Strategic alignment and value maximization for IT project portfolios

Managing project portfolios has been a challenge to many IT organizations due to the size and complexity of their initiatives that are often cross-functional, fast changing, and transformational in nature. A governance process on project solicitation, evaluation, and monitoring is thus essential to ensure the resulting portfolio creates tangible values, balances across priorities, and supports business objectives. An optimization model to streamline the decision processes for IT portfolios and programs is proposed. We consider project characteristics such as the extent of strategic alignment, expected benefit, development cost, and cross-project synergy to maximize the portfolio value. We also consider team proficiency and resource availability to determine a project portfolio that could be implemented within the overall development time. The multi-objective model identifies the optimal mix among project types and the solution procedure efficiently produces recommendations that are superior to those found with current empirical techniques. We also describe an evolutionary algorithm to find approximate solutions to the optimization model. Possible extensions on how the optimization procedure can go beyond projects to also streamline decisions such as the renewal or replacement of in-flight applications is discussed.     

基于战略对应的IT项目组合选择模型

把信息技术项目当作组合来管理可以通过平衡风险和收益来促进企业目标和IT应用的结合,但由于决策信息的不确定性和IT项目目标与企业战略的难以对应,企业面临IT项目组合选择的挑战。构建基于战略对应的IT项目组合选择模型,其中模糊集和模糊层次分析法用来刻画不确定信息和评估IT项目风险、成本及收益,关键成功因素法用来提高IT项目与企业战略的对应,并建立模糊0－1整数规划。利用定性可能性理论把模糊组合选择模型转化为一般可求解的整数规划形式,最后用一个案例说明模型的用法。     

A milp bi-objective model for static portfolio selection of R&;D projects with synergies

This paper presents a multi-objective MILP model for portfolio selection of research and development (R&D) projects with synergies. The proposed model incorporates information about the funds assigned to different activities as well as about synergies between projects at the activity and project level. The latter aspects are predominant in the context of portfolio selection of R&D projects in public organizations. Previous works on portfolio selection of R&D projects considered interdependencies mainly at the project level. In a few works considering activity level information the models and solution techniques were restricted to problems with a few projects. We study a generalization of our previous model and show that incorporating interdependencies and activity funding information is useful for obtaining portfolios with better quality. Numerical results are presented to demonstrate the efficiency of the proposed approach for large models.     

A fuzzy assessment framework to select among transportation investment projects in Turkey

Selecting the best transportation investment project (TIP) is often a difficult task, since many social, environmental and economic criteria have to be considered simultaneously. Evaluating a set of different projects, especially the best set of alternatives, portfolios, is even more complex. Pursuing the goal of selecting the best TIP portfolio, we propose a fuzzy assessment method to aid the selection process of a multi-criterion project by utilizing the concept of entropy and interval normalization procedure in a fuzzy analytic hierarchy process (F-AHP). Then, regarding this informative phase, we propose a fuzzy linear programming model to select the best TIP portfolio under uncertain cost pressure. A real case study is conducted to illustrate the efficiency of the proposed method.     

A metaheuristic approach for selecting a common platform for modular products based on product performance and manufacturing cost

A known strategy to implement Mass Customization is Product Modularization. To take advantage of the benefits of modularity, the selection of a common platform is required. This selection must be done with optimization criteria based on functionality and economics. In this paper we propose metaheuristic procedures to solve the problem of selecting a common platform for a modular product. This selection is based on an aggregate objective function that combines product performance and manufacturing cost. The problem is divided into two hierarchical problems that must be solved sequentially. The mathematical models have a non-linear integer formulation. Because of the computational complexity to solve optimally these models, metaheuristic procedures are proposed to solve each sub-problem. These procedures are based on Scatter Search and Tabu Search. A case study is presented with a small instance that is solved with these procedures and by total enumeration. The results of the metaheuristic procedures coincide with the optimal values found by total enumeration. The run times are reasonable and it is expected a greater benefit for a larger instance with similar results quality.     

