航线网络中需求无约束估计的非参数选择模型

传统的航空客运需求无约束估计方法仅针对平行直达航班中的顾客需求“溢出”和“再 现”问题,未能考虑航空网络中直达和中转联程航班之间的网络替代效应． 基于顾客偏好排序列表定义了航空网络顾客类型集合,建立了考虑顾客策略行为的网络型非参数离散选择模型．考虑到网络环境下历史预售数据的不完备性,站在线上和线下交易平台的角度,分别建立了非截尾和截尾需求情况下的完备数据对数似然函数． 采用 EM 算法对顾客到达率和概率质量函数进行联合估计,并提出了网络环境下的顾客“初始需求”、“再现需求”和“溢出需求”无约束估计计算方法．通过数值模拟验证了所提方法的可行性和有效性,相较于现有方法能准确反映产品间网络替代效应对顾客选择行为影响,从而更加有效地避免对历史顾客“初始需求”的高估问题．     

A quick response production strategy to market demand

The reason for considering the quick response production strategy to market demand is due to the rapid technology change, which results in decreasing market price and obsolescence. This study considers a production strategy of locating final production line in response to the changes in market demand and the continuous deterioration in stock. The demand rate is assumed to decrease exponentially with time while the price is assumed to decrease linearly with time. The purpose of this study is to derive the most economical site of final-production line that assembles products with short life cycle. The model considered in this research takes into account the sales revenue, the deteriorating cost, the carrying cost, the variable cost and the fixed cost of production. Although there is a higher labour and material cost when the production site is located near market point, the total profit increases due to quicker responsive time, smaller import tax, lower inventory and lesser deteriorating cost.     

弹性需求下网络设计问题和电子路票问题研究

应用双(二)层规划模型研究弹性需求下网络设计问题与电子路票收取问题,其中只考虑在部分路段进行路段能力扩充和收取电子路票.上层决策者(网络规划者)选择路段能力增加和收取电子路票的数量来获得最优的社会总福利.下层决策者(网络用户)选择路径来最小化他们的出行成本(路径出行时间与所付出电子路票的价值的和).应用下层规划问题的Ka-rush-Kuhn-Tucker(KKT)条件,将双层规划模型转化为单层规划模型.为了解决互补条件所造成的求解困难,本文构造了松弛算法进行求解,并用数值试验研究了模型和算法的可行性.数值结果表明,本文的模型在缓解交通拥挤方面可以得到更好的效果,而且只在部分路段进行路段能力扩充和收取电子路票更加方便实用.在可交易电子路票方案中,更多出行的用户需要购买电子路票来为他们的额外出行付费,而减少出行的用户则可以卖出多余电子路票得到补偿,同时电子路票的交易价格是在完全竞争的市场上形成的,因此本文中的可交易电子路票机制是收入中性的.     

International issues of the design and usage of websites for e-commerce: Hotel and airline examples

Technology has globalized businesses. International business is difficult due to differences in languages and cultures. In terms of technology, in some countries, it is difficult to get data; while in others, data is input in different ways. This paper examines how international differences affect website design, implementation and usage. We analyze websites of airlines and hotels. While some websites take into consideration language and culture differences, we find significant room for improvement in both industries. We found support for localization strategy in marketing, communication and transactions. Theoretical and practical implications, including language translators, currency converters and pull-down fields, are discussed.     

Reliability: Its implications in production systems design

Research on production systems design has in recent years tended to concentrate on ‘software’ factors such as organisational aspects, work design, and the planning of the production operations. In contrast, relatively little attention has been paid to maximising the contributions made by fixed assets, particularly machines and equipment. However, as the cost of unproductive machine time has increased, reliability, particularly of machine tools, has become ever more important. Reliability theory and research has traditionally been based in the main on electrical and electronic equipment whereas mechanical devices, especially machine tools, have not received sufficiently objective treatment. A recently completed research project has considered the reliability of machine tools by taking sample surveys of purchasers, maintainers and manufacturers. Breakdown data were also collected from a number of engineering companies and analysed using both manual and computer techniques. Results obtained have provided an indication of those factors most likely to influence reliability and which in turn could lead to improved design and selection of machine tool systems. Statistical analysis of long-term field data has revealed patterns of trends of failure which could help in the design of more meaningful maintenance schemes.     

Heuristics for the network design problem with connectivity requirements

We consider the NP-complete problem of finding a spanning \(k\)-tree of minimum weight in a complete weighted graph. This problem has a number of applications in designing reliable backbone telecommunication networks. We propose effective algorithms based on a greedy strategy and several variable neighborhood search metaheuristics. We also develop an integer linear programming model for calculating a lower bound. Preliminary numerical experiments using random and real-word data sets are reported to show the effectiveness of our approach. In addition, we compare our approach with known metaheuristics.     

