scholarly journals Storage Allocation in Automated Container Terminals: the Upper Level

2016 ◽  
Vol 23 (s1) ◽  
pp. 160-174 ◽  
Author(s):  
Xia Mengjue ◽  
Zhao Ning ◽  
Mi Weijian
Keyword(s):  
Programming Model ◽  
Level Structure ◽  
Pso Algorithm ◽  
Container Terminals ◽  
Mixed Integer ◽  
Future Research ◽  
Storage Allocation ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Upper Level

Abstract Nowadays automation is a trend of container terminals all over the world. Although not applied in current automated container terminals, storage allocation is indispensable in conventional container terminals, and promising for automated container terminals in future. This paper seeks into the storage allocation problem in automated container terminals and proposed a two level structure for the problem. A mixed integer programming model is built for the upper level, and a modified Particle Swarm Optimization (PSO) algorithm is applied to solve the model. The applicable conditions of the model is investigated by numerical experiments, so as the performance of the algorithm in different problem scales. It is left to future research the lower level of the problem and the potential benefit of storage allocation to automated container terminals.

scholarly journals Optimization of Continuous Berth Scheduling by Taking into Account Double-Line Ship Mooring

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Cheng Luo ◽  
Hongying Fei ◽  
Dana Sailike ◽  
Tingyi Xu ◽  
Fuzhi Huang
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Minimum Cost ◽  
Pso Algorithm ◽  
Container Terminals ◽  
Mixed Integer ◽  
Double Line ◽  
Support Tool ◽  
Berth Scheduling ◽  
Operation Cost

“Double-Line Ship Mooring” (DLSM) mode has been applied as an initiative operation mode for solving berth allocation problems (BAP) in certain giant container terminals in China. In this study, a continuous berth scheduling problem with the DLSM model is illustrated and solved with exact and heuristic methods with an objective to minimize the total operation cost, including both the additional transportation cost for vessels not located at their minimum-cost berthing position and the penalties for vessels not being able to leave as planned. First of all, this problem is formulated as a mixed-integer programming model and solved by the CPLEX solver for small-size instances. Afterwards, a particle swarm optimization (PSO) algorithm is developed to obtain good quality solutions within reasonable execution time for large-scale problems. Experimental results show that DLSM mode can not only greatly reduce the total operation cost but also significantly improve the efficiency of berth scheduling in comparison with the widely used single-line ship mooring (SLSM) mode. The comparison made between the results obtained by the proposed PSO algorithm and that obtained by the CPLEX solver for both small-size and large-scale instances are also quite encouraging. To sum up, this study can not only validate the effectiveness of DLSM mode for heavy-loaded ports but also provide a powerful decision support tool for the port operators to make good quality berth schedules with the DLSM mode.

scholarly journals Adaptive Autotuning Mathematical Approaches for Integrated Optimization of Automated Container Terminal

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Meisu Zhong ◽  
Yongsheng Yang ◽  
Yamin Zhou ◽  
Octavian Postolache
Keyword(s):  
Programming Model ◽  
Container Terminal ◽  
Operation Mode ◽  
Pso Algorithm ◽  
Container Terminals ◽  
Automated Guided Vehicles ◽  
Mixed Integer ◽  
Integrated Scheduling ◽  
Loading And Unloading ◽  
Unloading Time

With the development of automated container terminals (ACTs), reducing the loading and unloading time of operation and improving the working efficiency and service level have become the key point. Taking into account the actual operation mode of loading and unloading in ACTs, a mixed integer programming model is adopted in this study to minimize the loading and unloading time of ships, which can optimize the integrated scheduling of the gantry cranes (QCs), automated guided vehicles (AGVs), and automated rail-mounted gantries (ARMGs) in automated terminals. Various basic metaheuristic and improved hybrid algorithms were developed to optimize the model, proving the effectiveness of the model to obtain an optimized scheduling scheme by numerical experiments and comparing the different performances of algorithms. The results show that the hybrid GA-PSO algorithm with adaptive autotuning approaches by fuzzy control is superior to other algorithms in terms of solution time and quality, which can effectively solve the problem of integrated scheduling of automated container terminals to improve efficiency.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

