A disaster relief commodity supply chain network considering emergency relief volunteers: a case study

PurposeThe number of natural and man-made disasters is remarkable and threatened human lives at the time of occurrence and also after that. Therefore, an efficient response following a disaster can eliminate or mitigate the adverse effects. This paper aims to help address those challenges related to humanitarian logistics by considering disaster network design under uncertainty and the management of emergency relief volunteers simultaneously.Design/methodology/approachIn this paper, a robust fuzzy stochastic programming model is proposed for designing a relief commodity supply chain network in a disaster by considering emergency relief volunteers. To demonstrate the practicality of the proposed model, a case study is presented for the 22 districts of Tehran and solved by an exact method.FindingsThe results indicate that there are many parameters affecting the design of a relief commodity supply chain network in a disaster, and also many parameters should be controlled so that, the catastrophe is largely prevented and the lives of many people can be saved by sending the relief commodity on time.Practical implicationsThis model helps decision-makers and authorities to explore optimal location and allocation decisions without using complex optimization algorithms.Originality/valueTo the best of the authors’ knowledge, employee workforce management models have not received adequate attention despite their role in relief and recovery efforts. Hence, the proposed model focuses on the problem of managing employees and designing a disaster logistics network simultaneously. The robust fuzzy stochastic programming method is applied for the first time for controlling the uncertainties in the design of humanitarian relief supply chains.

Fundamental Concepts

In this chapter, the authors discuss some basic concepts of probability theory and possibility theory that are useful when reading the subsequent chapters of this book. The multi-objective fuzzy stochastic programming models developed in this book are based on the concepts of advanced topics in fuzzy set theory and fuzzy random variables (FRVs). Therefore, for better understanding of these advanced areas, the authors at first presented some basic ideas of probability theory and probability density functions of different continuous probability distributions. Afterwards, the necessity of the introduction of the concept of fuzzy set theory, some important terms related to fuzzy set theory are discussed. Different defuzzification methodologies of fuzzy numbers (FNs) that are useful in solving the mathematical models in imprecisely defined decision-making environments are explored. The concept of using FRVs in decision-making contexts is defined. Finally, the development of different forms of fuzzy goal programming (FGP) techniques for solving multi-objective decision-making (MODM) problems is underlined.