Market Good Flexibility in Capacity Auctions

We consider the allocation of limited production capacity among several competing agents through auctions. Our focus is on the contribution of flexibility in market good design to effective capacity allocation. The application studied is a capacity allocation problem involving several agents, each with a job, and a facility owner. Each agent generates revenue by purchasing capacity and scheduling its job at the facility. Ascending auctions with various market good designs are compared. We introduce a new market good that provides greater flexibility than those previously considered in the literature. We allow ask prices to depend both on agents’ utility functions and on the number of bids at the previous round of the auction, in order to model and resolve resource conflicts. We develop both optimal and heuristic solution procedures for the winner determination problem. Our computational study shows that flexibility in market good design typically increases system value within auctions. A further increase is achieved if each agent is allowed to bid for multiple market goods at each round. On average, the multiple flexible market goods auction provides over 95% of the system value found by centralized planning.     

Capacity Planning and Allocation for Web‐Based Applications

Motivated by the technology division of a financial services firm, we study the problem of capacity planning and allocation for Web‐based applications. The steady growth in Web traffic has affected the quality of service (QoS) as measured by response time (RT), for numerous e‐businesses. In addition, the lack of understanding of system interactions and availability of proper planning tools has impeded effective capacity management. Managers typically make decisions to add server capacity on an ad hoc basis when systems reach critical response levels. Very often this turns out to be too late and results in extremely long response times and the system crashes. We present an analytical model to understand system interactions with the goal of making better server capacity decisions based on the results. The model studies the relationships and important interactions between the various components of a Web‐based application using a continuous time Markov chain embedded in a queuing network as the basic framework. We use several structured aggregation schemes to appropriately represent a complex system, and demonstrate how the model can be used to quickly predict system performance, which facilitates effective capacity allocation decision making. Using simulation as a benchmark, we show that our model produces results within 5% accuracy at a fraction of the time of simulation, even at high traffic intensities. This knowledge helps managers quickly analyze the performance of the system and better plan server capacity to maintain desirable levels of QoS. We also demonstrate how to utilize a combination of dedicated and shared resources to achieve QoS using fewer servers.     

基于多目标二层规划的服务供应链服务能力协同决策模型

为了解决服务供应链系统运作过程中存在的服务能力建设过量或不足问题,本文构建了以服务集成商作为服务供应链整体计划、控制和协调中心的上层计划者、各服务供应商作为具有相对自主权的下层计划者的服务供应链协同决策机制,应用多目标二层规划方法建立了服务供应链服务能力分配的优化模型,同时给出了模型的具体算法框图和流程。最后,通过实证算例对服务能力协同决策优化模型进行了计算和有效性验证。     

Multi-site aggregate production planning with multiple objectives: A goal programming approach

This paper addresses the problem of aggregate production planning (APP) for a multinational lingerie company in Hong Kong. The multi-site production planning problem considers the production loading plans among manufacturing factories subject to certain restrictions, such as production import/export quotas imposed by regulatory requirements of different nations, the use of manufacturing factories/locations with regard to customers' preferences, as well as production capacity, workforce level, storage space and resource conditions of the factories. In this paper, a multi-objective model is developed to solve the production planning problems, in which the profit is maximized but production penalties resulting from going over/under quotas and the change in workforce level are minimized. To enhance the practical implications of the proposed model, different managerial production loading plans are evaluated according to changes in future policy and situation. Numerical results demonstrate the robustness and effectiveness of the developed model.     

基于Benders分解算法的跨区互联电力系统协调规划模型

研究跨区互联电力系统的协调规划,对于提高投资效率实现更大范围的资源配置具有较强现实意义。本文首先描述多区域电力系统扩张规划问题,并建立多区域扩张规划模型,旨在寻求最优的扩容方案,以最小投入来满足多区域电力系统负荷增长需求;其次,采用Benders分解算法将多区域扩张规划问题分解为一个规划主问题和一个运行子问题,通过主子问题之间的迭代求解,获得最终的最优解;最后,对某个典型的包含7个区域的多区域电力系统进行模拟仿真,验证了本文所构建模型及算法的有效性。     

