A decision model for interdependent information system project selection

Existing methods for information system (IS) project selection neglect an important aspect of information technology, namely the interdependencies that exist among various IS applications (projects). Recognizing and modeling these project interdependencies provides valuable cost savings and greater benefits to organizations. In this paper, an IS project selection model is developed that identifies and models benefit, resource and technical interdependencies among candidate projects. The proposed model is formulated as a nonlinear 0–1 programming problem and represents a significant addition to existing IS, capital budgeting and R&D project selection models. The model is converted, using linearization techniques, and tested (validated) by applying it to real-world IS project selection data. By comparing the performance of this model with existing project selection models, the contribution of this model is highlighted.     

Optimization analysis for design and planning of multi-project programs

Program management concerns the long term planning, coordination, and control of major technological, engineering, scientific, and/or developmental activities. In general, programs tend to be exceptionally large, consisting of several parallel or sequential projects or groups of projects. While a large number of modeling efforts have focused at the project level, this paper looks at the application of optimization techniques to the initial design and development of multi-project programs. The classic work breakdown structure (WBS) is used as a framework to provide an aggregate model to investigate the effects of funding levels, resource allocation, and program, project, and component durations. Decomposition, sensitivity analysis, and parametric programming are utilized to provide the decision maker detailed information for establishing program parameters, conditions, and bounds.     

基于关键链的敏捷软件开发多项目网络模型

敏捷软件开发因其效率和文档量远低于传统方法在一提出就得到广泛应用,但仍无法有效解决软件开发多项目管理中的资源受限调度问题.将关键链思想应用到包含多个项目的敏捷软件开发问题中,在分析敏捷软件开发多项目网络模型的基础上,建立了数学优化模型;提出了一种适宜敏捷软解开发的多项目网络迭代调度假设与规则,并设计了相应的算法,具体包括关键链选择算法和调度算法;最后进行了实例分析,所得结果与遗传算法的相比从52个单位时间的迭代周期减少到42,使得工期节省了近20%.     

Multi-mode resource constrained multi-project scheduling and resource portfolio problem

This paper introduces a multi-project problem environment which involves multiple projects with assigned due dates; activities that have alternative resource usage modes; a resource dedication policy that does not allow sharing of resources among projects throughout the planning horizon; and a total budget. Three issues arise when investigating this multi-project environment. First, the total budget should be distributed among different resource types to determine the general resource capacities, which correspond to the total amount for each renewable resource to be dedicated to the projects. With the general resource capacities at hand, the next issue is to determine the amounts of resources to be dedicated to the individual projects. The dedication of resources reduces the scheduling of the projects’ activities to a multi-mode resource constrained project scheduling problem (MRCPSP) for each individual project. Finally, the last issue is the efficient solution of the resulting MRCPSPs. In this paper, this multi-project environment is modeled in an integrated fashion and designated as the resource portfolio problem. A two-phase and a monolithic genetic algorithm are proposed as two solution approaches, each of which employs a new improvement move designated as the combinatorial auction for resource portfolio and the combinatorial auction for resource dedication. A computational study using test problems demonstrated the effectiveness of the solution approach proposed.     

An algorithm for solving the multi-period online fulfillment assignment problem

This paper develops an optimal solution procedure for the multi-period online fulfillment assignment problem to determine how many and which of a retailer/e-tailer’s capacitated regional warehouse locations should be set up to handle online sales over a finite planning horizon. To reduce the number of candidate solutions in each period, dominance rules from the facility location literature are extended to handle the nonlinear holding and backorder cost implications of our problem. Computational results indicate that multi-period considerations can play a major role in determining the optimal set of online fulfillment locations. In 92% of our test problems, the multi-period solution incorporated fewer openings and closings than myopic single period solutions. To illustrate the use of the model under changing demands, the multi-period solution yielded different supply chain configurations than the myopic single period solution in over 37% of the periods.     

