scholarly journals Berth scheduling at marine container terminals

2019 ◽  
Vol 5 (1) ◽  
pp. 30-66 ◽  
Author(s):  
Masoud Kavoosi ◽  
Maxim A. Dulebenets ◽  
Olumide Abioye ◽  
Junayed Pasha ◽  
Oluwatosin Theophilus ◽  
...  
Keyword(s):  
Population Based ◽  
Container Terminals ◽  
Metaheuristic Algorithms ◽  
Computational Time ◽  
Scheduling Problem ◽  
Metaheuristic Algorithm ◽  
Content Type ◽  
Marine Container Terminals ◽  
Problem Instances ◽  
Berth Scheduling

Purpose Marine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT. Design/methodology/approach A universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following: evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands. Findings The conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed UIMA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu search and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s. Research limitations/implications Some of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic. Practical implications The developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational time. Originality/value A novel island-based metaheuristic algorithm is designed to solve the spatially constrained berth scheduling problem. The proposed island-based algorithm adopts several types of metaheuristic algorithms to cover different areas of the search space. The considered metaheuristic algorithms rely on different operators. Such feature is expected to facilitate the search process for superior solutions.

scholarly journals Innovative Metaheuristic Algorithms for Efficient Berth Scheduling at Marine Container Terminals; Masoud Kavoosi; Dissertation; 2019

2020 ◽  
Author(s):  
Masoud Kavoosi
Keyword(s):  
Global Economy ◽  
Critical Role ◽  
Turnaround Time ◽  
Liner Shipping ◽  
Container Terminals ◽  
Metaheuristic Algorithms ◽  
Decision Problems ◽  
Computational Time ◽  
Marine Container Terminals ◽  
Berth Scheduling

International trade plays a critical role for the global economy. A significant portion of general consumption goods is transported by vessels in a containerized form. Increasing volumes of the containerized trade impose additional pressure on marine container terminals (MCTs), which are responsible for service of incoming vessels from different liner shipping companies. MCTs are considered as important nodes in supply chains, as they allow transfer of containers from vessels to one of the alternative inland modes (generally, truck or rail). Managing operations within MCTs is a quite challenging task not only due to increasing seaborne trade volumes, but also due to increasing number of liner shipping alliances, deployment of megaships by liner shipping companies, inability to expand the terminal size due to spatial constraints, and other factors. Another important goal for the MCT operator is to ensure the timely service of vessels, as the vessel service delays may result in potential disruption of liner shipping schedules, and ultimately cause product delivery delays to the final customers.This dissertation focuses on addressing complex decision problems, which are faced by the MCT operators. The seaside operations, which deal with loading and unloading of vessels, will be of a primary interest, as they affect the total turnaround time of vessels. The latter is considered as one of the key performance indicators for the MCT operations. As many of decision problems related to the seaside operations, cannot be solved using the exact optimization algorithms (e.g., Simplex, Branch-and-Bound, Brach-and-Cut) within a reasonable computational time for the realistic size problem instances, a number of metaheuristic algorithms will be proposed (with a primary focus on Evolutionary Algorithms, which have been widely used in different fields for solving combinatorial decision problems). The proposed metaheuristic algorithms are expected to serve as efficient decision making tools for the MCT operators and facilitate exchange of freight flows between water and inland transportation modes.

scholarly journals A novel continuous berth scheduling model at multiple marine container terminals with tidal considerations

2017 ◽  
Vol 2 (2) ◽  
pp. 142-157 ◽  
Author(s):  
Ali Dadashi ◽  
Maxim A. Dulebenets ◽  
Mihalis M. Golias ◽  
Abdolreza Sheikholeslami
Keyword(s):  
Mathematical Model ◽  
Time Of Day ◽  
Container Terminals ◽  
Channel Depth ◽  
Content Type ◽  
Access Channel ◽  
Marine Container Terminals ◽  
Bandar Abbas ◽  
Berth Scheduling ◽  
Operational Data

Purpose The paper aims to propose a new mathematical model for allocation and scheduling of vessels at multiple marine container terminals of the same port, considering the access channel depth variations by time of day. Design/methodology/approach This paper proposes a new mathematical model for allocation and scheduling of vessels at multiple marine container terminals of the same port, considering the access channel depth variations by time of day. The access channel serves as a gate for vessels entering or leaving the port. During low-depth tidal periods the vessels with deep drafts have to wait until the depth of the access channel reaches the required depth. Findings A number of numerical experiments are performed using the operational data collected from Port of Bandar Abbas (Iran). Results demonstrate that the suggested methodology is able to improve the existing port operations and significantly decrease delayed vessel departures. Originality/value The contribution of this study to the state of the art is a novel mathematical model for allocation and scheduling of vessels at multiple terminals of the same port, taking into consideration channel depth variations by time of day. To the best of the authors’ knowledge, this is the first continuous berth scheduling linear model that addresses the tidal effects on berth scheduling (both in terms of vessel arrival and departure at/from the berth) at multiple marine container terminals.

