6,371 research outputs found

    Collaborative truckload transportation procurement with multiple coalitions

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    Gönderici işbirliği, son yıllarda ortaya çıkmış yeni bir işbirliği türüdür ve tedarik zinciri yönetiminde kurumlar arası yatay işbirliği sınıfına girmektedir. Sert rekabet koşulları, kaynak yetersizliği, iklim değişimi, güvenlik sorunları ve yeni kanuni düzenlemeler firmalar üzerindeki baskıyı artırmış ve geleneksel düşünce kalıplarını zorlayan yeni çözümler aramaya itmiştir. İşbirliği; daha geniş ve bütün sistemi kapsayan bir bakış açısı getirmesi nedeniyle yeni fırsatlar sunan bir strateji olarak görülmektedir. Gönderici işbirliğinde taşıyıcı firmalardan taşımacılık hizmeti alan bir grup gönderici firma bir araya gelir; ve taşıyıcı firmalarla grup olarak pazarlık yaparlar. Göndericiler işbirliği yapmak istediklerinde; hangi göndericilerin işbirliğine dahil edileceği, hangi göndericilerin rotalarının arka arkaya ekleneceği, ve oluşturulan rota birleştirme çözümünden doğan toplam maliyetin göndericilere ve hatta her bir rotaya dağıtılması konularında en iyi kararları vermek durumundadırlar. Literatürdeki çalışmalar işbirliği yapan göndericilerin tek bir koalisyon kurduğunu varsaymış ve bu tek koalisyonunun kurulması, devamı ve genişletilmesi konularını ele almışlardır. Verilen bir koalisyon ve bu koalisyon için hesaplanan en küçük maliyetli çözüm için adil bir maliyet paylaşımı olup olmadığını konu almışlardır. Buna ek olarak, verilen bir koalisyon, çözüm ve maliyet paylaşma mekanizması için koalisyona katılmak isteyen yeni bir göndericinin koalisyona alınıp alınmaması kararını konu alan çalışmalar da mevcuttur. Büyük ölçekli gönderici işbirliği ağlarında sadece tek bir koalisyona izin verilmesi koordinasyonu zorlaştırmakta ve kabul edilebilir maliyet dağıtımına sahip geniş çaplı bir çözüm bulunmasını zorlaştırmaktadır. Bu çalışmada birden fazla ayrışık koalisyon içerebilen tam kamyon yükü gönderici işbirliği ağları ele alınmıştır. Her biri adil maliyet dağıtımına sahip, ayrık koalisyonlardan oluşan en düşük toplam maliyetli koalisyon yapısının bulunması amaçlanmıştır. Gönderici ve rota sayıları arttıkça ve operasyonel kısıtlar eklendikçe bu kararları en iyi biçimde vermek gittikçe zorlaşmaktadır. Gerçek hayat durumlarında problem boyutlarının çok büyük olması beklendiği için özellikle büyük ölçekli problem örneklerinin çözümüne yönelik sezgisel algoritmalar geliştirilmiştir.We study formation of stable coalitions given a set of shippers and their lanes corresponding to regularly scheduled truckload shipment. In this thesis, selecting participants, deciding who should participate with whom, calculating the lowest cost operational solution and allocating the system-wide cost to the participants stand out as important problems. Collaborating shippers try to identify tours which consist of regularly scheduled shipment with minimal empty truck movements. Then, they must allocate the total cost of the collaborative solution to the participated firms and individual lanes such that the collaborative solution remains attractive to the participants. In the literature, solving the optimization problem minimization the total cost and allocating the calculated minimum cost are treated as successive but distinct phases. The cost minimizing optimization problem is solved with well-known operation research methods, while cooperative game theory concepts are used for cost allocation. The minimum cost solution may render finding an acceptable cost allocation impossible. Besides, similar works in the literature assume that the collaborating firms will forge a single grand coalition. However, as the collaboration grows in size, a single grand coalition may become impractical and also it might leave several lanes out of the coalition, depriving shippers of significant cost savings. In this study, we propose algorithm to design coalition structure which consist of multiple disjoint stable coalitions. Each coalition must have a minimum cost collaborative solution with an acceptable cost allocation. Due to the complexity of the task hand, we devised a heuristic to find good quality solutions to this problem

