The Dynamic Broker Dominant Supply-Demand Matching Decision Using Multiagent System

Supply-demand matching is critical for the increase of response speed, the improvement of resource utilization, and the building of long-term partnership. This study will contribute to the present supply-demand matching studies by (1) summarizing the dynamic broker dominant supply-demand matching problem (B-SDMP), (2) proposing the dynamic broker dominant supply-demand matching approach, inclusive of (a) the building of the distributed constraint satisfaction model based on the dynamic negotiation among agents and (b) the design of an asynchronous backtracking algorithm according to interaction among agents, and (3) the design and development of a multiagent dynamic supply-demand matching system. To verify the validity and usability of the method, the system test and case simulation are conducted. The matching solutions yielded from this B-SDMP analysis can help the buyers find the appropriate sellers on one commodity/service under dynamic environment and stimulate the building of long-term partnership among the sellers, buyers, and broker as a stable supply chain.

Multi-Layer Distributed Constraint Satisfaction for Multi-criteria Optimization Problem

This article introduces a new approach to solve the multimodal transportation network planning problem (MTNP). In this problem, the commodities must be transported from an international network by at least two different transport modes. The main purpose is to identify the best multimodal transportation strategy. The present contribution focuses on efficient optimization methods to solve MTNP. This includes the assignment and the scheduling problems. The authors split the MTNP into layered. Each layer is presented by an agent. These agents interact, collaborate, and communicate together to solve the problem. This article defines MTNP as a distributed constraint satisfaction multi-criteria optimization problem (DCSMOP). This latter is a description of the constraint optimization problem (COP), where variables and constraints are distributed among a set of agents. Each agent can interact with other agents to share constraints and to distribute complementary tasks. Experimental results are the proof of this work efficiently.