Database placement in communication networks for minimizing the overall transmission cost

The minimum spanning tree write policy for the maintenance of the consistency of a distributed database, where replicated data exist, has been proposed in [1]. In this paper, we first present a data placement heuristic algorithm in general networks for minimizing the overall transmission cost for processing the typical demands of queries (by a “simple” process strategy) and updates (by the minimum spanning tree write policy). Several interesting optimality estimation results of this algorithm are shown, while the computational intractability of the complete optimization, with respect to the simple strategy, is shown as well. Secondly, we apply a classical climbing hill technique to obtain a dynamic database placement algorithm based on an employed optimizer—a collection of distributed query process algorithms. This is guaranteed to output a “locally optimal” data allocation. The implementation results also show that those two heuristics work well in practice.     

Robust optimization for performance tuning of modern database systems

Issues regarding design and management of database systems have been studied by applying operations research (OR) techniques. The purpose of this study is to propose a new alternative towards database performance tuning for query-processing needs of modern database systems from the perspective of operations research using robust optimization. We use a query-driven approach to specify database structures (schema) so that they are robust to uncertainty and dynamics of queries in a changing environment and allow fast and timely information retrieval and exchange. Instead of applying hardware tuning or traditional database tuning techniques, we examine queries by their types and properties to derive database structures that are robust at efficiently processing future queries of any type. This query-driven approach improves the efficiency of processing queries by setting up database structures based on the queries’ information needs. This new methodology provides a new approach of tuning database performance that is robust to unexpected changes and dynamics. To further demonstrate the idea, we develop a robust optimization model using a non-linear von Neumann–Morgenstern expected utility function and present two computational examples.     

财政支农专项转移支付制度制定:矫正工商资本下乡异化行为

制定怎样的财政支农专项转移支付制度才能正确引导与矫正工商资本下乡行为？为了探索出能够有效矫正工商资本下乡异化行为的财政制度,文章采用动态博弈的方法剖析工商资本下乡企业的行为。经深入研究发现,国家财政支付制度的实施成效取决于分配制度(“勉励”还是“嘉奖”)及工商资本下乡企业的发展阶段。随着企业的逐渐壮大,在政策机制中融入“嘉奖”制度较事后的“勉励”制度,对于发展农业提升企业利益与反哺农民改善民生的两种行为,可更好的引导工商资本下乡正确与合理的分配资金。     

Optimal dynamic multi-attribute hashing for range queries

This paper examines a partial match retrieval scheme which supports range queries for highly dynamic databases. The scheme relies on order preserving multi-attribute hashing. In general, designing optimal indexes is NP-hard. Greedy algorithms used to determine the optimal indexes for simple partial match queries are not directly applicable because there are a larger number of queries to consider in determining the optimal indexes. In this paper we present heuristic algorithms which provide near-optimal solutions. The optimisation scheme we propose can be used to design other dynamic file structures such as the grid file, BANG file and multilevel grid file to further enhance their retrieval performance taking into consideration the query distribution.     

What are the actual costs of cyber risk events?

Cyber risks are high on the business agenda of every company, but they are difficult to assess due to the absence of reliable data and thorough analyses. This paper is the first to consider a broad range of cyber risk events and actual cost data. For this purpose, we identify cyber losses from an operational risk database and analyze these with methods from statistics and actuarial science. We use the peaks-over-threshold method from extreme value theory to identify “cyber risks of daily life” and “extreme cyber risks”. Human behavior is the main source of cyber risk and cyber risks are very different compared with other risk categories. Our models can be used to yield consistent risk estimates, depending on country, industry, size, and other variables. The findings of the paper are also useful for practitioners, policymakers and regulators in improving the understanding of this new type of risk.     

Knowledge-based improvement: simulation and artificial intelligence for identifying and improving human decision-making in an operations system

The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as ‘knowledge-based improvement’ (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant.     

Dynamic,customer-oriented improvement of supply networks

The paper considers the dynamic coordination of a supply network consisting of one supplier company and multiple customer companies. The ongoing business relationships are based on general contracts. But also the informal understandings and agreements that are facilitated by ongoing business relationships are taken into account: the supplier tries to reduce the prevailing information asymmetry by performing regular customer satisfaction surveys. This information together with the contract attributes is used by the supplier to improve the performance of its business processes and/or the contract attributes that contribute the most to improving total customer satisfaction. We propose a four-stage decision-making procedure which is mainly based on statistical analyses (dependency analysis, logit model) and a managerial procedure describing whether the supplier should renegotiate the contract with a specific customer to improve the performance of the overall network. The statistical analysis is illustrated by a real-world case study of a medium-sized German company and its customers.     

层次分析法在零售市场饱和度评价中的应用

随着经济的发展,实业界投资零售商业的热情空前高涨.但零售商业要健康发展,有赖于对零售市场饱和度作出准确判断.利用层次分析法建立了一个饱和度评价体系,把定性的问题定量化,为政府部门制定和完善商业规划、为企业投资零售商业领域提供了一种易于操作的解决办法.     

