Optimization models and methods for storage yard operations in maritime container terminals

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 175-182).Container terminals, where containers are transferred between different modes of transportation both on the seaside and landside, are crucial links in intercontinental supply chains. The rapid growth of container shipping and the increasing competitive pressure to lower rates result in demand for higher productivity. In this thesis, we design new models and methods for the combinatorial optimization problems representing storage yard operations in maritime container terminals. The goal is to increase the efficiency of yard cranes by decreasing unproductive container moves (also called relocations). We consider three problems with applicability to real-time operations. First, we study the container relocation problem that involves finding a sequence of container moves that minimizes the number of relocations needed to retrieve all containers, while respecting a given order of retrieval. We propose a new binary integer program model, perform an asymptotic average case analysis, and show that our methods can apply to other storage systems where stacking occurs. Second, we relax the assumption that the full retrieval order of containers is known in advance and study the stochastic container relocation problem. We introduce a new model, compare it with an existing one, and develop two new algorithms for both models based on decision trees and new heuristics. We show that techniques in this chapter apply more generally to finite horizon stochastic optimization problems with bounded cost functions. Third, we consider the integrated container relocation problem and yard crane scheduling problem to find an optimal sequence of scheduled crane moves that perform the required container movements. Taking into account practical constraints, we present a new model, propose a binary integer program using a network flow-type formulation, and design an efficient heuristic procedure for real-time operations based on properties of our mathematical formulation. We relate this problem to pick-up and delivery problems with a single vehicle and capacities at every node. In all three chapters, the efficiency of all our algorithms are shown through extensive computational experiments on available problem instances from the literature and/or on real data.by Virgile Galle.Ph. D

Yard Crane Scheduling for container storage, retrieval, and relocation

© 2018 Elsevier B.V. This paper introduces a novel optimization problem resulting from the combination of two major existing problems arising at storage yards in container terminals. The Yard Crane Scheduling Problem is typically concerned with routing the crane given a sequence of storage and retrieval requests to perform, while the Container Relocation Problem tackles the minimization of relocations when retrieving containers in a simpler setting. This paper is the first to consider a model that integrates these two problems by scheduling storage, retrieval and relocations requests and deciding on storage and relocation positions. We formulate this problem as an integer program that jointly optimizes current crane travel time and future relocations. Based on the structure of the proposed formulation and the linear programming relaxation of subproblems, we propose a heuristic local search scheme. Finally, we show the value of our solutions on both simulated instances as well as real data from a port terminal

A new 0-1 formulation of the restricted container relocation problem based on a binary encoding of congurations

Submitted to EJOR June 2017The Container Relocation Problem (CRP), also called Block Relocation Problem (BRP), is concerned with finding a sequence of moves of containers that minimizes the number of relocations needed to retrieve all containers, while respecting a given order of retrieval. The restricted CRP enforces that only containers blocking the target container can be relocated. We improve upon and enhance an existing binary encoding and using it, formulate the restricted CRP as a binary integer programming problem in which we exploit structural properties of the optimal solution. This integer programming formulation reduces significantly the number of variables and constraints compared to existing formulations. Its efficiency is shown through computational results on small and medium sized instances taken from the literature

A new binary formulation of the restricted Container Relocation Problem based on a binary encoding of configurations

© 2017 Elsevier B.V. The Container Relocation Problem (CRP), also called Block Relocation Problem (BRP), is concerned with finding a sequence of moves of containers that minimizes the number of relocations needed to retrieve all containers, while respecting a given order of retrieval. The restricted CRP enforces that only containers blocking the target container can be relocated. We improve upon and enhance an existing binary encoding and using it, formulate the restricted CRP as a binary integer programming problem in which we exploit structural properties of the optimal solution. This integer programming formulation reduces significantly the number of variables and constraints compared to existing formulations. Its efficiency is shown through computational results on small and medium sized instances taken from the literature

An average-case asymptotic analysis of the Container Relocation Problem

The Container Relocation Problem (CRP) involves finding a sequence of moves of containers that minimizes the number of relocations needed to retrieve all containers in a given order. In this paper, we focus on average case analysis of the CRP when the number of columns grows asymptotically. We show that the expected minimum number of relocations converges to a simple and intuitive lower-bound for which we give an analytical formula. Keywords: CRP; Asymptotic analysis; Expected lower boun