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.
A novel scenario-based robust bi-objective optimization model for humanitarian logistics network under risk of disruptions