A robust optimization model for stochastic aggregate production planning

The aggregate production planning (APP) problem considers the medium-term production loading plans subject to certain restrictions such as production capacity and workforce level. It is not uncommon for management to often encounter uncertainty and noisy data, in which the variables or parameters are stochastic. In this paper, a robust optimization model is developed to solve the aggregate production planning problems in an environment of uncertainty in which the production cost, labour cost, inventory cost, and hiring and layoff cost are minimized. By adjusting penalty parameters, decision-makers can determine an optimal medium-term production strategy including production loading plan and workforce level while considering different economic growth scenarios. Numerical results demonstrate the robustness and effectiveness of the proposed model. The proposed model is realistic for dealing with uncertain economic conditions. The analysis of the tradeoff between solution robustness and model robustness is also presented.     

Capacity management through Lagrangean relaxation: an application to tyres production

A new integrated approach to capacity management in complex manufacturing systems is developed and the resulting framework is applied to a case study in tyre production. A hierarchical multilayered decomposition of the planning process is proposed, in which lower layers provide an increased level of detail and accuracy in capacity representation and analysis. Thus, a large and comprehensive model describing the manufacturing system is subdivided into smaller sub-models via relaxation and decomposition techniques. The Lagrangean multipliers provide a bi-directional link among different layers, reducing the risk of sub-optimization and infeasibility of the aggregate plans. Each of the sub-models is easier to solve than the original one and involves a different set of decision variables. Moreover, they relate to different levels of the management hierarchy, so that there emerges a strict correspondence between the decomposition scheme and the decision process underlying capacity management.     

考虑通行约束和运力限制的灾后应急物资联合调度优化研究

考虑到灾后路网受损难以运输应急物资，本文研究了应急响应中车辆-直升机联合调度的路径优化问题。针对受灾地区的实时路况，通往灾区的救援工具受到数量以及装载量的约束，本文将受灾点等待救援的平均时间最短以及应急网络总费用最低设为目标，构建运力受限条件下带通行约束的救援物资联合运输多目标优化模型，然后根据随机邻域搜索变异和分级交叉的思想构建出一种带精英策略的非支配排序混合进化算法（NSHEA-II）得到模型的解，并利用算例分析对该算法进行可行性检验。结果发现，本文构建的NSHEA-II算法相对NSGA-II算法能够得到较好的结果且波动性较小，这为决策者制定救援物资的配送方案提供有效的技术支撑。     

基于GBOM的供应链网络及其有生产能力约束的集成生产计划模型

提出了基于通用物料单构建供应链网络的方法,并以此为基础建立了其有生产能力约束的集成生产计划模型。由于任一成员都有生产能力上限,且生产能力可相互转化,故总生产能力须进行标准化。模型的目标是供应链联盟整体收益最大化,模型解反映了优化的集成生产计划和生产合作关系。     

城市道路交通网络容量的建模

交通网络承载着具有意志行为的个体组成的交通流,交通网络容量表示道路网络的交通承载能力,与交通网络的OD结构密切相关.本文在分析城市道路交通网络OD结构特征的基础上,提出表达道路网络容量的基本思想和模型,阐明交通网络容量受到交通流特性、网络要素容量、废气排放量、服务水平和系统效率等因素的影响,基于不同的考虑方法可以建立不同的网络容量模型.基于路段容量约束的网络容量描述了道路系统承载交通流的物理极限;基于路段容量约束和交通环境约束的网络容量则描述了道路系统承载交通流的环境极限;基于服务水平的网络容量描述了系统提供某种服务水平条件下的最大交通承载能力;基于效率的网络容量描述了网络资源使用效率和用户效益同时最大化条件下的道路网络承载能力.文章深入分析了所提出的各种模型之间的关系,在实际应用中可根据交通规划和管理所追求的具体目标,采取相应的模型.     

可变产能的按订单装配系统库存和生产决策研究

本文基于一个ATO 生产商的实践,考虑一个需求不确定并具有子装配件的单周期ATO 系统,为了应对较长的采购提前期和有限产能的挑战,生产商可以在得到确定的需求之前提前采购零件,装配子装配件以至于最终产品。由于产品物料清单结构中存在子装配件作为可选的中间件,因此完全使用零件和使用子装配件装配最终产品的不同,造成装配能力在时间上具有可变性。我们对此问题建立了利润最大化模型,分析在上述ATO 环境中的最优库存和生产决策。通过一系列最优解的性质,我们得到了该问题的有效解决方法。     

A Production–Inventory Model for a Push–Pull Manufacturing System with Capacity and Service Level Constraints

We study a hybrid push–pull production system with a two‐stage manufacturing process, which builds and stocks tested components for just‐in‐time configuration of the final product when a specific customer order is received. The first production stage (fabrication) is a push process where parts are replenished, tested, and assembled into components according to product‐level build plans. The component inventory is kept in stock ready for the final assembly of the end products. The second production stage (fulfillment) is a pull‐based assemble‐to‐order process where the final assembly process is initiated when a customer order is received and no finished goods inventory is kept for end products. One important planning issue is to find the right trade‐off between capacity utilization and inventory cost reduction that strives to meet the quarter‐end peak demand. We present a nonlinear optimization model to minimize the total inventory cost subject to the service level constraints and the production capacity constraints. This results in a convex program with linear constraints. An efficient algorithm using decomposition is developed for solving the nonlinear optimization problem. Numerical results are presented to show the performance improvements achieved by the optimized solutions along with managerial insights provided.     

Multi-stage scheduling for banks by mathematical programming

Despite moves toward electronic funds transfer systems, the United States banking industry must continue to cope with the paper processing requirements associated with a payments system still heavily reliant upon checks. Through the development of MICR (magnetic ink character recognition), computers have been utilized to sort documents and post customer accounts. However, a labor intensive key encoding function is required to prepare documents for processing. An attempt to eliminate the labor intensity is the recent development of optical scanning capture equipment which ‘reads’ printed or hand-written documents and inscribes them with MICR characters. However, the utilization of optical scanning technology requires the scheduling and coordination of five processing activities. A mathematical programming model has been developed which minimizes the combination of payroll and float costs while recognizing machine capacity constraints in the scheduling of optical capture systems. The formulation was specifically designed to respond to the bank check processing scheduling problem. However, with minor modifications, the model is directly transferable to any multi-echelon processing system where penalties are incurred when the units of output fail to meet prespecified deadlines.     

A multistage approach for production and workforce planning in long-cycle product environments

Aggregate production planning (APP) has been studied extensively for the past two decades. The APP problem, also called production and workforce scheduling, is to determine the optimal workforce and production level in each period of the planning horizon in order to satisfy demand forecasts for these periods. The advantages of the APP are low cost of data collection and computational cost of the running model; the accuracy of data; and, effective managerial understanding of the results. If the product of concern takes longer than one period, it is called a long-cycle product. Examples of long-cycle products are aircraft, ships, buildings and special machines. A detailed model incorporating dynamic productivity and long-cycle products considerations is presented to solve the problem of production and workforce planning. Using a multistage production system approach, a search technique is developed to solve this class of problems where the objective function is linear and some of the constraint coefficients are dynamically nonlinear. The model provides a better solution than an aggregate production planning model, often used to solve these problems.     

Rough-cut capacity planning for remanufacturing firms

Capacity planning is a critical element of any successful production planning and control system. A method of rough-cut capacity planning is developed, based on the bill-of-resources approach, that can be used to plan for capacity required for firms in a remanufacturing including overhaul repair operations environment. The modified bill-of-resources approach developed takes into account two major stochastic elements inherent in this environment; probabilistic material replacement factors and probabilistic routing files. A detailed example from an actual repair overhaul operation is presented to illustrate the technique.     

Route‐Independent Analysis of Available Capacity in Flexible Manufacturing Systems

In a job shop, because of large setup times, each operation is assigned to only one machine. There is no alternative routing. In a flexible manufacturing system, each manufacturing operation can often be performed on several machines. Therefore, with automated equipment, the capacity of a machine to perform certain operations is not independent of the capacity of other machines. Often, however, operations managers can use a route‐independent answer to production planning questions. For example, how much can be produced of a certain part type and when are important capacity questions in business negotiations, when the detailed routing and scheduling are not yet of interest or cannot be known. This paper provides a mathematical model for the route‐independent analysis of the capacity of flexible manufacturing systems based on a concept of operation types. An example is provided both to illustrate the use of operation types and to highlight the differences between the traditional route‐dependent and the proposed route‐independent formulations of capacity constraints. Some computational results are also given. Finally, a sensitivity analysis is developed to analyze the feasibility of production plans when production requirements and machine capacities can change.     

