Air and ground ambulance location-allocation-routing problem for designing a temporary emergency management system after a disaster

2020 ◽  
Vol 234 (8) ◽  
pp. 812-828 ◽  
Author(s):  
Pedram Memari ◽  
Reza Tavakkoli-Moghaddam ◽  
Fatemeh Navazi ◽  
Fariborz Jolai
Keyword(s):  
Emergency Management ◽  
Objective Function ◽  
Heuristic Algorithms ◽  
Critical Time ◽  
Performance Comparison ◽  
Routing Problem ◽  
Location Allocation ◽  
Ground Ambulance ◽  
Dynamic Location ◽  
Disaster Severity

Disasters cause a huge number of injured patients in a short time while existing emergency facilities encountered devastation and cannot respond properly. Here, the importance of implementing temporary emergency management becomes clear. This study aims to locate some temporary emergency stations across the area by maximal covering after a disaster. Furthermore, a multi-mode fleet is used for transferring patients using different modes of transportation (e.g. helicopter ambulance and bus ambulance). Since the type of patients may change over periods, medical servers can displace among temporary emergency stations dynamically according to disaster severity. For this purpose, a new bi-objective dynamic location-helicopter ambulance allocation-ambulance routing model with multi-medical servers is presented. The first objective function minimizes the operational costs related to the newly designed Emergency Medical Service along with the rate of human loss. The second objective function minimizes the critical time spent before the medical treatment. To validate the developed model, the augmented ε-constraint method is used and applied for the Tehran city, which shows the applicability of the model. Finally, two meta-heuristic algorithms are customized for large-sized problems, and the related results are compared based on multi-objective algorithms’ performance comparison metrics to find the more efficient one.

A Discrete Artificial Bee Colony Algorithm for the Reverse Logistics Location and Routing Problem

2017 ◽  
Vol 16 (05) ◽  
pp. 1339-1357 ◽  
Author(s):  
Kun Guo ◽  
Qishan Zhang
Keyword(s):  
Reverse Logistics ◽  
Vehicle Routing Problem ◽  
Artificial Bee Colony ◽  
Heuristic Algorithms ◽  
Routing Problem ◽  
Waste Products ◽  
Location Allocation ◽  
Bee Colony ◽  
Np Complete ◽  
Location Allocation Problem

Reverse logistics (RL) emerges as a hot topic in both research and business with the increasing attention on the collection and recycling of the waste products. Since Location and Routing Problem (LRP) in RL is NP-complete, heuristic algorithms, especially those built upon swarm intelligence, are very popular in this research. In this paper, both Vehicle Routing Problem (RP) and Location Allocation Problem (LAP) of RL are considered as a whole. First, the features of LRP in RL are analyzed. Second, a mathematical model of the problem is developed. Then, a novel discrete artificial bee colony (ABC) algorithm with greedy adjustment is proposed. The experimental results show that the new algorithm can approach the optimal solutions efficiently and effectively.

Optimizing Routes with Safety and Environmental Criteria in Transportation Management in Spain

2011 ◽  
Vol 4 (3) ◽  
pp. 38-59 ◽  
Author(s):  
Javier Faulin ◽  
Fernando Lera-López ◽  
Angel A. Juan
Keyword(s):  
Objective Function ◽  
Value Chain ◽  
Vehicle Routing Problem ◽  
Heuristic Algorithms ◽  
Environmental Cost ◽  
Routing Problem ◽  
Transportation Management ◽  
Logistic Management ◽  
Environmental Criteria ◽  
Internal Costs

The object of logistic management is to optimise the whole value chain of the distribution of goods and merchandise. One of the main aspects of such an analysis is the optimisation of vehicle routes to deliver final products to customers. There are many algorithms to optimise the related vehicle routing problem. The objective function of that problem usually involves distance, cost, number of vehicles, or profits. This study also takes safety and environmental costs into account. Thus, the authors develop variants to traditional heuristic algorithms, in which they include the traditional costs along with safety and environmental cost estimates for real scenarios in Spain. This methodology is called ASEC (Algorithms with Safety and Environmental Criteria). These considerations raise the value of the global objective function, but permit a more realistic cost estimate that includes not only the internal costs involved in the problem but also the related externalities. Finally, real cases are discussed, and solutions are offered using the new ASEC methodology.

