供应商综合实力的指标评价体系及模糊判断

供应链管理(SCM)是在IT技术广泛应用的基础上产生的一种先进、新颖的管理哲理与方法.在供应链(SC)中,合理地评价供应商的综合实力是优化选择具有敏捷性和相容性合作伙伴的关键,也对提高整个SC的竞争力有着极其重要的作用.构建了供应商综合实力的指标评价体系及评价因素集、权重集,提出一个通用的模糊判断模型及相应的算法,并利用该算法较好地解决了对供应商综合实力进行定量判断的问题.     

基于模糊多属性群决策的动态联盟盟员选择方法

对动态联盟盟员选择问题所采用的经典方法进行了分析,根据盟员选择过程中决策者判断模糊性的特点,考虑到不同决策者其决策可信性的区别,采用模糊环境下的多属性群决策方法,将定性指标转化为定量指标,从而求得动态联盟的最佳合作伙伴.     

基于偏好和动态直觉的产学研合作伙伴选择群决策分析

针对产学研合作伙伴选择的有限理性和偏好特性,基于直觉模糊多属性决策理论和相对熵理论,构建产学研合作伙伴选择群决策模型。运用动态直觉模糊加权几何算子(DIFWG)集成合作伙伴不同时段的个人准则决策矩阵,实现对产学研合作伙伴持续性的评价;运用直觉模糊有序加权平均算子(IFOWA)集成不同决策者的决策矩阵和偏好矩阵,并利用决策者对合作伙伴的主观偏好与对合作伙伴各准则的客观评价之间差距的极小化,基于加权平均思想,求取评价准则的客观权重;然后,引入相对熵求取评价对象理想的最优权重解,依据该解对各合作伙伴进行排序并选择;最终通过实证研究说明了该方法的有效性和可行性,充分利用直觉模糊理论,实现了产学研合作伙伴的“群偏好—多时段—群决策”的全面评价。     

基于偏好关系的制造工艺资源评价与选择的模糊决策方法

制造工艺资源的评价和选择是实现资源优化配置的关键技术.本文分析了协同工艺设计中制造工艺资源选择决策研究状况,应用模糊数和模糊决策理论,建立基于偏好关系的制造工艺资源模糊评价模型,研究一种基于偏好关系的隶属度函数求解算法和基于偏序关系的多方案制造工艺资源排序决策方法,最后给出一个算例验证本文提出的资源评价方法的合理性和有效性.     

基于模糊的创新动态联盟伙伴选择多属性决策模型

针对开放式创新合作伙伴选择过程中决策者判断模糊性的特点,考虑到不同的决策者其决策可信性的不同,提出了开放式创新合作伙伴选择的模糊多属性群决策模型。首先描述了开放式创新合作伙伴选择的多属性群决策问题;然后依据该种方法比较突出的特点,考虑到决策者对评价指标权重的不同,对TOPSIS方法加以改进;最后,通过算例说明了该方法的有效性和可行性。     

指标相关且信息模糊的合作创新伙伴选择方法

合作创新是快速提升企业创新能力的有效途径,合作伙伴选择的恰当与否直接影响合作创新绩效。通过对合作创新伙伴选择的影响因素分析,将其归结为是一类指标相关且信息模糊的多指标决策问题,并针对性地提出了一种基于Choquet积分的直觉模糊集的优劣势排序群体决策方法对合作伙伴进行选择。该方法先基于模糊相关指标构建评价方案的优势矩阵和劣势矩阵,再结合决策者权重的不确定信息构建相应的全局优势度和劣势度,由此得到合作伙伴的综合排序。最后通过实例说明方法的可行性和有效性。     

突破性创新研发联盟伙伴选择的模糊识别模型

突破性技术创新已经成为一国经济持续发展的主要动力.在突破性技术创新过程中,研发联盟伙伴选择的成功与否,直接会影响到突破性技术创新成败.本文将TOPSIS方法应用到突破性技术创新研发联盟伙伴选择过程中,并考虑到决策者对评价指标权重的不同,依据贴近度的公理化定义对TOPSIS方法进行了改进,并构造出了"相对"贴近度,以此来折衷地衡量研发联盟合作伙伴的优劣.     

