Decentralized Decision Making in Two Sided Manufacturing-as-a-Service Marketplaces

• URL: http://arxiv.org/abs/2506.12730v1
• Date: Sun, 15 Jun 2025 05:43:21 GMT
• Title: Decentralized Decision Making in Two Sided Manufacturing-as-a-Service Marketplaces
• Authors: Deepak Pahwa,
• Abstract summary: This dissertation focuses on developing tools for decision making enabling decentralization in MaaS marketplaces.<n>Two key decision making levers are typically used to optimize the operations of these marketplaces: pricing and matching.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advancements in digitization have enabled two sided manufacturing-as-a-service (MaaS) marketplaces which has significantly reduced product development time for designers. These platforms provide designers with access to manufacturing resources through a network of suppliers and have instant order placement capabilities. Two key decision making levers are typically used to optimize the operations of these marketplaces: pricing and matching. The existing marketplaces operate in a centralized structure where they have complete control over decision making. However, a decentralized organization of the platform enables transparency of information across clients and suppliers. This dissertation focuses on developing tools for decision making enabling decentralization in MaaS marketplaces. In pricing mechanisms, a data driven method is introduced which enables small service providers to price services based on specific attributes of the services offered. A data mining method recommends a network based price to a supplier based on its attributes and the attributes of other suppliers on the platform. Three different approaches are considered for matching mechanisms. First, a reverse auction mechanism is introduced where designers bid for manufacturing services and the mechanism chooses a supplier which can match the bid requirements and stated price. The second approach uses mechanism design and mathematical programming to develop a stable matching mechanism for matching orders to suppliers based on their preferences. Empirical simulations are used to test the mechanisms in a simulated 3D printing marketplace and to evaluate the impact of stability on its performance. The third approach considers the matching problem in a dynamic and stochastic environment where demand (orders) and supply (supplier capacities) arrive over time and matching is performed online.

Related papers

• PromptChain: A Decentralized Web3 Architecture for Managing AI Prompts as Digital Assets [0.0]
  We present PromptChain, a decentralized Web3 architecture that establishes AI prompts as first-class digital assets with verifiable ownership, version control, and monetization capabilities.<n>Our design includes: (1) a comprehensive metadata schema for cross-model compatibility, (2) a stake-weighted validation mechanism to align incentives, and (3) a token economy that rewards contributors proportionally to their impact.
  arXiv  Detail & Related papers  (2025-07-13T11:10:39Z)
• Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment [15.273192037219077]
  We study the dynamic pricing and replenishment problems under inconsistent decision frequencies. We integrate a decision tree-based machine learning approach, trained on comprehensive market data. In this approach, two agents handle pricing and inventory and are updated on different scales.
  arXiv  Detail & Related papers  (2024-10-28T15:12:04Z)
• MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
  We propose an immersion-aware model trading framework that facilitates data provision for services while ensuring privacy through federated learning (FL)<n>We design an incentive mechanism to incentivize metaverse users (MUs) to contribute high-value models under resource constraints.<n>We develop a fully distributed dynamic reward algorithm based on deep reinforcement learning, without accessing any private information about MUs and other MSPs.
  arXiv  Detail & Related papers  (2024-10-25T16:20:46Z)
• Sparse Attention-driven Quality Prediction for Production Process Optimization in Digital Twins [53.70191138561039]
  We propose to deploy a digital twin of the production line by encoding its operational logic in a data-driven approach. We adopt a quality prediction model for production process based on self-attention-enabled temporal convolutional neural networks. Our operation experiments on a specific tobacco shredding line demonstrate that the proposed digital twin-based production process optimization method fosters seamless integration between virtual and real production lines.
  arXiv  Detail & Related papers  (2024-05-20T09:28:23Z)
• Optimal Automated Market Makers: Differentiable Economics and Strong Duality [22.943723387429678]
  Optimal market making in the presence of multiple goods is not well understood. We show that finding an optimal market maker is dual to an optimal transport problem. We present conjectures of optimal mechanisms in settings which show further complex behavior.
  arXiv  Detail & Related papers  (2024-02-14T12:27:54Z)
• No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution [48.27759561064771]
  We consider the two-echelon supply chain model introduced in [Cachon and Zipkin, 1999] under two different settings. We design algorithms that achieve favorable guarantees for both regret and convergence to the optimal inventory decision in both settings. Our algorithms are based on Online Gradient Descent and Online Newton Step, together with several new ingredients specifically designed for our problem.
  arXiv  Detail & Related papers  (2022-10-23T08:45:39Z)
• Concepts and Algorithms for Agent-based Decentralized and Integrated Scheduling of Production and Auxiliary Processes [78.120734120667]
  This paper describes an agent-based decentralized and integrated scheduling approach. Part of the requirements is to develop a linearly scaling communication architecture. The approach is explained using an example based on industrial requirements.
  arXiv  Detail & Related papers  (2022-05-06T18:44:29Z)
• Will bots take over the supply chain? Revisiting Agent-based supply chain automation [71.77396882936951]
  Agent-based supply chains have been proposed since early 2000; industrial uptake has been lagging. We find that agent-based technology has matured, and other supporting technologies that are penetrating supply chains are filling in gaps. For example, the ubiquity of IoT technology helps agents "sense" the state of affairs in a supply chain and opens up new possibilities for automation.
  arXiv  Detail & Related papers  (2021-09-03T18:44:26Z)
• Fairness based Multi-Preference Resource Allocation in Decentralised Open Markets [8.516527663732617]
  We propose a three-step resource allocation approach that employs a reverse-auction paradigm. At the first step, priority label is attached to each bidding vendor based on the proposed priority mechanism. At the second step, the preference score is calculated for all the different kinds of preferences of the buyers.
  arXiv  Detail & Related papers  (2021-09-01T06:29:06Z)
• VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback [104.06766271716774]
  We study a multi-round welfare-maximising mechanism design problem in instances where agents do not know their values. We first define three notions of regret for the welfare, the individual utilities of each agent and that of the mechanism. Our framework also provides flexibility to control the pricing scheme so as to trade-off between the agent and seller regrets.
  arXiv  Detail & Related papers  (2020-04-19T18:00:58Z)

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.