NONLINEAR MATHEMATICAL PROGRAMMING FOR OPTIMAL MANAGEMENT OF CONTAINER TERMINALS

2009 ◽  
Vol 23 (27) ◽  
pp. 5333-5342 ◽  
Author(s):  
S. R. SEYEDALIZADEH GANJI ◽  
H. JAVANSHIR ◽  
F. VASEGHI
Keyword(s):  
Container Terminals ◽  
Mixed Integer ◽  
Berth Allocation ◽  
Berth Allocation Problem ◽  
Prior Model ◽  
Proposed Model ◽  
Integer Nonlinear Programming ◽  
Nonlinear Mathematical Programming ◽  
S Model ◽  
Berth Scheduling

Berth scheduling is the process of determining the time and position at which each arriving ship will berth. This paper attempts to minimize the serving time to ships, after introducing a proposed mathematical model, considers the berth allocation problem in form of mixed integer nonlinear programming. Then, to credit the proposed model, the results of Imai et al.'s model have been used. The results indicate that because the number of nonlinear variables in the proposed model is less than prior model, so by using the proposed model, we can obtain the results of model in less time rather than prior model.

scholarly journals A novel mathematical formulation for solving the dynamic and discrete berth allocation problem by using the Bee Colony Optimisation algorithm

2020 ◽  
Author(s):  
Luigi Pio Prencipe ◽  
Mario Marinelli
Keyword(s):  
Mathematical Formulation ◽  
Real Data ◽  
Allocation Problem ◽  
Mixed Integer ◽  
Berth Allocation ◽  
Berth Allocation Problem ◽  
Solution Approach ◽  
Bee Colony ◽  
Proposed Model ◽  
Berth Scheduling

AbstractBerth allocation is one of the crucial points for efficient management of ports. This problem is complex due to all possible combinations for assigning ships to available compatible berths. This paper focuses on solving the Berth Allocation Problem (BAP) by optimising port operations using an innovative model. The problem analysed in this work deals with the Discrete and Dynamic Berth Allocation Problem (DDBAP). We propose a novel mathematical formulation expressed as a Mixed Integer Linear Programming (MILP) for solving the DDBAP. Furthermore, we adapted a metaheuristic solution approach based on the Bee Colony Optimisation (BCO) for solving large-sized combinatorial BAPs. In order to assess the solution performance and efficiency of the proposed model, we introduce a new set of instances based on real data of the Livorno port (Italy), and a comparison between the BCO algorithm and CPLEX in solving the DDBAP is performed. Additionally, the application of the proposed model to a real berth scheduling (Livorno port data) and a comparison with the Ant Colony Optimisation (ACO) metaheuristic are carried out. Results highlight the feasibility of the proposed model and the effectiveness of BCO when compared to both CPLEX and ACO, achieving computation times that ensure a real-time application of the method.

scholarly journals Rescheduling Strategy for Berth Planning in Container Terminals: An Empirical Study from Korea

2021 ◽  
Vol 9 (5) ◽  
pp. 527
Author(s):  
Armi Kim ◽  
Hyun-Ji Park ◽  
Jin-Hyoung Park ◽  
Sung-Won Cho
Keyword(s):  
Mixed Integer Linear Programming ◽  
Arrival Time ◽  
Programming Model ◽  
Container Terminal ◽  
Container Terminals ◽  
Mixed Integer ◽  
Berth Allocation ◽  
Trade Volume ◽  
Penalty Costs ◽  
Proposed Model

The rapid increase in international trade volume has caused frequent fluctuation of the vessels’ arrival time in container terminals. In order to solve this problem, this study proposes a methodology for rescheduling berth and quay cranes caused by updated information on the arrival time of vessels. A mixed-integer linear programming model was formulated for the berth allocation and crane assignment problem, and we solved the problem using a rolling-horizon approach. Numerical experiments were conducted under three scenarios with empirical data from a container terminal located in Busan, Korea. The experiments revealed that the proposed model reduces penalty costs and overall delayed departure time compared to the results of the terminal planner.

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.

