Explicit Solution for a Class of Allocation Problems

1975 ◽  
pp. 351-385 ◽  
Author(s):  
Giovanni Castellani
Keyword(s):  
Explicit Solution ◽  
Allocation Problems

A Forward-Solving Numerical Technique for Dynamic Consumption and Portfolio Allocation Problems

2005 ◽  
Author(s):  
George Chacko ◽  
Maxim Golts ◽  
Mihir A. Desai ◽  
Vladimir Novakovsky
Keyword(s):  
Numerical Technique ◽  
Portfolio Allocation ◽  
Allocation Problems

Complexity of Allocation Problems in Spectrum Markets with Interference Complementarities

2013 ◽  
Vol 31 (3) ◽  
pp. 489-499 ◽  
Author(s):  
Hang Zhou ◽  
Randall Berry ◽  
Michael L. Honig ◽  
Rakesh Vohra
Keyword(s):  
Allocation Problems ◽  
Spectrum Markets

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.

Sign in / Sign up

Export Citation Format

Share Document