Incentive Schemes for Semiconductor Capacity Allocation: A Game Theoretic Analysis

We study incentive issues that arise in semiconductor capacity planning and allocation. Motivated by our experience at a major U. S. semiconductor manufacturer, we model the capacity‐allocation problem in a game‐theoretic setting as follows: each product manager (PM) is responsible for a certain product line, while privately owning demand information through regular interaction with the customers. Capacity‐allocation is carried out by the corporate headquarters (HQ), which allocates manufacturing capacity to product lines based on demand information reported by the PMs. We show that PMs have an incentive to manipulate demand information to increase their expected allocation, and that a carefully designed coordination mechanism is essential for HQ to implement the optimal allocation. To this end, we design an incentive scheme through bonus payments and participation charges that elicits private demand information from the PMs. We show that the mechanism achieves budget‐balance and voluntary‐participation requirements simultaneously. The results provide important insights into the treatment of misaligned incentives in the context of semiconductor capacity‐allocation.     

山区配电网震后恢复及资源调度集成优化研究

地震的发生往往会对配电网产生较大破坏,影响供电质量。山区路网地形地貌的复杂性及受地震破坏等,增加了资源调度的困难,阻碍了山区配电网的恢复。因此,本文针对山区配电网震后恢复及资源调度集成优化问题,建立以恢复绩效最大为目标的非线性混合整数规划模型,并根据模型特点,引入A*算法求解各节点间的最优路径与所需时间,通过改进具有快速稳健特性的细菌群趋药性算法(BCCOA)对模型进行求解,得到山区配电网恢复及资源调度策略,并采用IEEE30,IEEE57和IEEE118节点系统,验证所述策略的可行性和有效性。结果表明:(1)改进的BCCOA相对于枚举法,在求解精度和计算时间上都有较好优势;(2)当资源调度能力一定,改变抢修能力时,配电网恢复绩效平均提升36.40%。而当抢修能力一定,改变资源调度能力时,由于受抢修能力的影响,配电网恢复绩效平均提升7.99%,但资源调度延迟率下降了61.71%,可提高配电网恢复绩效。而同时改变两者的能力时,配电网恢复绩效平均提升38.23%,提升了209.14%,表明资源调度在山区配电网震后恢复中的重要性,说明山区配电网震后恢复及资源调度集成优化研究符合实际决策需求;...     

属性集容量确定的夹挤式测度模式及其推算模型

迄今,为解决多属性偏好关联决策属性集容量判断指数复杂性难题所提出的λ模糊测度模式与k-可加模糊测度模式,以及建构在它们之上的属性集容量确定的推算模型,尚存在着适用性差的技术不足。为此,以平衡容量判断的可操作性和容量推算的准确性为视角,提出了一种新容量测度模式,即关于容量判断与推算的夹挤式测度模式,并在此基础上通过引入决策者较易判断给出的容量序信息构建了相应的容量推算模型。基于数值模拟的对比分析表明：新模式不仅在应用可行性上高于k-可加模糊测度模式,而且从容量推算的准确性上看也明显优于λ模糊测度模式和k-可加模糊测度模式,因而对实际决策具有更强的适用性。     

基于补偿合约的供应链定价与能力设计的协调问题研究

研究和分析了在需求不确定并且受价格影响的情况下,供应链中制造商与供应商的产品定价与能力设计的协调问题。制造商从供应商处采购用于产品生产的关键零部件,供应链面临的潜在需求服从随机分布,有效需求则受产品定价的影响。当供应商的生产能力出现约束时,制造商可以从外部其他渠道获取关键零部件,但是需要付出一个更高的采购价格。制造商确定产品的销售价格,供应商确定生产能力。分析比较了在集成供应链与独立决策的供应链中的定价与能力计划策略,提出了一种能够有效协调制造商和供应商的决策行为的补偿合约。最后,进行了数值分析,证明补偿合约的有效性。