Trip planning for visitors in a service system with capacity constraints

Luo, Xinggang; Liu, Xinrui; Ji, Pengli; Shang, Xuan-Zhu; Zhang, Zhong-Liang

doi:10.1016/j.cor.2022.105974

Cited by 6 publications

(10 citation statements)

References 47 publications

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“…To solve this problem, Wang et al (2017) established a nonlinear model and designed a Branch and Bound (B&B) algorithm, a Sequential Algorithm (SA), and a Probabilistic Iterated Local Search (PILS) for solving the MAOPCC. Based on the work of Wang et al (2017), Luo et al (2022) developed a MILP model of the MAOPCC. They designed a new B&B algorithm and a Variable Neighborhood Search (VNS) algorithm based on the linear model to solve the MAOPCC and verified the performance of the algorithms through numerical experiments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…First, a nominal mathematical model for the MAOPCCSW is established in Section 3.1, which is consistent with the formula used by Luo et al (2022). Next, a two-stage model for the MAOPCCSW is developed in Section 3.2, which is easier to solve.…”

Section: Two-stage Approachmentioning

confidence: 99%

“…To solve the MAOPCCSW for large instances, we develop a heuristic method, which specifically utilizes the structure of the MAOPCCSW. The heuristic for solving the MAOPCCSW is based on the framework proposed by Luo et al (2022).…”

Section: Variable Neighborhood Searchmentioning

confidence: 99%

“…To solve the MAOPCC, Wang et al (2017) established a non-linear optimization model for the MAOPCC and designed exact and heuristic algorithms according to the characteristics of the problem. Based on the work of Wang et al (2017), Luo et al (2022) developed a MILP model of the MAOPCC and presented more efficient algorithms to solve it. Tourism is one of the most important application fields of the MAOPCC (Luo et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Based on the work of Wang et al (2017), Luo et al (2022) developed a MILP model of the MAOPCC and presented more efficient algorithms to solve it. Tourism is one of the most important application fields of the MAOPCC (Luo et al, 2022). In a tourism system, several attractions with capacity are located in different places within a region.…”

Section: Introductionmentioning

confidence: 99%

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A two-stage approach to the multiple-agent orienteering problem with stochastic weight and capacity constraints

Liu,

Luo,

Zhang

et al. 2023

Preprint

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The Multiple-Agent Orienteering Problem with Capacity Constraints (MAOPCC) is one kind of routing problem that finds applications in both tourism and transportation industries. The MAOPCC aims to find feasible routeS with maximum profit considering time constraints. In this paper, we extend the MAOPCC to the Multiple-Agent Orienteering Problem with Stochastic Weight and Capacity Constraints (MAOPCCSW) to address the uncertainty in practical situations. The problem is solved using a two-stage stochastic model with recourse and hard time constraints. The model considers the effect of stochastic weights on the expected total profit value during the modeling stage. The two-stage model is solved with Sample Average Approximation (SAA), which converges to the optimal solution with a high computational cost. Therefore, for solving large instances, a heuristic method is developed, which utilizes the problem structure of the MAOPCCSW and explicitly considers relevant uncertainties. In our experimental analysis, we demonstrate the effectiveness of the MAOPCCSW method, which outperforms both the standard deterministic method and the deterministic method amended with real-time information.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Two-stage Approachmentioning

confidence: 99%

Section: Variable Neighborhood Searchmentioning

confidence: 99%