scholarly journals Optimization of Warehouse Location and Supplies Allocation for Emergency Rescue under Joint Government–Enterprise Cooperation Considering Disaster Victims’ Distress Perception

2021 ◽  
Vol 13 (19) ◽  
pp. 10560
Author(s):  
Jiaxin Geng ◽  
Hanping Hou ◽  
Shaoqing Geng
Keyword(s):  
Pain Perception ◽  
Simulated Annealing Algorithm ◽  
Allocation Model ◽  
Emergency Relief ◽  
2008 Wenchuan Earthquake ◽  
Location Allocation ◽  
Disaster Victims ◽  
Emergency Rescue ◽  
Allocation Process ◽  
Government Enterprise

The location and allocation of emergency supplies are an important part of emergency rescue work. The existing emergency location and allocation process is inefficient, costly, and neglects the psychology of victims. To improve the emergency relief work and solve the current problems, this paper introduces the victims’ pain perception cost into the model, takes the lowest cost of the whole emergency rescue system as the goal, constructs a government–enterprise joint emergency material location–allocation model, and uses the simulated annealing algorithm to solve the model. This paper takes the 2008 Wenchuan earthquake in Sichuan Province as the background and verifies the validity and rationality of the model through a comparative analysis of case simulations. The results show that the model and algorithm can effectively solve the emergency supplies location–allocation problem considering the victims’ pain perception, reflecting the idea of human-centered sustainable development and providing support for building a sustainable emergency relief system.

Creating land allocation zones for forest management: a simulated annealing approach

2004 ◽  
Vol 34 (8) ◽  
pp. 1669-1682 ◽  
Author(s):  
Mark Boyland ◽  
John Nelson ◽  
Fred L Bunnell
Keyword(s):  
Simulated Annealing ◽  
Forest Management ◽  
Simulated Annealing Algorithm ◽  
Allocation Model ◽  
Management Zones ◽  
Allocation Process ◽  
Growth Zones ◽  
Annealing Algorithm ◽  
Allocation Decisions ◽  
Coastal British Columbia

This paper describes the Zone Allocation Model (ZAM) that uses the simulated annealing algorithm to create forest management zones. ZAM partitions the landscape into the Timber, Habitat, and Old Growth zones by allocating small land tiles into contiguous areas. The zone allocation process is guided by landscape-level targets and size and shape objectives. An ecological representation objective proportionally distributes all ecosystem types into each of the three zones. Priority objectives control allocation of identified lands that are targeted for specific zones. All objectives are combined within an objective function, with a penalty-weighting system specifying relative importance of each objective. The ZAM model found 1.7%–4.4% of theoretical optimum scores from small to large problems, respectively. A demonstration on a 1.2 × 106–ha landscape from coastal British Columbia illustrates the iterative exploration of compromises between objectives that leads to informed zone allocation decisions.

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.

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