scholarly journals Berth Allocation Problem with Quay Crane Assignment for Container Terminals Based on Rolling-Horizon Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Ling Xiao ◽  
Zhi-Hua Hu
Keyword(s):  
Large Scale ◽  
Dynamic Scheduling ◽  
Time Window ◽  
Optimal Solution ◽  
Container Terminals ◽  
Rolling Horizon ◽  
Scheduling Problems ◽  
Berth Allocation Problem ◽  
Arrival Times ◽  
Rolling Window

In order to solve the large-scale integral dynamic scheduling of continuous berths and quay cranes problem, a method based on rolling-horizon strategy is proposed. A multiobjective optimization model that is established minimizes the total penalty costs considering vessels’ deviations to their preferred berthing positions, delayed times for berthing comparing to their estimated arrival times, and delayed times for departure comparing to their estimated departure times. Then, the scheduling process was divided into a set of continual scheduling interval according to the dynamic arrival sequences. Meanwhile, rolling-horizon strategies for setting rolling and frozen windows and the parameter updating strategy are designed. The input parameters of the model in the next rolling window are updated according to the optimal results of each time window which have been obtained. The model is solved by choosing appropriate rolling and freezing window lengths that represents the numbers of adjacent vessels in the sequence of calling vessels. The holistic optimal solution is obtained by gradually rolling and combining the results of each window. Finally, a case study indicated that the rolling schedule can solve large-scale scheduling problems, and the efficiency of the proposed approach relates to the size of rolling window, freeze ship quantity, and rolling frequency.

scholarly journals A Fuzzy Optimization Model for the Berth Allocation Problem and Quay Crane Allocation Problem (BAP + QCAP) with n Quays

2021 ◽  
Vol 9 (2) ◽  
pp. 152
Author(s):  
Edwar Lujan ◽  
Edmundo Vergara ◽  
Jose Rodriguez-Melquiades ◽  
Miguel Jiménez-Carrión ◽  
Carlos Sabino-Escobar ◽  
...  
Keyword(s):  
Optimization Model ◽  
Fuzzy Optimization ◽  
Optimal Solution ◽  
Allocation Problem ◽  
Berth Allocation ◽  
Triangular Fuzzy Numbers ◽  
Berth Allocation Problem ◽  
Arrival Times ◽  
Decision Variables ◽  
Short Time

This work introduces a fuzzy optimization model, which solves in an integrated way the berth allocation problem (BAP) and the quay crane allocation problem (QCAP). The problem is solved for multiple quays, considering vessels’ imprecise arrival times. The model optimizes the use of the quays. The BAP + QCAP, is a NP-hard (Non-deterministic polynomial-time hardness) combinatorial optimization problem, where the decision to assign available quays for each vessel adds more complexity. The imprecise vessel arrival times and the decision variables—berth and departure times—are represented by triangular fuzzy numbers. The model obtains a robust berthing plan that supports early and late arrivals and also assigns cranes to each berth vessel. The model was implemented in the CPLEX solver (IBM ILOG CPLEX Optimization Studio); obtaining in a short time an optimal solution for very small instances. For medium instances, an undefined behavior was found, where a solution (optimal or not) may be found. For large instances, no solutions were found during the assigned processing time (60 min). Although the model was applied for n = 2 quays, it can be adapted to “n” quays. For medium and large instances, the model must be solved with metaheuristics.

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.

A FMS Dynamic Scheduling Optimization Strategy and Simulation Research

2013 ◽  
Vol 389 ◽  
pp. 692-697
Author(s):  
Ji Zhuang Hui ◽  
Xiang Ding ◽  
Kai Gao
Keyword(s):  
Production Scheduling ◽  
Dynamic Scheduling ◽  
Scheduling Problems ◽  
Optimization Strategy ◽  
Scheduling Optimization ◽  
Simulation Research ◽  
Scheduling Strategy ◽  
Machine Failure ◽  
Rolling Window ◽  
Scheduling Optimization Algorithm

This paper studied the FMS dynamic scheduling problem which was based on Petri net FMS static scheduling optimization algorithm, which in accorder to solve the FMS actual production scheduling problems. A rolling window dynamic re-scheduling strategy was proposed which based on event driven and cycle driven. Then take the emergency machine failure often appearing in the actual workshop for example, this scheduling strategy was analyzed and applied to dynamic simulation and finally the effectiveness of the dynamic scheduling strategy was verified.

Dynamic Scheduling Model and Algorithm for Dispatching Trains in High-Speed Railway Passenger Station

2010 ◽  
Author(s):  
Yixiang Yue ◽  
Leishan Zhou
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Time Window ◽  
Real Life ◽  
Greedy Heuristic ◽  
Railway Station ◽  
High Speed Railway ◽  
Railway Passenger

Regarding the railway station tracks and train running routes as machines, all trains in this railway station as jobs, dispatching trains in high-speed railway passenger stations can be considered as a special type of Job-Shop Problem (JSP). In this paper, we proposed a multi-machines, multi-jobs JSP model with special constraints for Operation Plan Scheduling Problem (OPSP) in high-speed railway passenger stations, and presented a fast heuristic algorithm based on greedy heuristic. This algorithm first divided all operations into several layers according to the yards attributes and the operation’s urgency level. Then every operation was allotted a feasible time window, each operation was assigned to a specified “machine” sequenced or backward sequenced within the time slot, layer by layer according to its priority. As we recorded and modified the time slots dynamically, the searching space was decreased dramatically. And we take the South Beijing High-speed Railway Station as example and give extensive numerical experiment. Computational results based on real-life instance show that the algorithm has significant merits for large scale problems; can both reduce tardiness and shorten cycle times. The empirical evidence also proved that this algorithm is industrial practicable.

scholarly journals Integrating Queueing Theory and Scheduling for Dynamic Scheduling Problems

2014 ◽  
Vol 50 ◽  
pp. 535-572 ◽  
Author(s):  
D. Terekhov ◽  
T. T. Tran ◽  
D. G. Down ◽  
J.C. Beck
Keyword(s):  
Queueing Theory ◽  
Dynamic Scheduling ◽  
Queueing Network ◽  
Scheduling Problems ◽  
Short Term ◽  
Arrival Times ◽  
Processing Times ◽  
Long Run ◽  
Resource Requirements ◽  
First Time

Dynamic scheduling problems consist of both challenging combinatorics, as found in classical scheduling problems, and stochastics due to uncertainty about the arrival times, resource requirements, and processing times of jobs. To address these two challenges, we investigate the integration of queueing theory and scheduling. The former reasons about long-run stochastic system characteristics, whereas the latter typically deals with short-term combinatorics. We investigate two simple problems to isolate the core differences and potential synergies between the two approaches: a two-machine dynamic flowshop and a flexible queueing network. We show for the first time that stability, a fundamental characteristic in queueing theory, can be applied to approaches that periodically solve combinatorial scheduling problems. We empirically demonstrate that for a dynamic flowshop, the use of combinatorial reasoning has little impact on schedule quality beyond queueing approaches. In contrast, for the more complicated flexible queueing network, a novel algorithm that combines long-term guidance from queueing theory with short-term combinatorial decision making outperforms all other tested approaches. To our knowledge, this is the first time that such a hybrid of queueing theory and scheduling techniques has been proposed and evaluated.

scholarly journals Multiconstrained Network Intensive Vehicle Routing Adaptive Ant Colony Algorithm in the Context of Neural Network Analysis

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Shaopei Chen ◽  
Ji Yang ◽  
Yong Li ◽  
Jingfeng Yang
Keyword(s):  
Neural Network ◽  
Vehicle Routing ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Routing Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Neural Network Models ◽  
Network Systems

Neural network models have recently made significant achievements in solving vehicle scheduling problems. Adaptive ant colony algorithm provides a new idea for neural networks to solve complex system problems of multiconstrained network intensive vehicle routing models. The pheromone in the path is changed by adjusting the volatile factors in the operation process adaptively. It effectively overcomes the tendency of the traditional ant colony algorithm to fall easily into the local optimal solution and slow convergence speed to search for the global optimal solution. The multiconstrained network intensive vehicle routing algorithm based on adaptive ant colony algorithm in this paper refers to the interaction between groups. Adaptive transfer and pheromone update strategies are introduced based on the traditional ant colony algorithm to optimize the selection, update, and coordination mechanisms of the algorithm further. Thus, the search task of the objective function for a feasible solution is completed by the search ants. Through the division and collaboration of different kinds of ants, pheromone adaptive strategy is combined with polymorphic ant colony algorithm. It can effectively overcome some disadvantages, such as premature stagnation, and has a theoretical significance to the study of large-scale multiconstrained vehicle routing problems in complex traffic network systems.

