# A Network-Based Model for Optimization of Container Location Assignment at Inland Terminals
## 2 Literature Review
### Container Allocation at Seaports  
Studies on seaports have focused on improving quay-side efficiency to speed up the processing of container vessels, but faster quay-side operations will not lead to greater overall productivity without effective storage yard strategies [35]. To address this, the authors of [36] proposed a model to increase seaport productivity by preventing subsystem overcapacity. The authors of [37] developed an analytical model to simultaneously determine the optimal storage space and number of yard cranes for inbound containers that could minimize total cost. Paper [38] investigated container yard congestion issues and proposed a physics-based model for truck interruptions that could minimize the total time spent in moving containers in the container yard. The concept of yard template regeneration was proposed [39]. In that approach, a multiple-objective mixed-integer programmingmodel was formulated that considers the minimum transportation cost, minimum template disturbance, and maximum space utilization for container terminals. The authors of [40] developed a model to determine the numbers of bays, rows, and tiers in a seaport container yard block that maximize throughput and storage capacities. To improve storage yard management at seaports, several studies have tackled the problems of container storage space allocation and location assignment (stacking). A model for predicting the number of relocation steps to access a required container from the seaport container yard was proposed by the authors of [41]. A mathematical model for the solution of the block relocation problem in order to minimize the number of relocations required to retrieve the blocks in the specified order was presented [42]. In that approach, a binary linear program was used for block relocation, while a simple heuristic was used to define relocation rules for shifting containers located on top of the one to be retrieved. Based on the model of [42], the author of [43] developed a mathematical solution for the block relocation problem. In the paper [44], the authors used a genetic algorithm and a tabu search algorithm, on their own and together, to integrate a container-transfer model with a container-location model to optimize seaport container location and handling. Paper [45] defined a dynamic generalized assignment problem to allocate containers in port storage yards. A consignment strategy with a static yard template to reduce the level of relocations in the yard for outbound containers and improve operation efficiency and land productivity was proposed in paper [35]. To implement their strategy, the researchers allocated yard storage within the sections. To make yard allocation more efficient, two space-sharing approaches were proposed in which storage space was dynamically reserved for different vessels during different shifts. To improve storage yard management at seaports, paper [46] presented an integer linear programming model that integrates the two interrelated decision problems, space allocation and yard crane deployment. The main goal was to minimize the costs of yard crane operation and interblock movements. A heuristic approach, based on a divide-and-conquer strategy, was used to solve the integrated problem efficiently; a harmony search algorithm and constraint satisfaction techniques were used to solve subproblems within the heuristic approach. In paper [47], the authors considered the restricted container relocation problem and analyzed the selection of appropriate slots for relocated outbound containers during the retrieval process in order to minimize the total number of container relocations. A beam search algorithm was employed to solve this problem, while heuristics were used to ensure efficient beam search node selection. In paper [4], the authors developed a fuzzy knowledge-based system for container yard management with the aim of lowering unproductive manipulations. Fuzzy theory was introduced to solve multi-dimensional problems with multiple objectives.