Modeling and solving truck-load consolidation problems by using multi-agent technology

Logistics organizations mainly the ones providing land transportation services are facing with difficulties while making effective operational decisions. This is especially the case in making load/capacity/route planning and load consolidation decisions where customer orders are generally unpredictable and subject to sudden changes. Classical modeling and decision support systems are mostly insufficient to provide satisfactory solutions in a reasonable time for solving such dynamic problems. Agent-based approaches especially multi-agent paradigms which can be considered as relatively new members of system science and software engineering are providing effective mechanisms for modeling dynamic systems. These systems are generally operating under unpredictable environments and having high degree of complex interactions. It seems that multi-agent paradigms have a big potential to handle complex problems in land transportation logistics. Based on this motivation, in this thesis a multi-agent based load consolidation decision making approach is proposed. In the proposed approach the load consolidation decisions for the less-than-truckload orders which are dynamically dispatched to the system are made by the software agents. The less-than-truckload orders are assigned/consolidated to the trucks by the negotiation mechanism constructed within the model. The proposed approach considers the truck travel distances, number of order rejections, average costs of the transportation operations and system profitability so as to measure the performance of the proposed system. The proposed system is tested with some static problem sets and via running it under some stochastic environment.