Addressing a Humanitarian Relief Chain Considering Uncertain Demand and Deprivation Costs by a Hybrid LP-GA Method: An Earthquake in Kermanshah

Abstract Today, a great deal of attention to numerous disasters such as earthquakes, floods and terrorist attacks is motivated by humanitarian logistics. A comprehensive plan for relief logistic items under uncertainty is a challengeable concern for both academic and logistics practitioners. This study contributes another robust plan for the humanitarian logistics for the earthquake disaster in Kermanshah, Iran. The proposed framework evaluates both operational and disruption risks simultaneously to study the Humanitarian Relief Chain (HRC) network after an earthquake. The main novelty is the simultaneous consideration of the deprivation costs and demand under uncertainty. The deprivation cost leads to a reduction in high social costs for the decision-makers of the HRC. The proposed HRC also guarantees the delivery of the essential supplies to beneficiaries under both operational and disruption risks. As an optimization model, it seeks to minimize total costs consisting of inventory holding cost, shortage cost, deprivation costs and transportation cost and maximizes each facility's weighted resilience level as the second objective. A robust optimization model is established to deal with uncertain levels of the transport network paths, supply condition, amount of demand and deprivation costs which are assumed uncertain. The resilience parameters used for the second objective are obtained by a Best Worst Method (BWM). Another significant contribution was a hybrid approach combining the LP-metric method and Genetic Algorithm (GA) as the LP–GA approach for optimizing large-scale instances. Regarding the analyses, including tuning, validation and comparison of the proposed approach, its performance is showed by several standard multi-objective assessment metrics. As a final point, the achieved outcomes demonstrate that the suggested model is highly sensitive to uncertain parameters. This encourages further development and application of the proposed HRC with the use of a hybrid LP-GA approach as a strong technique for solving optimization problems.

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A Hybrid Approach of Heuristic and Neural Network for Packing Problems

20th Design Automation Conference: Volume 2 — Robust Design Applications; Decomposition and Design Optimization; Optimization Tools and Applications ◽

10.1115/detc1994-0119 ◽

1994 ◽

Author(s):

Zuo Dai ◽

Jianzhong Cha

Keyword(s):

Neural Network ◽

Large Scale ◽

Optimization Problems ◽

Travelling Salesman Problem ◽

Hybrid Approach ◽

Two Dimensions ◽

Packing Problems ◽

Combinatorial Optimization Problems ◽

Complete Problems ◽

Np Complete

Abstract Artificial Neural Networks, particularly the Hopfield-Tank network, have been effectively applied to the solution of a variety of tasks formulated as large scale combinatorial optimization problems, such as Travelling Salesman Problem and N Queens Problem [1]. The problem of optimally packing a set of geometries into a space with finite dimensions arises frequently in many applications and is far difficult than general NP-complete problems listed in [2]. Until now within accepted time limit, it can only be solved with heuristic methods for very simple cases (e.g. 2D layout). In this paper we propose a heuristic-based Hopfield neural network designed to solve the rectangular packing problems in two dimensions, which is still NP-complete [3]. By comparing the adequacy and efficiency of the results with that obtained by several other exact and heuristic approaches, it has been concluded that the proposed method has great potential in solving 2D packing problems.

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Multi objective optimization for humanitarian logistics operations through the use of mobile technologies

Journal of Humanitarian Logistics and Supply Chain Management ◽

10.1108/jhlscm-01-2015-0002 ◽

2016 ◽

Vol 6 (3) ◽

pp. 399-418 ◽

Cited By ~ 5

Author(s):

Marco Antonio Serrato-Garcia ◽

Jaime Mora-Vargas ◽

Roman Tomas Murillo

Keyword(s):