Optimizing Routes with Safety and Environmental Criteria in Transportation Management in Spain

2013 ◽  
pp. 144-165
Author(s):  
Javier Faulin ◽  
Fernando Lera-López ◽  
Angel A. Juan
Keyword(s):  
Objective Function ◽  
Value Chain ◽  
Heuristic Algorithms ◽  
Environmental Cost ◽  
Routing Problem ◽  
Transportation Management ◽  
Logistic Management ◽  
Environmental Criteria ◽  
Internal Costs ◽  
Global Objective

The object of logistic management is to optimise the whole value chain of the distribution of goods and merchandise. One of the main aspects of such an analysis is the optimisation of vehicle routes to deliver final products to customers. There are many algorithms to optimise the related vehicle routing problem. The objective function of that problem usually involves distance, cost, number of vehicles, or profits. This study also takes safety and environmental costs into account. Thus, the authors develop variants to traditional heuristic algorithms, in which they include the traditional costs along with safety and environmental cost estimates for real scenarios in Spain. This methodology is called ASEC (Algorithms with Safety and Environmental Criteria). These considerations raise the value of the global objective function, but permit a more realistic cost estimate that includes not only the internal costs involved in the problem but also the related externalities. Finally, real cases are discussed, and solutions are offered using the new ASEC methodology.

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.

scholarly journals Performance Comparison between Particle Swarm Optimization and Differential Evolution Algorithms for Postman Delivery Routing Problem

2021 ◽  
Vol 11 (6) ◽  
pp. 2703
Author(s):  
Warisa Wisittipanich ◽  
Khamphe Phoungthong ◽  
Chanin Srisuwannapa ◽  
Adirek Baisukhan ◽  
Nuttachat Wisittipanit
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Metaheuristic Methods ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms ◽  
Current Practices

Generally, transportation costs account for approximately half of the total operation expenses of a logistics firm. Therefore, any effort to optimize the planning of vehicle routing would be substantially beneficial to the company. This study focuses on a postman delivery routing problem of the Chiang Rai post office, located in the Chiang Rai province of Thailand. In this study, two metaheuristic methods—particle swarm optimization (PSO) and differential evolution (DE)—were applied with particular solution representation to find delivery routings with minimum travel distances. The performances of PSO and DE were compared along with those from current practices. The results showed that PSO and DE clearly outperformed the actual routing of the current practices in all the operational days examined. Moreover, DE performances were notably superior to those of PSO.

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.

A Conceptual Framework for Dynamic Location—Allocation Analysis

1974 ◽  
Vol 6 (5) ◽  
pp. 547-564 ◽  
Author(s):  
E S Sheppard
Keyword(s):  
Programming Models ◽  
Theoretical Level ◽  
Fully Integrated ◽  
Location Allocation ◽  
Plant Construction ◽  
Temporal Aspects ◽  
Dynamic Location ◽  
Spatio Temporal ◽  
Construction Model ◽  
Taxonomic Tree

To date, much of the work published on the problems of plant construction has been restricted to either determining the timing of the building, or forming static location—allocation models, with little attempt to combine the spatial and temporal aspects into one solution. By construction of a taxonomic tree, this paper demonstrates that the capacity expansion and the location—allocation solutions are just the simplest instances of a whole class of models. Formulation of these various possibilities is undertaken and it is shown that a fully integrated spatio—temporal plant-construction model can be derived, at least at the theoretical level. Although the derivations are in the form of deterministic programming models, the concluding section of the paper suggests possible ways in which these might be reformulated to allow for the fact that most planning takes place in an uncertain environment.

Sign in / Sign up

Export Citation Format

Share Document