Relief Commodities Research Articles

Multistage stochastic programming with a random number of stages: Applications in hurricane disaster relief logistics planning

We consider a logistics planning problem of prepositioning relief commodities in preparation for an impending hurricane landfall. We model the problem as a multi-period network flow problem where the objective is to minimize the total expected logistics cost of operating the network to meet the demand for relief commodities. We assume that the hurricane’s attributes evolve over time according to a Markov chain model, and the demand quantity at each demand point is calculated based on the hurricane’s attributes (intensity and location) at the terminal stage, which corresponds to the hurricane’s landfall. We introduce a fully adaptive multi-stage stochastic programming (MSP) model that allows the decision-maker to adapt their logistics decisions over time according to the evolution of the hurricane’s attributes. In addition, we develop a novel extension of the standard MSP model to address the challenge of having a random number of stages in the planning horizon due to the uncertain landfall time of the hurricane. We benchmark the performance of the adaptive decision policy given by the MSP models with alternative decision policies, including a static policy, a rolling-horizon policy, a wait-and-see policy, and a decision-tree-based policy, all based on two-stage stochastic programming models. Our numerical results and sensitivity analyses provide key insights into the value of MSP in the hurricane disaster relief logistics planning problem.

A scenario-based game theory integrating with a location-allocation-routing problem in a pre- and post-disaster humanitarian logistics network under uncertainty

PurposePre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.Design/methodology/approachIn this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.FindingsManagers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.Originality/valueThe model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

Application of cloud computing and big data in three-stage dynamic modeling of disaster relief logistics and wounded transportation: a case study.

Collecting and sharing information about affected areas is an important activity for optimal decision-making in relief processes. Defects such as over-sending some items to affected areas and mistakes in transferring injured people to medical centers in accidents are due to improper management of this information. Because cloud computing as a processing and storage platform for big data is independent of the device and location and can also perform high-speed processing, its use in disasters has been highly regarded by researchers. In this environment, a three-stage dynamic procedure for evacuation operations and logistics issues is presented. The first stage of the proposed model is image processing and tweet mining in a cloud center in order to determine the disaster parameters. In stage II, a mixed-integer multi-commodity model is presented for the relief commodity delivery, wounded people transportation with capacity constraints, and locating of the possible on-site clinics and local distribution centers near disaster areas. In stage III, by using a system of equations, detailed vehicle load/unload instructions are obtained. Finally, the effectiveness of the proposed model on the data of an earthquake disaster in Iran is investigated. The results of comparing the proposed approach with a two-stage algorithm show that the total number of unsatisfied demand for all types of commodities in the proposed approach was better than the other. Also, the number of survivors in the three-stage model is significantly higher than in the two-stage one. The better performance of the proposed algorithm is due to the fact that online data is continuously available and that decisions such as sending relief items and dispatching are made more effectively.

An extended version of adaptive large neighborhood search for a relief commodities distribution network design under uncertainty

Natural and technology-induced disasters have posed significant threats to human life all around the world and caused many damages and losses so far. The current study addresses a location-routing problem to make an efficient and timely distribution plan in response to a possible earthquake. This problem considers uncertainty in such parameters as demand, access to routes, travel time, and the number of available vehicles. To deal with these uncertainties, stochastic programming (SP) is performed while the objective function is to minimize the time of carrying relief commodities (RCs) to affected areas. The problem is coded in the CPLEX solver to obtain optimal solutions to small-scale problems, and an adaptive large neighborhood search (ALNS) is proposed to solve mixed-integer linear formulas for large-scale problems. To validate the formulation and evaluate the performance of the proposed ALNS, several types of tests are devised. To shows the efficiency of the proposed ALNS, two other metaheuristic algorithms, the Genetic algorithm (GA) and simulated annealing algorithm (SA), are used as well. The results of the calculations suggest the satisfactory performance of the suggested algorithm and the effectiveness of the model for the desirable delivery of humanitarian aids to affected areas.

Scenario-based redesigning of a relief supply-chain network by considering humanitarian constraints, triage, and volunteers’ help

In disaster management problems, supply and distribution chains of relief commodities have been one of the most critical parts of planning for both pre- and post-disaster phases. Providing relief items (medical supplies) for injured people can directly affect the vitality rate, and this would perfectly explain the necessity of implementing such practical studies in this field. Thus, a scenario-based, multi-period, multi-objective, two-stage, location-allocation model for providing relief commodities to demand points in an uncertain environment is proposed in this study. Humanitarian constraints (equitizing the number of distributed items and the service time as well as prioritizing areas in receiving the commodities) are also considered in the model to make it closer to reality. Such other practical contributions as redesigning the supply chain and addressing volunteers’ help in the supply and distribution processes are formulated as well. Moreover, using the triage concept for distributing vital items is presented for the first time in this study. The proposed non-linear model is initially linearized and then solved by applying the epsilon-constraint technique. Due to the NP-Hardness property of the model, an NSGA-II and a hybrid algorithm are also presented to solve larger-sized instances. Solving numerical examples and conducting sensitivity analysis verify the applicability and efficiency of the proposed model as well as the solution algorithms and provide some managerial insights to improve the performance of the relief logistics.