Decision Making ◽

Information System ◽

Developing Countries ◽

Multiobjective Optimization ◽

Mobile Technology ◽

Optimization Model ◽

Humanitarian Logistics ◽

Social Costs ◽

Content Type ◽

Deprivation Costs

Purpose The purpose of this paper is to present the development and implementation of a multiobjective optimization model and information system based on mobile technology, to support decision making in humanitarian logistics operations. Design/methodology/approach The trade-off between economic and social (deprivation) costs faced by governmental and nongovernmental organizations (NGOs) involved in humanitarian logistics operations is modeled through a Pareto frontier analysis, which is obtained from a multiobjective optimization model. Such analysis is supported on an information system based on mobile technology. Findings Results show useful managerial insights for decision-makers by considering both economic and social costs associated to humanitarian logistics operations. Such insights include the importance of timely and accurate information shared through mobile technology. Research limitations/implications This research presents a multiobjective approach that considers social costs, which are modeled through deprivation functions. The authors suggest that a future nonlinear approach be also considered, since there will be instances where the deprivation cost is a nonlinear function throughout time. Also, the model and information system developed may not be suitable for other humanitarian aid instances, considering the specific characteristics of the events considered on this research. Practical implications The inclusion of several types of goods, vehicles, collecting points off the ground, distributions points on the ground, available roads after a disaster took place, as well as volume and weight constraints faced under these scenarios, are considered. Social implications Deprivation costs faced by affected population after a disaster took place are considered, which supports decision making in governmental and NGOs involved in humanitarian logistics operations toward welfare of such affected population in developing countries. Originality/value A numerical illustration in the Latin American context is presented, the model and information system developed can be used in other developing countries or regions that face similar challenges toward humanitarian logistics operations.

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Computationally Efficient Hybrid Method for Transmission Network Expansion Planning

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2016-0145 ◽

2017 ◽

Vol 18 (2) ◽

Cited By ~ 1

Author(s):

Ashu Verma ◽

Soumya Das ◽

P. R. Bijwe

Keyword(s):

Power System ◽

Large Scale ◽

Optimization Problems ◽

Hybrid Approach ◽

Solution Process ◽

System Optimization ◽

Mixed Integer ◽

Transmission Network ◽

Network Expansion ◽

Expansion Planning

Abstract Transmission network expansion planning (TNEP) is an important and computationally very demanding problem in power system. Many computational approaches have been proposed to handle TNEP in the past. The problem is mixed integer, large scale and its complexity increases exponentially with the size of the system. Metaheuristic techniques have gained a lot of importance in last few years to solve the power system optimization problems, due to their ability to handle complex optimization functions and constraints. Many of them have been successfully applied for TNEP. The biggest challenge in these techniques is the requirement of large computational efforts. This paper uses a two-stage solution process to solve the TNEP problems. The first stage uses compensation based method to generate a quick, suboptimal solution. The valuable information contained in this solution is used to generate a set of heuristics aimed at drastically reducing the number of population for fitness evaluations required in the 2nd stage with application of metaheuristic method. The resulting hybrid approach produces very good quality solutions very efficiently. Results for 24 bus and 93 bus test systems have been obtained with the proposed method to ascertain the potential of the method in comparison to earlier approaches.

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Fuzzy Simheuristics for Optimizing Transportation Systems: Dealing with Stochastic and Fuzzy Uncertainty

Applied Sciences ◽

10.3390/app11177950 ◽

2021 ◽

Vol 11 (17) ◽

pp. 7950

Author(s):

Rafael D. Tordecilla ◽

Leandro do C. Martins ◽

Javier Panadero ◽

Pedro J. Copado ◽

Elena Perez-Bernabeu ◽

...

Keyword(s):

Large Scale ◽

Optimization Problems ◽

Smart Cities ◽

Hybrid Approach ◽

Transportation Systems ◽

Telecommunication Networks ◽

Routing Problem ◽

Fuzzy Uncertainty ◽

Hard Problems ◽

Team Orienteering

In the context of logistics and transportation, this paper discusses how simheuristics can be extended by adding a fuzzy layer that allows us to deal with complex optimization problems with both stochastic and fuzzy uncertainty. This hybrid approach combines simulation, metaheuristics, and fuzzy logic to generate near-optimal solutions to large scale NP-hard problems that typically arise in many transportation activities, including the vehicle routing problem, the arc routing problem, or the team orienteering problem. The methodology allows us to model different components–such as travel times, service times, or customers’ demands–as deterministic, stochastic, or fuzzy. A series of computational experiments contribute to validate our hybrid approach, which can also be extended to other optimization problems in areas such as manufacturing and production, smart cities, telecommunication networks, etc.