    Dagstuhl Reports : Volume 1, Issue 2, February 2011

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    Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

    A competitive solution for cooperative truckload delivery

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    This paper introduces a solution for gain sharing in consortia of logistic providers where joint planning of truckload deliveries enables the reduction of empty kilometers. The highly competitive nature of freight transport markets necessitates solutions that distinguish among the logistics providers based on their characteristics, even in situations with two players only. We introduce desirable properties in these situations and propose a solution that satisfies such properties. By comparing the existing solutions against the introduced properties we demonstrate the advantages of our proposed solution

    Gain-Sharing in Urban Consolidation Centers

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    Urban consolidation centers provide the logistical infrastructure for cooperation among less-than-truckload carriers with contiguous destinations. The rising number of initiatives to establish and operate urban consolidation centers and their low success rates signal the need for better mechanisms to manage cooperation in this context. We introduce and study cooperative situations comprising a set of carriers with time sensitive deliveries who can consolidate their cargo to obtain savings. We introduce the class of Dispatch Consolidation (DC) games and search for ways to fairly allocate the obtained savings among the participating carriers. When delivery capacities are not restrictive, i.e. when waiting costs trigger truck dispatches, we show that stable allocations in the core always exist and can, in their entirety, be found by solving a compact linear program. With restrictive capacities, however, the core of a DC game may become empty. We introduce the notion of component-wise core for DC games to preserve stability first and foremost among the carriers whose deliveries are dispatched together in the chosen optimal solutions. The novelty of our approach is to link the stability requirements of an allocation rule with the structure of selected solutions for the underlying optimization problems. We characterize the component-wise cores of DC games, prove their non-emptiness, and suggest proportionally calculated allocations therein. Finally, we discuss a refinement of component-wise core allocations that minimizes envy among the carriers who are dispatched separately

    Competitive solutions for cooperating logistics providers

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    This paper discusses solutions for gain sharing in consortia of logistic providers where joint planning of truckload deliveries enables the reduction of empty kilometres. The highly competitive nature of freight transport markets necessitates solutions that distinguish among the logistics providers based on their characteristics, even in situations with two players only. We introduce desirable properties in these situations and propose a solution that satisfies such properties. By comparing the existing solutions against the introduced properties we demonstrate the advantages of our proposed solution

    INSPIRE Newsletter Fall 2022

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    https://scholarsmine.mst.edu/inspire-newsletters/1011/thumbnail.jp

    A unified race algorithm for offline parameter tuning

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    This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods

    Sublinear Computation Paradigm

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    This open access book gives an overview of cutting-edge work on a new paradigm called the “sublinear computation paradigm,” which was proposed in the large multiyear academic research project “Foundations of Innovative Algorithms for Big Data.” That project ran from October 2014 to March 2020, in Japan. To handle the unprecedented explosion of big data sets in research, industry, and other areas of society, there is an urgent need to develop novel methods and approaches for big data analysis. To meet this need, innovative changes in algorithm theory for big data are being pursued. For example, polynomial-time algorithms have thus far been regarded as “fast,” but if a quadratic-time algorithm is applied to a petabyte-scale or larger big data set, problems are encountered in terms of computational resources or running time. To deal with this critical computational and algorithmic bottleneck, linear, sublinear, and constant time algorithms are required. The sublinear computation paradigm is proposed here in order to support innovation in the big data era. A foundation of innovative algorithms has been created by developing computational procedures, data structures, and modelling techniques for big data. The project is organized into three teams that focus on sublinear algorithms, sublinear data structures, and sublinear modelling. The work has provided high-level academic research results of strong computational and algorithmic interest, which are presented in this book. The book consists of five parts: Part I, which consists of a single chapter on the concept of the sublinear computation paradigm; Parts II, III, and IV review results on sublinear algorithms, sublinear data structures, and sublinear modelling, respectively; Part V presents application results. The information presented here will inspire the researchers who work in the field of modern algorithms
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