Inventory and transshipment decisions in the rationing game under capacity uncertainty

In this paper, we consider the inventory decisions of two retailers who are supplied by a single supplier with uncertain capacity. When capacity is allocated in proportion to the retailers׳ orders, the retailers compete for the capacity by inflating their orders (i.e., the rationing game). In addition, we allow the retailers to implement transshipment between them such that they cooperate by transshipping the surplus stock of one to another who is out of stock. Our analysis of Nash equilibrium orders shows that, while order inflation in the equilibrium orders persists in the rationing game with transshipment, it may not occur if the amount of capacity shortage is small and the transshipment prices are low. Thus, carefully chosen transshipment prices may alleviate order inflation behavior. We also characterize centralized orders that maximize the total profit of the retailers and compare them to equilibrium orders. In particular, we investigate coordinating transshipment prices that induce the retailers to choose centralized orders. Our numerical analysis shows that, even for two identical retailers, coordinating transshipment prices exist in a more limited range of parameter values in the rationing game than they do outside the rationing game due to capacity uncertainty and limitation.     

Approaches to implementing manufacturing information systems

Since computers lie behind almost all aspects of new technology, industry now has the chance to develop true integrated Manufacturing Information Systems. This paper examines the developments in this direction which new technology makes available, and why companies should embrace them. Case studies are presented in which the progress achieved and problems encountered by three companies in realising their manufacturing information system are discussed. General conclusions are drawn from them and recommendations for successful implementation are put forward.     

Disassembly to order system under uncertainty

This paper presents a multi-criteria optimization model of a disassembly-to-order (DTO) system under uncertainty. The goal of the proposed model is to determine the best combination of the number of each product type to be taken back from the last user and/or collectors. The EOL products are then disassembled for the retrieval of reusable components and materials and resold in order to meet a certain level of demand under a variety of physical, financial and environmental constraints. The surplus components are recycled, stored for usage in subsequent periods or properly disposed. The problem is modeled as a multi-criteria decision-making problem under uncertainty, where the aspiration levels for various goals are more likely to be in the “approximately more (less) than” and/or “more (less) is better” form. We employ fuzzy goal programming technique to solve the problem. When solved, the model provides the number of EOL products to be taken back as well as the number of items reused, recycled, stored and disposed. The values of a host of other performance measures are also obtained, including total profit, materials and items sales revenues, take back cost, transportation costs as well as costs of preparation of EOL products, destructive disassembly, non-destructive disassembly, recycling, storage and disposal. A case example is presented to illustrate the model's implementation.     

Designing information systems for strategic decisions

In this article the authors argue that recent events have given rise to a need for a new class of information related to strategic decision-making. They describe some of the characteristics of “ideal” systems to meet this need and suggest a program of less sophisticated systems which will develop towards that ideal.     

A hierarchical approach to multi-project planning under uncertainty

We survey several viewpoints on the management of the planning complexity of multi-project organisations under uncertainty. Based on these viewpoints we propose a positioning framework to distinguish between different types of project-driven organisations. This framework is meant to aid project management in the choice between the various existing planning approaches. We also discuss the current state of the art of hierarchical planning approaches both for traditional manufacturing and for project environments. Next, we introduce a generic hierarchical project planning-and-control framework that serves to position planning methods for multi-project planning under uncertainty. We discuss various techniques for dealing with the uncertainty inherent to the different hierarchical stages in a multi-project organisation. In the last part of this paper we discuss two cases from practice and relate these to the positioning framework that is put forward in the paper.     

Using expected values to simplify decision making under uncertainty

A simulation study examines the impact of a simplification strategy that replaces distributional attribute evaluations with their expected values and uses those expectations in an additive value model. Several alternate simplified forms and approximation approaches are investigated, with results showing that in general the simplified models are able to provide acceptable performance that is fairly robust to a variety of internal and external environmental changes, including changes to the distributional forms of the attribute evaluations, errors in the assessment of the expected values, and problem size. Certain of the simplified models are shown to be highly sensitive to the form of the underlying preference functions, and in particular to extreme non-linearity in these preferences.     

