Christian Stummer

Interactive R&D portfolio analysis with project interdependencies and time profiles of multiple objectives

This paper describes a three-phase approach to assist research and development managers in obtaining the most attractive project portfolio. The screening procedure of the first phase identifies project proposals that are worthy of further evaluation keeping the number of projects entering the subsequent phase within a manageable size. In the second phase, a multiobjective integer linear programming model determines the solution space of all efficient (i.e., Pareto-optimal) portfolios. It takes into account time profiles of the objectives, various project interdependencies, logical and strategic requirements, as well as resource and benefit constraints. The third phase, finally, aims to find a portfolio which fits the decision-makers notions. Starting with an arbitrarily selected candidate he/she iteratively sets aspiration levels for objectives or modifies upper and lower bounds. Thus, the solution space is explored until a satisfying compromise between the figures both in benefit and resource categories is reached. Our approach is numerically tractable, and, as a key feature, requires no a priori assumptions about the decision-makers preferences. Its application is illustrated by an example.

Agent-based simulation of innovation diffusion: a review

Mathematical modeling of innovation diffusion has attracted strong academic interest since the early 1960s. Traditional diffusion models have aimed at empirical generalizations and hence describe the spread of new products parsimoniously at the market level. More recently, agent-based modeling and simulation has increasingly been adopted since it operates on the individual level and, thus, can capture complex emergent phenomena highly relevant in diffusion research. Agent-based methods have been applied in this context both as intuition aids that facilitate theory-building and as tools to analyze real-world scenarios, support management decisions and obtain policy recommendations. This review addresses both streams of research. We critically examine the strengths and limitations of agent-based modeling in the context of innovation diffusion, discuss new insights agent-based models have provided, and outline promising opportunities for future research. The target audience of the paper includes both researchers in marketing interested in new findings from the agent-based modeling literature and researchers who intend to implement agent-based models for their own research endeavors. Accordingly, we also cover pivotal modeling aspects in depth (concerning, e.g., consumer adoption behavior and social influence) and outline existing models in sufficient detail to provide a proper entry point for researchers new to the field.

Research and development project selection and resource allocation: a review of quantitative modelling approaches

This paper reviews the literature on quantitative modelling for research and development (R&;D) project selection and resource allocation. The topic has been a subject of operations research for about four decades. Its importance stems from the fact that R&D projects are a core element of corporate renewal, heavily influence a firms market success and, if not properly chosen and trimmed, may waste large amounts of resources or even ruin the enterprise. Our survey classifies and characterizes the various modelling approaches.

Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection

Abstract One of the most important, common and critical management issues lies in determining the “best” project portfolio out of a given set of investment proposals. As this decision process usually involves the pursuit of multiple objectives amid a lack of a priori preference information, its quality can be improved by implementing a two-phase procedure that first identifies the solution space of all efficient (i.e., Pareto-optimal) portfolios and then allows an interactive exploration of that space. However, determining the solution space is not trivial because brute-force complete enumeration only solves small instances and the underlying NP-hard problem becomes increasingly demanding as the number of projects grows. While meta-heuristics in general provide an attractive compromise between the computational effort necessary and the quality of an approximated solution space, Pareto ant colony optimization (P-ACO) has been shown to perform particularly well for this class of problems. In this paper, the beneficial effect of P-ACO’s core function (i.e., the learning feature) is substantiated by means of a numerical example based on real world data. Furthermore, the original P-ACO approach is supplemented by an integer linear programming (ILP) preprocessing procedure that identifies several efficient portfolio solutions within a few seconds and correspondingly initializes the pheromone trails before running P-ACO. This extension favors a larger exploration of the search space at the beginning of the search and does so at a low cost.

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.

Multi-objective decision analysis for competence-oriented project portfolio selection

This paper develops a multi-objective optimization model for project portfolio selection taking employee competencies and their evolution into account. The objectives can include economic gains as well as gains expressed in terms of aggregated competence increments according to pre-defined profiles. In order to determine Pareto-optimal solutions, the overall problem is decomposed into a master problem addressing the portfolio selection itself, and a slave problem dealing with a suitable assignment of personnel to the work packages of the selected projects over time. We provide an asymptotic approximation of the problem by a linearized formulation, which allows an efficient and exact solution of the slave problem. For the solution of the master problem, we compare the multi-objective metaheuristics NSGA-II and P-ACO. Experimental results both for synthetically generated test instances and for real-world test instances, based on an application case from the E-Commerce Competence Center Austria, are presented.

Determining location and size of medical departments in a hospital network: a multiobjective decision support approach.

Decisions on the location and size of medical departments in a given hospital network are prime examples of priority setting in health care, which is an issue of growing political importance. As such decisions are regularly characterized by multiple and often conflicting objectives in real-life, this paper integrates the fields of hospital planning and multiobjective decision support. The proposed two-phase solution procedure for our corresponding mathematical programming model does not require a priori preference information. Instead, it seeks efficient solutions by means of multiobjective tabu search in the first phase, while applying clustering in the second phase to allow the decision makers to interactively explore the solution space until the “best” configuration is determined. The real-world applicability of our approach is illustrated through a numerical example based on hospital data from Germany.

A multicriteria decision support system for competence-driven project portfolio selection

The systematic and proactive development of human resources is of major importance in organizations that rely heavily on the competencies of their employees when engaging in innovative endeavors. Human capital, however, is not only a resource required for conducting research, but also the eventual result of that research. When selecting a research portfolio, the decision-maker (DM) thus needs to take into consideration both current and future competence requirements, as well as other financial and nonfinancial objectives and constraints. We introduce a proper multicriteria decision support system (MCDSS) that first determines the set of Pareto-efficient solutions and then allows the DM to interactively filter and/or explore this set in various ways. Its practical application is demonstrated by means of a showcase at the Electronic Commerce Competence Center (EC3) in Vienna, Austria.

New Multiobjective Metaheuristic Solution Procedures for Capital Investment Planning

Capital investment planning is a periodic management task that is particularly challenging in the presence of multiple objectives as trade-offs have to be made with respect to the preferences of the decision-makers. The underlying mathematical model is a multiobjective combinatorial optimization problem that is NP-hard. One way to tackle this problem is first to determine the set of all efficient portfolios and then to explore this set in order to identify a final preferred portfolio. In this study, we developed heuristic procedures to find efficient portfolios because it is impossible to enumerate all of them within a reasonable computation time for practical problems. We first added a neighborhood search routine to the Pareto Ant Colony Optimization (P-ACO) procedure to improve its performance and then developed a Tabu Search procedure and a Variable Neighborhood Search procedure. Step-by-step descriptions of these three new procedures are provided. Computational results on benchmark and randomly generated test problems show that the Tabu Search procedure outperforms the others if the problem does not have too many objective functions and an excessively large efficient set. The improved P-ACO procedure performs better otherwise.

An agent-based simulation approach for the new product diffusion of a novel biomass fuel

Marketing activities support the market introduction of innovative goods or services by furthering their diffusion and, thus, their success. However, such activities are rather expensive. Managers must therefore decide which specific marketing activities to apply to which extent and/or to which target group at which point in time. In this paper, we introduce an agent-based simulation approach that supports decision-makers in these concerns. The practical applicability of our tool is illustrated by means of a case study of a novel, biomass-based fuel that will likely be introduced on the Austrian market within the next 5 years.