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Collective leadershipOrganizations must develop new strategies and forms of leadership to move from hierarchical leadership structures to a more vertical, cooperative leadership approach. So, how can collective leadership help you and your organization build the leadership capabilities today that you will need in the future, and how can this leadership approach be effectively broached within organizations? Although leadership development starts with a focus on the individual, leadership development should ultimately transform the entire organization, enabling them to operate within a collective leadership approach.
A prediction model for ranking branch-and-bound procedures for the resource-constrained project scheduling problemThe branch-and-bound (B&B) procedure is one of the most widely used techniques to get optimal solutions for the resource-constrained project scheduling problem (RCPSP). Recently, various components from the literature have been assembled by Coelho and Vanhoucke (2018) into a unified search algorithm using the best performing lower bounds, branching schemes, search strategies, and dominance rules. However, due to the high computational time, this procedure is only suitable to solve small to medium-sized problems. Moreover, despite its relatively good performance, not much is known about which components perform best, and how these components should be combined into a procedure to maximize chances to solve the problem. This paper introduces a structured prediction approach to rank various combinations of components (configurations) of the integrated B&B procedure. More specifically, two regression methods are used to map project indicators to a full ranking of configurations. The objective is to provide preference information about the quality of different configurations to obtain the best possible solution. Using such models, the ranking of all configurations can be predicted, and these predictions are then used to get the best possible solution for a new project with known network and resource values. A computational experiment is conducted to verify the performance of this novel approach. Furthermore, the models are tested for 48 different configurations, and their robustness is investigated on datasets with different numbers of activities. The results show that the two models are very competitive, and both can generate significantly better results than any single-best configuration.
Manufacturing project scheduling considering human factors to minimize total cost and carbon footprintsMake-to-order (MTO) or engineer-to-order (ETO) systems produce complex and highly customized products and, therefore, there is a need for advanced project scheduling approaches for production planning in these systems. An important aspect of production scheduling is the assignment of operators with specific human factors to activities in a manufacturing project. This assignment impacts the duration of the activities, the total wage cost of the project and even the energy consumption during production. With increasing concern regarding low-carbon production in manufacturing, the human factors of operators thus cannot be ignored in the decision-making process in production project scheduling. In this context, our study considers an extension of the well-known resource-constrained project scheduling problem for manufacturing. This problem is represented as a bi-objective optimization problem with the conjoint objectives of minimizing the total cost of the project and its carbon footprint. Two variants of a genetic algorithm-based memetic algorithm (MA) are proposed to solve this problem and a set of artificial, realistic project instances are generated to evaluate the proposed solution procedure. Experimental results show that the proposed MA outperforms the well-known non-dominated sorting genetic algorithms (NSGA-II and NSGA-III) and its enhanced approach (ENSGA-II) in terms of both solution quality and computational efficiency. The experiments are conducted on both real-life case study data from an MTO project in the furniture industry and a large set of artificial data instances. Our research allows project managers to select appropriate operators to execute activities based on human factors, wage and power consumption with the objectives of minimum total cost and carbon footprint.
The boundary conditions for growth: Exploring the non-linear relationship between organic and acquisitive growth and profitabilityThe nature of the relationship between growth and profitability remains inconclusive, despite prior research. To contribute to a better understanding of the growth-profitability relationship, we examine its non-linear character. We achieve this by deconstructing growth into organic and acquisitive modes, and by theorizing how the particular costs and benefits of each mode affect the profitability, which we measure as return on assets. Furthermore, we propose that the interaction of these two modes can also affect profitability. By studying these relationships with a panel data set of established German firms during a 13-year period, we uncover an inverted U-shaped relationship between growth and profitability that is mainly driven by acquisitive growth. These decreasing returns at higher levels of acquisitive growth are related to the higher internal costs of managing acquisitions. Consistent with our logic, we find that organic growth has a declining positive profitability effect. The interaction of both growth modes also shows that increasing acquisitive growth negatively impacts the positive effect of organic growth on profitability.