Optimizing berth allocation and storage space in direct transshipment operations at container terminals

2017 ◽  
Vol 19 (3) ◽  
pp. 474-503 ◽  
Author(s):  
Qingcheng Zeng ◽  
Yuanjun Feng ◽  
Zigen Chen
Keyword(s):  
Container Terminals ◽  
Storage Space ◽  
Berth Allocation ◽  
And Storage

Multiple Equipment Integrated Scheduling and Storage Space Allocation in Rail–Water Intermodal Container Terminals Considering Energy Efficiency

2019 ◽  
Vol 2673 (3) ◽  
pp. 199-209 ◽  
Author(s):  
Yijia Yang ◽  
Xiaoning Zhu ◽  
Ali Haghani
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Operation Time ◽  
Container Terminals ◽  
Optimization Approach ◽  
Storage Space ◽  
Integrated Scheduling ◽  
Space Allocation ◽  
Storage Space Allocation ◽  
And Storage

The rail–water coordinated operation area in a container terminal is the key place to operate the transshipment of intermodal containers between the rail and the sea—the handling efficiency in which can affect the overall transport turnover efficiency. A complicated operational process for various handling equipment exists in this coordinated operation area and can lead to a large amount of energy consumption and environmental pollution. This study proposes an integrated optimization approach to manage the multiple equipment integrated scheduling and storage space allocation problem in an energy-efficient way. A bi-objective optimization model is proposed to minimize the overall operation time and energy consumption, in which the handling operations of imported and exported intermodal containers are considered simultaneously. A genetic algorithm based heuristic algorithm is developed to solve the problem. Results from computational experiments indicate the feasibility and effectiveness of the proposed model and algorithm, verifying that a near-optimum solution can be obtained for large-scale problems efficiently, which contributes to the improvement of operation services in rail–water intermodal container terminals.

Optimal berth allocation under regular and emergent vessel arrivals

2020 ◽  
pp. 147509022093788
Author(s):  
Abbas Al-Refaie ◽  
Hala Abedalqader
Keyword(s):  
Waiting Time ◽  
Container Terminal ◽  
Allocation Problem ◽  
Container Terminals ◽  
Future Research ◽  
Satisfaction Level ◽  
Berth Allocation ◽  
Scheduling Problems ◽  
Berth Allocation Problem ◽  
Quay Crane Assignment

This research proposes two optimization models to deal with the berth allocation problem. The first model considers the berth allocation problem under regular vessel arrivals to minimize the flow time of vessels in the marine container terminal, minimize the tardiness penalty costs, and maximize the satisfaction level of vessels’ operators on preferred times of departure. The second model optimizes the berth allocation problem under emergency conditions by maximizing the number of assigned vessels, minimizing the vessel’s waiting time, and maximizing the satisfaction level on the served ships. Two real examples are provided for model illustration under regular and emergent vessel arrivals. Results show that the proposed models effectively provide optimal vessel scheduling in the terminal, reduce costs at an acceptable satisfaction level of vessels’ operators, decrease the waiting time of vessels, and shorten the delay in departures under both regular and emergent vessel arrivals. In conclusion, the proposed models may provide valuable assistance to decision-makers in marine container terminals on determining optimal berth allocation under daily and emergency vessel arrivals. Future research considers quay crane assignment and scheduling problems.

scholarly journals A Two-Stage Offline-to-Online Multiobjective Optimization Strategy for Ship Integrated Energy System Economical/ Environmental Scheduling Problem

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qing An ◽  
Jun Zhang ◽  
Xin Li ◽  
Xiaobing Mao ◽  
Yulong Feng ◽  
...  
Keyword(s):  
Multiobjective Optimization ◽  
Real World ◽  
Operation Time ◽  
Energy System ◽  
Scheduling Problem ◽  
Storage Space ◽  
Optimization Strategy ◽  
Two Stage ◽  
Integrated Energy System ◽  
And Storage

The economical/environmental scheduling problem (EESP) of the ship integrated energy system (SIES) has high computational complexity, which includes more than one optimization objective, various types of constraints, and frequently fluctuated load demand. Therefore, the intelligent scheduling strategies cannot be applied to the ship energy management system (SEMS) online, which has limited computing power and storage space. Aiming at realizing green computing on SEMS, in this paper a typical SIES-EESP optimization model is built, considering the form of decision vectors, the economical/environmental optimization objectives, and various types of real-world constraints of the SIES. Based on the complexity of SIES-EESPs, a two-stage offline-to-online multiobjective optimization strategy for SIES-EESP is proposed, which transfers part of the energy dispatch online computing task to the offline high-performance computer systems. The specific constraints handling methods are designed to reduce both continuous and discrete constraints violations of SIES-EESPs. Then, an establishment method of energy scheduling scheme-base is proposed. By using the big data offline, the economical/environmental scheduling solutions of a typical year can be obtained and stored with more computing resources and operation time on land. Thereafter, a short-term multiobjective offline-to-online optimization approach by SEMS is considered, with the application of multiobjective evolutionary algorithm (MOEA) and typical schemes corresponding to the actual SIES-EESPs. Simulation results show that the proposed strategy can obtain enough feasible Pareto solutions in a shorter time and get well-distributed Pareto sets with better convergence performance, which can well adapt to the features of real-world SIES-EESPs and save plenty of operation time and storage space for the SEMS.

