A Fuzzy Profit Maximization Model Using Communities Viable Leaders for Information Diffusion in Dynamic Drivers Collaboration Networks

The railway network design problem is one of the critical issues in the transportation sector due to its significance and variety of necessary applications. The major issue of this field relates to the decision of whether to increase the railways’ capacity or construct a new route to meet demand. Although the budget is a great concern of the managers for making such a decision, environmental factors should be necessarily included in the decision-making process. Therefore, this research proposes a novel robust bi-objective mixed-integer linear programming (MILP) model to simultaneously minimize the total cost and environmental impact under uncertain conditions and within a given time horizon. The proposed problem addresses strategic and operational decisions through railway project selection and product flow determination. To deal with the bi-objectiveness of the model and tackle the complexity of the problem, a nondominated sorting genetic algorithm (NSGA-II) is employed. The proposed NSGA-II could reach near-optimal Pareto solutions in a reasonable solution time and showed a reliable performance for being employed in large-sized instances. It also indicates that the proposed NSGA-II can be utilized for solving large-sized samples in a very short time.

show abstract

“…Developing other uncertainty handling techniques, such as fuzzy programming [32], to be compared to the proposed robust optimization; II.…”

Section: Conclusion Managerial Insights and Future Workmentioning

confidence: 99%

A Robust Optimization Model for Multi-Period Railway Network Design Problem Considering Economic Aspects and Environmental Impact

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Because of the advantages such as rationality and comprehensiveness of GDM results, GDM methods have been widely used in industrial design, supply chain management, operations research, and risk evaluation ( Maghsoodi et al, 2019 , Banaeian et al, 2018 , Islam et al, 2019 , Zhang et al, 2021 , He et al, 2021 ). However, in a complex decision environment, it may occur that the evaluation attributes cannot be determined, or the decision-maker is unable to perform the evaluation due to too many attributes, their own knowledge structure, or limited ability level ( Kropat et al, 2016 , Kropat and Weber, 2018 , Tirkolaee and Aydin, 2022 , Kalantari et al, 2022 , Goli et al, 2021 ). Thus, the decision-makers choose a very effective and simple method to compare alternatives two by two to determine the preference between alternatives, constructing a judgment matrix that reflects the preference relations (PRs) ( Orlovsky, 1978 , Grobelny et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Incomplete pythagorean fuzzy preference relation for subway station safety management during COVID-19 pandemic

Zhang

Zhou

et al. 2023

Expert Systems with Applications

View full text Add to dashboard Cite

Leader-based diffusion optimization model in transportation service procurement under heterogeneous drivers’ collaboration networks

2022

Self Cite

View full text Add to dashboard Cite

One of the key issues in transportation systems is allocating shipping orders to the most appropriate drivers, in the shortest time and with the maximum profit. Many studies were carried out in the transportation service procurement process for allocating orders, but none of them considered driver-to-driver interactions and applied information diffusion concepts as a framework to maximize the profit, due to the lack of a framework to model the interactions. In this paper, we present a weighted drivers' collaboration network to form the interactions. To predict the behavior of drivers, a new community detection algorithm is developed to extract communities and their leaders, in terms of the speed and the power of receiving and diffusing shipping orders. Also, we present a profit maximization model using information diffusion power of community leaders. The results show the model is able to allocate shipping orders to the most suitable drivers, in the best possible time and with the highest profit. To demonstrate the performance of the developed algorithm, we present a numerical example. Finally, a case study is applied to solve the optimization problem. The results show that the optimized behavior of companies in allocating orders to drivers is based on their risk level, reputation, and the average number of their customers.

show abstract

A Fuzzy Profit Maximization Model Using Communities Viable Leaders for Information Diffusion in Dynamic Drivers Collaboration Networks

Cited by 8 publications

References 57 publications

A Robust Optimization Model for Multi-Period Railway Network Design Problem Considering Economic Aspects and Environmental Impact

A Robust Optimization Model for Multi-Period Railway Network Design Problem Considering Economic Aspects and Environmental Impact

Incomplete pythagorean fuzzy preference relation for subway station safety management during COVID-19 pandemic

Leader-based diffusion optimization model in transportation service procurement under heterogeneous drivers’ collaboration networks

Contact Info

Product

Resources

About