Minimum cost network design in strategic alliances

Strategic alliances are established between firms to improve their competitiveness in markets and generally appear in the form of joint ventures. Such collaborative efforts require centralized planning, and the survival of the alliance largely depends on the success of joint planning processes. In this regard, we investigate the opportunities that centralized collaboration can offer to firms when designing their service networks. Apart from the classical fixed and variable costs associated with the network design, we also consider transaction costs induced by the formation of the alliance, which can broadly be defined as cost components related to the coordination and monitoring of the people, efforts and resources. We concentrate on bilateral alliances and develop alternative models for solving their associated network design problem. We also adopt a state-of-the-art heuristic to solve large-scale instances. Our findings confirm that accounting for the transaction cost in network design is vital for the alliance. These transaction costs can be high enough to even render the collaboration unattractive. Hence, careful data collection and model treatment are required before deciding whether to form an alliance.     

Demand and supply network design scope for personalized manufacturing

This paper first presents personalizing and personalized manufacturing as a means for firms to meet the rising challenge, faced by a large variety of manufacturers, to offer innovative highly personalized products with short and reliable delivery delays in a digital and global economy that is highly turbulent and competitive. It positions personalizing versus the current trend of mass customising. It characterizes complementary types of personalizing and highlights the key elements for their successful implementation from a demand and supply network perspective. Finally, it illustrates the scope and addresses how personalizing affects the design of the demand and supply network, emphasizing the scope of this design influence, the variety of options for each element of the network, and the interrelationships between the design decisions.     

供应链中二级分销网络的优化设计模型

从供应链的集成和协调的角度出发 ,提出了在多工厂、多产品、多客户环境下 ,考虑需求分配的二级分销网络优化设计模型 .模型体现了在设计分销网络时 ,根据制造企业各分厂的生产能力和各客户区 (需求地 )对不同产品的需求情况 ,合理的将对产品的需求分配到各个分厂 ,制定相应的生产计划 ,以降低生产和分销环节的总费用 .文中还讨论了采用启发式算法同传统的分枝定界法相结合以提高问题的求解速度 ,并给出了一个数值例子 .     

Exact approaches for the directed network design problem with relays

We study the directed network design problem with relays (DNDPR) whose aim is to construct a minimum cost network that enables the communication of a given set of origin-destination pairs. Thereby, expensive signal regeneration devices need to be placed to cover communication distances exceeding a predefined threshold. Applications of the DNDPR arise in telecommunications and transportation. We propose two new integer programming formulations for the DNDPR. The first one is a flow-based formulation with a pseudo-polynomial number of variables and constraints and the second is a cut-based formulation with an exponential number of constraints. Fractional distance values are handled efficiently by augmenting both models with an exponentially-sized set of infeasible path constraints. We develop branch-and-cut algorithms and also consider valid inequalities to strengthen the obtained dual bounds and to speed up convergence. The results of our extensive computational study on diverse sets of benchmark instances show that our algorithms outperform the previous state-of-the-art method based on column generation.     

Fiber optical network design problems: A case for Turkey

In this paper, we consider problems originating from one of the largest Internet service providers operating in Turkey. The company mainly faces two different design problems: the green field design (area with no Internet access) and the copper field re-design (area with limited access over copper networks). In the green field design problem, the aim is to design a least cost fiber optical network that will provide high bandwidth Internet access from a given central station to a set of aggregated demand nodes. Such an access can be provided either directly by installing fibers or indirectly by utilizing passive splitters. Insertion loss, bandwidth level and distance limitations should simultaneously be considered in order to provide a least cost design to enable the required service level. In the re-design of the copper field application, the aim is to improve the current service level by augmenting the network with fiber optical wires, specifically by adding cabinets to copper rings in the existing infrastructure and by constructing direct fiber links from cabinets to distant demand nodes. Mathematical models are constructed for both problem specifications. Extensive computational results based on realistic data from Kartal (45 nodes) and Bakırköy (74 nodes) districts in Istanbul show that the proposed models are viable exact solution methodologies for moderate dimensions.     

Multi-echelon supply chain network design in agile manufacturing

In this paper, we consider a supply chain network design problem in an agile manufacturing scenario with multiple echelons and multiple periods under a situation where multiple customers have heavy demands. Decisions in our supply chain design problem include selection of one or more companies in each echelon, production, inventory, and transportation. We formulate the problem integrating all decisions to minimize the total operational costs including fixed alliance costs between two companies, production, raw material holding, finished products holding, and transportation costs under production and transportation capacity limits. A Lagrangian heuristic is proposed in this paper. Optimizing a Lagrangian relaxation problem provides a lower bound, while a feasible solution is generated by adjustment techniques based on the solution of subproblems at each iteration. Computational results indicate the high quality solutions with less than 5% optimality gap are provided quickly by the approach in this paper. Further, compared to initiative managerial alternatives, an improvement of 15% to 25% is not unusual in certain cases for the proposed approach.     