scholarly journals Making Education Collections Safe to Touch: Safety Assessments and Data Maintenance

2018 ◽  
Vol 2 ◽  
pp. e28197
Author(s):  
Kelsey Falquero ◽  
Katherine Roberts ◽  
Jessica Nakano
Keyword(s):  
Natural History ◽  
Specimen Preparation ◽  
Museum Specimens ◽  
National Museum ◽  
Storage Space ◽  
Collections Management ◽  
Vertebrate Zoology ◽  
Education And Outreach ◽  
Safety Assessments ◽  
And Storage

Q?rius is an interactive learning venue at the Smithsonian National Museum of Natural History (NMNH) designed specifically for a teen audience. The space gives visitors a chance to interact with museum specimens, especially in the Collections Zone. The Q?rius collections are non-accessioned education collections, belonging to the Office of Education and Outreach (E&O). The collections include the Museum’s seven disciplines – Anthropology, Botany, Entomology, Invertebrate Zoology, Mineral Sciences, Paleobiology, and Vertebrate Zoology. Starting in 2013, collections staff began performing safety assessments on specimens before their rehousing and storage in the publicly accessible Collections Zone. Risks assessed include sharpness, ingestibility, radioactivity, and contaminants (such as arsenic, mercury, and lead, which were historically used in specimen preparation or for pest management). Specimen and object fragility was also assessed. The goal of these assessments was to minimize risks to our visitors and to our collections. The safety assessments allow collections staff to make housing recommendations that would ensure the safety of NMNH’s visitors and the preservation of E&O’s collections in a publicly accessible storage space. This practice now extends to other pre-existing learning venues that contain publicly accessible portions of the E&O Collection, further minimizing risks. Staff have started adding the data gathered by these safety assessments to our collections management system, to protect the data from loss and to make the information easily accessible to staff. This poster relates to a second poster, Establishing Legal Title for Non-Accessioned Collections.

Optimizing Solution for Storage Space Allocation Problem in Container Terminal Using Genetic Algorithm

2016 ◽  
pp. 159-184
Author(s):  
Gamal Abd El-Nasser A. Said ◽  
El-Sayed M. El-Horbaty
Keyword(s):  
Genetic Algorithm ◽  
Service Time ◽  
Container Terminal ◽  
Study Data ◽  
Allocation Problem ◽  
Container Terminals ◽  
Storage Space ◽  
Space Allocation ◽  
Storage Space Allocation ◽  
Np Hard Problem

Seaport container terminals are essential nodes in sea cargo transportation networks. In container terminal, one of the most important performance measures in container terminals is the service time. Storage space allocation operations contribute to minimizing the vessel service time. Storage space allocation problem at container terminals is a combinatorial optimization NP-hard problem. This chapter proposes a methodology based on Genetic Algorithm (GA) to optimize the solution for storage space allocation problem. A new mathematical model that reflects reality and takes into account the workload balance among different types of storage blocks to avoid bottlenecks in container yard operations is proposed. Also the travelling distance between vessels berthing positions and storage blocks at container yard is considered in this research. The proposed methodology is applied on a real case study data of container terminal in Egypt. The computational results show the effectiveness of the proposed methodology.

Selecting and Allocating Cubes in Multi-Node OLAP Systems

2011 ◽  
pp. 99-131
Author(s):  
Jorge Loureiro ◽  
Orlando Belo
Keyword(s):  
Cost Estimation ◽  
Cost Model ◽  
Storage Space ◽  
Communication Costs ◽  
Estimation Algorithms ◽  
Parallel Tasks ◽  
Maintenance Time ◽  
Dimension Space ◽  
And Storage ◽  
Linear Cost

OLAP queries are characterized by short answering times. Materialized cube views, a pre-aggregation and storage of group-by values, are one of the possible answers to that condition. However, if all possible views were computed and stored, the amount of necessary materializing time and storage space would be huge. Selecting the most beneficial set, based on the profile of the queries and observing some constraints as materializing space and maintenance time, a problem denoted as cube views selection problem, is the condition for an effective OLAP system, with a variety of solutions for centralized approaches. When a distributed OLAP architecture is considered, the problem gets bigger, as we must deal with another dimension—space. Besides the problem of the selection of multidimensional structures, there’s now a node allocation one; both are a condition for performance. This chapter focuses on distributed OLAP systems, recently introduced, proposing evolutionary algorithms for the selection and allocation of the distributed OLAP Cube, using a distributed linear cost model. This model uses an extended aggregation lattice as framework to capture the distributed semantics, and introduces processing nodes’ power and real communication costs parameters, allowing the estimation of query and maintenance costs in time units. Moreover, as we have an OLAP environment, whit several nodes, we will have parallel processing and then, the evaluation of the fitness of evolutionary solutions is based on cost estimation algorithms that simulate the execution of parallel tasks, using time units as cost metric.

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