Global synchromodal transportation involves the movement of container shipments between inland terminals located in different continents using ships, barges, trains, trucks, or any combination among them through integrated planning at a network level. One of the challenges faced by global operators is the matching of accepted shipments with services in an integrated global synchromodal transport network with dynamic and stochastic travel times. The travel times of services are unknown and revealed dynamically during the execution of transport plans, but the stochastic information of travel times are assumed available. Matching decisions can be updated before shipments arrive at their destination terminals. The objective of the problem is to maximize the total profits that are expressed in terms of a combination of revenues, travel costs, transfer costs, storage costs, delay costs, and carbon tax over a given planning horizon. We propose a sequential decision process model to describe the problem. In order to address the curse of dimensionality, we develop a reinforcement learning approach to learn the value of matching a shipment with a service through simulations. Specifically, we adopt the Q-learning algorithm to update value function estimations and use the ϵ-greedy strategy to balance exploitation and exploration. Online decisions are created based on the estimated value functions. The performance of the reinforcement learning approach is evaluated in comparison to a myopic approach that does not consider uncertainties and a stochastic approach that sets chance constraints on feasible transshipment under a rolling horizon framework.

Agricultural supply chain is in general open and dynamic attributed and negotiation is a key strategy to realize collaboration among different entities involved. However, traditional static and offline negotiation strategy may not function well, an agricultural supply chain centered on third party logistics will provide a new collaborative relationship among agricultural materials suppliers, agricultural cooperatives, farmers, agricultural products demanders and third party logistics providers. This paper proposes a novel adaptive negotiation strategy for agricultural supply chain centered on third party logistics. The negotiation strategy consists of an evaluation function in terms of integrity and risk preferences, a concession function in terms of concession preference, time and resources, and a generation function in terms of opponent’s behaviors. Afterwards, an procedure was proposed to solve the negotiation strategy. Finally, a simulation experiment was employed to verify the effectiveness of the negotiation strategy.

The Asymmetric information among different entities can contribute to the imbalance between supply and demand in agricultural supply chain. Negotiation is an effective method to address information symmetry. Many researchers can be found in the literature on agent-based negotiation in industrial supply chain, but very few in agricultural supply chain, primarily because of the interior instability. This paper presents an agent-based negotiation framework for agricultural supply chain to address the information symmetry by introducing the third party logistics. The third party logistics, with more functions than a traditional broker, integrates logistics services and intermediary services to guarantee the relative stability in dynamic agricultural environments. A negotiation interaction process is also designed to facilitate the interaction among these agents. Finally, a case is used to validate the proposed framework.