Multi-echelon supply chain network design in agile manufacturing

In this paper, we consider a supply chain network design problem in an agile manufacturing scenario with multiple echelons and multiple periods under a situation where multiple customers have heavy demands. Decisions in our supply chain design problem include selection of one or more companies in each echelon, production, inventory, and transportation. We formulate the problem integrating all decisions to minimize the total operational costs including fixed alliance costs between two companies, production, raw material holding, finished products holding, and transportation costs under production and transportation capacity limits. A Lagrangian heuristic is proposed in this paper. Optimizing a Lagrangian relaxation problem provides a lower bound, while a feasible solution is generated by adjustment techniques based on the solution of subproblems at each iteration. Computational results indicate the high quality solutions with less than 5% optimality gap are provided quickly by the approach in this paper. Further, compared to initiative managerial alternatives, an improvement of 15% to 25% is not unusual in certain cases for the proposed approach.     

A new approximation algorithm for the Selective Single-Sink Buy-at-Bulk problem in network design

The Selective Single-Sink Buy-at-Bulk problem was proposed by Awerbuch and Azar (FOCS 1997). For a long time, the only known non-trivial approach to approximate this problem is the tree-embedding method initiated by Bartal (FOCS 1996). In this paper, we give a thoroughly different approximation approach for the problem with approximation ratio $O(\sqrt{q})$ , where q is the number of source terminals in the problem instance. Our approach is based on a mixed strategy of LP-rounding and the greedy method. When the number q (which is always at most n) is relatively small (say, q=o(log² n)), our approximation ratio $O(\sqrt{q})$ is better than the currently known best ratio O(logn), where n is the number of vertices in the input graph.     

Using branch-and-price approach to solve the directed network design problem with relays

We present node-arc and arc-path formulations, and develop a branch-and-price approach for the directed network design problem with relays (DNDR). The DNDR problem can be used to model many network design problems in transportation, service, and telecommunication system, where relay points are necessary. The DNDR problem consists of introducing a subset of arcs and locating relays on a subset of nodes such that in the resulting network, the total cost (arc cost plus relay cost) is minimized, and there exists a directed path linking the origin and destination of each commodity, in which the distances between the origin and the first relay, any two consecutive relays, and the last relay and the destination do not exceed a predefined distance limit. With the node-arc formulation, we can directly solve small DNDR instances using mixed integer programming solver. With the arc-path formulation, we design a branch-and-price approach, which is a variant of branch-and-bound with bounds provided by solving linear programs using column generation at each node of the branch-and-bound tree. We design two methods to efficiently price out columns and present computational results on a set of 290 generated instances. Results demonstrate that our proposed branch-and-price approach is a computationally efficient procedure for solving the DNDR problem.     

A demand classification scheme for spare part inventory model subject to stochastic demand and lead time

In this study, we aim to develop a demand classification methodology for classifying and controlling inventory spare parts subject to stochastic demand and lead time. Using real data, the developed models were tested and their performances were evaluated and compared. The results show that the Laplace model provided superior performance in terms of service level, fill rate (FR) and inventory cost. Compared with the current system based on normal distribution, the proposed Laplace model yielded significant savings and good results in terms of the service level and the FR. The Laplace and Gamma optimisation models resulted in savings of 82 and 81%, respectively.     

Two-level network design with intermediate facilities: An application to electrical distribution systems

We consider the two-level network design problem with intermediate facilities. This problem consists of designing a minimum cost network respecting some requirements, usually described in terms of the network topology or in terms of a desired flow of commodities between source and destination vertices. Each selected link must receive one of two types of edge facilities and the connection of different edge facilities requires a costly and capacitated vertex facility. We propose a hybrid decomposition approach which heuristically obtains tentative solutions for the vertex facilities number and location and use these solutions to limit the computational burden of a branch-and-cut algorithm. We test our method on instances of the power system secondary distribution network design problem. The results show that the method is efficient both in terms of solution quality and computational times.     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

Consumer response to car fronts: eliciting biological preparedness with product design

Building on assumptions derived from evolutionary theory, we investigated viewers’ reactions to the design of car fronts (i.e., an automobile’s face), which were designed to be threatening using basic principles of anthropomorphism. Previous research suggests two opposite human reactions when presented with threatening stimuli: Initially, threatening objects attract human attention (e.g., when exploring a scene for the first time), but afterwards, people tend to avoid such threatening stimuli (as they are likely to induce discomfort in the viewer). This proposition is tested within a product design context using eye tracking methodology. Results showed that automotive stimuli not only activate affective dimensions of customers, but also lead to specific automatic reactions that can be explained by evolutionary theory. Practical implications for product design and marketing are discussed.