IS/IT项目选择决策的灰色逼近理想解排序法

IS/IT项目选择决策是一个多属性决策问题.针对传统逼近理想解排序法(TOPSIS)在确定属性权重系数上的缺陷,并考虑到在实际IS/IT项目选择决策过程中部分决策信息的不足,提出了基于灰色TOPSIS改进算法.算法运用区间灰数表达指标权重和指标评价值,定义备择项目与正、负理想解的灰色关联度,依此计算各备则项目的贴近度并实现最终排序.仿真实例验证了该方法的合理和有效性.     

Competence-driven project portfolio selection,scheduling and staff assignment

This paper presents a new model for project portfolio selection, paying specific attention to competence development. The model seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub-problem, scheduling and staff assignment for a candidate set of selected projects must also be optimized. We provide a nonlinear mixed-integer program formulation for the overall problem, and then propose heuristic solution techniques composed of (1) a greedy heuristic for the scheduling and staff assignment part, and (2) two (alternative) metaheuristics for the project selection part. The paper outlines experimental results on a real-world application provided by the E-Commerce Competence Center Austria and, for a slightly simplified instance, presents comparisons with the exact solution computed by CPLEX.     

A model and approach to the challenge posed by optimal power systems planning

Currently, there is a need to plan and analyze the electric power transmission system in greater detail and over larger geographic areas. Existing models approach the problem from different perspectives. Each model addresses different aspects of and has different approximations to the optimal planning process. In order to scope out the huge challenge of optimal transmission planning, this paper presents a new modeling approach for inter-regional planning and investment in a competitive environment. This modeling approach incorporates the detailed generator, topology and operational aspects found in production cost planning models into a larger framework that can find optimal sets of transmission expansion projects. The framework proposed here can be used in an auction to award investment contracts or as a part of a more general policy analysis. The solution yields the set of transmission projects that have the highest expected benefits, while also representing generic generation expansions under the same objective. The model is a two-stage, mixed-integer, multi-period, N-1-reliable model with investment, unit commitment, and transmission switching. The combination of combinatorial, stochastic and operational elements means this model may be computationally intractable without judicious modelling aggregations or approximations to reduce its size and complexity. Nevertheless we show via a dual problem that analysing the economics and sensitivity of the solution is computationally more straightforward.     

A multi-period order selection problem in flexible manufacturing systems

We consider a multi-period order selection problem in flexible manufacturing systems, which is the problem of selecting orders to be produced in each period during the upcoming planning horizon with the objective of minimising earliness and tardiness costs and subcontracting costs. The earliness and tardiness costs are incurred if an order is not finished on time, while subcontracting cost is incurred if an order is not selected within the planning horizon (and must be subcontracted) due to processing time capacity or tool magazine capacity. This problem is formulated as a 0–1 integer program which can be transformed into a generalised assignment problem. To solve the problem, a heuristic algorithm is developed using a Lagrangian relaxation technique. Effectiveness of the algorithm is tested on randomly generated problems and results are reported.     

Selecting most efficient information system projects in presence of user subjective opinions: a DEA approach

Information System (IS) project selection is a critical decision making task that can significantly impact operational excellence and competitive advantage of modern enterprises and also can involve them in a long-term commitment. This decision making is complicated due to availability of numerous IS projects, their increasing complexities, importance of timely decisions in a dynamic environment, as well as existence of multiple qualitative and quantitative criteria. This paper proposes a Data Envelopment Analysis approach to find most efficient IS projects while considering subjective opinions and intuitive senses of decision makers. The proposed approach is validated by a real world case study involving 41 IS projects at a large financial institution as well as 18 artificial projects which are defined by the decision makers.     

