Storage Space Allocation Strategy for Digital Data with Message Importance

This paper mainly focuses on the problem of lossy compression storage based on the data value that represents the subjective assessment of users when the storage size is still not enough after the conventional lossless data compression. To this end, we transform this problem to an optimization, which pursues the least importance-weighted reconstruction error in data reconstruction within limited total storage size, where the importance is adopted to characterize the data value from the viewpoint of users. Based on it, this paper puts forward an optimal allocation strategy in the storage of digital data by the exponential distortion measurement, which can make rational use of all the storage space. In fact, the theoretical results show that it is a kind of restrictive water-filling. It also characterizes the trade-off between the relative weighted reconstruction error and the available storage size. Consequently, if a relatively small part of total data value is allowed to lose, this strategy will improve the performance of data compression. Furthermore, this paper also presents that both the users’ preferences and the special characteristics of data distribution can trigger the small-probability event scenarios where only a fraction of data can cover the vast majority of users’ interests. Whether it is for one of the reasons above, the data with highly clustered message importance is beneficial to compression storage. In contrast, from the perspective of optimal storage space allocation based on data value, the data with a uniform information distribution is incompressible, which is consistent with that in the information theory.

A Novel Two-Stage Heuristic for Solving Storage Space Allocation Problems in Rail–Water Intermodal Container Terminals

In the past, most researchers have paid attention to the storage space allocation problem in maritime container terminals, while few have studied this problem in rail–water intermodal container terminals. Therefore, this paper proposes a storage space allocation problem to look for a symmetry point between the efficiency and effectivity of rail–water intermodal container terminals and the unbalanced allocations and reallocation operations of inbound containers in the railway operation area, which are two interactive aspects. In this paper, a two-stage model on the storage space allocation problem is formulated, whose objective is to balance inbound container distribution and minimize overlapping amounts, considering both stacking principles, such as container departure time, weight and stacking height, and containers left in railway container yards from earlier planning periods. In Stage 1, a novel simulated annealing algorithm based on heuristics is introduced and a new heuristic algorithm based on a rolling horizon approach is developed in Stage 2. Computational experiments are implemented to verify that the model and algorithm we introduce can enhance the storage effect feasibly and effectively. Additionally, two comparison experiments are carried out: the results show that the approach in the paper performs better than the regular allocation approach and weight constraint is the most important influence on container storage.

The outbound container slot allocation based on the stowage plan in rail–water intermodal container terminals

In the past, most researchers focused on the storage space allocation problem or container block allocation problem in maritime container terminals, while few studied the container slot allocation problem in rail–water intermodal container terminals. Container slot allocation problem is proposed to reduce relocation operations of containers in railway container yards and improve the efficiency of rail–water intermodal container terminals. In this paper, a novel outbound container slot allocation model is introduced to reduce the rehandling operations, considering stowage plan, containers left from earlier planning periods and container departure time. A novel heuristic algorithm based on the rolling planning horizon approach is developed to solve the proposed problem effectively. Computational experiments are carried out to validate that the proposed model and algorithm are feasible and effective to enhance the storage effect. Meanwhile, some other experiments are conducted to verify that our approach is better than the regular allocation approach, which is a common method in marine and railway container terminals, and container weight is the most important influence factor when storing containers.