In this paper, the extended Resource Renting Problem (RRP/extended) is presented. The RRP/extended is a time-constrained project scheduling problem, in which the total project cost is minimised. In the RRP/extended, this total project cost is determined by a number of extra costs, which are defined in this paper. These costs are based on the costs that are used in the traditional Resource Renting Problem and the Total Adjustment Cost Problem. Therefore, the RRP/extended represents a union of these two problems. To solve the RRP/extended, a scatter search is developed. The building blocks of this scatter search are specifically designed for the RRP/extended. We introduce two crossovers and an improvement method. The efficiency of these building blocks will be shown in the paper. Furthermore, a sensitivity analysis is presented in which the five costs have diverse values.
Due to the adoption of more and more complex incentive contract structures for projects, designing the best contract for a specific situation has become an increasingly daunting task for project owners. Through the combination of findings from contracting literature with knowledge from the domain of project management, a quantitative model for the contract design problem is constructed. The contribution of this research is twofold. First of all, a comprehensive and quantitative methodology to analyse incentive contract design is introduced, based on an extensive review of the existing literature. Secondly, based on this methodology, computational experiments are carried out, which result in a set of managerial guidelines for incentive contract design. Our analysis shows that substantial improvements can often be attained by using contracts which include incentives for cost, duration as well as scope simultaneously. Moreover, nonlinear and piecewise linear formulae to calculate the incentive amounts are shown to improve both the performance and robustness across different projects.
Baert, Caroline; Meuleman, Miguel; Debruyne, Marion; Wright, Mike (2016)
This study examines the role of resource orchestration for the exploration and exploitation of opportunities through portfolio entrepreneurship. Adopting a single-case study approach, we identify eight distinctive resource orchestration subprocesses that we group into three aggregate resource orchestration processes that enable the development and exploitation of a set of resources and capabilities across a portfolio of ventures. Our findings extend the literature on enduring entrepreneurship by building theory on how resource orchestration across a portfolio of ventures facilitates the emergence of synergies when exploring and exploiting opportunities.
Chen, Yufen; Vanhaverbeke, Wim; Du, Jingshu (2016)
This paper investigates to what extent internal R&D efforts and different types of external knowledge sources jointly affect innovation performance of firms in emerging economies. Based on a survey about external knowledge sourcing activities of Chinese innovative firms, we categorize external knowledge sources into four groups: science-based partners, horizontal connections, value chain partners, and technology service providers. We find that both internal R&D activities and external knowledge sourcing have a positive effect on firms' innovation performance. Strong internal R&D capabilities also increase the effect of sourcing from value chain partners and horizontal connections, but we do not find support for complementarity between internal R&D and collaborations with universities and research labs. These findings jointly suggest that the mixture of different types of external knowledge partners in combination with internal R&D capabilities is crucial in understanding the role of open innovation in emerging economies.
This paper presents a new solution approach to solve the resource-constrained project scheduling problem in the presence of three types of logical constraints. Apart from the traditional AND constraints with minimal time-lags, these precedences are extended to OR constraints and bidirectional (BI) relations. These logical constraints extend the set of relations between pairs of activities and make the RCPSP definition somewhat different from the traditional RCPSP research topics in literature. It is known that the RCPSP with AND constraints, and hence its extension to OR and BI constraints, is NP-hard. The new algorithm consists of a set of network transformation rules that removes the OR and BI logical constraints to transform them into AND constraints and hereby extends the set of activities to maintain the original logic. A satisfiability (SAT) solver is used to guarantee the original precedence logic and is embedded in a metaheuristic search to resource feasible schedules that respect both the limited renewable resource availability as well as the precedence logic. Computational results on two well-known datasets from literature show that the algorithm can compete with the multi-mode algorithms from literature when no logical constraints are taken into account. When the logical constraints are taken into account, the algorithm can report major reductions in the project makespan for most of the instances within a reasonable time.