Equitable post-disaster relief distribution: a robust multi-objective multi-stage optimization approach

PurposeThis 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/approachA 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.FindingsThe 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/implicationsIn 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 implicationsIn 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/valueA 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.

An updated review on blood supply chain quantitative models: A disaster perspective

After the occurrence of a disaster, relief operations are needed to help survivors and injured people. Among the relief commodities required for affected areas, blood products are critically important because a shortage can threaten the lives of injured people. Accordingly, the blood supply chain (BSC) in disaster situations has been a growing area of research focus in recent years. Indeed, there has been an increase in the number of papers published on BSCs since 2018, and most of these papers relate to disaster situations. Meanwhile, the most recent review paper on BSCs explicitly excluded disaster situations. Consequently, in the current paper we present a state-of-the-art review of quantitative models for BSCs whose main theme is disaster situations. Our review design is based on a taxonomy from a 2015 review paper. A detailed analysis on solution methods is also presented. In addition, we highlight both the academic research gap as well as the requirements of health system and relief organizations to provide practical directions for future research. Naturally, although we designed a comprehensive procedure for exploring papers, it is possible that some papers have been missed due to our search strategy and this should be considered as a limitation of the current research.

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

Humanitarian aid in disasters is critical to saving lives and alleviating human suffering. This paper presents a novel scenario-based robust bi-objective optimization model that integrates medical facility location, casualty transportation, and relief commodity allocation considering triage. The proposed model aims to minimize the total deprivation cost of casualties due to the delayed access to medical services and the total operation cost. Following a set of disruption scenarios, the scenario-based robust approach is applied to protect solutions against the risk of disruptions in temporary medical centers. Considering the uncertain number of casualties under each scenario, the robust method which denotes the uncertainty as interval data is adopted. We utilize the ε-constraint method to deal with the bi-objective model. Additionally, we consider real case studies of the Wenchuan Earthquake to validate the proposed model. Several numerical experiments are conducted to examine the effects of uncertainties and capacities of medical facilities on the main objective value. The performance of considering the uncertainty and facility disruption is also discussed.

A cooperative game theory approach for location-routing-inventory decisions in humanitarian relief chain incorporating stochastic planning

This paper describes a new simulation based mathematical model for locating distribution centres, vehicle routing, and inventory problems under earthquake conditions. In this paper, the proposed network includes affected areas, suppliers, distribution centres, and hospitals. Additionally, the basic infrastructures of the city, which are very fragile at the time of the earthquake, are identified. Then, the demand of each relief commodity is calculated depending on the different earthquake scenarios using simulation. The estimated demand is incorporated into the mathematical model as an uncertain parameter. The proposed methodology is designed as a two-stage model so that in the first stage the location and inventory of distribution centres are addressed. Thereafter, in the second stage the routing decisions are taken for the distribution of relief commodities from the distribution centres and suppliers to the affected areas. Due to the NP-hardness of the second stage model, this model is solved using multi-objective stochastic fractal search. This algorithm is one of the population-based and stochastic optimization techniques and inspired by the natural phenomenon of fractal growth. It should be noted that in the second stage, a cooperative game theory of coalition type is considered, which resulted in synergies that minimize the relief golden time. The kind of cooperation is the use of potential of co-operators’ vehicle. In this stage, the possibility for the players to cooperate by sharing people and commodities transportation requests is considered. Also, to validate the model, a real case study is provided for a possible earthquake in Tehran. Finally, the comparison between the simulation results and the values obtained from the real system evaluates the performance of the implemented model. Considering normality and a 95% confidence interval, it can be concluded that the proposed model provides a precise representation of the real system's performance.

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.

A possibilistic mathematical programming model to control the flow of relief commodities in humanitarian supply chains

In emergency situations, disaster relief organizations are faced with the difficult decision of how to allocate scarce resources in an efficient manner in order to provide the best possible relief action. This paper aims to provide an analytical model that will help relief organizations in reducing human suffering following a disaster while maintaining an acceptable level of cost efficiency. A mathematical model is introduced to optimize the relief distribution problem which considers the social cost —the total sum of logistics and deprivation costs. The fuzzy nature of the deprivation cost function is addressed with possibilistic mixed integer programming with fuzzy objectives to reflect variation in deprivation costs perceptions. The model is solved using the Rolling Horizon method in a sequence of iterations. In each iteration, part of the planning horizon is modeled in detail and the rest of the time horizon is represented in an aggregated manner. The model is tested both empirically and on a case study of internal displacement in northwest Syria. Computational results showed that considering the demographic structure in affected areas and reflecting it to the deprivation cost function helped to reach better prioritization in distribution of commodities. The rolling horizon methodology is also found to be efficient in solving large scale instances and in capturing the dynamic changes in demand and supply parameters.

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.

Multiobjective location-routing problem of relief commodities with reliability.

The applicability and efficiency of location-routing problems for relief commodities motivate several researchers to develop optimization models and algorithms with regards to real-world cases. This study by presenting a multiobjective mixed integer mathematical model for the location-routing of the medical relief problem at the time of a disaster, proposes a new extension to this applicable model with reliability considerations. The proposed model focuses on the location of temporary relief centers and delivers the pharmaceutical commodities to centers at the shortest possible time with reliability assurance. The model includes three simultaneous objectives of minimizing response time, minimizing operational costs, and maximizing the reliability of the transportation network. As far as we know, this study firstly optimizes these three objectives simultaneously. Another novelty is to add the uncertainty of the problem. In this regard, the inherent uncertainty is formulated by a scenario-based approach. Considering the multiobjectiveness of the proposed model, the Epsilon constraint method as a solution algorithm has been used to solve the model. Results are tested for numerical examples via different scenarios. The results represent the excellent performance of the model to minimize the costs and to increase the reliability for the proposed location-routing problem of relief commodities.

A robust simulation-optimization approach for pre-disaster multi-period location–allocation–inventory planning

Natural disasters such as earthquakes always threaten human societies. Therefore, preparedness planning seems to be necessary to reduce casualties and accelerate relief efforts. Decisions in the preparedness phase include determining the optimal location for distribution centers and suppliers, allocating them to affected areas, and determining the amount of inventory of distribution centers. In this research, a robust simulation-optimization approach is presented for the planning in the preparedness phase. Although the demand for relief commodities has been considered as a given parameters. Due to our best knowledge there is no research in which the demand for relief commodities including drinking water, food, blood, medicine, tents and non-drinking water, during the disaster is related to the interactive relations of critical infrastructures of the city affected by the earthquake, and the power of the earthquake. Most of the unplanned occasions are occurred when co-incidences of probabilities are happen together. When a critical urban infrastructure fails due to earthquake it may cause some difficulties in the other infrastructures. Co-incidences may cause some horrible tragedy. First, the critical infrastructures of the city affected by the earthquake are identified and the interactions of these infrastructures are simulated. Considering the interactions of critical infrastructures in a city, various scenarios for earthquake are designed and tested through simulation approach in order to estimate the mean and variance of demand for relief commodities. The stochastic demand behavior which is the output of the simulation approach is assumed as a stochastic parameter of a mathematical model for multi-period location–allocation–Inventory problem. Robust optimization approach is used to deal with uncertainty. The mathematical model determines the location of distribution centers and suppliers, and how they are allocated to the affected areas. The proposed mathematical model is solved using a customized genetic algorithm for a case study in Tehran.

Integration of carrier selection and supplier selection problem in humanitarian logistics

Carrier selection is a crucial issue in humanitarian relief operations, and the shortage of transportation vehicles can considerably affect relief distribution. In this paper, the significance of commodity and vehicle suppliers and guaranteeing prevention of shortage via on-time supplying of each relief commodity by the suppliers is emphasized by suggesting the integration of carrier selection and supplier selection for developing contractual agreements in humanitarian logistics. For this close relationship to be described, a scenario-based two-stage stochastic programming model for the establishment of joint decision-making in preparedness and response phases is developed so that a contract can be created on the quantity of reservation of both commodities and vehicles by the suppliers, as well as the transmission of the reserved commodities in the form of commodity purchase and vehicle rent in the response phase. An efficient two-step solution technique is suggested as a solution method for real scale problems, the effectiveness of which can be seen through the results. To illustrate the applicability of the model, a case study is suggested. The application of the model is discussed via numerical studies as well as a sensitivity analysis. The results have provided an appropriate managerial insight for responding to disaster for relief agencies.

