scholarly journals HADES: A Multi-Agent Platform to Reduce Congestion Anchoring Based on Temporal Coordination of Vessel Arrivals—Application to the Multi-Client Liquid Bulk Terminal in the Port of Cartagena (Spain)

2021 ◽  
Vol 11 (7) ◽  
pp. 3109
Author(s):  
Pilar Jiménez ◽  
José María Gómez-Fuster ◽  
Pablo Pavón-Mariño
Keyword(s):  
Mixed Integer ◽  
Berth Allocation ◽  
Limited Information ◽  
Key Factors ◽  
Berth Allocation Problem ◽  
Mixed Integer Linear Program ◽  
Agent Platform ◽  
Liquid Bulk ◽  
Multi Agent ◽  
Time Changes

Ports are key factors in international trade, and new port terminals are quite costly and time consuming to build. Therefore, it is necessary to optimize existing infrastructure to achieve sustainability in logistics. This problem is more complex in multi-client port terminals, where quay infrastructure is shared among terminal operators who often have conflicting interests. Moreover, the berth allocation problem in liquid bulk terminals implies demanding restrictions due to the reduced flexibility in berth allocation for these types of goods. In this context, this paper presents HADES, a multi-agent platform, and the experience of its pilot use in the Port of Cartagena. HADES is a software platform where agents involved in vessel arrivals share meaningful but limited information. This is done to alleviate potential congestion in multi-client liquid bulk terminals, promoting a consensus where overall congestion anchoring is reduced. A study is presented using a mixed integer linear program (MILP) optimization model to analyze the maximum theoretical reduction in congestion anchoring, depending on the flexibility of vessel arrival time changes. Results show that 6 h of flexibility is enough to reduce congestion anchoring by half, and 24 h reduces it to negligible values. This confirms the utility of HADES, which is also briefly described.

scholarly journals Berth allocation problem: formulation and a Tunisian case study.

2019 ◽  
Vol 51 (3) ◽  
pp. 85-100
Author(s):  
Lobna Kallel ◽  
Ezzeddine Benaissa ◽  
Hichem Kamoun ◽  
Mounir Benaissa
Keyword(s):  
Waiting Time ◽  
Problem Formulation ◽  
Flow Time ◽  
Allocation Problem ◽  
Mixed Integer ◽  
Berth Allocation ◽  
Berth Allocation Problem ◽  
Mixed Integer Linear Program ◽  
Knowing That

This paper examines one of the most important operational problems in seaport terminals, namely the Berth Allocation Problem (BAP) which finds an optimal assignment of ships to the berths that minimize the total waiting time of all ships and reduce congestion in ports. Our problem is to affect and schedule n ships on m berths to minimize the processing time and the waiting time for all the ships in the port. Therefore, ships stay time in the port known by the flow time, while respecting the physical constraints existing at the port (such as the depth of the water berth and the draft of the ship’s water), knowing that each ship can only accommodate one ship at a time. It is as if it was a case of n tasks and m machines in parallel, and we wanted to schedule the passage of different tasks on different machines, knowing that each task can only pass on one machine and that the interruption of the task is not allowed. For example, if a job started on a machine, it will remain on this machine up to its completion. In our case, tasks are ships and machines are berths that are opting to minimize the total flow time and, therefore, to decrease the residence time of all the ships in the port. In a first step, a Mixed Integer Linear Program model is designed to address the BAP with the aim of minimizing the flow time of the ships in the port, our sample can be used for both static and dynamic berth allocation cases. In a second step, this model is illustrated with a real case study in the Tunisian port of Rades and solved by a commercial solver CPLEX. Calculation results are presented and compared with those obtained by port authorities in Radès.

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.

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.

A Multi-Objective Model for the Simultaneous Planning Problems

2020 ◽  
pp. 111-135
Author(s):  
Kallel Lobna ◽  
Kamoun Hichem ◽  
Benaissa Mounir
Keyword(s):  
Linear Program ◽  
Integer Linear Program ◽  
Mixed Integer ◽  
Program Model ◽  
Berth Allocation Problem ◽  
Mixed Integer Linear Program ◽  
Stay Time ◽  
Travelling Time ◽  
Storage Area ◽  
Objective Model

This chapter examines two of the most important operational problems in seaport terminals, first, the berth allocation problem (BAP) which finds an optimal assignment of ships to the berths that minimise the total waiting time of all ships. Then we consider the ships containers to storage areas assignment problem (SSAP) which finds an allocation of ship containers to storage area that minimises the travelling time and containers dispersion. In the first step, a mixed integer linear program model is designed to address the BA problem with the aim of minimising the ships stay time in the port (known as the scheduling theory by the flow time). In a second step, the output of the first model is used in another mixed integer linear program model to solve the SSA problem with a view at reducing both travelling time and containers dispersion while satisfying storage capacities for the case where the containers of one ship can be partitioned into two different and consecutive storage area when needed. The experimental part is conducted on a real case, namely the Tunisian port of Radès.

scholarly journals Coordinated Energy Scheduling of a Distributed Multi-Microgrid System Based on Multi-Agent Decisions

