fruit fly optimization algorithm (IFFOA) based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model. For enhancing the better performance of the IFSDL-FRA technique, an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.