14 research outputs found
Models and Algorithms for Inbound and Outbound Truck to Door Scheduling
Cross-docking is a logistic strategy that facilitates rapid movement of consolidated products between suppliers and retailers within a supply chain. It is also a warehousing strategy that aims at reducing or eliminating storage and order picking, two of which are known to be major costly operations of any typical warehouse. This strategy has been used in the retailing, manufacturing, and automotive industries. In a cross-dock, goods are unloaded from incoming trucks, consolidated according to their destinations, and then, loaded into outgoing trucks with little or no storage in between.
In this thesis, we address an integrated cross-dock door assignment and truck scheduling problem in which the assignment and sequencing of incoming trucks to strip doors and outgoing trucks to stack doors is optimized to minimize the total time to process all trucks. We present a mixed integer programming formulation to model this problem and some valid inequalities to strengthen the formulation. We also present two metaheuristics to obtain high quality solutions in reasonable CPU times. These algorithms use a mix of composite dispatching rules, constructive heuristics, local search heuristics which are embedded into a greedy randomized adaptive search procedure (GRASP) and an iterated local search (ILS). Results of computational experiments are presented to assess the performance of the proposed algorithms, in comparison with a general purpose solver
ワセキケイ ロンリシキ ノ ジュウソク カノウセイ モンダイ ニ タイスル アルゴリズム ノ カイリョウ
京都大学0048新制・課程博士博士(情報学)甲第12459号情博第213号新制||情||46(附属図書館)UT51-2006-J450京都大学大学院情報学研究科通信情報システム専攻(主査)教授 岩間 一雄, 教授 湯淺 太一, 教授 小野寺 秀俊学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA
Fairness in Recommendation: Foundations, Methods and Applications
As one of the most pervasive applications of machine learning, recommender
systems are playing an important role on assisting human decision making. The
satisfaction of users and the interests of platforms are closely related to the
quality of the generated recommendation results. However, as a highly
data-driven system, recommender system could be affected by data or algorithmic
bias and thus generate unfair results, which could weaken the reliance of the
systems. As a result, it is crucial to address the potential unfairness
problems in recommendation settings. Recently, there has been growing attention
on fairness considerations in recommender systems with more and more literature
on approaches to promote fairness in recommendation. However, the studies are
rather fragmented and lack a systematic organization, thus making it difficult
to penetrate for new researchers to the domain. This motivates us to provide a
systematic survey of existing works on fairness in recommendation. This survey
focuses on the foundations for fairness in recommendation literature. It first
presents a brief introduction about fairness in basic machine learning tasks
such as classification and ranking in order to provide a general overview of
fairness research, as well as introduce the more complex situations and
challenges that need to be considered when studying fairness in recommender
systems. After that, the survey will introduce fairness in recommendation with
a focus on the taxonomies of current fairness definitions, the typical
techniques for improving fairness, as well as the datasets for fairness studies
in recommendation. The survey also talks about the challenges and opportunities
in fairness research with the hope of promoting the fair recommendation
research area and beyond.Comment: Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST
Bertsobot: gizaki-robot arteko komunikazio eta elkarrekintzarako portaerak
216 p.Bertsobot: Robot-Portaerak Gizaki-Robot Arteko Komunikazio eta ElkarrekintzanBertsotan aritzeko gaitasuna erakutsiko duen robot autonomoa garatzeada gure ikerketa-lanaren helburu behinena. Bere egitekoa, bertsoa osatzekoinstrukzioak ahoz jaso, hauek prozesatu eta ahalik eta bertsorik egokienaosatu eta kantatzea litzateke, bertsolarien oholtza gaineko adierazkortasunmaila erakutsiz gorputzarekin. Robot-bertsolariak, gizaki eta roboten artekoelkarrekintza eta komunikazioan aurrera egiteko modua jarri nahi luke, lengoaianaturala erabiliz robot-gizaki arteko bi noranzkoko komunikazioan