scholarly journals Berth Scheduling Problem Considering Traffic Limitations in the Navigation Channel

2018 ◽  
Vol 10 (12) ◽  
pp. 4795 ◽  
Author(s):  
Ya Xu ◽  
Kelei Xue ◽  
Yuquan Du
Keyword(s):  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Container Terminals ◽  
Mixed Integer ◽  
Computational Time ◽  
Scheduling Problem ◽  
Navigation Channel ◽  
Temporary Closure ◽  
Berth Scheduling

In view of the trend of upsizing ships, the physical limitations of natural waterways, huge expenses, and unsustainable environmental impact of channel widening, this paper aims to provide a cost-efficient but applicable solution to improve the operational performance of container terminals that are enduring inefficiency caused by channel traffic limitations. We propose a novel berth scheduling problem considering the traffic limitations in the navigation channel, which appears in many cases including insufficient channel width, bad weather, poor visibility, channel accidents, maintenance dredging of the navigation channel, large vessels passing through the channel, and so on. To optimally utilize the berth and improve the service quality for customers, we propose a mixed-integer linear programming model to formulate the berth scheduling problem under the one-way ship traffic rule in the navigation channel. Furthermore, we develop a more generalized model which can cope with hybrid traffic in the navigation channel including one-way traffic, two-way traffic, and temporary closure of the navigation channel. For large-scale problems, a hybrid simulated annealing algorithm, which employs a problem-specific heuristic, is presented to reduce the computational time. Computational experiments are performed to evaluate the effectiveness and practicability of the proposed method.

Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning

2011 ◽  
Vol 2 (4) ◽  
pp. 1-11 ◽  
Author(s):  
Xin-She Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Design Problem ◽  
Firefly Algorithm ◽  
Parameter Tuning ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
New Algorithms ◽  
Automatic Parameter Tuning

Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm further to introduce chaos-enhanced firefly algorithm with automatic parameter tuning, which results in two more variants of FA. The author first compares the performance of these algorithms, and then uses them to solve a benchmark design problem in engineering. Results obtained by other methods will be compared and analyzed.

Robust berth scheduling at marine container terminals via hierarchical optimization

2014 ◽  
Vol 41 ◽  
pp. 412-422 ◽  
Author(s):  
Mihalis Golias ◽  
Isabel Portal ◽  
Dinçer Konur ◽  
Evangelos Kaisar ◽  
Georgios Kolomvos
Keyword(s):  
Container Terminals ◽  
Hierarchical Optimization ◽  
Marine Container Terminals ◽  
Berth Scheduling

A Multi-Objective Decision and Analysis Approach for the Berth Scheduling Problem

2012 ◽  
pp. 1-20
Author(s):  
Mihalis M. Golias ◽  
Maria Boilé ◽  
Sotirios Theofanis ◽  
Heidi A. Taboada
Keyword(s):  
Resource Allocation ◽  
Container Terminal ◽  
Solution Space ◽  
Allocation Problem ◽  
Container Terminals ◽  
Analysis Approach ◽  
Scheduling Problem ◽  
Resource Allocation Problem ◽  
Multi Objective ◽  
Berth Scheduling

Berth scheduling can be described as the resource allocation problem of berth space to vessels in a container terminal. When defining the allocation of berths to vessels container terminal operators set several objectives which ideally need to be optimized simultaneously. These multiple objectives are often non-commensurable and gaining an improvement on one objective often causes degrading performance on the other objectives. In this paper, the authors present the application of a multi-objective decision and analysis approach to the berth scheduling problem, a resource allocation problem at container terminals. The proposed approach allows the port operator to efficiently select a subset of solutions over the entire solution space of berth schedules when multiple and conflicting objectives are involved. Results from extensive computational examples using real-world data show that the proposed approach is able to construct and select efficient berth schedules, is consistent, and can be used with confidence.

Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning

2013 ◽  
pp. 125-136 ◽  
Author(s):  
Xin-She Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Design Problem ◽  
Firefly Algorithm ◽  
Parameter Tuning ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
New Algorithms ◽  
Automatic Parameter Tuning

Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm further to introduce chaos-enhanced firefly algorithm with automatic parameter tuning, which results in two more variants of FA. The author first compares the performance of these algorithms, and then uses them to solve a benchmark design problem in engineering. Results obtained by other methods will be compared and analyzed.