Population-based Metaheuristics for the Dynamic Minimum Cost Hybrid Berth Allocation Problem

2021 ◽  
Vol 30 (04) ◽  
pp. 2150017
Author(s):  
Nataša Kovač ◽  
Tatjana Davidović ◽  
Zorica Stanimirović
Keyword(s):  
Container Terminal ◽  
Mathematical Formulation ◽  
Minimum Cost ◽  
Population Based ◽  
Allocation Problem ◽  
Maritime Transportation ◽  
Mixed Integer ◽  
Berth Allocation ◽  
Berth Allocation Problem ◽  
Metaheuristic Methods

This study considers the Dynamic Minimum Cost Hybrid Berth Allocation Problem (DMCHBAP) with fixed handling times of vessels. The objective function to be minimized consists of three components: costs of positioning, waiting, and tardiness of completion for all vessels. A mathematical formulation of DMCHBAP, based on Mixed Integer Linear Programming (MILP), is proposed and used within the framework of commercial CPLEX 12.3 solver. As the speed of finding high-quality solutions is of crucial importance for an efficient and reliable decision support system in container terminal, two population-based metaheuristic approaches to DMCHBAP are proposed: combined Genetic Algorithm (cGA) and improvement-based Bee Colony Optimization (BCOi). Both cGA and BCOi are evaluated and compared against each other and against state-of-the-art solution methods for DMCHBAP on five sets of problem instances. The conducted computational experiments and statistical analysis indicate that population-based metaheuristic methods represent promising approaches for DMCHBAP and similar problems in maritime transportation.

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.

A cooperative search for berth scheduling

2016 ◽  
Vol 31 (5) ◽  
pp. 498-507 ◽  
Author(s):  
Eduardo Lalla-Ruiz ◽  
Belén Melián-Batista ◽  
José Marcos Moreno-Vega
Keyword(s):  
Container Terminal ◽  
Search Space ◽  
Computational Experiments ◽  
Berth Allocation ◽  
Cooperative Search ◽  
Berth Allocation Problem ◽  
High Quality ◽  
Grouping Strategy ◽  
Relevant Role ◽  
Berth Scheduling

AbstractWith the growing demand of freight transport by means of container vessels as well as the important competition among terminals, managers and stakeholders seek to improve the exploitation of the container terminal resources efficiently. In this context, arises the Berth Allocation Problem, which aims to allocate and schedule incoming vessels along the quay. Its appropriate solution plays a relevant role in enhancing the terminal productivity. Thus, for addressing this problem, we propose a cooperative search, where the individuals are organized into groups and each member shares information with its group partners. This grouping strategy allows to diversify as well as intensify the search in some regions by means of information shared among the individuals of each group. The computational experiments for this problem reveal that our approach reports high-quality solutions and identifies promising regions within the search space in short computational times.

scholarly journals Median location problem with two probabilistic line barriers: Extending the Hook and Jeeves algorithm

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Saber Shiripour ◽  
Nezam Mahdavi-Amiri
Keyword(s):  
Location Problem ◽  
Programming Model ◽  
Distance Functions ◽  
Mixed Integer ◽  
Lower And Upper Bounds ◽  
Rectilinear Distance ◽  
Proposed Model ◽  
Integer Nonlinear Programming ◽  
Nonlinear Programming Model ◽  
New Facility

<p style='text-indent:20px;'>We consider a median location problem in the presence of two probabilistic line barriers on the plane under rectilinear distance. It is assumed that the two line barriers move on their corresponding horizontal routes uniformly. We first investigate different scenarios for the position of the line barriers on the plane and their corresponding routes, and then define the visibility and invisibility conditions along with their corresponding expected barrier distance functions. The proposed problem is formulated as a mixed-integer nonlinear programming model. Our aim is to locate a new facility on the plane so that the total weighted expected rectilinear barrier distance is minimized. We present efficient lower and upper bounds using the forbidden location problem for the proposed problem. To solve the proposed model, the Hooke and Jeeves algorithm (HJA) is extended. We investigate various sample problems to test the performance of the proposed algorithm and appropriateness of the bounds. Also, an empirical study in Kingston-upon-Thames, England, is conducted to illustrate the behavior and applicability of the proposed model.</p>

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