A bi-layer model for berth allocation problem based on proactive-reactive strategy

Dai, Hongwei; Ma, Jun; Yu, Yang; Sun, Jun; Dai, Ying-Chun

doi:10.1016/j.cie.2023.109200

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…Constraints ( 5) and (6) determine the delay time of the vessel, which is non-negative. Constraint (7) stipulates that the vessel is berthed in a certain berth in the visiting port. Constraints ( 8) to (10) prevent conflicts of any two vessels in the same visiting port.…”

Section: Proposed Optimization Modelmentioning

confidence: 99%

“…Early research mainly focuses on the model formulation and algorithm innovation of the discrete and continuous BAP [3][4][5]. Further, more efficient BAP models were developed, such as the multi-objective programming model [6] and bi-layer programming model [7]. According to the reviews on the BAP, Bierwirth and Meisel [8] stated the heuristic algorithm is dominant in solving algorithms [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Variable Neighborhood Search for Multi-Port Berth Allocation with Vessel Speed Optimization

Song,

Ji,

2024

JMSE

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This paper delves into the multi-port berth allocation problem (MBAP), enriching the traditional berth allocation problem (BAP) with vessel speed optimization (VSO). In the MBAP, it is assumed that there is cooperation between the port and the shipping companies, and the operation of the vessels and the ports is planned to maximize the overall benefits. Exploring this potential collaboration between ports and shipping entities has the potential to mitigate, or even resolve, the challenges plaguing maritime transportation, e.g., port congestion and suboptimal vessel schedules, to ultimately enhance the efficiency of maritime trade. In this paper, a new mixed-integer linear programming (MILP) model for the MBAP is formulated, which attempts to minimize the total cost incurred during operations, with various constraints such as vessel sailing, the vessel space–time relationship in ports, and the planning period. Meanwhile, an innovative variable neighborhood search (VNS) algorithm is presented, in which the initial solution generation method and neighborhood structures are proposed according to the MBAP characteristics. Furthermore, two sets of MBAP instances are generated to test the proposed MILP and VNS, of which the first set is based on real-world port data and the second on existing studies. The numerical experiments verify that the VNS can efficiently and reliably solve instances of all scales, with each neighborhood structure contributing uniquely to the iterative process. In addition, by analyzing the impact of varying oil prices on the MBAP, the study offers valuable management insights. Finally, a case study based on real data from a port group in the Yangtze River Basin is presented to further demonstrate the necessity of considering vessel service time window and planning period in the MBAP as well as the important role of the VSO in scheduling.

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Section: Proposed Optimization Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variable Neighborhood Search for Multi-Port Berth Allocation with Vessel Speed Optimization

Song,

Ji,

2024

JMSE

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“…Tan and He [26] formulated the BACAP under the uncertain vessels' arrival times as a stochastic programming model and proposed a two-stage metaheuristic framework based on GA for solving this problem. Dai et al [27] constructed a bi-layer model for BAP to reduce the impact of uncertain arrival time. They used adaptive search and heuristic adjustment strategies to improve the cross-entropy algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Proactive-Reactive-Based Approach for Continuous Berth Allocation and Quay Crane Assignment Problems with Hybrid Uncertainty

Wang,

Cheng,

2024

JMSE

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Port operations have been suffering from hybrid uncertainty, leading to various disruptions in efficiency and tenacity. However, these essential uncertain factors are often considered separately in literature during berth and quay crane assignments, leading to defective, even infeasible schedules. This paper addressed the integrated berth allocation and quay crane assignment problem (BACAP) with stochastic vessel delays under different conditions. A novel approach that combines both proactive and reactive strategies is proposed. First, a mixed-integer programming model is formulated for BACAP with quay crane maintenance activities under the ideal state of no delay. Then, for minor delays, buffer time is added to absorb the uncertainty of the arrival time of vessels. Thus, a robust optimization model for minimizing the total service time of vessels and maximizing the buffer time is developed. Considering that the schedule is infeasible when a vessel is seriously delayed, a reactive model is built to minimize adjustment costs. According to the characteristics of the problem, this article combined local search with the genetic algorithm and proposed an improved genetic algorithm (IGA). Numerical experiments validate the efficiency of the proposed algorithm with CPLEX and Squeaky Wheel Optimization (SWO) in different delay conditions and problem scales. An in-depth analysis presents some management insights on the coefficient setting, uncertainty, and buffer time.

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A predictive-reactive strategy for flight test task scheduling with aircraft grounding

Tian,

Xiao,

Shen

2024

Complex Intell. Syst.

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In flight test engineering, the flight test duration (FTD) affects the aircraft’s delivery node and directly impacts costs. In the actual flight test process, the environmental status updates frequently, and various uncertain events are often encountered, which affect the flight test progress and project implementation. Therefore, when scheduling flight test tasks, rescheduling should be taken into account. This paper proposes a predictive-reactive strategy based on a deep reinforcement learning approach to solve the flight test task scheduling problem with consideration of aircraft grounding. In the predictive stage, a constructive heuristic algorithm is designed to generate an initial schedule. The rescheduling problem is solved by the appropriate rescheduling method that aims to optimize the FTD deviation, task reallocation, and workload cost simultaneously. The problem is modeled as a Markov decision process, including the well-designed state features, rewards, and actions based on different rescheduling methods. The policy is trained by the proximal policy optimization algorithm. At last, numerical results are provided to demonstrate the effectiveness and superiority of the proposed approach.

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A bi-layer model for berth allocation problem based on proactive-reactive strategy

Cited by 6 publications

References 40 publications

Variable Neighborhood Search for Multi-Port Berth Allocation with Vessel Speed Optimization

Variable Neighborhood Search for Multi-Port Berth Allocation with Vessel Speed Optimization

A Proactive-Reactive-Based Approach for Continuous Berth Allocation and Quay Crane Assignment Problems with Hybrid Uncertainty

A predictive-reactive strategy for flight test task scheduling with aircraft grounding

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