The interplay of empirical and deductive reasoning in proving “if” and “only if” statements in a Dynamic Geometry environment

This paper is situated within the ongoing enterprise to understand the interplay of students’ empirical and deductive reasoning while using Dynamic Geometry (DG) software. Our focus is on the relationships between students’ reasoning and their ways of constructing DG drawings in connection to directionality (i.e., “if” and “only if” directions) of geometry statements. We present a case study of a middle-school student engaged in discovering and justifying “if” and “only if” statements in the context of quadrilaterals. The activity took place in an online asynchronous forum supported by GeoGebra. We found that student's reasoning was associated with the logical structure of the statement. Particularly, the student deductively proved the “if” claims, but stayed on empirical grounds when exploring the “only if” claims. We explain, in terms of a hierarchy of dependencies and DG invariants, how the construction of DG drawings supported the exploration and deductive proof of the “if” claims but not of the “only if” claims.     

Dynamic data structures for fat objects and their applications

We present several efficient dynamic data structures for point-enclosure queries, involving convex fat objects in or . Our planar structures are actually fitted for a more general class of objects – (β,δ)-covered objects – which are not necessarily convex, see definition below. These structures are more efficient than alternative known structures, because they exploit the fatness of the objects. We then apply these structures to obtain efficient solutions to two problems: (i) finding a perfect containment matching between a set of points and a set of convex fat objects, and (ii) finding a piercing set for a collection of convex fat objects, whose size is optimal up to some constant factor.     

The dynamic vehicle rescheduling problem

The pre-planned schedules of a transportation company are often disrupted by unforeseen events. As a result of a disruption, a new schedule has to be produced as soon as possible. This process is called the vehicle rescheduling problem, which aims to solve a single disruption and restore the order of transportation. However, there are multiple disruptions happening over a “planning unit” (usually a day), and all of them have to be addressed to achieve a final feasible schedule. From an operations management point of view the quality of the final solution has to be measured by the combined quality of every change over the horizon of the “planning unit”, not by evaluating the solution of each disruption as a separate problem. The problem of finding an optimal solution where all disruptions of a “planning unit” are addressed will be introduced as the dynamic vehicle rescheduling problem (DVRSP). The disruptions of the DVRSP arrive in an online manner, but giving an optimal final schedule for the “planning unit” would mean knowing all information in advance. This is not possible in a real-life scenario, which means that heuristic solution methods have to be considered. In this paper, we present a recursive and a local search algorithm to solve the DVRSP. In order to measure the quality of the solutions given by the heuristics, we introduce the so-called quasi-static DVRSP, a theoretical problem where all the disruptions are known in advance. We give two mathematical models for this quasi-static problem, and use their optimal solutions to evaluate the quality of our heuristic results. The heuristic methods for the dynamic problem are tested on different random instances.     

Time lag size in multiple operations flow shop scheduling heuristics

This paper considers a multistage flow shop where jobs require multiple operations at each stage and a finish-to-start time lag between any two consecutive operations of a job: the next operation of a job cannot start until the time lag after the former operation of that job has elapsed. The effect of the size of this time lag is considered when studying the effectiveness of solution approaches for this problem. Since the problem of minimizing the makespan is shown to be NP-hard even for the two-stage case, we present a lower bound based heuristic approach that is used to construct several heuristic procedures. These heuristics use lower bounds on the minimum makespan to solve the problem. The effectiveness of these heuristics is empirically evaluated for various time lag sizes by solving a large number of randomly generated problems. We show that the relative performance of the heuristics depends on the size of the time lag. If the ratio of mean time lag and mean processing time is 20% or more, heuristics that construct an active schedule perform less well than heuristics that construct a non-delay schedule. The opposite holds true if this ratio is smaller. The performance of the widely used Shortest Processing Time heuristic (SPT) deteriorates quickly if the size of the time lags increases. We propose instead to use the Earliest Finish Time heuristic (EFT) in case time lags are present. EFT performs much better in this case and is identical to SPT if all time lags are zero. The use of the lower bound based heuristics results in an improvement of the makespan performance of up to 50% as compared with the performance of some simple dispatching heuristics that take the presence of multiple operations and time lags into account. This effect increases with the size of the time lags.     

Agent-based approach to complex systems modeling

In this paper, we propose a method for detection of local system structures in a complex database. The complex database is viewed as consisting of mixed numeric and nominal attributes, and the local system structure as expressed by “if–then” rules. The detection of local system structures is an important task, and is concerned with inter-dependent issues. The issues involved in the detection of “if–then” rules include finding the objects that share common interests and then finding if–then rules that characterize those objects. To deal with these issues, an agent-based approach is proposed. Each agent has the role of collecting data points (objects) based on their similarity, for mixed data and detecting a rule. The similarity is introduced so that the agent can handle a mixed database. Each agent will occupy a part of the database as its territory according to the predefined algorithm with which agents try to expand or reduce their territories.     