Production planning with uncertainty in the quality of raw materials: a case in sawmills

Motivated by sawmill production planning, this paper investigates multi-period, multi-product (MPMP) production planning in a manufacturing environment with non-homogeneous raw materials, and consequently random process yields. A two-stage stochastic program with recourse is proposed to address the problem. The random yields are modelled as scenarios with stationary probability distributions during the planning horizon. The solution methodology is based on the sample average approximation (SAA) scheme. The stochastic sawmill production planning model is validated through the Monte Carlo simulation. The computational results for a real medium capacity sawmill highlight the significance of using the stochastic model as a viable tool for production planning instead of the mean-value deterministic model, which is a traditional production planning tool in many sawmills.     

基于较优方案最大区别度的组合权重赋值法

现有多方案决策中的指标权重计算方法可能产生评价结果中较优方案不突出的难题,进而提出了一种新的方法——基于较优方案最大区别度的组合权重赋值法.方法可根据方案间区别度最大的原则将主观赋权法和客观赋权法中所确定的指标权重进行集结,综合考量待评方案,并建立多方案决策结果最大化和决策结果间方差最大化非线性优化模型,采用理想点法对具体的多目标规划问题进行求解.方法的运用有利于扩大备选方案之间的差距,进而突出较优方案,最后通过实例说明了该方法的优越性.     

研究生录取问题的数学模型

针对研究生录取问题,建立了模糊综合评价模型和一般指派问题的规划模型.首先,利用模糊综合评价模型对学生的综合成绩加以量化.其次,利用一般指派问题的规划模型制定了学生和导师的最佳双向选择方案.最后,给出了一种更能体现“双向选择”的研究生录取方案.实例分析的结果表明:按本文的方法所确定的“双向选择”的录取方案是科学的、合理的.     

Multiple-Project Scheduling with Controllable Project Duration and Hard Resource Constraint: Some Solvable Cases

In many large-scale project scheduling problems, multiple projects are either taking place at the same time or scheduled into a tight sequence in order to efficiently share a common resource. One example of this is the computing resource allocation at an Application Service Provider (ASP) which provides data processing services for multiple paying customers. Typical services provided by ASPs are data mining, payroll processing, internet-based storage backup services and Customer Relation Management (CRM) services. The processing mode of an ASP can be either batch or concurrent, depending on the type service rendered. For example, for CPU intensive or long processing time required services, it would be more economical to processes one customer request at a time in order to minimize the context switching overhead. While the data transaction processes within a service request are subject to certain precedence relationships, the requests from different customers to an ASP are independent of each other, and the total time required to process a service request depends on the computing resource allocated to that request. The related issue of achieving an optimal use of resources at ASPs leads to problem of project scheduling with controllable project duration.In this paper, we present efficient algorithms for solving several special cases of such multi-project scheduling problems with controllable project duration and hard resource constraints. Two types of problems are considered. In type I, the duration of each project includes a constant and a term that is inversely proportional to the amount of resource allocated. In type II, the duration of each individual project is a continuous decreasing function of the amount of resource allocated.     

A market-based multi-agent system model for decentralized multi-project scheduling

We consider a multi-project scheduling problem, where each project is composed of a set of activities, with precedence relations, requiring specific amounts of local and shared (among projects) resources. The aim is to complete all the project activities, satisfying precedence and resource constraints, and minimizing each project schedule length. The decision making process is supposed to be decentralized, with as many local decision makers as the projects. A multi-agent system model, and an iterative combinatorial auction mechanism for the agent coordination are proposed. We provide a dynamic programming formulation for the combinatorial auction problem, and heuristic algorithms for both the combinatorial auction and the bidding process. An experimental analysis on the whole multi-agent system model is discussed.     

Broadening the boundaries: an application of critical systems thinking to IS planning in Colombia

Most current information systems (IS) planning methodologies are focused on achieving plans that provide competitive advantage to business and solve the problems of information needs by using the latest technologies available. This paper presents an alternative approach to IS planning based on critical systems thinking—a research perspective that encourages the analysis of stakeholders' understandings of social contexts prior to the selection and/or design of planning methods. The approach is underpinned by a combination of the systems theories of autopoiesis and boundary critique, and it enables participants to reflect on different concerns and values during IS planning. This approach was applied in a project with a Colombian University, and it helped participants to uncover and address important human issues not usually seen as relevant by traditional approaches to IS planning. In addition, it led the authors to undertake further research on ethics after the project had been concluded.     

