An Optimization Approach for the Train Load Planning Problem in Seaport Container Terminals

2021 ◽  
pp. 121-133
Author(s):  
Daniela Ambrosino ◽  
Davide Anghinolfi ◽  
Massimo Paolucci ◽  
Silvia Siri
Keyword(s):  
Container Terminals ◽  
Planning Problem ◽  
Optimization Approach ◽  
Load Planning

scholarly journals A LEXICOGRAPHIC OPTIMIZATION APPROACH FOR BERTH SCHEDULE RECOVERY PROBLEM IN CONTAINER TERMINALS

Transport ◽  
2016 ◽  
Vol 31 (1) ◽  
pp. 76-83 ◽  
Author(s):  
Qian Zhang ◽  
Qingcheng Zeng ◽  
Hualong Yang
Keyword(s):  
Container Terminals ◽  
Optimization Approach ◽  
Technical Problems ◽  
Trade Off ◽  
Practical Applications ◽  
Lexicographic Optimization ◽  
Multi Stage ◽  
Schedule Recovery ◽  
Equipment Failures ◽  
Recovery Problem

In container terminals, the planned berth schedules often have to be revised because of disruptions caused by severe weather, equipment failures, technical problems and other unforeseen events. In this paper, the problem of berth schedule recovery is addressed to reduce the influences caused by disruptions. A multi-objective, multi-stage model is developed considering the characteristics of different customers and the trade-off of all parties involved. An approach based on the lexicographic optimization is designed to solve the model. Numerical experiments are provided to illustrate the validity of the proposed Model A and algorithms. Results indicate that the designed Model A and algorithm can tackle the berth plan recovery problem efficiently because the beneficial trade-off among all parties involved are considered. In addition, it is more flexible and feasible with the aspect of practical applications considering that the objective order can be adjusted by decision makers.

Adaptive PPS durch simulationsbasierte Optimierung*/Adaptive PPC by simulation-based optimization

2017 ◽  
Vol 107 (04) ◽  
pp. 288-292
Author(s):  
M. Kück ◽  
J. Ehm ◽  
T. Hildebrandt ◽  
M. Prof. Freitag ◽  
E. M. Prof. Frazzon
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Production Systems ◽  
Semiconductor Industry ◽  
Small Scale ◽  
Planning Problem ◽  
Optimization Approach ◽  
Flexible Production Systems ◽  
Simulation Based ◽  
Simulation Based Optimization

Der Trend zur Fertigung individualisierter Produkte in kleinen Losgrößen erfordert hochflexible Produktionssysteme. Durch die damit verbundene Systemdynamik wird die Reihenfolgeplanung zu einem komplexen Planungsproblem. Der Beitrag beschreibt ein simulationsbasiertes Optimierungsverfahren, welches Echtzeitinformationen zur adaptiven Selektion geeigneter Prioritätsregeln verwendet. Das Potenzial des Ansatzes wird anhand eines Anwendungsfalls aus der Halbleiterindustrie demonstriert.   The trend to manufacturing individualized products in small-scale series demands highly flexible production systems. Because of the dynamic nature of such production systems, scheduling becomes a complex planning problem with frequent need for rescheduling. This article describes a data-driven simulation-based optimization approach using real-time information for adaptive job shop scheduling. The potential of the approach is demonstrated by a use case from semiconductor industry.

scholarly journals New solution approaches for the train load planning problem

2019 ◽  
Vol 8 (3) ◽  
pp. 299-325
Author(s):  
Daniela Ambrosino ◽  
Claudia Caballini
Keyword(s):  
Planning Problem ◽  
Load Planning

Optimal Multistage Expansion Planning of a Gas Turbine Cogeneration Plant

1996 ◽  
Vol 118 (4) ◽  
pp. 803-809 ◽  
Author(s):  
R. Yokoyama ◽  
K. Ito ◽  
Y. Matsumoto
Keyword(s):  
Gas Turbine ◽  
Energy Demand ◽  
Numerical Study ◽  
District Heating ◽  
Development Project ◽  
Planning Problem ◽  
Optimization Approach ◽  
Cogeneration Plant ◽  
Expansion Planning ◽  
Heating And Cooling

A multistage expansion planning problem is discussed concerning a gas turbine cogeneration plant for district heating and cooling using an optimization approach. An optimal sizing method for single-stage planning proposed by the authors is extended to this case. Equipment capacities and utility maximum demands at each expansion stage are determined so as to minimize the levelized annual total cost subject to increasing energy demands. A numerical study on a simple-cycle gas turbine cogeneration plant to be installed in a district development project clarifies the relationship between optimal expansion planning and energy demand trend, and shows the effectiveness of the proposed method.

Robust optimization approach to production system with failure in rework and breakdown under uncertainty: evolutionary methods

2015 ◽  
Vol 35 (1) ◽  
pp. 81-93 ◽  
Author(s):  
Masoud Rabbani ◽  
Neda Manavizadeh ◽  
Niloofar Sadat Hosseini Aghozi
Keyword(s):  
Mathematical Model ◽  
Robust Optimization ◽  
Production Planning ◽  
Production System ◽  
Demand Uncertainty ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Optimization Approach ◽  
Content Type ◽  
Evolutionary Methods

Purpose – This paper aims to consider a multi-site production planning problem with failure in rework and breakdown subject to demand uncertainty. Design/methodology/approach – In this new mathematical model, at first, a feasible range for production time is found, and then the model is rewritten considering the demand uncertainty and robust optimization techniques. Here, three evolutionary methods are presented: robust particle swarm optimization, robust genetic algorithm (RGA) and robust simulated annealing with the ability of handling uncertainties. Firstly, the proposed mathematical model is validated by solving a problem in the LINGO environment. Afterwards, to compare and find the efficiency of the proposed evolutionary methods, some large-size test problems are solved. Findings – The results show that the proposed models can prepare a promising approach to fulfill an efficient production planning in multi-site production planning. Results obtained by comparing the three proposed algorithms demonstrate that the presented RGA has better and more efficient solutions. Originality/value – Considering the robust optimization approach to production system with failure in rework and breakdown under uncertainty.

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