Uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation model for earthquake evacuation planning

2019 ◽  
Vol 350 ◽  
pp. 105-132 ◽  
Author(s):  
Peiman Ghasemi ◽  
Kaveh Khalili-Damghani ◽  
Ashkan Hafezalkotob ◽  
Sadigh Raissi
Keyword(s):  
Evacuation Planning ◽  
Allocation Model ◽  
Location Allocation ◽  
Multi Objective ◽  
Vehicle Location

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.

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

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

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