Simulation-based optimization for housekeeping in a container transshipment terminal

Cordeau, Jean‐François; Legato, Pasquale; Mazza, Rina Mary; Trunfio, Roberto

doi:10.1016/j.cor.2014.08.001

Cited by 40 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In simulation-based optimization, candidates are evaluated on the basis of simulation results, while in pure optimization, these evaluations are done by math expressions. Just recently, simulation-based optimization method has been successfully used to optimize ambulance fleet allocation and base station locations [28], to determine the best design and control factors of a paint shop production line in an automotive company [29], and to decide the best vehicle schedules for housekeeping in a container transshipment terminal [30]. To the extent of our knowledge, this method has not been used in ACT.…”

Section: Related Work and Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Simulation-Based Optimization for Storage Allocation Problem of Outbound Containers in Automated Container Terminals

Zhao

Xia

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Storage allocation of outbound containers is a key factor of the performance of container handling system in automated container terminals. Improper storage plans of outbound containers make QC waiting inevitable; hence, the vessel handling time will be lengthened. A simulation-based optimization method is proposed in this paper for the storage allocation problem of outbound containers in automated container terminals (SAPOBA). A simulation model is built up by Timed-Colored-Petri-Net (TCPN), used to evaluate the QC waiting time of storage plans. Two optimization approaches, based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), are proposed to form the complete simulation-based optimization method. Effectiveness of this method is verified by experiment, as the comparison of the two optimization approaches.

show abstract

Section: Related Work and Methodologymentioning

confidence: 99%

“…Colored Petri Net (CPN) [30] and Timed Petri Net (TPN) [32] are two extensions of Petri Net. In a CPN, tokens have properties and data values attached to them, called token color, after which distinction between tokens is made possible [33][34][35].…”

Section: Methodsmentioning

confidence: 99%

Simulation-Based Optimization for Storage Allocation Problem of Outbound Containers in Automated Container Terminals

Zhao

Xia

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…There are two main approaches to addressing the operational decision problems of container terminals [8]. The first approach takes into account the complex dynamic environment of a container terminal.…”

Section: Literature Review On Integrated Decision Problemsmentioning

confidence: 99%

Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals

et al. 2021

View full text Add to dashboard Cite

At container terminals, many cargo handling processes are interconnected and occur in parallel. Within short time windows, many operational decisions need to be made and should consider both time efficiency and equipment utilization. During operation, many sources of disturbance and, thus, uncertainty exist. For these reasons, perfectly coordinated processes can potentially unravel. This study analyzes simulation-based optimization, an approach that considers uncertainty by means of simulation while optimizing a given objective. The developed procedure simultaneously scales the amount of utilized equipment and adjusts the selection and tuning of operational policies. Thus, the benefits of a simulation study and an integrated optimization framework are combined in a new way. Four meta-heuristics—Tree-structured Parzen Estimator, Bayesian Optimization, Simulated Annealing, and Random Search—guide the simulation-based optimization process. Thus, this study aims to determine a favorable configuration of equipment quantity and operational policies for container terminals using a small number of experiments and, simultaneously, to empirically compare the chosen meta-heuristics including the reproducibility of the optimization runs. The results show that simulation-based optimization is suitable for identifying the amount of required equipment and well-performing policies. Among the presented scenarios, no clear ranking between meta-heuristics regarding the solution quality exists. The approximated optima suggest that pooling yard trucks and a yard block assignment that is close to the quay crane are preferable.

show abstract

“…Many analyses have been conducted in this field. Those analyses concerned among others: »» intermodal transport effectiveness (Monios, Bergqvist 2017;Santos et al 2015;Wiegmans, Konings 2015); »» handling process optimization (Boysen, Fliedner 2010;Cordeau et al 2015;Fedtke, Boysen 2017;Guo et al 2018); »» studies about intermodal carriages both in Poland and in the world (Flodén, Woxenius 2017;Grzelakowski 2012;Saeedi et al 2017); »» empty intermodal transport units management (Furió et al 2013;Hjortnaes et al 2017); »» localizations of the intermodal terminals (Limbourg, Jourquin 2009;Lin et al 2014;Brzeziński, Pyza 2020); »» environmental impact of intermodal transport (Arnold et al 2004;Braekers et al 2009;Craig et al 2013;Dimoula et al 2016;García-Álvarez et al 2013;Hjortnaes et al 2017;Jacyna et al 2017;Kreutzberger et al 2006;Li et al 2013Li et al , 2014López-Navarro 2014;Monios, Bergqvist 2017;EC 2015;Ritchie, Roser 2017;Southworth, Peterson 2000). However, current literature lacks reliable case studies showing a connection between the aspect of intermodal network modelling and GHG emission.…”

Section: Aspect Of Intermodal Network Modelling In Terms Of Environmental Impactmentioning

confidence: 99%

Carbon Dioxide Emission From Diesel Engine Vehicles in Intermodal Transport

Brzeziński

Pyza

2021

Transport

View full text Add to dashboard Cite

Currently, many logistics operators operate in both domestic and foreign markets using various forms of transport organization. Choosing a corresponding technology and appropriate form of transport has an influence not only delivery time and costs, but also has an impact on the environment as a whole. There is a plethora of public research available in global literature discussing various ways of exploiting transport. On the other hand, there is a lack of complex studies detailing carbon emissions coming from transport activity. Specifically, where a theory of organic fuel combustion in the form of a chemical reaction with oxygen is considered. To fill this gap, we offer an innovative Emission Model of Industrial Sources (EMIS) method. This method makes it possible to determine the amount of CO2 emitted into the atmosphere during various transport methods. It also enables us to estimate, in terms of CO2 output, a threshold where transport of containers via combined mode becomes more favourable for the environment, than road transport. We ran a simulation of our algorithm to create boundary conditions. This let us prepare a regression function of CO2 emission, for intermodal and road transport as a function of various transport distances. The simulation results suggest that our approach may be used by supervisory institutions, which are responsible further developing and utilizing combined transport.

show abstract

Simulation-based optimization for housekeeping in a container transshipment terminal

Cited by 40 publications

References 23 publications

Simulation-Based Optimization for Storage Allocation Problem of Outbound Containers in Automated Container Terminals

Simulation-Based Optimization for Storage Allocation Problem of Outbound Containers in Automated Container Terminals

Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals

Carbon Dioxide Emission From Diesel Engine Vehicles in Intermodal Transport

Contact Info

Product

Resources

About