The determination of the most suitable inertia weight strategy for particle swarm optimization via the minimax mixed-integer linear programming model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Volkan Soner Özsoy
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Pso Algorithm ◽  
Complex Problem ◽  
Mixed Integer ◽  
Swarm Optimization ◽  
Content Type ◽  
Inertia Weight

Purpose This paper aims to consider each strategy of the particle swarm optimization (PSO) as a unit in data envelopment analysis (DEA) and uses the minimax mixed-integer linear programming DEA approach to find the most suitable inertia weight strategy. A total of 15 inertia weight strategies were empirically examined in a suite of 42 benchmark problems in the view of DEA. Design/methodology/approach PSO is very sensitive to inertia weight strategies, and therefore, an important amount of research attempts has been concentrated on these strategies. There is no research into the determination of the most suitable inertia weight strategy; however, there are a large number of comparisons related to the inertia weight strategies. DEA is one of the performance evaluation methods, and its models classify the set of strategies into two distinct sets as efficient and inefficient. However, only one of the strategies should be used in the PSO algorithm. Some effective models were proposed to find the most efficient strategy. Findings The experimental studies demonstrate that an approach is a useful tool in the determination of the most suitable strategy. Besides, if the author encounters a new complex problem whose properties are known, it will help the author to choose the best strategy. Practical implications A heavy oil thermal cracking three lumps model for the simplification of the reaction system was used because it is an important complicated chemical process. In addition, the soil water retention curve (SWRC) plays an important role in diverse facets of agricultural engineering. As the SWRC can be regarded as a nonlinear function between the water content and the soil water potential, Van Genuchten model is proposed to describe this function. To determinate these model parameters, an optimization problem is formulated, which minimizes the difference between the measured and modeled data. Originality/value In this paper, the PSO algorithm is integrated with minimax mixed-integer linear programming to find the most suitable inertia weight strategy. In this way, the best strategy could be chosen for a new more complex problem.

A DECISION SUPPORT SYSTEM FOR THE ALLOCATION OF YARD CRANES AND BLOCKS IN CONTAINER TERMINALS

2011 ◽  
Vol 28 (06) ◽  
pp. 803-829 ◽  
Author(s):  
CANRONG ZHANG ◽  
ZHIHAI ZHANG ◽  
LI ZHENG ◽  
LIXIN MIAO
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Programming Model ◽  
Solution Procedure ◽  
Container Terminals ◽  
Mixed Integer ◽  
Scarce Resources ◽  
Iterative Decomposition ◽  
Gantry Cranes

This paper examines the allocation of yard cranes and blocks for yard activities in container terminals. In this paper, the yard cranes are confined to rail mounted gantry cranes (RMGC), which are characterized by the restricted traveling range on a pair of rails. Since RMGCs and yard blocks are tightly bound to each other, when allocating them, we should make sure that the RMGCs allocated for a yard activity are able to together cover the blocks allocated for the corresponding yard activity. In addition, considering that there are four basic activities occurring in the yard which compete with each other for the scarce resources and have different requirements and priorities in the allocation of blocks and yard cranes, we treat them in a single model rather than in multiple independent models as were generally done in literature. A mixed integer programming model is constructed, and an iterative decomposition solution procedure is proposed for the problem. Based on the solution procedure, a decision support system is developed and implemented for a terminal in Tianjin seaport. Using the actual data, the numerical experiments show the effectiveness and efficiency of the decision support system.

A New Dispatching Model of Automated Lifting Vehicles in Automated Container Terminal with Limited Buffer Space

2013 ◽  
Vol 446-447 ◽  
pp. 1334-1339 ◽  
Author(s):  
Seyed Hamidreza Sadeghian ◽  
Mohd Khairol Anuar Bin Mohd Ariffin ◽  
Say Hong Tang ◽  
Napsiah Binti Ismail
Keyword(s):  
Programming Model ◽  
Container Terminal ◽  
Container Terminals ◽  
Mixed Integer ◽  
Transport Systems ◽  
Buffer Space ◽  
Integrated Scheduling ◽  
Quay Cranes ◽  
Automated Lifting Vehicle ◽  
Large Container

Automation of the processes at the quays of the world's large container ports is one of the answers to the required ever-increasing transshipment volumes within the same timeframe. For such purpose, using new generation of vehicles is unavoidable. One of the automatic vehicles that can be used in container terminals is Automated Lifting Vehicle (ALV). Integrated scheduling of handling equipments with quay cranes can increase the efficiency of automated transport systems in container. In this paper, an integrated scheduling of quay cranes and automated lifting vehicles with limited buffer space is formulated as a mixed integer linear programming model. This model minimizes the makespan of all the loading and unloading tasks for a pre-defined set of cranes in a scheduling problem.

