Distributed Production Planning Models in Production Networks

Author(s):  
Paolo Renna

Production networks can be dynamically structured and involving multiple production sites with different objectives. This organizational structure is able to match agility and efficiency to compete in the global market. In this environment is impossible for a single organization to control whole production networks; thus, a decentralized approach has been developed to manage the production networks. However, the coordinate mechanism in decentralized control is more important to obtain a high level of performance. The research proposes innovative coordination strategies for coordinate production networks by Multi Agent Architecture. A link between negotiation strategies and a production planning algorithm has been developed in order to support the coordination strategies proposed. In particular, two protocols to reach an agreement between customer and the production network have been proposed: negotiation and an expected profit approaches. Moreover, two coordination strategies have been proposed: index efficiency and ranking price approaches. Finally, the possibility of divide the orders in lots by the customer is proposed. A simulation environment based on open source code and Multi Agent Architecture has been developed to test the proposed approaches. The experiments have been conducted in different conditions of workload and mar-up; the results of the simulation provide the information necessary to select the suitable coordination and protocol mechanisms in a distributed production planning problem.

2012 ◽  
pp. 1666-1682
Author(s):  
Samir Hamichi ◽  
Diana Mangalagiu ◽  
Zahia Guessoum

In this chapter, the authors present a multi-agent model aimed to investigate emergent organizational structures in production networks and their reification by means of pheromone-based algorithms. The model considers agents (firms) embedded in a production network, interacting among them through business-to-business relations. The evolution of the network structure is endogenous, as it takes into account the individual behavior of the firms and their interactions. The firms are adaptive agents taking investment decisions according to their business efficiency. They adapt their prices to be competitive and get a larger share of the market. Also, they adapt their business relations with their suppliers in order to reduce costs of inputs and get orders satisfied. The agent’s proactivity, with very simple decision mechanisms at the micro level, leads to the emergence of meta-stable business clusters and supply chains at the macro level. Pheromone-based algorithms reify dynamically these clusters as explicit graphs. The results of the authors’ simulations show the impact of the transportation cost and the geographical reach on the regionalization of production and on wealth patterns. Individual firms, with local B2B interactions and decisions, form stable production systems based on the supply/demand and market growth mechanisms leading to the maturation of the market.


10.5772/5808 ◽  
2005 ◽  
Vol 2 (1) ◽  
pp. 2 ◽  
Author(s):  
S. Heinrich ◽  
H. Durr ◽  
T. Hanel ◽  
J. Lassig

The goal is the development of a simultaneous, dynamic, technological as well as logistical real-time planning and an organizational control of the production by the production units themselves, working in the production network under the use of Multi-Agent-Technology. The design of the multi-agent-based manufacturing management system, the models of the single agents, algorithms for the agent-based, decentralized dispatching of orders, strategies and data management concepts as well as their integration into the SCM, basing on the solution described, will be explained in the following.


Author(s):  
Samir Hamichi ◽  
Diana Mangalagiu ◽  
Zahia Guessoum

In this chapter, the authors present a multi-agent model aimed to investigate emergent organizational structures in production networks and their reification by means of pheromone-based algorithms. The model considers agents (firms) embedded in a production network, interacting among them through business-to-business relations. The evolution of the network structure is endogenous, as it takes into account the individual behavior of the firms and their interactions. The firms are adaptive agents taking investment decisions according to their business efficiency. They adapt their prices to be competitive and get a larger share of the market. Also, they adapt their business relations with their suppliers in order to reduce costs of inputs and get orders satisfied. The agent’s proactivity, with very simple decision mechanisms at the micro level, leads to the emergence of meta-stable business clusters and supply chains at the macro level. Pheromone-based algorithms reify dynamically these clusters as explicit graphs. The results of the authors’ simulations show the impact of the transportation cost and the geographical reach on the regionalization of production and on wealth patterns. Individual firms, with local B2B interactions and decisions, form stable production systems based on the supply/demand and market growth mechanisms leading to the maturation of the market.


2020 ◽  
Vol 16 (1) ◽  
pp. 95-117
Author(s):  
Anna Beckers

AbstractReviewing the burgeoning legal scholarship on global value chains to delineate the legal image of the global value chain and then comparing this legal image with images on global production in neighbouring social sciences research, in particular the Global Commodity Chain/Global Value Chain and the Global Production Network approach, this article reveals that legal research strongly aligns with the value chain image, but takes less account of the production-centric network image. The article then outlines a research agenda for legal research that departs from a network perspective on global production. To that end, it proposes that re-imagining the law in a world of global production networks requires a focus in legal research on the legal construction of global production and its infrastructure and a stronger contextualization of governance obligations and liability rules in the light of the issue-specific legal rules that apply to said infrastructure.


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