An integrated production and transportation scheduling problem with order acceptance and resource allocation decisions

Aminzadegan, Sajede; Tamannaei, Mohammad; Fazeli, Majid

doi:10.1016/j.asoc.2021.107770

Cited by 33 publications

(6 citation statements)

References 68 publications

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“…Thus, researchers have designed many heuristic-based algorithms to reduce the solution space. For example, the greedy heuristic algorithm [11], insertion heuristic algorithm [4], variable neighborhood search heuristic algorithm [33], tabu search algorithm [34], neighborhood search algorithm [35], simulated-annealing algorithm [28], and genetic algorithm [36] have been applied to gradually modify initial solutions by performing local adjustments. Heuristic algorithms are highly effective for solving combinatorial optimization problems and obtaining approximate optimal solutions.…”

Section: Related Workmentioning

confidence: 99%

Bus Rescheduling for Long-Term Benefits: An Integrated Model Focusing on Service Capability and Regularity

Deng,

He,

Zhong

et al. 2024

Applied Sciences

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Unplanned disruptions, such as vehicle breakdowns, in a public transportation system can lead to severe delays and even service interruptions, preventing the successful implementation of subsequent plans and the overall stability of transit services. A common solution to address such issues is implementing a bus bridging service using an experience-based response strategy, involving the deployment of spare buses to continue affected services. However, with this approach, it becomes impractical and challenging to generate a feasible and rational rescheduling scheme for the remaining transit services when spare buses are insufficient or widespread disruptions occur. In response to this challenge, we propose an innovative model that integrates service capability and regularity, aiming to minimize rescheduling costs through timetable adjustments and scheduling reassignments. We apply dynamic programming to comprehensively consider the hysteresis effects of disruptions and achieve a long-term optimal rescheduling scheme. To efficiently solve the proposed model, the large neighborhood search algorithm is improved by incorporating operational rules. Finally, several experiments are conducted under an actual transit operation scenario in Shenzhen. The results demonstrate that our method significantly reduces trip cancellations and, simultaneously, diminishes the increase in the departure interval resulting from the adjusted schedule by 23.27%.

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Section: Related Workmentioning

confidence: 99%

Bus Rescheduling for Long-Term Benefits: An Integrated Model Focusing on Service Capability and Regularity

Deng,

He,

Zhong

et al. 2024

Applied Sciences

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“…Chu et al proposed a particle swarm genetic hybrid algorithm (Chu et al, 2022). Aminzadegan et al proposed two meta-heuristic solutions based on Adaptive Genetic Algorithm (AGA) and Tabu Search Algorithm (TS) (Aminzadegan et al, 2021). These efforts have demonstrated the significant potential of optimization algorithms in solving resource scheduling problems in MSAR operations.…”

Section: Literature Reviewmentioning

confidence: 99%

Accident black spot clustering oriented maritime search and rescue resource allocation and optimization

Sun,

Hou,

Chen

et al. 2024

Front. Mar. Sci.

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Efficient and rapid deployment of maritime search and rescue(MSAR) resources is a prerequisite for maritime emergency search and rescue, in order to improve the efficiency and accuracy of MSAR. This paper proposes an integrated approach for emergency resource allocation. The approach encompasses three main steps: identifying accident black spots, assessing high-risk areas, and optimizing the outcomes through a synergistic combination of an optimization algorithm and reinforcement learning. In the initial step, the paper introduces the iterative self-organizing data analysis technology (ISODATA) for identifying accident spots at sea. A comparative analysis is conducted with other clustering algorithms, highlighting the superiority of ISODATA in effectively conducting dense clustering. This can effectively carry out dense clustering, instead of the situation where the data spots are too dispersed or obvious anomalies that affect the clustering. Furthermore, this approach incorporates entropy weighting to reassess the significance of accident spots by considering both the distance and the frequency of accidents. This integrated approach enhances the allocation of search and rescue forces, ensuring more efficient resource utilization. To address the MSAR vessel scheduling problem at sea, the paper employs the non-dominated sorting genetic algorithm II combined with reinforcement learning (NSGAII-RL). Comparative evaluations against other optimization algorithms reveal that the proposed approach can save a minimum of 7% in search and rescue time, leading to enhanced stability and improved efficiency in large-scale MSAR operations. Overall, the integrated approach presented in this paper offers a robust solution to the ship scheduling problem in maritime search and rescue operations. Its effectiveness is demonstrated through improved resource allocation, enhanced timeliness, and higher efficiency in responding to maritime accidents.

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“…To better benchmark our method, we adapt AM [16] to mT-SPTWR as a baseline, which is known as the state-of-the-art DRL method for TSP. Besides, we adopt three competitive and representative conventional metaheuristic algorithms including tabu search (TS) [31], simulated annealing (SA) [32] and bees algorithm (BA) [33] as additional baselines. Moreover, we conduct detailed ablation studies on the components in our proposed framework to verify their effectiveness.…”

Section: A Settingsmentioning

confidence: 99%

Learning to Solve Multiple-TSP With Time Window and Rejections via Deep Reinforcement Learning

Zhang

Cao

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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We propose a manager-worker framework 1 based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), i.e., multiplevehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections. Particularly, in the proposed framework, a manager agent learns to divide mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a Graph Isomorphism Network (GIN) based policy network. A worker agent learns to solve sub-routing tasks by minimizing the cost in terms of both tour length and rejection rate for each vehicle, the maximum of which is then fed back to the manager agent to learn better assignments. Experimental results demonstrate that the proposed framework outperforms strong baselines in terms of higher solution quality and shorter computation time. More importantly, the trained agents also achieve competitive performance for solving unseen larger instances.

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An integrated production and transportation scheduling problem with order acceptance and resource allocation decisions

Cited by 33 publications

References 68 publications

Bus Rescheduling for Long-Term Benefits: An Integrated Model Focusing on Service Capability and Regularity

Bus Rescheduling for Long-Term Benefits: An Integrated Model Focusing on Service Capability and Regularity

Accident black spot clustering oriented maritime search and rescue resource allocation and optimization

Learning to Solve Multiple-TSP With Time Window and Rejections via Deep Reinforcement Learning

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