An approach to the valuation and decision of ERP investment projects based on real options

The risks and uncertainties inherent in most enterprise resources planning (ERP) investment projects are vast. Decision making in multistage ERP projects investment is also complex, due mainly to the uncertainties involved and the various managerial and/or physical constraints to be enforced. This paper tackles the problem using a real-option analysis framework, and applies multistage stochastic integer programming in formulating an analytical model whose solution will yield optimum or near-optimum investment decisions for ERP projects. Traditionally, such decision problems were tackled using lattice simulation or finite difference methods to compute the value of simple real options. However, these approaches are incapable of dealing with the more complex compound real options, and their use is thus limited to simple real-option analysis. Multistage stochastic integer programming is particularly suitable for sequential decision making under uncertainty, and is used in this paper and to find near-optimal strategies for complex decision problems. Compared with the traditional approaches, multistage stochastic integer programming is a much more powerful tool in evaluating such compound real options. This paper describes the proposed real-option analysis model and uses an example case study to demonstrate the effectiveness of the proposed approach.     

高新技术项目区域风险多层次灰色评价

从区域产业风险、社会政治风险、政策法规风险、经济波动风险、金融与资本市场风险和人力资源风险等四个方面 ,设计了高新技术项目区域风险的综合评价指标体系 ,并描述了灰色系统理论应用于风险评价的基本过程 ,希望能有助于高新技术项目投资者的项目决策 .     

Robust multiobjective optimization with application to Internet routing

Robust optimization addressing decision making under uncertainty has been very well developed for problems with a single objective function and applied to areas of human activity such as portfolio selection, investment decisions, signal processing, and telecommunication-network planning. As these decision problems typically have several decisions or goals, we extend robust single objective optimization to the multiobjective case. The column-wise uncertainty model can be carried over to the multiobjective case without any additional assumptions. For the row-wise uncertainty model, we show under additional assumptions that robust efficient solutions are efficient to specific instance problems and can be found as the efficient solutions of another deterministic problem. Being motivated by the fact that Internet traffic must be maintained in a reliable yet affordable manner in situations of complex and dynamic usage, we apply the row-wise model to an intradomain multiobjective routing problem with polyhedral traffic uncertainty. We consider traditional objective functions corresponding to link utilizations and implement the biobjective case using the parametric simplex algorithm to compute robust efficient routings. We also present computational results for the Abilene network and analyze their meaning in the context of the application.     

PORTPLAN,decision support system for port terminals

Models underlying a decision support system (dss) for port terminal planning are described. Among others Markov models, queuing models and simple optimization models are considered. The integration of different models, each describing specific aspects of a decision situation, is one of the major problems in the development of a dss. This paper gives a good insight in this problem field.The paper gives also some theoretical results related to some models that might be interesting in other contexts.     

Capacitated facility location/network design problems

We introduce a combined facility location/network design problem in which facilities have constraining capacities on the amount of demand they can serve. This model has a number of applications in regional planning, distribution, telecommunications, energy management, and other areas. Our model includes the classical capacitated facility location problem (CFLP) on a network as a special case. We present a mixed integer programming formulation of the problem, and several classes of valid inequalities are derived to strengthen its LP relaxation. Computational experience with problems with up to 40 nodes and 160 candidate links is reported, and a sensitivity analysis provides insight into the behavior of the model in response to changes in key problem parameters.     

The vehicle routing problem with coupled time windows

In this article we introduce the vehicle routing problem with coupled time windows (VRPCTW), which is an extension of the vehicle routing problem with time windows (VRPTW), where additional coupling constraints on the time windows are imposed. VRPCTW is applied to model a real-world planning problem concerning the integrated optimization of school starting times and public bus services. A mixed-integer programming formulation for the VRPCTW within this context is given. It is solved using a new meta-heuristic that combines classical construction aspects with mixed-integer preprocessing techniques, and improving hit-and-run, a randomized search strategy from global optimization. Solutions for several randomly generated and real-world instances are presented.     

Efficient generation of alternative perspectives in public environmental policy formulation: applying co-evolutionary simulation–optimization to municipal solid waste management

In public policy formulation, it is generally preferable to create several quantifiably good alternatives that provide very different approaches to the particular situation. This is because public sector decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult—if not impossible—to quantify and capture at the time supporting decision models are constructed. There are invariably unmodelled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model’s solutions. Furthermore, public environmental policy formulation problems often contain considerable stochastic uncertainty and there are frequently numerous stakeholders with irreconcilable perspectives involved. Consequently, it is preferable to generate several alternatives that provide multiple, disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to the known modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables. By generating a set of very different solutions, it is hoped that some of these dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This study provides a co-evolutionary simulation–optimization modelling-to-generate-alternatives approach that can be used to efficiently create multiple solution alternatives that satisfy required system performance criteria in highly uncertain environments and yet are maximally different in their decision space. This new stochastic approach is very computationally efficient, since it permits the simultaneous generation of good solution alternatives in a single computational run of the SO algorithm. The efficacy and efficiency of this technique is specifically demonstrated using an earlier waste management case to enable direct comparisons to previous methods. Waste management systems provide an ideal setting for illustrating the modelling techniques used for such public environmental policy formulation, since they possess all of the prevalent incongruencies and system uncertainties inherent in complex planning processes.     

Facets and algorithms for capacitated lot sizing

The dynamic economic lot sizing model, which lies at the core of numerous production planning applications, is one of the most highly studied models in all of operations research. And yet, capacitated multi-item versions of this problem remain computationally elusive. We study the polyhedral structure of an integer programming formulation of a single-item capacitated version of this problem, and use these results to develop solution methods for multi-item applications. In particular, we introduce a set of valid inequalities for the problem and show that they define facets of the underlying integer programming polyhedron. Computational results on several single and multiple product examples show that these inequalities can be used quite effectively to develop an efficient cutting plane/branch and bound procedure. Moreover, our results show that in many instances adding certain of these inequalities a priori to the problem formulation, and avoiding the generation of cutting planes, can be equally effective.Supported by Grant #ECS-8316224 from the Systems Theory and Operations Research Program of the National Science Foundation.     

Decision support approach based on multiple objectives and resources for assessing the relocation plan of dangerous hillside aggregations

The overdevelopment of hillside areas, coupled with the effects of global climate change, has increased the likelihood of disasters with severe consequences, such as material damages and loss of lives. Thus, there is a great urgency to create relocation plans for dangerous hillside aggregations that are not suitable for human settlement. The government of Taipei City in Taiwan has not properly addressed the assessment needs for decisions involving priority and budget allocation for these relocation projects. This study proposes a hybrid approach combining the Delphi method, fuzzy-logic inference system (FLIS), and the multi-objective programming (MOP) method to assist the city government in dealing with complex aggregation relocation decision problems. Twenty-four projects are tested by the proposed approach. Compared to the original relocation plan, results from this study reveal that the proposed approach is more objective and effective in relocation prioritization, project selection, budget utilization, and resource planning. These results will provide the city government with useful information for improving the relocation plan, thereby reducing the potential for human and material losses due to inadequate decision-making.     

The fuzzy decision problem: An approach to the point estimation problem with fuzzy information

This paper is devoted to the extension of the Bayesian method for the point estimation, when the available information is ‘vague’.In the nonfuzzy case, the parametric estimation can be approached as a particularization in the statistical decision problem. This motivates us to accomplish the mentioned extension by looking at the parametric estimation in the fuzzy case as a special situation in the fuzzy decision problem (defined by Tanaka, Okuda and Asia).In this way, concepts in the fuzzy decision problem are first ‘expressed’ in the estimation terminology. Then, on the basis of these concepts, we shall introduce some notions and state some interesting results. Finally, several illustrative examples will be exposed.     

On minty variational principle for nonsmooth vector optimization problems with approximate convexity

In this paper, we consider a vector optimization problem involving locally Lipschitz approximately convex functions and give several concepts of approximate efficient solutions. We formulate approximate vector variational inequalities of Stampacchia and Minty type and use these inequalities as a tool to characterize an approximate efficient solution of the vector optimization problem.     

Integrating long-term care insurance purchase decisions with saving and investment for retirement

Risk related to long-term care (LTC) is high for the elderly. Planning for LTC is now regarded as the ‘third leg’ of retirement planning. In this paper, planning for LTC is integrated with saving and investment decisions for an integrated approach to retirement planning. Optimal LTC insurance purchase decisions are obtained by developing a trade-off between post-retirement LTC costs and LTC insurance premiums paid and coverage received. Integrating insurance purchase with wealth evolution, consisting of saving and investment decisions, allows addressing affordability issues.Two-way branching models are used for the stochastic health events and asset returns. The problem, formulated as a nonlinearly constrained mixed-integer optimization problem, is solved using a heuristic. Sensitivity analyses are performed for initial health and wealth status. Some important aspects of an individual’s behavioral preferences are also addressed in this framework to provide more robust decision support.     

A stochastic programming model for aggregate production planning

This paper looks at a problem of production planning at the level of the work centre, rather than of the individual machine. The approach is based on methodology which combines aspects of decision analysis with mathematical programming procedures.A model is developed which can take into account various real life factors in the planning phase, including delaying production, and the probability of production being scrapped, or of extra work being required. An extension to the multiple objective situation is outlined, and some computational results are reported.     

基于径向基函数网络的区域物流能力投资结构优化

针对如何有效地提高区域物流能力,以推动区域经济增长的问题,构建了区域物流能力的投资结构优化模型.首先详细分析了优化区域产业投资结构能增强区域物流能力的原因,从产业结构的角度揭示了区域物流能力与产业投资分配之间复杂的非线性关系;然后采用径向基函数神经网络实现了它们之间的非线性映射,进而建立了有约束条件限制的非线性规划投资结构优化模型;最后以四川省2005年的产业投资实际数据为基础,采用改进遗传算法对该模型进行求解,获得了优化问题的近似最优解以及投资结构的优化方向.优化结果表明:建立的模型对产业投资结构的优化是合理、有效的,从而提供了一个能提高区域物流能力的实用且切实可行的新方法.     

Discontinuity in decision-making when objectives conflict: a military command decision case study

In previous work, we considered the representation of human decision-making processes in closed-form simulation models of conflict. An important element of this representation is the rapid planning process that embodies the processing of information for situation assessment to support a course of action decision (eg in a military headquarters). The application of this work is in support of operational analysis models for defence procurement and balance of investment. This paper describes the application of non-linear multi-attribute utility theory in conflict scenarios in order to extend the representation of the rapid planning process to account for a wider set of subjective attributes of the decision-maker. The results show, through examination of experimental data, that decision-making can be modelled through a particular class of utility functions. These utilities embody a geometry which allows us to classify the types of decision being made when there are conflicting objectives and when decision-makers adopt very different and subjective appraisals of constraints and beliefs in outcome. The experimental results help to demonstrate that the subjective nature of the situation assessment, and the personality, training, experience and history of the decision-maker are central to the functional representations. This paper presents a way to capture this deeper representation of human decision-making in a way that is potentially useful for quantitative modelling using the rapid planning process as a basis.     

A transdisciplinary approach for formalized integrated planning and decision-making in complex systems

Multi-sectoral systems, such as cities or regional economies, face strategic challenges of optimal development due to the complexity of interacting perspectives, interests, and preferences of decision-makers and stakeholders. In coping with these challenges, integrated approaches in strategic planning and decision support promise to generate more efficient and effective results than sectoral approaches. In this article, we follow the concept of operational research (OR) by applying a formalized approach to cross-sectoral integrated planning and decision-making in complex, i.e., multi-sectoral systems. The presented Transdisciplinary Integrated Planning and Synthesis (TIPS) approach relies on (soft) OR methods combined in a multi-methodological framework that is adapted to the cognitive skills and habits of the stakeholders and experts involved in mutual and joint learning processes (transdisciplinarity). This methodological contribution structures, formalizes, and empirically illustrates cross-sectoral integrated strategic planning step by step, and evaluates the TIPS methodology against requirements for integrated approaches derived from literature.     

Design of an IT-driven decision support system for vehicle routing and scheduling

This paper presents the development of a decision support system used by an oil downstream company for routing and scheduling purposes. The studied problem refers to a complex delivery process of oil products from a number of distribution centers to all customers. The latest rapid advance of operations research (OR) applications, in the form of advanced planning and scheduling (APS) systems, has shown that OR algorithms can be applied in practice if (a) they are embodied in packaged information technology (IT) solutions, (b) the interface problems to mainstream ERP software applications are solved. In this study the utilisation of advanced IT systems supports effectively the planning and management of distribution operations. The combination of a supply chain management (SCM) application with a geographical information system (GIS) integrated with an enterprise resource planning (ERP) software resulted to this innovative decision support tool. The objectives of this new tool are: optimum use of the distribution network resources, transportation cost reduction and customer service improvement. The paper concludes with the benefits of the new system, emphasising at how new technologies can support transportation processes with the help of operations research algorithms embedded in software applications.     

非常规油气资源开发投资决策优化模型研究

非常规油气资源作为最现实的可替代能源,对其进行勘探和开发对于降低日益加大的石油供需矛盾缺口和确保国家能源安全均具有重要的战略意义。然而,非常规油气资源勘探开发十分复杂,开发投资决策好坏已经成为制约其能否实现规模化和产业化的关键问题,科学投资决策问题已逐步成为石油企业高层管理者的主要职责。针对非常规油气资源开发投资的多阶段多目标决策优化难题,以可供开发区块的资源分配为重点研究对象,从解决不同区块投资规模入手,运用多阶段决策、多目标决策和不确定多属性方案优选的方法理论,通过剖析非常规油气开发投资决策过程及其复杂性特征,将开发投资决策过程进行形式化描述并在计算机中加以实现,从而得以实现开发投资决策方案的动态性调整。本项研究不仅有助于深化多目标动态优化决策理论的研究,还为解决非常规油气资源开发投资决策难题提供一种新的思路和方法。     

Strange: An interactive method for multi-objective linear programming under uncertainty

In the field of investment planning within a time horizon, problems typically involve multiple objectives, and basic data are uncertain. In a large number of cases, these decision problems can be written as linear programming problems in which time dependent uncertainties affect the coefficients and the right hand side of constraints. Given the possibility of defining plausible scenarios on basic data, discrete sets of such coefficients are given, each with its subjective probability of occurrence. The corresponding structure is then characteristic for Multi-Objective Stochastic Linear Programming (MOSLP).In the paper, an interactive procedure is described to obtain a best compromise for such a MOSLP problem. This algorithm, called Strange, extends the Stem method to take the random aspects into account. It involves in particular, the concepts of stochastic programming with recourse. In its interactive steps, the efficiency projection techniques are used to provide the decision-maker with detailed graphical information on efficient solution families.As an illustration of the successive steps, a didactic example is solved in some detail, and the results of a case study in energy planning are given.