A network model for solving the yard truck routing and scheduling problem

2016 ◽  
Vol 27 (2) ◽  
pp. 353-370
Author(s):  
Feng-Ming Tsai ◽  
Chung-Cheng Lu ◽  
Yu-Ming Chang
Keyword(s):  
Network Model ◽  
Waiting Time ◽  
Queuing Theory ◽  
Operating Cost ◽  
Container Terminals ◽  
Scheduling Problem ◽  
Content Type ◽  
Routing And Scheduling ◽  
Space Network ◽  
Time Space

Purpose – The purpose of this paper is to improve the efficiency of loading and discharging operations in container terminals. Accounting for an increase in the size of ships, the yard truck (YT) routing and scheduling problem has become an important issue to terminal operators. Design/methodology/approach – A (binary) integer programming model is developed using the time-space network technique to optimally move YTs between quay cranes (QC) and yard cranes (YC) in the time and space dimensions. The objective of the model is to minimize the total operating cost, and the model employs the M/M/S model in the queuing theory to determine the waiting time of YTs. The developed model can obtain the optimal number of YTs and their scheduling and routing plans simultaneously, as shown by the computational results. Findings – The results also show that the model can be applied to practical operations. In this research, an experimental design of the QC and YC operation networks was considered with the import and export containers carried by YTs. The model can be used to tackle a real world problem in an international port, and the analysis results could be useful references for port operators in actual practice. Research limitations/implications – The purpose of this research only focusses on YTs routing and scheduling problem, however, the container terminal operation problems are interrelated with berth allocation and yard stacking plan. The managerial application of this study is to analyze the trade-off between truck numbers and truck waiting time can be used for terminal operators to adjust the truck assignment. This research can assist an operator to determine the optimal fleet size and schedule in advance to avoid wasted costs and congestion in the quayside and yard block. Originality/value – This research solves the YT scheduling and routing problem for container discharging and loading processes with a time-space network model, which has not been previously reported, through an empirical research.

scholarly journals Models and Solution Algorithms for Efficient Truck Scheduling at Cross-docking Terminals with Time-sensitive Products

2021 ◽  
Author(s):  
Oluwatosin Theophilus
Keyword(s):  
Mathematical Model ◽  
Metaheuristic Algorithms ◽  
Computational Time ◽  
Neighborhood Search ◽  
Perishable Products ◽  
Service Cost ◽  
Cross Docking ◽  
Truck Scheduling ◽  
Size Problem ◽  
Problem Instances

This dissertation focuses on the scheduling of trucks (both in- and outbound trucks) at a CDT, where some of the delivered products are perishable in nature. The short lifespan of perishable products (i.e., foods and drugs) poses critical challenges to the CDT operations management. Perishable goods are time-sensitive products that require minimal handling time to preserve their quality and profitability. Cross-docking is expected to facilitate the distribution of perishable products within supply chains. There are many challenges involved in the management of the cross-docking terminals with perishable products, including determination of the service order of the trucks (inbound and outbound) carrying perishable products, selection of preemption strategies for certain trucks (i.e., a given truck can leave the door, so another truck can be docked for service), allocation of suitable temporary storage space for products, quality loss due to late delivery or errors in temperature control.This dissertation aims to develop a mathematical model for scheduling the arriving trucks at a cross-dock terminal, taking product decay into consideration throughout the handling process. The objective of the mathematical model minimizes the total truck service cost, which includes (1) waiting cost; (2) service cost; (3) cost of product storage; (4) cost of delay in truck departure; and (5) the cost associated with the decay of products that are perishable in nature. A number of linearization techniques are discussed in order to linearize the original nonlinear mathematical model (where the nonlinearity is caused by the adopted product decay function). The complexity of the linearized model is evaluated in this dissertation. Moreover, the candidate solution approaches for the proposed mathematical model are described.The developed model was solved using the exact optimization technique. In particular, the model was solved to optimality using CPLEX. However, it was observed that the computational time increased as the problem size increased due to the model complexity. Four alternative solution approaches namely: (1) Evolutionary Algorithm (EA); (2) Variable Neighborhood Search (VNS); (3) Tabu Search (TS); and (4) Simulated Annealing (SA), which are common metaheuristic algorithms, were developed and compared with CPLEX using small-size problem instances. These metaheuristics were able to achieve optimal solutions for the small-size problem instances and required relatively low computational times. The metaheuristic algorithms were further compared, and EA was found to outperform the others (VNS, TS, and SA) based on the balance between the objective function and computational time values. A set of analyses were carried out using EA, and managerial insights that could be of interest to supply chain stakeholders were drawn. The proposed mathematical model, the developed EA, and the managerial insights could assist the CDT manager in making efficient and timely truck scheduling decisions in any planning horizon.

Analysis of Firefly Algorithms and Automatic Parameter Tuning

2015 ◽  
pp. 36-49 ◽  
Author(s):  
Xin-She Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Design Problem ◽  
Firefly Algorithm ◽  
Parameter Tuning ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
New Algorithms ◽  
Automatic Parameter Tuning

Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this chapter, the authors analyze the standard firefly algorithm and study the chaos-enhanced firefly algorithm with automatic parameter tuning. They first compare the performance of these algorithms and then use them to solve a benchmark design problem in engineering. Results obtained by other methods are compared and analyzed. The authors also discuss some important topics for further research.

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