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Article
Publication date: 31 August 2020

Jae-Dong Hong and Ki‐Young Jeong

Finding efficient disaster recovery center location-allocation-routing (DRCLAR) network schemes play a vital role in the disaster recovery logistics network (DRLN) design. The…

Abstract

Purpose

Finding efficient disaster recovery center location-allocation-routing (DRCLAR) network schemes play a vital role in the disaster recovery logistics network (DRLN) design. The purpose of this paper is to propose and demonstrate how to design efficient DRCLAR network schemes under the risk of facility disruptions as a part of the disaster relief activities.

Design/methodology/approach

A goal programming (GP) model is formulated to consider four performance measures simultaneously for the DRCLAR design. The cross-evaluation based-super efficiency data envelopment analysis (DEA) approach is applied to better evaluate the DRCLAR network schemes generated by solving the GP model so that more efficient network schemes can be identified.

Findings

The proposed approach identifies more efficient DRCLAR network schemes consistently among various network schemes generated by GP. We find that combining these two methods compensates for each method's weaknesses and enhances the discriminating power of the DEA method for effectively identifying and ranking the network schemes.

Originality/value

This study presents how to generate balanced DRCLAR network schemes and how to evaluate various network schemes for identifying efficient ones. The proposed procedure of developing and evaluating them could be extended for designing some disaster recovery/relief supply chain systems with conflicting performance measures.

Details

Journal of Humanitarian Logistics and Supply Chain Management, vol. 10 no. 4
Type: Research Article
ISSN: 2042-6747

Keywords

Article
Publication date: 3 June 2021

Mohammad Mahdi Ershadi and Hossein Shams Shemirani

Proper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is…

Abstract

Purpose

Proper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of injured people, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified injured people.

Design/methodology/approach

The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized injured people in the network. Besides, the total transportation activities of different types of vehicles are considered as another objective function. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize transportation activities as the second objective function while maintaining the optimality of the first objective function.

Findings

The performances of the proposed model were analyzed in different cases and its robust approach for different problems was shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.

Practical implications

The proposed methodology can be applied to find the best response plan for all crises.

Originality/value

In this paper, we have tried to use a multi-objective optimization model to guide and correct response programs to deal with the occurred crisis. This is important because it can help emergency managers to improve their plans.

Details

Journal of Humanitarian Logistics and Supply Chain Management, vol. 12 no. 1
Type: Research Article
ISSN: 2042-6747

Keywords

Article
Publication date: 24 June 2022

Sogand Soghrati Ghasbeh, Nadia Pourmohammadzia and Masoud Rabbani

This paper aims to address a location-distribution-routing problem for distributing relief commodities during a disaster under uncertainty by creating a multi-stage model that can…

Abstract

Purpose

This paper aims to address a location-distribution-routing problem for distributing relief commodities during a disaster under uncertainty by creating a multi-stage model that can consider information updates during the disaster. This model aims to create a relief network that chooses distribution centers with the highest value while maximizing equity and minimizing response time.

Design/methodology/approach

A hybrid algorithm of adaptive large neighborhood search (ALNS) and multi-dimensional local search (MDLS) is introduced to solve the problem. Its results are compared to ALNS and an augmented epsilon constraint (AUGMECON) method.

Findings

The results show that the hybrid algorithm can obtain high-quality solutions within reasonable computation time compared to the exact solution. However, while it yields better solutions compared to ALNS, the solution is obtained in a little longer amount of time.

Research limitations/implications

In this paper, the uncertain nature of some key features of the relief operations problem is not discussed. Moreover, some assumptions assumed to simplify the proposed model should be verified in future studies.

Practical implications

In order to verify the effectiveness of the designed model, a case study of the Sarpol Zahab earthquake in 2017 is illustrated and based on the results and the sensitivity analyses, some managerial insights are listed to help disaster managers make better decisions during disasters.

Originality/value

A novel robust multi-stage linear programming model is designed to address the location-distribution-routing problem during a disaster and to solve this model an efficient hybrid meta-heuristic model is developed.

Details

Journal of Humanitarian Logistics and Supply Chain Management, vol. 12 no. 4
Type: Research Article
ISSN: 2042-6747

Keywords

Article
Publication date: 14 September 2021

Peiman Ghasemi, Fariba Goodarzian, Angappa Gunasekaran and Ajith Abraham

This paper proposed a bi-level mathematical model for location, routing and allocation of medical centers to distribution depots during the COVID-19 pandemic outbreak. The…

Abstract

Purpose

This paper proposed a bi-level mathematical model for location, routing and allocation of medical centers to distribution depots during the COVID-19 pandemic outbreak. The developed model has two players including interdictor (COVID-19) and fortifier (government). Accordingly, the aim of the first player (COVID-19) is to maximize system costs and causing further damage to the system. The goal of the second player (government) is to minimize the costs of location, routing and allocation due to budget limitations.

Design/methodology/approach

The approach of evolutionary games with environmental feedbacks was used to develop the proposed model. Moreover, the game continues until the desired demand is satisfied. The Lagrangian relaxation method was applied to solve the proposed model.

Findings

Empirical results illustrate that with increasing demand, the values of the objective functions of the interdictor and fortifier models have increased. Also, with the raising fixed cost of the established depot, the values of the objective functions of the interdictor and fortifier models have raised. In this regard, the number of established depots in the second scenario (COVID-19 wave) is more than the first scenario (normal COVID-19 conditions).

Research limitations/implications

The results of the current research can be useful for hospitals, governments, Disaster Relief Organization, Red Crescent, the Ministry of Health, etc. One of the limitations of the research is the lack of access to accurate information about transportation costs. Moreover, in this study, only the information of drivers and experts about transportation costs has been considered. In order to implement the presented solution approach for the real case study, high RAM and CPU hardware facilities and software facilities are required, which are the limitations of the proposed paper.

Originality/value

The main contributions of the current research are considering evolutionary games with environmental feedbacks during the COVID-19 pandemic outbreak and location, routing and allocation of the medical centers to the distribution depots during the COVID-19 outbreak. A real case study is illustrated, where the Lagrangian relaxation method is employed to solve the problem.

Details

The International Journal of Logistics Management, vol. 34 no. 4
Type: Research Article
ISSN: 0957-4093

Keywords

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