Theoretical aspects of multicriteria flight gate scheduling: deterministic and fuzzy models

This paper addresses the airport flight gate scheduling problem with multiple objectives. The objectives are to maximize the total flight gate preferences, to minimize the number of towing activities, and to minimize the absolute deviation of the new gate assignment from a so-called reference schedule. The problem examined is a multicriteria multi-mode resource-constrained project scheduling problem with generalized precedence constraints or time windows. While in previous approaches the problem has been simplified to a single objective counterpart, we tackle it directly by a multicriteria metaheuristic, namely Pareto Simulated Annealing, in order to get a representative approximation of the Pareto front. Possible uncertainty of input data is treated by means of fuzzy numbers.     

Multicriteria decisions on interdependent infrastructure transportation projects using an evolutionary-based framework

When evaluating transportation infrastructure projects and determining which of them will be carried out from a set of projects and given a budget constraint, several criteria need to be considered in the decision. Standard evaluation practices imply the aggregation of impacts into one utility function which is later optimized. Nevertheless these techniques used for translation of different measuring units into monetary terms are highly controversial. Multicriteria techniques can explicitly deal with different measuring units, however, they are not suitable to model interdependence relationships of projects that share a common characteristic (same route, location or target population, for instance). In this research we model this transportation planning problem, the multi-objective transportation infrastructure project selection problem (MTIPSP), as a constrained multi-objective optimization problem with quadratic objective functions, using a variation of the multi-objective 0–1 knapsack problem plus some additional constraints. Given the combinatorial nature of the problem, an evolutionary-based framework is used for the identification of Pareto solutions, and later, those with non-attractive properties are filtered using a Knee Identification Procedure. The final selection of the projects portfolio is made using a well known multicriteria decision aid method and including the decision makers’ preferences based on the existing context.     

Quantifying requirements volatility effects

In an organization operating in the bancassurance sector we identified a low-risk IT subportfolio of 84 IT projects comprising together 16,500 function points, each project varying in size and duration, for which we were able to quantify its requirements volatility. This representative portfolio stems from a much larger portfolio of IT projects. We calculated the volatility from the function point countings that were available to us. These figures were aggregated into a requirements volatility benchmark. We found that maximum requirements volatility rates depend on size and duration, which refutes currently known industrial averages. For instance, a monthly growth rate of 5% is considered a critical failure factor, but in our low-risk portfolio we found more than 21% of successful projects with a volatility larger than 5%. We proposed a mathematical model taking size and duration into account that provides a maximum healthy volatility rate that is more in line with the reality of low-risk IT portfolios. Based on the model, we proposed a tolerance factor expressing the maximal volatility tolerance for a project or portfolio. For a low-risk portfolio its empirically found tolerance is apparently acceptable, and values exceeding this tolerance are used to trigger IT decision makers. We derived two volatility ratios from this model, the π-ratio and the ρ-ratio. These ratios express how close the volatility of a project has approached the danger zone when requirements volatility reaches a critical failure rate. The volatility data of a governmental IT portfolio were juxtaposed to our bancassurance benchmark, immediately exposing a problematic project, which was corroborated by its actual failure. When function points are less common, e.g. in the embedded industry, we used daily source code size measures and illustrated how to govern the volatility of a software product line of a hardware manufacturer. With the three real-world portfolios we illustrated that our results serve the purpose of an early warning system for projects that are bound to fail due to excessive volatility. Moreover, we developed essential requirements volatility metrics that belong on an IT governance dashboard and presented such a volatility dashboard.     

Bi-objective optimisation model for installation scheduling in offshore wind farms

A bi-objective optimisation using a compromise programming approach is proposed for installation scheduling of an offshore wind farm. As the installation cost and the completion period of the installation are important aspects in the construction of an offshore wind farm, the proposed method is used to deal with those conflicting objectives. We develop a mathematical model using integer linear programming (ILP) to determine the optimal installation schedule considering several constraints such as weather condition and the availability of vessels. We suggest two approaches to deal with the multi-objective installation scheduling problem, namely compromise programming with exact method and with metaheuristic techniques. In the exact method the problem is solved by CPLEX whereas in the metaheuristic approach we propose Variable Neighbourhood Search (VNS) and Simulated Annealing (SA). Moreover, greedy algorithms and a local search for solving the scheduling problem are introduced. Two generated datasets are used for testing our approaches. The computational experiments show that the proposed metaheuristic approaches produce interesting results as the optimal solution for some cases is obtained.     