Parameter estimation in stochastic scenario generation systems

Scenario analysis offers an effective tool for addressing the stochastic elements in multi-period financial planning models. Critical to any scenario generation process is the estimation of the input parameters of the underlying stochastic model for economic factors. In this paper, we propose a new approach for estimation, known as the integrated parameter estimation (IPE). This approach combines the significant features of other well-known estimation techniques within a non-convex multiple objective optimization framework, with the objective weights controlling the relative importance of the features. We solve the non-convex optimization problem using adaptive memory programming – a variation of tabu search. Based on a short interest rate model using UK treasury rates from 1980 to 1995, the integrated approach compares favorably with maximum likelihood and the generalized method of moments. We also evaluate performance with Towers Perrin's CAP:Link scenario generation system.     

Uncertainty Modelling in Software Development Projects (With Case Study)

A project scheduling model tailored specifically for software development projects is proposed in this study. The model incorporates uncertainties related to activity durations and network topology. The first type of uncertainty exists due to error-prone coding which might result in elongated task durations caused by validation and debugging sessions. Furthermore, in practice, macro-activities represent groups of sub-tasks in order to simplify the planning and monitoring of the project. Due to the aggregation, it is more difficult to be precise on the duration of a macro-activity.The uncertainty related to the network topology is due to common database design issues or program modules shared among parallel tasks in the project network. These tasks become associated with each other through uncertain Start-to-Start (SS) precedence relationships. On the other hand, SS lags may also be the outcome of technological precedence relationships among pairs of activities. However, the imprecision underlying the work content of a predecessor activity leads to uncertain SS lags.Software development projects are human-intensive projects and hence, the duration of a task depends on the skill of the person assigned to the job as well as his/her learning rate. Thus, a task may be realized by alternative staff members which results in different expected task durations. Hence, a realistic model proposed for software development projects should incorporate staff assignment features under the uncertainties discussed above. In this study, we develop a mathematical model for software development projects and propose heuristic solution methods to be used by the project co-ordinator in preparing the project plan. The heuristic algorithms developed here are tested on real data provided by a consulting firm undertaking software development projects from manufacturing companies in Turkey.     

Multi-period aggregate production planning in a news-vendor framework

The paper focuses on the control decisions in the area of multi-period, aggregate production planning. The plan consists of a number of sequential planning periods stretching out to a particular time horizon. Owing to machine downtime, quality, supply, and maintenance problems, true productivity differs from the expected. Therefore, at the end of each planning period, the planner must have immediate feedback about production outcome. The results are analysed for variations from the plan (shortfalls or surpluses) and any differences are used to modify subsequent plans. The goal is to minimize the expected total costs including productivity, overtime as well as over- and under-production costs. Although the presented solution is generally not of base-stock type, a correspondence between it and the solution of the classical newsboy problem is revealed. This paper was developed during a consultation assignment in the food industry where the planning results were tested, and the case is presented.     

A Minimum-Cost Analysts Assignment Model for Asset Evaluation of Insolvent Saving and Loan Institutions

Audit staff planning has been a challenging problem for accounting, auditing and real estate firms. This paper presents a mathematical model and a solution methodology for determining the minimum-cost analysts assignment, when analysts should travel from geographically dispersed locations to evaluate assets of an insolvent Saving and Loan institution. Computational experiments with the solution algorithm on 27 randomly generated projects show (a) that the solution methodology efficiently generates an optimal solution, and (b) provides the decision maker with alternative next best plans through ex post sensitivity analysis. Although specific, variations of the model and algorithm presented here can be applied to a variety of audit staff assignment problems in accounting and real estate firms.