基于演化博弈的制造企业低碳技术采纳决策机制研究

为实现CO₂减排目标,针对我国企业应对气候变化整体表现较差的问题,利用演化博弈理论和方法构建了企业低碳技术采纳决策模型,并对策略选择进行稳定性分析。理论研究和数值仿真结果表明:市场机制下只有当采纳低碳技术能够获得额外净收益时企业群体才会向全部选择采纳的“理想状态”演化,反之,企业选择不采纳策略,此时政府实施低碳技术投入和碳减排两种补贴政策可诱导系统向“理想状态”演化,但应将补贴系数控制在一定范围内发挥其最大激励效用。     

A Tabu search-based approach for scheduling job-shop type flexible manufacturing systems

The problem of scheduling parts in a job-shop type flexible manufacturing system (FMS) is investigated when each part can have alternative process plans and each operation required of a part can be performed on alternative machines. The mixed-(binary) integer programming model developed for the problem is proven strongly NP-hard. A higher-level heuristic solution algorithm based on a concept known as ‘tabu search’ is developed to determine the best (near-optimal) solution for problems of industrial merit. A comparison of six different versions of tabu search-based heuristics (TSH 1-TSH 6) is performed to investigate the impact of using long-term memory and the use of fixed versus variable tabu-list sizes. A carefully constructed statistical experiment, based on randomized complete-block design, is used to test the performance on four problem structures ranging from 4–14 parts. The results show that, as the problem size increases, TSH 3 with fixed tabu-list size and long-term memory is preferred over the other heuristics. Further, the branch-and-bound technique, by failing to identify as good a solution as that determined by the heuristics (TSH 1-TSH 6), let alone an optimal solution, for a small problem reinforces the need for developing efficient heuristics for solving real problems encountered in industry practice.     

Integration of business function models into an aggregate enterprise systems model

Introduced are methods that combine models of distinct business functions into an aggregate model of an enterprise system to assist management’s strategic decision making. Models of individual business functions are reviewed, and equations quantifying relationships presented. Using methods of system theory, including block diagrams, non-dimensionalization, and state equation methods, these business function sub-models were assembled into a composite enterprise systems model. The formulated aggregate model is illustrated with industry examples for tire companies; nonetheless the aggregate model can be used to assess other industries. Values of parameters for the system model were determined from data obtained from annual reports of publicly owned companies. Simulations closely matched the companies’ published performance over ensuing years. The developed aggregate enterprise model has significant predictive capabilities for modern corporations.     

双边市场下商家和电子商务平台的演化博弈分析

互联网平台经济中存在的“二选一”和“多选一”现象引起了国内外社会各界的广泛关注,在此现实情境下,本文作了具体参数设置和相应假设,构建了双边市场下商家和电子商务平台关于排他性竞争行为的演化博弈模型,得到商家和电子商务平台在不同条件下的演化稳定均衡策略,并讨论了参数变化对演化稳定均衡策略的影响。最后,通过数值算例分析对系统演化稳定均衡结果进行了验证。研究结果对商家和电子商务平台企业的管理决策,以及政府监管部门对互联网经济下不正当竞争行为的有效监管有着一定启示意义。     

A single-resource revenue management problem with random resource consumptions

We study a single-resource multi-class revenue management problem where the resource consumption for each class is random and only revealed at departure. The model is motivated by cargo revenue management problems in the airline and other shipping industries. We study how random resource consumption distribution affects the optimal expected profit and identify a preference acceptance order on classes. For a special case where the resource consumption for each class follows the same distribution, we fully characterize the optimal control policy. We then propose two easily computable heuristics: (i) a class-independent heuristic through parameter scaling, and (ii) a decomposition heuristic that decomposes the dynamic programming formulation into a collection of one-dimensional problems. We conduct extensive numerical experiments to investigate the performance of the two heuristics and compared them with several widely studied heuristic policies. Our results show that both heuristics work very well, with class-independent heuristic slightly better between the two. In particular, they consistently outperform heuristics that ignore demand and/or resource consumption uncertainty. Our results demonstrate the importance of considering random resource consumption as another problem dimension in revenue management applications.     

基于自组织的虚拟企业控制机理研究

基于虚拟企业开放性、非线性、涨落性和非平衡性的特质,本文借鉴普里高津“功能-涨落-结构”三角循环转化关系,提出了虚拟企业的自组织管理模式,探讨了虚拟企业自组织的控制过程并构建了复杂虚拟企业控制模型,深入挖掘了虚拟企业通过自组织形式进行生产服务或运营管理的机理。通过分析自组织对虚拟企业运作的内在影响,揭示了虚拟企业自组织的演化机理。     

Towards “zero abandonments” in call center performance

The call center industry is a big business in today's global economy. Staffing costs account for over half of a call center's total operations costs. Some large call centers, in practice, operate at very close to maximum capacity, believing that such an operations policy is efficient. However, by operating at levels close to 100% utilization, a call center is “living dangerously”. If, for example, call volumes even slightly exceed forecasts, customer calls will queue. As queue lengths and durations increase, customers will tend to abandon their calls. We provide some “rule-of-thumb” formulas that evaluate the cost of abandonments. These formulas may be used to justify an investment in additional agents required to improve the quality of service and reduce abandonments. Standard Erlang-C queueing formulas imply that abandonments can be significantly reduced with a small investment in additional agents. Thus, by improving customer service and hiring additional staff, a call center can improve profitability. We illustrate our analysis with realistic data, based on our work with large-scale customer service centers.