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An agent-based model for resource allocation during relief distribution

Journal of Humanitarian Logistics and Supply Chain Management ◽

10.1108/jhlscm-07-2013-0023 ◽

2014 ◽

Vol 4 (2) ◽

pp. 265-285 ◽

Cited By ~ 17

Author(s):

Rubel Das ◽

Shinya Hanaoka

Keyword(s):

Large Scale ◽

Transportation Cost ◽

Social Benefit ◽

Humanitarian Logistics ◽

Agent Based Model ◽

Decomposition Approach ◽

Content Type ◽

Agent Based ◽

The Social ◽

Relief Distribution

Purpose – The purpose of this paper is to propose a model for allocating resources in various zones after a large-scale disaster. This study is motivated by the social dissatisfaction caused by inefficient relief distribution. Design/methodology/approach – This study introduces an agent-based model (ABM) framework for integrating stakeholders’ interests. The proposed model uses the TOPSIS method to create a hierarchy of demand points for qualitative and quantitative parameters. A decomposition algorithm has been proposed to solve fleet allocation. Findings – Relief distribution based on the urgency of demand points increases social benefit. A decomposition approach generates higher social benefit than the enumeration approach. The transportation cost is lower in the enumeration approach. Research limitations/implications – This study does not consider fleet contracts explicitly, but rather assumes a linear cost function for computing transportation costs. Practical implications – The outcomes of this study can be a valuable tool for relief distribution planning. This model may also help reduce the social dissatisfaction caused by ad hoc relief distribution. Originality/value – This study introduces an ABM for humanitarian logistics, proposes a decomposition approach, and explores the ontology of stakeholders of humanitarian logistics specific to last-mile distribution.

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Numerical Optimal Control for Problems with Random Forced SPDE Constraints

ISRN Applied Mathematics ◽

10.1155/2014/974305 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

R. Naseri ◽

A. Malek

Keyword(s):

Numerical Simulation ◽

Optimal Control ◽

Large Scale ◽

Polynomial Chaos ◽

Optimization Problems ◽

Hybrid Approach ◽

Chaos Expansion ◽

The Finite Element Method ◽

Stochastic Diffusion Equation ◽

Spatial Part

A numerical algorithm for solving optimization problems with stochastic diffusion equation as a constraint is proposed. First, separation of random and deterministic variables is done via Karhunen-Loeve expansion. Then, the problem is discretized, in spatial part, using the finite element method and the polynomial chaos expansion in the stochastic part of the problem. This process leads to the optimal control problem with a large scale system in its constraint. To overcome these difficulties the adjoint technique for derivative computation to implementation of the optimal control issue in preconditioned Newton’s conjugate gradient method is used. By some numerical simulation, it is shown that this hybrid approach is efficient and simple to implement.

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Developing an Optimization Tool for Selecting Gravel Roads Maintenance Strategies using a Genetic Algorithm

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120915201 ◽

2020 ◽

Vol 2674 (5) ◽

pp. 108-119

Author(s):

Waleed Aleadelat ◽

Omar Albatayneh ◽

Khaled Ksaibati

Keyword(s):

Optimization Model ◽

Large Scale ◽

Local Community ◽

Planning Process ◽

Optimization Problems ◽

Service Level ◽

Local Economy ◽

Treatment Type ◽

Gravel Roads ◽

Local Agencies

As part of the efforts by Wyoming Technology Transfer Center (WYT2/LTAP) to develop a gravel roads management system (GRMS) in Wyoming, this research study developed a user-friendly tool, using JavaScript, which implements an optimization model based on genetic algorithms (GA). The developed tool will help decision makers and local agencies in managing gravel roads efficiently. Using this tool, a decision maker will be able to identify the most appropriate treatment type for each road, based on service level, estimated project costs, predicted road conditions, and whether to fund a project or not. The optimization model aims to maximize the overall condition of the gravel roads network subject to the average daily traffic (ADT) on each road. The developed tool can be applied to large-scale optimization problems (i.e., gravel roads network). The tool operates with minimal data requirements that are in line with procedures regularly followed at these agencies. In addition to having an engineered outcome, this tool can help local agencies in allocating their limited available funds efficiently, enhancing the planning process, maximizing the social welfare of the local economy, and promoting a sense of general satisfaction within the local community. A case study using data from Laramie County was used to validate this tool. The initial results were promising and in line with previous efforts to manage gravel roads in Wyoming.

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