Measures of return under uncertainty

The purpose of this paper is twofold. First a methodlogy for contrasting accounting and economic measures of return will be briefly described. This methodology overcomes many of the limitations of previous comparisons of these measures. Second, the results of applying the new methodology to an area of current accounting concern—adjusting for price level changes—will be presented.     

Multiobjective investment planning under uncertainty

In this paper we consider the relevance of various planning methods and decision criteria to multiobjective investment planning under uncertainty. Assuming that a natural reaction to uncertainty is to operate so as to leave open as many good options as possible (as opposed to maximizing subjective expected utility) we argue that the planning process should concentrate on analyzing the effects of the initial decision, and that for this exercise the classical methods of mixed integer programming are inappropriate. We demonstrate how the technique of dynamic programming can be extended to take account of multiple objectives and use dynamic programming as a framework in which we analyze the robustness of an initial decision in the face of various types of uncertainty. In so doing we also analyze the risks involved in both the planning and decision making functions.     

不确定环境下链与链竞争纵向联盟与收益分享

基于需求不确定性环境,构建两个制造商、两个排他性零售商构成的链与链价格竞争模型,识别了纵向联盟的形成机制及制造商与零售商收益分享合同的选择范围,并分析了产品竞争、价格风险对联盟及收益分享合同选择的影响.研究结论表明,联盟的选择不受价格风险的影响而取决于产品的竞争强度,但基于联盟的零售商收益分享比例范围将随着价格波动的增加而逐渐提高.     

不确定性环境中组织结构设计

针对现代组织的高度不确定性环境特征 ,从组织决策角度探讨组织结构设计问题 .建立了不确定性环境中组织决策模型 .引入组织结构价值概念 ,采用定性与定量相结合的方法 ,推导出组织环境、组织决策与组织结构三者之间关系的数学表达式 ,给出对不确定性环境中组织结构设计有意义的一些结论 .特定的组织结构设计取决于多种因素 ,最后对这些因素及其对组织结构设计的影响进行了初步研究 .     

Material planning for production kits under uncertainty

A kit is a specific collection of components and/or tools needed for completing a procedure or producing a product. A multiperiod material planning system for production kits under the demand and procurement lead-time uncertainty is considered when component sharing among kits is plausible. Simulation experiments show that component sharing improves the system's service measure of average kit availability and average backorder quantity per period. Also, carrying component safety stock only enhances the benefits of component sharing by reducing the average backorder quantity at the expense of increased inventories, but does not improve the average kit availability.     

Configuration of flexibility strategies under supply uncertainty

In this paper, we study a buyer׳s configuration of flexibility strategies under supply uncertainty. His main supplier׳s production process is uncertain, and he can either choose pricing flexibility (setting prices depending on the available supply) or operational flexibility (requesting a contingent order from a backup supplier). As the buyer may or may not find a suitable contingent supplier ex post, we study two scenarios that the backup supplier׳s supply is infinite, and that this supply is random. We also include the factor that the main supplier may determine the wholesale price. We demonstrate that the adoption of flexibility strategies is controlled by threshold policies in different scenarios whether the main supplier determines the wholesale price or not. We also investigate how the buyer׳s attribute (finding a suitable contingent supplier) affects the configuration of flexibility strategies.     

Output planning for material requirements under uncertainty

This paper considers a production planning and control system requiring that, for each work station, a time-phased series of input and output values be estimated. It is assumed that the projected input levels can vary significantly across time periods and that anticipated output levels can be determined by management, subject to limits specified by available manpower and subcontracting. The actual input and output levels for each period depart from the planned levels according to a probability distribution determined empirically. An approach is presented that allows management to evaluate a chosen set of output levels to determine the probability that sufficient amounts of work will be available at a work station each period.     

Multimode time-cost-robustness trade-off project scheduling problem under uncertainty

The time/cost trade-off problem is a well-known project scheduling problem that has been extensively studied. In recent years, many researchers have begun to focus on project scheduling problems under uncertainty to cope with uncertain factors, such as resource idleness, high inventory, and missing deadlines. To reduce the disturbance from uncertain factors, the aim of robust scheduling is to generate schedules with time buffers or resource buffers, which are capped by project makespan and project cost. This paper addresses a time-cost-robustness trade-off project scheduling problem with multiple activity execution modes under uncertainty. A multiobjective optimization model with three objectives (makespan minimization, cost minimization, and robustness maximization) is constructed and three propositions are proposed. An epsilon-constraint method-based genetic algorithm along with three improvement measures is designed to solve this NP-hard problem and to develop Pareto schedule sets, and a large-scale computational experiment on a randomly generated dataset is performed to validate the effectiveness of the proposed algorithm and the improvement measures. The final sensitivity analysis of three key parameters shows their distinctive influences on the three objectives, according to which several suggestions are given to project managers on the effective measures to improve the three objectives.

