267 research outputs found
Learning optimization models in the presence of unknown relations
In a sequential auction with multiple bidding agents, it is highly
challenging to determine the ordering of the items to sell in order to maximize
the revenue due to the fact that the autonomy and private information of the
agents heavily influence the outcome of the auction.
The main contribution of this paper is two-fold. First, we demonstrate how to
apply machine learning techniques to solve the optimal ordering problem in
sequential auctions. We learn regression models from historical auctions, which
are subsequently used to predict the expected value of orderings for new
auctions. Given the learned models, we propose two types of optimization
methods: a black-box best-first search approach, and a novel white-box approach
that maps learned models to integer linear programs (ILP) which can then be
solved by any ILP-solver. Although the studied auction design problem is hard,
our proposed optimization methods obtain good orderings with high revenues.
Our second main contribution is the insight that the internal structure of
regression models can be efficiently evaluated inside an ILP solver for
optimization purposes. To this end, we provide efficient encodings of
regression trees and linear regression models as ILP constraints. This new way
of using learned models for optimization is promising. As the experimental
results show, it significantly outperforms the black-box best-first search in
nearly all settings.Comment: 37 pages. Working pape
Fair task allocation in transportation
Task allocation problems have traditionally focused on cost optimization.
However, more and more attention is being given to cases in which cost should
not always be the sole or major consideration. In this paper we study a fair
task allocation problem in transportation where an optimal allocation not only
has low cost but more importantly, it distributes tasks as even as possible
among heterogeneous participants who have different capacities and costs to
execute tasks. To tackle this fair minimum cost allocation problem we analyze
and solve it in two parts using two novel polynomial-time algorithms. We show
that despite the new fairness criterion, the proposed algorithms can solve the
fair minimum cost allocation problem optimally in polynomial time. In addition,
we conduct an extensive set of experiments to investigate the trade-off between
cost minimization and fairness. Our experimental results demonstrate the
benefit of factoring fairness into task allocation. Among the majority of test
instances, fairness comes with a very small price in terms of cost
Creating incentives to prevent execution failures: an extension of VCG mechanism
When information or control in a multiagent planning system is private to the agents, they may misreport this information or refuse to execute an agreed outcome, in order to change the resulting end state of such a system to their benefit. In some domains this may result in an execution failure. We show that in such settings VCG mechanisms lose truthfulness, and that the
utility of truthful agents can become negative when using VCG payments (i.e., VCG is not strongly individually rational). To deal with this problem, we introduce an extended payment structure which takes into account the actual execution of the promised outcome. We show that this extended mechanism can guarantee a nonnegative utility and is (i) incentive compatible in a Nash equilibrium, and (ii) incentive compatible in dominant strategies if and only if all agents can be verified during execution
Digital twin applications in urban logistics:an overview
Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external factors like pollution and congestion. To counter this, smart cities deploy technologies such as digital twins (DT)s to achieve sustainability. Research suggests that DTs can be beneficial in optimizing the physical systems they are linked with. The concept has been extensively studied in many technology-driven industries like manufacturing. However, little work has been done with regards to their application in urban logistics. In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics applications. To do this, we survey previous research on DT applications in urban logistics as we found that a holistic overview is lacking. Using this knowledge in combination with the identification of key factors in urban logistics, we produce a conceptual model for the general design of an urban logistics DT through a knowledge graph. We provide an illustration on how the conceptual model can be used in solving a relevant problem and showcase the integration of relevant DDO methods. We finish off with a discussion on research opportunities and challenges based on previous research and our practical experience
Digital twin applications in urban logistics:an overview
Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external factors like pollution and congestion. To counter this, smart cities deploy technologies such as digital twins (DT)s to achieve sustainability. Research suggests that DTs can be beneficial in optimizing the physical systems they are linked with. The concept has been extensively studied in many technology-driven industries like manufacturing. However, little work has been done with regards to their application in urban logistics. In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics applications. To do this, we survey previous research on DT applications in urban logistics as we found that a holistic overview is lacking. Using this knowledge in combination with the identification of key factors in urban logistics, we produce a conceptual model for the general design of an urban logistics DT through a knowledge graph. We provide an illustration on how the conceptual model can be used in solving a relevant problem and showcase the integration of relevant DDO methods. We finish off with a discussion on research opportunities and challenges based on previous research and our practical experience
Digital Twin Applications in Urban Logistics:An Overview
Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external effects pertaining to pollution and congestion. In order to counter this, smart cities deploy technological tools to achieve sustainability. Such tools include Digital Twins (DT)s which are virtual replicas of real-life physical systems that control the systems. Research points that DTs can be very beneficial in how they control a physical system by constantly optimizing its performance. The concept has been extensively studied in other technology-driven industries like manufacturing. However, little work has been done with regards to their application in urban logistics. In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics networks. To do this, we provide a characterization of key factors in urban logistics for dynamic-decision making. We also survey previous research on DT applications in urban logistics as we found that a holistic overview is lacking. Using this knowledge in combination with the characterization, we produce a conceptual model that describes the ontology, learning capabilities and optimization prowess of an urban logistics digital twin through its quantitative models. We finish off with a discussion on potential research benefits and limitations based on previous research and our practical experience
Parcel loss prediction in last-mile delivery: deep and non-deep approaches with insights from Explainable AI
Within the domain of e-commerce retail, an important objective is the
reduction of parcel loss during the last-mile delivery phase. The
ever-increasing availability of data, including product, customer, and order
information, has made it possible for the application of machine learning in
parcel loss prediction. However, a significant challenge arises from the
inherent imbalance in the data, i.e., only a very low percentage of parcels are
lost. In this paper, we propose two machine learning approaches, namely, Data
Balance with Supervised Learning (DBSL) and Deep Hybrid Ensemble Learning
(DHEL), to accurately predict parcel loss. The practical implication of such
predictions is their value in aiding e-commerce retailers in optimizing
insurance-related decision-making policies. We conduct a comprehensive
evaluation of the proposed machine learning models using one year data from
Belgian shipments. The findings show that the DHEL model, which combines a
feed-forward autoencoder with a random forest, achieves the highest
classification performance. Furthermore, we use the techniques from Explainable
AI (XAI) to illustrate how prediction models can be used in enhancing business
processes and augmenting the overall value proposition for e-commerce retailers
in the last mile delivery
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