Purpose-Various types and implementations of crowdsourcing have emerged on the market; many of them are related to logistics. While we can identify plenty of crowd logistics applications using information technology capabilities and information sharing in practice, theories behind this phenomenon have received only limited attention. This paper accounts for filling this research gap by analyzing the crowd's contributions in logistics of goods and information. Thereby, this paper aims to provide the necessary basis for a novel interdisciplinary research field. Design and Methodology-This paper is part of an ongoing research endeavor in the field of location-based crowdsourcing (LBCS). It represents conceptual work that builds on a literature review enriched with an in-depth analysis of real-world examples in the field of crowd logistics. Using a scoring method, we provide an example how a company may evaluate the alternatives of crowd logistics. Approach-The main approach is an analysis of variants of how the social crowd may be integrated in logistics processes. The work is conceptual in its core. Thereby, we use real-world examples of crowdsourcing applications to underpin the evaluated variants of crowd logistics. Findings-The paper presents relevant theoretical background on crowd logistics. We differentiate between variants of crowd logistics with their flow of materials, goods, and information. Thereby we zoom in the type, significance, and process flow of the crowd's contributions. We discuss potential advantages and challenges of logistics with the performing crowd and deeply discuss opportunities and challenges from a business and from an individual's perspective. Finally, we highlight a route map for future research directions in this novel interdisciplinary research field. Limitations of the research-As this work is conceptual in its core, generalizations may be drawn only with great care. Still, we are in a position to propose a route map for further research in this area in this paper. Also the integration of an analysis of a scale of real-world applications allows us to highlight our research's practical relevance and implications. Contributions-The main contribution of this paper is an in-depth analysis and consolidation of innovative crowd logistics applications in order to provide an overview on recent implementations. We propose a categorization scheme and contribute with a route map for further research in the field of crowd logistics.
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-ACOÕs 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.
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