Interaction between intelligent agent strategies for real-time transportation planning

In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to dfferent collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents other jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a multi-agent system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead policies instead of a myopic policy (10-20%) and (ii) the joint effect of two look-ahead policies is larger than the effect of an individual policy. To provide an indication of the savings that might be realized with a central solution methodology, we benchmark our results against an integer programming approach

Look-ahead strategies for dynamic pickup and delivery problems

In this paper we consider a dynamic full truckload pickup and delivery problem with time-windows. Jobs arrive over time and are offered in a second-price auction. Individual vehicles bid on these jobs and maintain a schedule of the jobs they have won. We propose a pricing and scheduling strategy based on dynamic programming where not only the direct costs of a job insertion are taken into account, but also the impact on future opportunities. Simulation is used to evaluate the benefits of pricing opportunities compared to simple pricing strategies in various market settings. Numerical results show that the proposed approach provides high quality solutions, in terms of profits, capacity utilization, and delivery reliability

Interaction between intelligent agent strategies for real-time transportation planning

In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to different collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents offer jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead strategies instead of a myopic strategy (10–20%) and (ii) the joint effect of two look-ahead strategies is larger than the effect of an individual strategy. To provide an indication of the savings that might be realized under centralized decision making, we benchmark our results against an integer programming approach

PERFORMANCE AND ANALYSIS OF SPOT TRUCK-LOAD PROCUREMENT MARKETS USING SEQUENTIAL AUCTIONS

Competition in a transportation marketplace is studied under different supply/demand conditions, auction formats, and carriers' behavioral assumptions. Carriers compete in a spot truck-load procurement market (TLPM) using sequential auctions. Carrier participation in a TLPM requires the ongoing solution of two distinct problems: profit maximization problem (chose best bid) and fleet management problem (best fleet assignment to serve acquired shipments). Sequential auctions are used to model an ongoing transportation market, where carrier competition is used to study carriers' dynamic vehicle routing technologies and decision making processes. Given the complexity of the bidding/fleet management problem, carriers can tackle it with different levels of sophistication. Carriers' decision making processes and rationality/bounded rationality assumptions are analyzed. A framework to study carrier behavior in TL sequential auctions is presented. Carriers' behavior is analyzed as a function of fleet management technology, auction format, carrier bounded rationality, market settings, and decision making complexity. The effects of fleet management technology asymmetries on a competitive marketplace are studied. A methodology to compare dynamic fleet management technologies is developed. Under a particular set of bounded rationality assumptions, bidding learning mechanisms are studied; reinforcement learning and fictitious play implementations are discussed. The performance of different auction formats is studied. Simulated scenarios are presented and their results discussed

Solving the winner determination problem with discounted bids in transportation auctions

Continuing advances in modern technologies have transformed the procedure of transportation procurement through auctions in supply chain management (SCM). This study examines the online combinatorial auction (CA), which serves customers placed at the nodes of a transportation network, with particular consideration given to carbon emissions. The CA mechanism allows early shipments of the carriers to improve their load consolidation and reduce their repositioning trips. Sustainability and carbon emissions are considered by prioritizing the carrier’s carbon reduction performances. Two models are examined under the carbon emission regulations (Carbon tax and Cap-and-offset) to choose the winners in the CA. Our aim is to minimize the cost of transportation procurement and reduce carbon emissions by incorporating the green reputation-based winner determination problem within the procurement model. Computational experiments reveal the positive impact of prioritization and discounted offers in reducing both transportation costs and the number of empty trips. Indeed, our results show the introduction of the discounted bids allows a reduction of about 2% in the transportation cost for the shipper and 24% of empty movements, on average, for the carriers

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems