A Novel Hybrid Algorithm for Multiobjective Location-Allocation Problem in Emergency Logistics

Increased frequency of disasters keeps reminding us of the importance of effective resource distribution in postdisaster. To reduce the suffering of victims, this paper focuses on how to establish an effective emergency logistics system. We first propose a multiobjective optimization model in which the location and allocation decisions are made for a three-level logistics network. Three objectives, deprivation costs, unsatisfied demand costs, and logistics cost, are adopted in the proposed optimization model. Several cardinality and flow balance constraints are considered simultaneously. Then, we design a novel effective IFA-GA algorithm by combining the firefly algorithm and genetic algorithm to solve this complex model effectively. Furthermore, three schemes are proposed to improve the effectiveness of the IFA-GA algorithm. Finally, the numerical results provide several insights on the theory and practice of relief distribution, which also illustrate the validity of the proposed solution algorithm.

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

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.

Pre-Positioning Facility Location and Resource Allocation in Humanitarian Relief Operations Considering Deprivation Costs

Alleviating human sufferings during and in the aftermath of disasters is one of the most important goals in humanitarian relief logistics. The lack of relief commodities, especially life-saving items, is a life-threatening loss to victims and must be considered when making emergency supply allocation and transportation decisions, even in the pre-disaster prepositioning phase. This paper proposes a scenario-based stochastic program that integrates the decisions of prepositioning facility locations, quantities of stocked emergency supplies, and service allocations in each scenario in the same modeling framework. The estimation of victims’ losses for waiting for emergency supplies is measured in the typical deprivation cost function and treated as one of the main bases of decision making, besides traditional transportation costs, in determining the service allocation strategies in each scenario. Specifically, a case study with data from the hurricane threat in the Gulf Coast area of the US was conducted to demonstrate the application of this model and the significance of considering victims’ welfare loss in humanitarian relief logistics. Some interesting managerial insights were also drawn from a series of numerical experiments and sensitivity analyses.

An applied approach to multi-criteria humanitarian supply chain planning for pandemic response

PurposeWhen a large-scale outbreak such as the COVID-19 pandemic happens, organizations that are responsible for delivering relief may face a lack of both provisions and human resources. Governments are the primary source for the humanitarian supplies required during such a crisis; however, coordination with humanitarian NGOs in handling such pandemics is a vital form of public-private partnership (PPP). Aid organizations have to consider not only the total degree of demand satisfaction in such cases but also the obligation that relief goods such as medicine and foods should be distributed as equitably as possible within the affected areas (AAs).Design/methodology/approachGiven the challenges of acquiring real data associated with procuring relief items during the COVID-19 outbreak, a comprehensive simulation-based plan is used to generate 243 small, medium and large-sized problems with uncertain demand, and these problems are solved to optimality using GAMS. Finally, post-optimality analyses are conducted, and some useful managerial insights are presented.FindingsThe results imply that given a reasonable measure of deprivation costs, it can be important for managers to focus less on the logistical costs of delivering resources and more on the value associated with quickly and effectively reducing the overall suffering of the affected individuals. It is also important for managers to recognize that even though deprivation costs and transportation costs are both increasing as the time horizon increases, the actual growth rate of the deprivation costs decreases over time.Originality/valueIn this paper, a novel mathematical model is presented to minimize the total costs of delivering humanitarian aid for pandemic relief. With a focus on sustainability of operations, the model incorporates total transportation and delivery costs, the cost of utilizing the transportation fleet (transportation mode cost), and equity and deprivation costs. Taking social costs such as deprivation and equity costs into account, in addition to other important classic cost terms, enables managers to organize the best possible response when such outbreaks happen.

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

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.