Monopole-Gear Design Based on Neural Network and Modified Particle Swarm Optimization

2013 ◽  
Vol 477-478 ◽  
pp. 368-373 ◽  
Author(s):  
Hai Rong Fang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Results ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Gear Design ◽  
Annealing Algorithm

In order to raise the design efficiency and get the most excellent design effect, this paper combined Particle Swarm Optimization (PSO) algorithm and put forward a new kind of neural network, which based on PSO algorithm, and the implementing framework of PSO and NARMA model. It gives the basic theory, steps and algorithm; The test results show that rapid global convergence and reached the lesser mean square error MSE) when compared with Genetic Algorithm, Simulated Annealing Algorithm, the BP algorithm with momentum term.

The AFSA-GA Algorithm for the Quay Crane Scheduling Problem of the Loading and Unloading Operations

2017 ◽  
Vol 9 (3) ◽  
pp. 59-71
Author(s):  
Yi Liu ◽  
Sabina Shahbazzade ◽  
Jian Wang
Keyword(s):  
Programming Model ◽  
Global Optimum ◽  
Container Terminals ◽  
Mixed Integer ◽  
Quay Crane Scheduling ◽  
Crane Scheduling ◽  
Ga Algorithm ◽  
Simultaneous Loading ◽  
Loading And Unloading ◽  
Dual Cycle

In order to improve the efficiency of container terminals, eliminate the empty quay cranes movements, the simultaneous loading and unloading operations in same ship-bay is advanced. The AFSA-GA algorithm is proposed to solve the mixed integer programming model of the dual-cycle operation, which take advantage of the strong local search ability of GA and the global optimum search ability of AFSA. The experiment shows that AFSA-GA algorithm can improve the operation efficiency of quay crane significantly.

Work continuity constraints in repetitive project scheduling considering soft logic

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Zou ◽  
Guangchuan Wu ◽  
Qian Zhang
Keyword(s):  
Programming Model ◽  
Mixed Integer ◽  
Future Research ◽  
Small Scale ◽  
Content Type ◽  
Project Duration ◽  
Additional Information ◽  
Work Interruptions ◽  
Conflicting Objectives ◽  
Soft Logic

PurposeRepetitive projects play an important role in the construction industry. A crucial point in scheduling this type of project lies in enabling timely movement of crews from unit to unit so as to minimize the adverse effect of work interruptions on both time and cost. This paper aims to examine a repetitive scheduling problem with work continuity constraints, involving a tradeoff among project duration, work interruptions and total project cost (TPC). To enhance flexibility and practicability, multi-crew execution is considered and the logic relation between units is allowed to be changed arbitrarily. That is, soft logic is considered.Design/methodology/approachThis paper proposes a multi-objective mixed-integer linear programming model with the capability of yielding the optimal tradeoff among three conflicting objectives. An efficient version of the e-constraint algorithm is customized to solve the model. This model is validated based on two case studies involving a small-scale and a practical-scale project, and the influence of using soft logic on project duration and total cost is analyzed via computational experiments.FindingsUsing soft logic provides more flexibility in minimizing project duration, work interruptions and TPC, especial for non-typical projects with a high percentage of non-typical activities.Research limitations/implicationsThe main limitation of the proposed model fails to consider the learning-forgetting phenomenon, which provides space for future research.Practical implicationsThis study assists practitioners in determining the “most preferred” schedule once additional information is provided.Originality/valueThis paper presents a new soft logic-based mathematical programming model to schedule repetitive projects with the goal of optimizing three conflicting objectives simultaneously.

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