scholarly journals A Dynamic Scheduling Method for Carrier Aircraft Support Operation under Uncertain Conditions Based on Rolling Horizon Strategy

2018 ◽  
Vol 8 (9) ◽  
pp. 1546 ◽  
Author(s):  
Peilong Yuan ◽  
Wei Han ◽  
Xichao Su ◽  
Jie Liu ◽  
Jingyu Song
Keyword(s):  
Renewable Resources ◽  
Large Scale ◽  
Dynamic Scheduling ◽  
Dynamic Environment ◽  
Optimization Method ◽  
Scheduling Problem ◽  
Rolling Horizon ◽  
Scheduling Strategy ◽  
Mutation Strategy ◽  
Event Driven

The efficient scheduling of carrier aircraft support operations in the flight deck is important for battle performances. The supporting operations and maintenance processes involve multiple support resources, complex scheduling process, and multiple constraints; the efficient coordination of these processes can be considered a multi-resource constrained multi-project scheduling problem (MRCMPSP), which is a complex non-deterministic polynomial-time hard (NP-hard) problem. The renewable resources include the operational crews, resource stations, and operational spaces, and the non-renewable resources include oil, gas, weapons, and electric power. An integer programming mathematical model is established to solve this problem. A periodic and event-driven rolling horizon (RH) scheduling strategy inspired by the RH optimization method from predictive control technology is presented for the dynamic scheduling environment. The periodic horizon scheduling strategy can track the changes of the carrier aircraft supporting system, and the improved event-driven mechanism can avoid unnecessary scheduling with effective resource allocation under uncertain conditions. The dual population genetic algorithm (DPGA) is designed to solve the large-scale scheduling problem. The activity list encoding method is proposed, and a new adaptive crossover and mutation strategy is designed to improve the global exploration ability. The double schedule for leftward and rightward populations is integrated into the genetic process of alternating iterations to improve the convergence speed and decrease the computation amount. The computational results show that our approach is effective at solving the scheduling problem in the dynamic environment, as well as making better decisions regarding disruption on a real-time basis.

Scheduling

2010 ◽  
pp. 91-110
Author(s):  
Valentin Cristea ◽  
Ciprian Dobre ◽  
Corina Stratan ◽  
Florin Pop
Keyword(s):  
Distributed Systems ◽  
Large Scale ◽  
Dynamic Scheduling ◽  
Scheduling Algorithms ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Advance Reservation ◽  
Systems Architecture ◽  
Static Versus Dynamic ◽  
Scheduling Models

This chapter presents the scheduling problem in large scale distributed systems. Most parts of the chapter are devoted to discussion of scheduling algorithms and models. The main challenges of scheduling problem are approached here. The implementation issues are also covered. The chapter has three parts. The first part covers basics like scheduling models, scheduling algorithms for independent tasks and DAG scheduling Algorithms for tasks with dependencies. The first part of the chapter presents a classification of scheduling problems, methods that are relevant for the solution procedures, and computational complexity. The scheduling models are presented based on systems architecture described in Resource Management chapter. This firs part also provides a critical analysis of most important algorithms from different points of view, such as static versus dynamic policies, objective functions, applications models, adaptation, QoS constraints and strategies dealing with dynamic behavior of resources. The second part covers new scheduling mechanism like resources co-allocation and advance reservation. Multi-criteria optimization mechanisms for users and systems constrain (e.g. load-balancing, minimization of execution time) are described and analyzed in this chapter. This part uses algorithm and methods to highlight the importance of these topics. The dynamic scheduling is also the subject of this part. It is also presented the implementation issues for scheduler tools. Since it is not possible to cover the whole area of scheduling in one chapter, some restrictions are imposed. Firstly, the chapter presents only Scheduling for Large Scale Distributed Systems (LSDS), without single system scheduling. Secondly, some interesting topics of fault tolerance (re-scheduling) are not analyzed in this chapter.

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