Collaboration Planning of Stakeholders for Sustainable City Logistics Operations

• URL: http://arxiv.org/abs/2107.14049v1
• Date: Mon, 12 Jul 2021 22:54:17 GMT
• Title: Collaboration Planning of Stakeholders for Sustainable City Logistics Operations
• Authors: Taiwo Adetiloye
• Abstract summary: City logistics involves movements of goods in urban areas respecting the municipal and administrative guidelines. We investigate the problems of collaboration planning of stakeholders to achieve sustainable city logistics operations. Two categories of models are proposed to evaluate the collaboration strategies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: City logistics involves movements of goods in urban areas respecting the municipal and administrative guidelines. The importance of city logistics is growing over the years especially with its role in minimizing traffic congestion and freeing up of public space for city residents. Collaboration is key to managing city logistics operations efficiently. Collaboration can take place in the form of goods consolidation, sharing of resources, information sharing, etc. We investigate the problems of collaboration planning of stakeholders to achieve sustainable city logistics operations. Two categories of models are proposed to evaluate the collaboration strategies. At the macro level, we have the simplified collaboration square model and advance collaboration square model and at the micro level we have the operational level model. These collaboration decision making models, with their mathematical elaborations on business-to-business, business-to-customer, customer-to-business, and customer-to-customer provide roadmaps for evaluating the collaboration strategies of stakeholders for achieving sustainable city logistics operations attainable under non-chaotic situation and presumptions of human levity tendency. City logistics stakeholders can strive to achieve effective collaboration strategies for sustainable city logistics operations by mitigating the uncertainty effect and understanding the theories behind the moving nature of the individual complexities of a city. To investigate system complexity, we propose axioms of uncertainty and use spider networks and system dynamics modeling to investigate system elements and their behavior over time.

Related papers

• EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning [69.55982246413046]
  We propose explicit policy optimization (EPO) for strategic reasoning. EPO provides strategies in open-ended action space and can be plugged into arbitrary LLM agents to motivate goal-directed behavior. Experiments across social and physical domains demonstrate EPO's ability of long-term goal alignment.
  arXiv  Detail & Related papers  (2025-02-18T03:15:55Z)
• Collaborative Imputation of Urban Time Series through Cross-city Meta-learning [54.438991949772145]
  We propose a novel collaborative imputation paradigm leveraging meta-learned implicit neural representations (INRs) We then introduce a cross-city collaborative learning scheme through model-agnostic meta learning. Experiments on a diverse urban dataset from 20 global cities demonstrate our model's superior imputation performance and generalizability.
  arXiv  Detail & Related papers  (2025-01-20T07:12:40Z)
• Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration [51.452664740963066]
  Collaborative Gym is a framework enabling asynchronous, tripartite interaction among agents, humans, and task environments. We instantiate Co-Gym with three representative tasks in both simulated and real-world conditions. Our findings reveal that collaborative agents consistently outperform their fully autonomous counterparts in task performance.
  arXiv  Detail & Related papers  (2024-12-20T09:21:15Z)
• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents [101.17919953243107]
  GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs) We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%. We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
  arXiv  Detail & Related papers  (2024-04-25T15:59:16Z)
• Large language model empowered participatory urban planning [5.402147437950729]
  This research introduces an innovative urban planning approach integrating Large Language Models (LLMs) within the participatory process. The framework, based on the crafted LLM agent, consists of role-play, collaborative generation, and feedback, solving a community-level land-use task catering to 1000 distinct interests.
  arXiv  Detail & Related papers  (2024-01-24T10:50:01Z)
• A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters [4.8051028509814575]
  We propose a SIM-GAT model to predict visitation flows between community business clusters and their trade areas. A graph-based deep learning model, i.e., Graph AttenTion network (GAT), is used to capture the clusters and interdependencies of business.
  arXiv  Detail & Related papers  (2024-01-09T23:42:21Z)
• AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning [8.363841553742912]
  We introduce a Consensus-based Multi-Agent Reinforcement Learning framework for real-world land use readjustment. This framework serves participatory urban planning, allowing diverse intelligent agents as stakeholder representatives to vote for preferred land use types. By integrating Multi-Agent Reinforcement Learning, our framework ensures that participatory urban planning decisions are more dynamic and adaptive to evolving community needs.
  arXiv  Detail & Related papers  (2023-10-25T17:04:11Z)
• Multi-Temporal Relationship Inference in Urban Areas [75.86026742632528]
  Finding temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. We propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation learning and further handle the relationship sparsity.
  arXiv  Detail & Related papers  (2023-06-15T07:48:32Z)
• Dynamic Urban Planning: an Agent-Based Model Coupling Mobility Mode and Housing Choice. Use case Kendall Square [5.620109882646823]
  This paper presents an agent-based model that characterizes citizens' behavioural patterns with respect to housing and mobility choice. The realistic identification and representation of the criteria that affect this decision-making process will help understand and evaluate the impacts of potential housing incentives.
  arXiv  Detail & Related papers  (2021-06-28T10:54:44Z)
• Modelling Cooperation and Competition in Urban Retail Ecosystems with Complex Network Metrics [8.767281392253976]
  We first present a modeling framework which examines the role of new businesses in their respective local areas. Using a longitudinal dataset from location technology platform Foursquare, we model new venue impact across 26 major cities worldwide. We then devise a data-driven metric that captures the first-order correlation on the impact of a new venue on retailers within its vicinity. Lastly, we build a supervised machine learning model to predict the impact of a given new venue on its local retail ecosystem.
  arXiv  Detail & Related papers  (2021-04-28T19:18:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.