Designing a hybrid system dynamic model for analyzing the impact of strategic alignment on project portfolio selection

One of the key challenges in project organizations is the alignment of portfolio management with major corporate strategies. Usually, project-based organizations use shared resources to control and plan the project portfolio. Therefore, the exploitation of shared resources and project planning decisions made in this regard can change the progress of projects and affect the success rate of the projects. In this article, the integration of system dynamics with multi-objective decision making is applied to address project portfolio selection. The project portfolio has been modeled using four basic dimensions including technology, complexity, innovation and time sensitivity. The aim is to plan and control the progress of project portfolio while maximizing the strategic adaptation subject to the changes of the human resources. For this purpose, a two-stage MO-PSO with TOPSIS is proposed for portfolio selection problem that can solve real-world instances of the problem in a reasonable time. The result of the sensitivity analysis indicated that the proposed decision support system (DSS) provides insights into the impact of strategic alignment on project portfolio selection. According to the simulation results, the integrated methodology of this research can assist in choosing the suitable projects to achieve a project's strategic goals following the organization strategy.     

A portfolio optimization model with three objectives and discrete variables

We formulate the portfolio selection as a tri-objective optimization problem so as to find tradeoffs between risk, return and the number of securities in the portfolio. Furthermore, quantity and class constraints are introduced into the model in order to limit the proportion of the portfolio invested in assets with common characteristics and to avoid very small holdings. Since the proposed portfolio selection model involves mixed integer decision variables and multiple objectives finding the exact efficient frontier may be very hard. Nevertheless, finding a good approximation of the efficient surface which provides the investor with a diverse set of portfolios capturing all possible tradeoffs between the objectives within limited computational time is usually acceptable. We experiment with the current state of the art evolutionary multiobjective optimization techniques, namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto Envelope-based Selection Algorithm (PESA) and Strength Pareto Evolutionary Algorithm 2 (SPEA2), for solving the mixed-integer multiobjective optimization problem and provide a performance comparison among them using metrics proposed by the community.     

Resource‐constrained project scheduling through the goal programming model: integration of the manager's preferences

The aim of this paper is to present a multi‐objective project scheduling approach to help project managers when deciding on a baseline schedule. The concepts of satisfaction functions and goal programming are incorporated to generate this baseline schedule that represents the best compromise among a set of conflicting project objectives. An efficient computerized procedure based on the tabu search algorithm is proposed and enables the handling of large planning and scheduling projects.     

A decision support system for coordinated disaster relief distribution

This paper presents a simulation and optimization based decision-support system (DSS) to facilitate disaster relief coordination between private and relief organizations. The DSS simulates disasters and plans shipments of relief goods via transfer points to demand points in the affected area. This enables decision-makers to analyze the last-mile distribution of goods by scheduling and routing trucks, off-road as well as unmanned aerial vehicles. Roads which are closed or opened during a disaster are considered allowing a dynamic adaptation to real world conditions and a comprehensive analysis over a rolling time-horizon. A mixed-integer problem formulation, an agent-based simulation, a heuristic-based scheduling and routing procedure as well as a Tabu Search metaheuristic are applied to analyze the given decision problem. Coordination between private and relief organization shows to be especially beneficial if time-losses resulting from closed roads are high and substantial unexpected demand occurs. Furthermore, results highlight the importance of selecting suitable transfer points and the potential of simulation and optimization based DSSs to improve disaster relief distribution.     

项目交互耦合网络演化

针对现有文献中较少考虑交互依赖关系对项目组合动态选择影响的问题,借鉴复杂网络理论,提出项目交互耦合网络概念,并从项目收益、资源成本、项目风险、项目状态、交互依赖关系及战略匹配程度等6个方面描述网络演化的影响要素.在此基础上,提出网络生成及调整规则,给出网络稳定状态定义,建立项目交互耦合网络.研究结果表明,与传统模型相比,项目交互耦合网络模型能得到更优的项目组合,能更有效地利用资源,规避风险,提高收益,同时,项目交互耦合网络能更好地了解项目结构,为项目组合管理提供支持.项目组合管理者应对那些节点介数大、聚集系数高的项目即核心项目进行重点扶持与保护,以防这些项目失败后引起其邻节点失败,甚至导致整个项目网络发生级联失败.     