Antons, David; Kleer, Robin; Salge, Torsten Oliver (Wiley, 2016)
During the three decades since its inception in 1984, the JPIM has shaped the evolution of innovation research as a scientific field. It helped create a topic landscape that is not only more diverse and rich in insights, but also more complex and fragmented in structure than ever before. We seek to map this landscape and identify salient development trajectories over time. In contrast to prior citation-based studies covering the first two decades of JPIM research, we benefit from recent advances in natural language processing and rely on a topic modeling algorithm to extract 57 distinct topics and the corresponding most common words, terms, and phrases from the entire full-text corpus of 1008 JPIM articles published between 1984 and 2013. Estimating the development trajectory of each topic based on yearly publication counts in JPIM allows us to identify “hot,” “cold,” “revival,” “evergreen,” and “wall-flower” topics. We map these topics onto the Product Development and Management Association (PDMA) Body of Knowledge categories and discover that these categories differ significantly not only in terms of their internal topic diversity and relative prevalence, but also—and arguably more importantly—in terms of their publication and citation trajectories over time. For instance, the PDMA category “Codevelopment and Alliances” exhibits only moderate topic diversity (7 out of 57 topics) and prevalence in JPIM (161 out of 1008 articles). That said, it is among the most dynamic categories featuring two evergreen topic (“Users and Innovation” and “Tools and Systems for Technology Transfer”) and three hot topics (“Open Innovation,” “Alliances and Cooperation,” and “Networks and Network Structure”) as well as a sharply growing annual number of citations received. Our findings are likely to be of interest to all those who are keen to (re)discover JPIM's topic landscape in search of hidden structures and development trajectories.
This paper explores the use of multivariate regression methods for project schedule control within a statistical project control framework. These multivariate regression methods monitor the activity level performance of an ongoing project from the earned value management/earned schedule (EVM/ES) observations that are made at a high level of the work breakdown structure (WBS). These estimates can be used to calculate the longest path in the project and to produce warning signals for project schedule control. The effort that is spent by the project manager is thereby reduced, since a drill-down of the WBS is no longer required for every review period. An extensive computational experiment was set up to test and compare four distinct multivariate regression methods on a database of project networks. The kernel principal component regression method, when used with a radial base function kernel, was found to outperform the other presented regression methods.
Moges, Helen-Tadesse; Van Vlasselaer, Véronique; Lemahieu, Wilfried; Baesens, Bart (2016)
Decision making processes and their outcomes can be affected by a number of factors. Among them, the quality of the data is critical. Poor quality data cause poor decisions. Although this fact is widely known, data quality (DQ) is still a critical issue in organizations because of the huge data volumes available in their systems. Therefore, literature suggests that communicating the DQ level of a specific data set to decision makers in the form of DQ metadata (DQM) is essential. However, the presence of DQM may overload or demand cognitive resources beyond decision makers' capacities, which can adversely impact the decision outcomes. To address this issue, we have conducted an experiment to explore the impact of DQM on decision outcomes, to identify different groups of decision makers who benefit from DQM and to explore different factors which enhance or otherwise hinder the use of DQM. Findings of a statistical analysis suggest that the use of DQM can be enhanced by data quality training or education. Decision makers with a certain level of data quality awareness used DQM more to solve a decision task than those with no data quality awareness. Moreover, those with data quality awareness reached a higher decision accuracy. However, the efficiency of decision makers suffers when DQM is used. Our suggestion would be that DQM can have a positive impact on decision outcomes if it is associated with some characteristics of decision makers, such as a high data quality knowledge. However, the results do not confirm that DQM should be included in data warehouses as a general business practice, instead organizations should first investigate the use and impact of DQM in their setting before maintaining DQM in data warehouses.
We model an inventory management setting in which the decision maker first uses newsvendor model to decide on the amount of ordered perishable inventory for a fixed consumption period, based on the best available forecast of demand at the time of ordering. After a relatively long lead time, the consumption period starts and she has to assign the received inventory to two priority customer classes given the -newly updated- rate of arrival of each class. The assignment of inventory requires two simultaneous decisions: 1) the reservation quantity for the high priority class and 2) the choice of inventory allocation mechanism (Standard Nesting SN or Theft Nesting TN), to minimize the expected units short of the high priority class while minimizing the wasted inventory at the end of the fixed consumption period. We assume that some partial information about the bottom line impact of a shortage in high priority customer class compared to the other can be conjectured. For both inventory allocation mechanisms, we then calculate the monetary benefit for all feasible reserved quantities to identify the optimal reserved quantity. We derive closed form expressions for the expected number of units short in each demand class under SN and TN allocation mechanisms. We showcase the management of electricity smart meter inventory in a multi-year implementation project consisting of multiple fixed consumption periods. Numerical experiments and graphical interpretations feature the optimum allocation policy and the cost minimizing reserved quantity under such policy.
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