Algorithms for the multiperiod workforce scheduling and routing problem with dependent tasks

Pontes, Vitor Notini; Pereira, Dilson Lucas

doi:10.1111/itor.12971

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Interesting matheuristic algorithms have been developed recently in Lima et al. (2024), Notini Pontes and Pereira (2022), and dos Santos and Borenstein (2024). Experimental results showed that their proposed approach solves instances that instead current exact methods cannot solve.…”

Section: The Finemath‐pasu Solution Approachmentioning

confidence: 99%

Patient admission scheduling problems with uncertain length of stay: optimization models and an efficient matheuristic approach

Guido

2023

Int Trans Operational Res

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In this paper, we study two well‐known problems that are attracting increasing attention in the last years: patient admission and patient‐to‐room assignment problems. These two problems are often complicated by some patients, which may have fluctuations in length of stay (LOS). We propose an optimization model that plans patient admissions and patient stays considering fluctuations in LOS and does not allow overcrowded rooms, as typically required in real‐world cases. We develop an efficient matheuristic approach based on large neighborhood search and fix and optimize heuristic. We tested our optimization model and some variations of the objective function on benchmark instances from the literature. The computational results indicate the effectiveness of the proposed methods in improving the quality of care offered to the patients. Furthermore, the proposed approach can provide a useful support to decision makers for patient flow management and avoid disruptions in access to care due to bed shortages.

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Section: The Finemath‐pasu Solution Approachmentioning

confidence: 99%

Patient admission scheduling problems with uncertain length of stay: optimization models and an efficient matheuristic approach

Guido

2023

Int Trans Operational Res

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A Dynamic Scheduling Method for Logistics Supply Chain Based on Adaptive Ant Colony Algorithm

Zhang,

Wang

2024

Int J Comput Intell Syst

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To reduce the dynamic scheduling cost of logistics supply chain and improve customer satisfaction, this paper proposes a dynamic scheduling method for logistics supply chain based on adaptive ant colony algorithm. First, determine the goal of dynamic scheduling in the logistics supply chain. Second, considering supplier satisfaction, transportation costs, and maximum delivery distance constraints, a dynamic scheduling model for logistics supply chain is constructed. Then by smoothing the pheromones and designing a transition function, adjusting factors are introduced to update the pheromone rules. Finally, based on the adaptive ant colony algorithm, the solution of the dynamic scheduling function of the logistics supply chain is solved to achieve the dynamic scheduling of the current logistics supply chain. The experimental results show that after 19 iterations, the method can search for the optimal route A1 group with a length of 33.85 km, with fewer iterations and shorter paths. The total cost is 114,290 yuan, and the degree of cargo loss is low, with a maximum of only 0.14%. The task completion time is short, customer satisfaction is above 0.85, and the scheduling accuracy is 99.9%. It can effectively control costs, improve customer satisfaction, and accurately arrange logistics supply chains.

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Algorithms for the multiperiod workforce scheduling and routing problem with dependent tasks

Cited by 2 publications

References 26 publications

Patient admission scheduling problems with uncertain length of stay: optimization models and an efficient matheuristic approach

Patient admission scheduling problems with uncertain length of stay: optimization models and an efficient matheuristic approach

A Dynamic Scheduling Method for Logistics Supply Chain Based on Adaptive Ant Colony Algorithm

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