scholarly journals Healthcare Facility Location-Allocation Optimization for China’s Developing Cities Utilizing a Multi-Objective Decision Support Approach

2018 ◽  
Vol 10 (12) ◽  
pp. 4580 ◽  
Author(s):  
Li Wang ◽  
Huan Shi ◽  
Lu Gan
Keyword(s):  
Heuristic Algorithms ◽  
Programming Model ◽  
Rapid Development ◽  
Supply And Demand ◽  
Healthcare Facility ◽  
Allocation Model ◽  
Location Allocation ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Allocation Problems

With rapid development of the healthcare network, the location-allocation problems of public facilities under increased integration and aggregation needs have been widely researched in China’s developing cites. Since strategic formulation involves multiple conflicting objectives and stakeholders, this paper presents a practicable hierarchical location-allocation model from the perspective of supply and demand to characterize the trade-off between social, economical and environmental factors. Due to the difficulties of rationally describing and the efficient calculation of location-allocation problems as a typical Non-deterministic Polynomial-Hard (NP-hard) problem with uncertainty, there are three crucial challenges for this study: (1) combining continuous location model with discrete potential positions; (2) introducing reasonable multiple conflicting objectives; (3) adapting and modifying appropriate meta-heuristic algorithms. First, we set up a hierarchical programming model, which incorporates four objective functions based on the actual backgrounds. Second, a bi-level multi-objective particle swarm optimization (BLMOPSO) algorithm is designed to deal with the binary location decision and capacity adjustment simultaneously. Finally, a realistic case study contains sixteen patient points with maximum of six open treatment units is tested to validate the availability and applicability of the whole approach. The results demonstrate that the proposed model is suitable to be applied as an extensive planning tool for decision makers (DMs) to generate policies and strategies in healthcare and design other facility projects.

Multi-Objective Pricing Optimization for a High-Speed Rail Network Under Competition

2019 ◽  
Vol 2673 (7) ◽  
pp. 215-226 ◽  
Author(s):  
Huizhuo Cao ◽  
Xuemei Li ◽  
Vikrant Vaze ◽  
Xueyan Li
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Pareto Frontier ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
High Speed Rail ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Single Objective

Multi-objective pricing of high-speed rail (HSR) passenger fares becomes a challenge when the HSR operator needs to deal with multiple conflicting objectives. Although many studies have tackled the challenge of calculating the optimal fares over railway networks, none of them focused on characterizing the trade-offs between multiple objectives under multi-modal competition. We formulate the multi-objective HSR fare optimization problem over a linear network by introducing the epsilon-constraint method within a bi-level programming model and develop an iterative algorithm to solve this model. This is the first HSR pricing study to use an epsilon-constraint methodology. We obtain two single-objective solutions and four multi-objective solutions and compare them on a variety of metrics. We also derive the Pareto frontier between the objectives of profit and passenger welfare to enable the operator to choose the best trade-off. Our results based on computational experiments with Beijing–Shanghai regional network provide several new insights. First, we find that small changes in fares can lead to a significant improvement in passenger welfare with no reduction in profitability under multi-objective optimization. Second, multi-objective optimization solutions show considerable improvements over the single-objective optimization solutions. Third, Pareto frontier enables decision-makers to make more informed decisions about choosing the best trade-offs. Overall, the explicit modeling of multiple objectives leads to better pricing solutions, which have the potential to guide pricing decisions for the HSR operators.

The Multi-Objective Optimization Allocation Model of Water Resources in Xiaokai River Irrigation Area

2015 ◽  
Vol 1092-1093 ◽  
pp. 1289-1294
Author(s):  
Xin Wang ◽  
Jing Xu ◽  
Ke Kong ◽  
Lei Yan ◽  
Fang Wu
Keyword(s):  
Water Resources ◽  
Optimization Model ◽  
Supply And Demand ◽  
Reference Value ◽  
Multi Objective Optimization ◽  
Allocation Model ◽  
Irrigation Area ◽  
Multi Objective ◽  
Groundwater Environment ◽  
Sequence Method

For the three big problems of water resources supply and demand contradiction, protection of groundwater environment and sediment over long distances in Xiaokai river irrigation area, the model of water utilization benefit maximization, groundwater level optimal control and the goal of sediment transport effect optimization model are established, and coupled into a multi-objective optimization model. The model is solved by using The delaminating sequence method, obtained the rational allocation plan of water resources in water years, and analyzing the rationality of the plan. The results show that, the scheme comprehensively considers the economic and environmental issues and has great reference value to promote sustainable development of irrigation area.

A study of multi-objective dynamic water resources allocation modeling of Huai River

2015 ◽  
Vol 15 (4) ◽  
pp. 817-824 ◽  
Author(s):  
Jing Peng ◽  
Ximin Yuan ◽  
Lan Qi ◽  
Qiliang Li
Keyword(s):  
Water Resources ◽  
Dynamic Balance ◽  
Supply And Demand ◽  
Low Flow ◽  
Resources Allocation ◽  
Time Interval ◽  
Water Resources Allocation ◽  
Allocation Model ◽  
Multi Objective ◽  
Huai River

Water resources supply and demand has become a serious problem. Water resources allocation is usually a multi-objective problem, and has been of concern for many researchers. In the north of China, the lack of water resources in the Huai River Basin has handicapped the development of the economy, especially badly in the low-flow period. So it is necessary to study water resources allocation in this area. In this paper, a multi-objective dynamic water resources allocation model has been developed. The developed model took the overall satisfaction of water users in a time interval as the objective function, applied an improved simplex method to solve the calculation, considered the overall users' satisfaction variation with time, and followed the principle that the variation of the system satisfaction within adjacent periods of time must be minimal. The established model was then applied to the Huai River, for the present situation (2010), short-term (2020) and long-term (2030) planning timeframes. From the calculation results, the overall satisfaction in late May and mid September in 2030 was 0.65 and 0.70. After using the model allocation optimization, the overall satisfaction was improved, increasing to 0.78 and 0.79, respectively, thus achieving the dynamic balance optimization of water resources allocation in time and space. This model can provide useful decision support in water resources allocation, when it is used to alleviate water shortages occurring in the low-flow period.

scholarly journals Enhancing community based health programs in Iran: a multi-objective location-allocation model

2016 ◽  
Vol 20 (4) ◽  
pp. 485-499 ◽  
Author(s):  
S. Khodaparasti ◽  
H. R. Maleki ◽  
S. Jahedi ◽  
M. E. Bruni ◽  
P. Beraldi
Keyword(s):  
Health Programs ◽  
Community Based ◽  
Allocation Model ◽  
Location Allocation ◽  
Multi Objective

scholarly journals QoS-aware and Multi-Objective Virtual Machine Dynamic Scheduling for Big Data Centers in Clouds

2021 ◽  
Author(s):  
Jirui Li ◽  
Rui Zhang ◽  
Yafeng Zheng
Keyword(s):  
Load Balancing ◽  
Virtual Machine ◽  
Cost Estimation ◽  
Dynamic Scheduling ◽  
Supply And Demand ◽  
Estimation Method ◽  
Multi Objective ◽  
Vm Scheduling ◽  
Conflicting Objectives ◽  
Migration Cost

Abstract Efficient resource scheduling is one of the most critical issues for big datacenters in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models.

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