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4077
Author(s):  
Yuyan Sun ◽  
Zexiang Cai ◽  
Ziyi Zhang ◽  
Caishan Guo ◽  
Guolong Ma ◽  
...  
Keyword(s):  
Lower Layer ◽  
Pso Algorithm ◽  
Control Agent ◽  
Mixed Integer ◽  
Hierarchical Optimization ◽  
Limited Information ◽  
Multi Stage ◽  
Coordinated Optimization ◽  
Multi Agent ◽  
Internal Operations

Regarding the different ownerships and autonomy of microgrids (MGs) in the distributed multi-microgrid (MMG) system, this paper establishes a multi-stage energy scheduling model based on a multi-agent system (MAS). The proposed mechanism enables a microgrid agent (MGA), a central energy management agent (CEMA), and a coordination control agent (CCA) to cooperate efficiently during various stages including prescheduling, coordinated optimization, rescheduling and participation willingness analysis. Based on the limited information sharing between agents, energy scheduling models of agents and coordinated diagrams are constructed to demonstrate the different roles of agents and their interactions within the MMG system. Distributed schemes are introduced for MG internal operations considering demand response, while centralized schemes under the control of the CCA are proposed to coordinate MGAs. Participation willingness is defined to analyze the MGA’s satisfaction degree of the matchmaking. A hierarchical optimization algorithm is applied to solve the above nonlinear problem. The upper layer establishes a mixed-integer linear programming (MILP) model to optimize the internal operation problem of each MG, and the lower layer applies the particle swarm optimization (PSO) algorithm for coordination. The simulation with a three-MG system verifies the rationality and effectiveness of the proposed model and method.

scholarly journals ORNInA: A decentralized, auction-based multi-agent coordination in ODT systems

2021 ◽  
pp. 1-17
Author(s):  
Alaa Daoud ◽  
Flavien Balbo ◽  
Paolo Gianessi ◽  
Gauthier Picard
Keyword(s):  
Synthetic Data ◽  
Continuous Optimization ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Multi Agent ◽  
Run Time ◽  
Agent Coordination

On-Demand Transport (ODT) systems have attracted increasing attention in recent years. Traditional centralized dispatching can achieve optimal solutions, but NP-Hard complexity makes it unsuitable for online and dynamic problems. Centralized and decentralized heuristics can achieve fast, feasible solution at run-time with no guarantee on the quality. Starting from a feasible not optimal solution, we present in this paper a new solution model (ORNInA) consisting of two parallel coordination processes. The first one is a decentralized insertion-heuristic based algorithm to build vehicle schedules in order to solve a particular case of the dynamic Dial-A-Ride-Problem (DARP) as an ODT system, in which vehicles communicate via Vehicle-to-vehicle communication (V2V) and make decentralized decisions. The second coordination scheme is a continuous optimization process namely Pull-demand protocol, based on combinatorial auctions, in order to improve the quality of the global solution achieved by decentralized decision at run-time by exchanging resources between vehicles (k-opt). In its simplest implementation, k is set to 1 so that vehicles can exchange only one resource at a time. We evaluate and analyze the promising results of our contributed techniques on synthetic data for taxis operating in Saint-Étienne city, against a classical decentralized greedy approach and a centralized one that uses a classical mixed-integer linear program (MILP) solver.

scholarly journals A Comprehensive Modeling of the Discrete and Dynamic Problem of Berth Allocation in Maritime Terminals

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2684
Author(s):  
Sami Mansri ◽  
Malek Alrashidi
Keyword(s):  
Dynamic Problem ◽  
Numerical Experiments ◽  
Parallel Machines ◽  
Berth Allocation ◽  
Berth Allocation Problem ◽  
Resolution Method ◽  
Agent Interactions ◽  
Contract Net ◽  
Multi Agent ◽  
Comprehensive Modeling

In this study, the discrete and dynamic problem of berth allocation in maritime terminals, is investigated. The suggested resolution method relies on a paradigm of optimization with two techniques: heuristic and multi-agent. Indeed, a set of techniques such as the protocol of negotiation named contract net, the multi-agent interactions, and Worst-Fit arrangement technique, are involved. The main objective of the study is to propose a solution for attributing m parallel machines to a set of activities. The contribution of the study is to provide a detailed modeling of the discrete and dynamic berth allocation problem by establishing the corresponding models using a multi-agent methodology. A set of numerical experiments are detailed to prove the performance of the introduced multi-agent strategy compared with genetic algorithm and tabu search.

Berth Allocation in a Bulk Terminal with Overall Quay Length Constraint

2021 ◽  
pp. 2159019
Author(s):  
Jiangbo Yao ◽  
Junfang Wu
Keyword(s):  
Genetic Algorithm ◽  
Pearl River Delta ◽  
Programming Model ◽  
Mixed Integer ◽  
Berth Allocation ◽  
Berth Allocation Problem ◽  
Large Size ◽  
Mip Model ◽  
Length Constraint ◽  
The Pearl River

Motivated by the operation of bulk terminal in the Pearl River Delta, we study a berth allocation problem (BAP) with 2 berths and overall quay length constraint. The quay length occupied by a vessel depends on its berthing directions. The feasible berthing direction changes in a tide cycle. The objective is to minimize the total service of all vessels. We develop a mixed integer programming (MIP) model for the problem. We also propose an efficient genetic algorithm to tackle the problem. Our computational experiments show that the mixed integral programming model can only be solved by CPLEX for small-size instances, the genetic algorithm obtains good approximation solutions for large-size instances.

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.

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