     

奈特不确定性下的欧式期权定价区间

在Black-Scholes期权定价模型中引入等级参数测度金融市场上的奈特不确定性程度,提出奈特不确定性下欧式期权定价的新模型.设置可行控制集合定义等级参数为奈特不确定性测度,借助可行域上的容度获得奈特不确定性对偶测度,基于Black-Scholes期权定价模型构建欧式看涨看跌期权的定价区间;运用倒向随机微分方程获得定价区间的表达式;最后,基于2015年2月9日上市的上证50ETF期权的日收益数据为样本予以实证,并与Black-Scholes期权定价特征对比.结果表明,奈特不确定性环境下的欧式期权均衡价格不再是某一确定值,而是某一定价区间;期权标的资产当前价格越大,定价区间越大;期权到期时间越长,定价区间越大;定价区间随着奈特不确定性程度的增强而不断变大.研究指出奈特不确定性的客观存在降低了市场流动性,内生解释了“非市场参与”之谜,外生说明了“有限市场参与”特征,为投资者决策提出建议和金融市场监管提供经验证据.     

Lot sizing with storage losses under demand uncertainty

We address a variant of the single item lot sizing problem affected by proportional storage (or inventory) losses and uncertainty in the product demand. The problem has applications in, among others, the energy sector, where storage losses (or storage deteriorations) are often unavoidable and, due to the need for planning ahead, the demands can be largely uncertain. We first propose a two-stage robust optimization approach with second-stage storage variables, showing how the arising robust problem can be solved as an instance of the deterministic one. We then consider a two-stage approach where not only the storage but also the production variables are determined in the second stage. After showing that, in the general case, solutions to this problem can suffer from acausality (or anticipativity), we introduce a flexible affine rule approach which, albeit restricting the solution set, allows for causal production plans. A hybrid robust-stochastic approach where the objective function is optimized in expectation, as opposed to in the worst-case, while retaining robust optimization guarantees of feasibility in the worst-case, is also discussed. We conclude with an application to heat production, in the context of which we compare the different approaches via computational experiments on real-world data.     

原材料价格波动下的库存与生产联合决策

价格波动的情况下如何进行原材料采购,并在产品的生产决策中如何权衡市场需求、产品价格以及采购与库存成本等问题是当前很多企业面临的普遍难题.对此类问题,假定原材料的价格波动服从马尔可夫过程,成品的市场需求为与价格相关的随机变量,进而建立了多周期库存采购与生产联合决策模型,并分析了系统的动态最优策略.模型同时考虑了原材料买进或者卖出决策和成品生产决策.研究结果表明,模型的最优策略,即原材料库存决策与成品生产决策均满足动态基库存策略.同时证明了一系列性质,这些性质展示了原材料价格波动下最优库存策略的特征.数值实验进一步验证了模型的最优策略,并研究了参数变化时最优策略的变化.     

Lot-sizing decisions under limited-time price incentives: A review

The problem of determining the optimal order quantities for an inventoried item, when the vendor offers a limited-time price reduction is both interesting and important. It is interesting in that it has received continued attention of academic researchers for over four decades and it is important given its economic implication in a variety of practical settings. The extensive literature on this problem represents a fascinating confluence of: (1) economic significance and relevance to managerial practice, (2) continuing academic research interest directed at precise mathematical analysis leading to the development of rigorous and complex models and policies, and (3) elegance and adequacy of simple models and heuristic policies which are validated by the results of rigorous models and computational analysis. In this paper, we survey this literature using simple classification frameworks and then review some of the key studies. We highlight their key assumptions and findings, bring across key implications for policy implementation and academic research and identify avenues for further research.