Storage Space Allocation at Marine Container Terminals Using Ant-based Control by Omor Sharif and Nathan Huynh Session 677: Innovations in intermodal terminal design and operations Sponsored by AT020, AT050, AW010

Overview of Storage Space Allocation Problem (SSAP)

Overview of SSAP Container yard is divided up into yard blocks Each yard block holds a group of containers Each yard block has a fixed total capacity Storage changes with time and among blocks Assign yard blocks to newly arriving containers

Objectives of SSAP Two competing objectives: Reduce Workload Imbalance – Proper distribution of newly arriving containers amongst blocks Minimize Container Transportation Cost – Minimize the distance traveled by ITs between berth and yard

Two Levels of SSAP Block level decision- Finds the block to store a container Objectives are ‘workload balancing’ & ‘distance minimizing’ Stack level decision- Finds an exact stack in a block Objective is to ‘minimize future reshuffling’

Why Workload Balancing? Reduces relocation of yard cranes Reduces turn time of vessels Reduces average container handling time Reduces congestion on road network Improves efficiency of related resources

What is Ant-Based Control? Falls under ‘Ant colony optimizations & algorithms’ Used to solve complex routing problems in network Inspired by collective intelligence of ants Can be used for shortest path and load balancing Utilizes pheromone laying behavior of ants Ants have a decision to make Situation several months later

Ant Based Control for SSAP A network approach to container terminal Network support artificial ant agents Ants move just like inbound & outbound containers Lays pheromone as a function of distance & congestion Probabilities in pheromone table guide ants & containers Fig: Ant choses path according to probability

Shortest Path Workload Balancing Ant age Increases with traveled distance Probability of selection decrease with ant age Workload Balancing Ants are delayed at congested blocks Delay is proportional to degree of congestion

Dynamic Interaction between Ants & Containers

Simulation and Experiments A simulation was modeled in NetLogo NetLogo - A multi-agent programmable environment

Parameter and Values Used in Model

Parameter and Values Used in Model

Performance Metric I - Transport Distance Converge to shortest path in 500 time-units Under no congestion converge to lower bound When congestion occurs distance converge to 2% above lower bound

Performance Metric II - Workload Imbalance Standard deviation gradually rises when no congestion When congestion occurs standard deviation falls Maintains a distribution to accommodate both objectives

Conclusions What’s New? Distributed allocation to individual containers Real-time approach requiring no advanced container arrival information Adaptive and scalable Synchronous instead of hierarchical approach Potential for model integration Future Work Incorporate ‘stack level’ decision Categorization of containers (Loaded, Empty etc.)

Thank You