Credibilistic multi-period portfolio optimization model with bankruptcy control and affine recourse

Avoiding the possibility of bankruptcy during the investment horizon is very important to multi-period portfolio management. This paper considers a multi-period fuzzy portfolio selection problem with bankruptcy control. A multi-period portfolio optimization model imposed by a bankruptcy control constraint in fuzzy environment is proposed on the basis of credibility theory. In the proposed model, a linearly recourse policy is used to reflect the influence of historical predication basis on current portfolio decision. Three optimization objectives, viz., maximizing the terminal wealth and minimizing the cumulative risk and the cumulative uncertainty of the returns of portfolios over the whole investment horizon, are taken into consideration. For solving the proposed model, a fuzzy programming approach is applied to transform it into a single objective programming model. Then, a hybrid particle swarm optimization algorithm is designed for solution. Finally, an empirical example is presented to illustrate the application of the proposed model and solution comparisons are also given to demonstrate the effectiveness of the designed algorithm.     

An evolutionary clustering search for the no-wait flow shop problem with sequence dependent setup times

This paper examines the m machine no-wait flow shop problem with setup times of a job separated from its processing time. The performance measure considered is the makespan. The hybrid metaheuristic Evolutionary Cluster Search (ECS_NSL) proposed in Nagano et al. (2012) is extended to solve the scheduling problem. The ECS_NSL performance is evaluated and the results are compared with the best method reported in the literature. Experimental tests show superiority of the ECS_NSL regarding the solution quality.     

Simulating the dynamic scheduling of project portfolios

A main challenge in project management is to provide methodologies that facilitate coordination among the projects in a portfolio or in a firm. Each incoming project in an existing portfolio affects its schedule, the resources availability and the planned performance. There are no analytical solutions for the problem of dynamic scheduling of resources for multiple projects in real time. Mathematical approaches, like integer programming or network based techniques, cannot describe the complexity of real problems (multi-projects environments have many interrelated elements), and have difficulties to adapt the analysis to dynamic changes.We propose a multi-agent system, where projects negotiate the procurement of resources through an auction mechanism all over the portfolio life. Both, projects and resources are modelled as agents. Projects demand resources for fulfilling their scheduled planned work, whereas resources offer their capabilities and workforce. This framework allows project portfolio management and the assessment in the decision of acceptance/rejection new projects.     

Quantitatives IT-Portfoliomanagement

Based on previously released research this paper focuses on the question of how IT projects should be allocated to a risk/return balanced IT portfolio. Therefore we develop an approach that exploits the structure of IS Architectures and scenarios to identify project risks as well as dependencies between projects. As a result, different clusters of efficient portfolios with distinctive risk/return-properties can be derived. The presented approach is designed to support management decisions in a pragmatic manner when selecting IT portfolios. By using real data of a major German financial services provider we exemplify the implementation and the results of the presented approach.     

A decision aid for the selection and scheduling of softwaremaintenance projects

Software maintenance is a challenging activity made more complex by pressures for immediate fixes in an environment of limited resources. To commit these resources in an optimal fashion, it is necessary to adopt a long-term perspective. This paper provides a framework that facilitates selection of software maintenance projects and their staffing. Variables involved in the modeling process include project utility, project cost; maintenance budgets, staff supply and scheduling factors. An integer programming model is used to identify a subset of projects to be undertaken together with a multi-period schedule of when to accomplish the work. Finally a procedure based on the transportation algorithm assigns staff to the various selected projects. The need to incorporate judgement and address nonquantifiable factors coupled with uncertainty regarding the values for several of the variables dictates that the process be repeated multiple times to generate solutions which are disparate, several of which could well be statisficing. The methodology forces a long-term perspective for maintenance and provides a structure for what could be a very ad hoc process. The overall focus of the model presented is that of a decision aid in that it interacts with the decision maker in support of the decision     

A new evolutionary clustering search for a no-wait flow shop problem with set-up times

This paper addresses the m-machine no-wait flow shop problem where the set-up time of a job is separated from its processing time. The performance measure considered is the total flowtime. A new hybrid metaheuristic Genetic Algorithm–Cluster Search is proposed to solve the scheduling problem. The performance of the proposed method is evaluated and the results are compared with the best method reported in the literature. Experimental tests show superiority of the new method for the test problems set, regarding the solution quality.