Solving a new bi-objective model for relief logistics in a humanitarian supply chain by bi-objective meta-heuristic algorithms

One of the most important factors in a humanitarian supply chain during a disaster is to respond quickly and efficiently. Delivering emergency commodities to the affected areas is critical in reducing consequences. Moreover, transferring the injured people through the fastest and the shortest time by using all available resources is vitally important. To this aim, a multi-echelon, multi-objective forward and backward relief network is proposed that considers the location of hospitals, local warehouses and hybrid centers, which are hospital-warehouse centers in the pre-disaster phase. In the post-disaster phase, the routing of relief commodities is considered in the forward route. In the backward route some vehicles that can transfer injured people after delivering commodities; hybrid transportation facilities; will take injured to hospitals and hybrid centers. According to the degree of hardness, a hybrid non-dominated sorting genetic algorithm (NSGA-II) with simulated annealing (SA) and variable neighborhood search (VNS) algorithms is proposed to solve the given problems. The results of this hybrid algorithm are compared with NSGA-II and multi-objective SA-VNS using five metrics (i.e., a number of Pareto, mean ideal distance, spacing, diversity and time) in order to emphasize that the proposed hybrid algorithm outperforms the two foregoing algorithms

Inventory relief chain model with deterioration and disposal of relief commodity

Our environment can be hit by disasters such as earthquake, flood, tornadoes, hurricanes, etc., and if any happens, organizations (private or government) start to provide facilities for evacuation and supply of relief commodities in the affected regions. This paper formulates an inventory-relief-chain (IRC) model with the relief supply chain for the relief commodity distribution. The results of the model provide the optimal number of intermediate distribution and relief centers. However, in the study, every center is considered to distribute relief commodities. The model is formulated with a core objective to optimize the total cost of the relief operation. An algorithm is proposed to find the optimal solution of the model. Furthermore, a numerical analysis is carried out on a numerical example to validate the efficiency of the algorithm. The sensitivity analysis is done over the optimal solution and the respective parameters of the model, at the same time representing the strength of the model,i.e., the extent these parameters are sensitive to the objective. The model can help relief organizations when managing relief supplies to the disaster areas.

Designing a Multi-Objective Three-Stage Location-Routing Model for Humanitarian Logistic Planning under Uncertainty

Natural and technological disasters threaten human life all around the world significantly and impose many damages and losses on them. The current study introduces a multi-objective three-stage location-routing problem in designing an efficient and timely distribution plan in the response phase of a possible earthquake. This problem considers uncertainty in parameters such as demands, access to routes, time and cost of travels, and the number of available vehicles. Accordingly, a three-stage stochastic programming approach is applied to deal with the uncertainties. The objective functions of the proposed problem include minimizing the unsatisfied demands, minimizing the arriving times, and minimizing the relief operations costs. A modified algorithm of the improved version of the augmented e-constraint method, which finds Pareto-optimal solutions in less computational time, is presented to solve the proposed multi-objective mixed-integer linear programming model. To validate the model and evaluate the performance of the methods several test problems are generated and solved by them. The computational results show the satisfactory performance of the proposed methods and effectiveness of the proposed model for delivery of relief commodities in the affected areas.

Scenario-Based Emergency Material Scheduling Using V2X Communications

Vehicle-to-everything (V2X) communications can be applied in emergency material scheduling due to their performance in collecting and transmitting disaster-related data in real time. The urgency of disaster depots can be judged based on the disaster area video, and the scenario coefficient can be evaluated for building a fairness model. This paper presents a scenario-based approach for emergency material scheduling (SEMS) using V2X communications. We propose a SEMS model, with the objectives of minimum time and maximum fairness in the cases of multiple supply depots, disaster depots, commodities and transport modes for logistics management of relief commodities. We design the SEMS algorithm based on the artificial fish-swarm algorithm to obtain an optimized solution. The results demonstrate that the SEMS model can enhance the fairness of relief scheduling, especially for disaster depots with small demands compared to the Gini and enhanced Theil fairness models. Moreover, the acquired vehicle speed via V2X communications updates the SEMS model in real time, which approaches a solution closer to reality.

A robust multi-objective humanitarian relief chain network design for earthquake response, with evacuation assumption under uncertainties

In this paper, we have proposed a multi-objective mathematical model for the humanitarian supply chain design problem that minimizes: (1) total number of the injured not transferred to hospitals and total number of the homeless not evacuated from the affected area, and (2) total unmet relief commodity needs. In this model, such parameters as the demand and travel time have been considered as uncertain and two discrete robust counterpart models (with “ellipsoidal” and “box and polyhedral” uncertainty sets) have been developed to model uncertainties. Results found from Tehran Case Study have revealed that the one with the “box and polyhedral” uncertainty set performs better than the “ellipsoidal” set.