International Financial Crisis has made the less-than-truckload (LTL) industry face with severe challenges of survival and development. More and more small and medium-sized LTL carriers choose to collaborate as the potential savings are large, often in the range 5–15%. A key question is how to distribute profits/savings among the participants. Since every LTL carriers are guided by their own self-interests and their contributions to the collaboration are quite different, the proposed allocation method should be a collectively and individually desirable solution. In this paper, we firstly analyze the profit opportunities from collaboration and present mechanisms to realize these benefits by two illustrative examples. Based on the cooperative game theory, we formulate the LTL collaboration game and discuss the well-known profit allocation concepts including Proportional Allocation, Shapley value and Nucleolus. We then propose a new al-location method named Weighted Relative Savings Model (WRSM) which is in the core and minimizes the maximum difference between weighted relative savings among the participants. Simulation result for real-life instances shows the effectiveness of WRSM

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Multi-tier shuttle warehousing systems are increasingly popular because of their high flexibility and robustness. These systems consist of a multi-tier shuttle sub-system that controls horizontal movement and a lift sub-system that manages vertical movement. The combination of shuttles and lifts undertakes inbound/outbound tasks instead of the stacker crane in conventional automated storage and retrieval systems. Scheduling different devices to reduce expected cycle time is an important concern. Thus, we propose a time sequence mathematical model of task operation on the basis of the movement characteristics of shuttles and lifts. The task scheduling problem between shuttles and lifts is converted into an assembly line parallel job problem by analysing the mathematical model, which generates the scheduling task queue model in the specified time window. In addition, a Pareto optimal based on an elitist non-dominated sorting genetic algorithm is adopted to solve the multi-objective optimisation function in the task scheduling problem. Finally, we illustrate the findings of the study through a practical example. Results show that the optimisation scheduling solution can reduce the total lift idle time and the total shuttle waiting time. Furthermore, it can improve warehousing efficiency and lower operation costs.

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