三人模糊联盟合作博弈的最小核心解

研究了联盟是模糊的合作博弈.利用多维线性扩展的方法定义了模糊联盟最小核心解,并推导出三人模糊联盟合作博弈最小核心的计算公式.研究结果发现,多维线性扩展的模糊联盟合作博弈最小核心解是对清晰联盟合作博弈最小核心解的扩展.最后给出三人模糊联盟合作博弈的一个具体事例,证明了此方法的有效性和适用性.     

基于不完全权重信息下模糊多属性决策的供应链合作伙伴选择

针对属性权重完全未知或只有部分权重信息且属性值为三角模糊数的供应链合作伙伴选择问题,给出了一种模糊多属性决策方法.提出了一种基于置信度的定性指标的量化方法,通过求解最优化决策模型确定属性的权重,然后根据各方案到模糊理想点的相对贴近度的大小选择最优的合作伙伴.     

多产品模糊存储模型及算法

提出了一个需求为模糊数,产品存储空间有模糊约束的多产品EOQ模型,并采用两种方法进行求解.一种是结合模糊仿真技术和遗传算法的混合算法进行求解,另一种是将模糊模型转化为清晰模型,再用算法求解.最后举出具体数值实例,对两种方法的求解结果进行比较.     

An option-based revenue management procedure for strategic airline alliances

An airline has to decide whether to accept an incoming customer request for a seat in the airplane or to reject it in hope that another customer will request the seat later at a higher price. Capacity control, as one of the instruments of revenue management, gives a solution to this decision problem. In the presence of strategic alliances capacity control changes. For the case of two airlines in the alliance and a single flight leg we propose an option-based capacity control process. The determination of booking limits for capacity control is done with real options. A simulation model is introduced to evaluate the booking process of the partner airlines within the strategic alliance, considering the option-based procedure. In an iterative process the booking limits are improved with simulation-based optimization. The results of the option-based procedure will be compared with the results of the simulation-based optimization, the results of a first-come-first-served (FCFS) approach and ex post optimal solutions.     

基于BP神经网络的航空公司机队可靠性评价模型

机队作为航空公司运输能力的关键,其安全性与可靠性直接影响到航空公司的经济效益.根据航空公司机队可靠性统计和数据采集方式以及实际应用情况,建立了航空公司机队可靠性评价指标体系.鉴于机队可靠性受多种复杂因素影响及各指标体系非线性等特点,结合人工神经网络基本原理和特性,提出了BP神经网络机队可靠性评价模型.最后应用该模型对航空公司机队可靠性进行了实例分析,得出了评价等级.结果表明,基于BP神经网络机队可靠性评价模型是可行的,该方法能够实现动态的评价,对提高航空公司机队可靠性评价技术水平具有现实的意义.     

Airline overbooking in the multi-class case

This paper presents an airline overbooking model at a class level for one service compartment–cabin. Class level demand data is used to determine the number of bookings that can be taken for each class. The model is optimised through the use of multi-dimensional search routines. The control level model developed is tested with data supplied by Ireland's national airline, Aer Lingus. The model shows a significant improvement over previous methods employed by Aer Lingus and was subsequently adopted by the airline.     

Optimal dispatching strategy on an airline network after a schedule perturbation

It often happens that one or more aeroplanes from an airline fleet are taken out of operation for technical reasons and the airline has to operate on the existing network with a reduced number of planes. This paper presents the results of an effort to define a new ad hoc schedule for this situation, so that the total passenger delay on an airline network is minimized. A network is formed, in which nodes represent flights on a given airline network, and arcs are the total time losses on individual flights. The problem of determining a new routing and scheduling plan for the airline fleet is solved by branch and-bound methods. A numerical example illustrates the efficiency of the model.     

A hybrid scatter search heuristic for personalized crew rostering in the airline industry

The crew scheduling problem in the airline industry is extensively investigated in the operations research literature since efficient crew employment can drastically reduce operational costs of airline companies. Given the flight schedule of an airline company, crew scheduling is the process of assigning all necessary crew members in such a way that the airline is able to operate all its flights and constructing a roster line for each employee minimizing the corresponding overall cost for personnel. In this paper, we present a scatter search algorithm for the airline crew rostering problem. The objective is to assign a personalized roster to each crew member minimizing the overall operational costs while ensuring the social quality of the schedule. We combine different complementary meta-heuristic crew scheduling combination and improvement principles. Detailed computational experiments in a real-life problem environment are presented investigating all characteristics of the procedure. Moreover, we compare the proposed scatter search algorithm with optimal solutions obtained by an exact branch-and-price procedure and a steepest descent variable neighbourhood search.     

Operational aircraft maintenance routing problem with remaining time consideration

The aircraft maintenance routing problem is one of the most studied problems in the airline industry. Most of the studies focus on finding a unique rotation that will be repeated by each aircraft in the fleet with a certain lag. In practice, using a single rotation for the entire fleet is not applicable due to stochasticity and operational considerations in the airline industry. In this study, our aim is to develop a fast responsive methodology which provides maintenance feasible routes for each aircraft in the fleet over a weekly planning horizon with the objective of maximizing utilization of the total remaining flying time of fleet. For this purpose, we formulate an integer linear programming (ILP) model by modifying the connection network representation. The proposed model is solved by using branch-and-bound under different priority settings for variables to branch on. A heuristic method based on compressed annealing is applied to the same problem and a comparison of exact and heuristic methods are provided. The model and the heuristic method are extended to incorporate maintenance capacity constraints. Additionally, a rolling horizon based procedure is proposed to update the existing routes when some of the maintenance decisions are already fixed.     

基于k-means和邻域粗糙集的航空客户价值分类研究

对客户价值进行分类, 识别重要价值客户, 对航空公司获利至关重要。本文提出了基于k-means和邻域粗糙集的航空客户价值分类模型。首先, 从客户的当前价值和潜在价值双视角出发, 建立了航空客户综合价值评价指标体系; 之后, 采用基于Elbow的k-means方法对航空客户进行聚类, 采用邻域粗糙集方法对决策系统进行指标约简, 根据约简后的决策系统完成客户价值初筛。评估前先使用SMOTE方法消除数据的不平衡性, 而后采用网格搜索组合分类器的方法对航空客户价值分类的效果进行评估和检验。最后, 根据评估结果对航空客户价值细分。文末, 对国内某航空公司的62988条真实客户记录进行了实证分析和验证, 其中, 潜在VIP客户群的分类准确率达到了92%, 从而为航空客户价值分类提供了一种新思路。     

Genetic algorithm based approach for the integrated airline crew-pairing and rostering problem

Airline crew scheduling problem is a complex and difficult problem faced by all airline companies.To tackle this problem, it was often decomposed into two subproblems solved successively. First, the airline crew-pairing problem, which consists on finding a set of trips – called pairings – i.e. sequences of flights, starting and ending at a crew base, that cover all the flights planned for a given period of time. Secondly, the airline crew rostering problem, which consists on assigning the pairings found by solving the first subproblem, to the named airline crew members. For both problems, several rules and regulations must be respected and costs minimized.It is sure that this decomposition provides a convenient tool to handle the numerous and complex restrictions, but it lacks, however, of a global treatment of the problem. For this purpose, in this study we took the challenge of proposing a new way to solve both subproblems simultaneously. The proposed approach is based on a hybrid genetic algorithm. In fact, three heuristics are developed here to tackle the restriction rules within the GA’s process.     

Modeling and Solving an Airline Schedule Generation Problem

Since opening a new flight connection or closing an existing flight has a great impact on the revenues of an airline, the generation of the flight schedule is one of the fundamental problems in airline planning processes.In this paper we concentrate on a special case of the problem which arises at charter companies. In contrast to airlines operating on regular schedules, the market for charter airlines is well-known and the schedule is allowed to change completely from period to period. Thus, precise adjustments to the demands of the market have a great potential for minimizing operating costs.We present a capacitated network design model and propose a combined branch-and-cut approach to solve this airline schedule generation problem. To tighten the linear relaxation bound, we add cutting planes which adjust the number of aircraft and the spill of passengers to the demand on each itinerary.For real-world problems from a large European charter airline we obtain solutions within a very few percent of optimality with running times in the order of minutes on a customary personal computer for most of the data sets.     

Choosing best alliance partners and allocating optimal alliance resources using the fuzzy multi-objective dummy programming model

Synergy effects are the motives to enter into strategic alliances; however due to lack of adequate preparation or planning, these alliances often fail. It is of no doubt that a successful strategic alliance depends on choosing the correct alliance partners and appropriate resource allocation. In this paper, the fuzzy multi-objective dummy programming model is proposed to overcome the above-mentioned problems. Two types of strategic alliances, joint ventures and mergers and acquisitions (M&A), are demonstrated to choose the best alliance partners and allocate the optimal alliance resources in a numerical example. Based on the results, our method can provide the optimal alliance cluster and satisfaction in strategic alliances.