Relief Chain Research Articles

Humanitarian relief supplies distribution: an application of inventory routing problem

In this paper, we study the distribution of humanitarian relief supplies. In humanitarian relief, supplies including food, water and medication are received in batches/waves from the suppliers and the donors. Then, these supplies are distributed to local dispensing sites located in the affected areas. Fast and fair distribution of these relief supplies is the key to the success of humanitarian relief operations. Motivated by the practices in humanitarian relief chain, we study an application of Inventory Routing Problem where the goal is equitable distribution of these supplies to the affected areas over a planning horizon. We measure the fairness of the distribution plan by the safety stock level at a demand location, and our goal is to maximize the minimum safety stock level at any location. Such a difference in the objective requires a solution approach that is significantly different than the ones proposed in the literature for classical cost-minimization routing problems. In order to address this distribution problem, we propose a three-phase (clustering, routing and improvement) solution approach. Due to nature of the problem, routing and allocation decisions significantly affect each other. The proposed approach (i) considers the interaction between routing and resource allocation decisions in a novel way to produce equitable relief supplies distribution plans, (ii) outperforms the existing algorithms by finding solutions with around 1.4% lower optimality gap on average, (iii) provides solutions with 2.6% optimality gap on average when compared to an upper bound, and (iv) finds a solution in < 5 min.

DEVELOPMENT OF HUMANITARIAN SUPPLY CHAIN PERFORMANCE CONCEPTUAL FRAMEWORK IN CREATING RESILIENT LOGISTICS NETWORK

Humanitarian logistics which is precisely known as humanitarian supply chain (HSC) plays a major role in reducing the impact of disaster on human life and livelihood by providing humanitarian aid in the forms of food, water, medicine, shelter and other supplies. Unfortunately, anecdotal evidences indicated that relief chain tends to be unstable, unpredictable and unresponsive to the needs of disaster victims. The 2004 Asian tsunami highlighted the lack of coordination between the relief chain linkages that hampered effective supply of aid. This phenomenon was further evident in our own context during the 2014 flood devastation in Peninsular Malaysia. Floodwaters and subsequent landslides blocked major roads, limiting access to evacuation centres and impeding the delivery of emergency relief supplies. Hence, an effective humanitarian supply chain management (HSCM) should be able to be deployed rapidly enabling provision of aid to beneficiaries. Notwithstanding the frequency and impact of disasters, humanitarian organizations today are under continuous pressure of improving their logistics performance. Departing from this need, this study aims to examine the criteria that influence the humanitarian aid actors in their decision making while increasing transparency and accountability of relief operations. Therefore, it is imperative for humanitarian sector to quantify the efficiency and effectiveness of a particular relief operation using set of performance metrics. A mixed methods approach comprising qualitative and quantitative survey will be used. The study intended to identify and define the metrics that would determine successful operational performance of disaster relief. This research will contribute mainly in the development of a HSCM performance model that (i) informs decision makers at the strategic, tactical and operational level in tracking progress, (ii) facilitate a more open and transparent communication and cooperation between humanitarian actors, and (iii) improve the logistics of disaster management both at the government and at non-governmental level.

DEVELOPMENT OF HUMANITARIAN SUPPLY CHAIN PERFORMANCE CONCEPTUAL FRAMEWORK IN CREATING RESILIENT LOGISTICS NETWORK

Humanitarian logistics which is precisely known as humanitarian supply chain (HSC) plays a major role in reducing the impact of disaster on human life and livelihood by providing humanitarian aid in the forms of food, water, medicine, shelter and other supplies. Unfortunately, anecdotal evidences indicated that relief chain tends to be unstable, unpredictable and unresponsive to the needs of disaster victims. The 2004 Asian tsunami highlighted the lack of coordination between the relief chain linkages that hampered effective supply of aid. This phenomenon was further evident in our own context during the 2014 flood devastation in Peninsular Malaysia. Floodwaters and subsequent landslides blocked major roads, limiting access to evacuation centres and impeding the delivery of emergency relief supplies. Hence, an effective humanitarian supply chain management (HSCM) should be able to be deployed rapidly enabling provision of aid to beneficiaries. Notwithstanding the frequency and impact of disasters, humanitarian organizations today are under continuous pressure of improving their logistics performance. Departing from this need, this study aims to examine the criteria that influence the humanitarian aid actors in their decision making while increasing transparency and accountability of relief operations. Therefore, it is imperative for humanitarian sector to quantify the efficiency and effectiveness of a particular relief operation using set of performance metrics. A mixed methods approach comprising qualitative and quantitative survey will be used. The study intended to identify and define the metrics that would determine successful operational performance of disaster relief. This research will contribute mainly in the development of a HSCM performance model that (i) informs decision makers at the strategic, tactical and operational level in tracking progress, (ii) facilitate a more open and transparent communication and cooperation between humanitarian actors, and (iii) improve the logistics of disaster management both at the government and at non-governmental level.

Robust bi-level optimization of relief logistics operations

• A hierarchical decision processes with two levels of decision as a robust bi-level optimization model. • Addressing both supply risk and relief items demand coverage robustness. • Optimizing relief operating costs as well as considering penalty cost for unsatisfied victims demand. • TOPSIS method is employed to identify candidate suppliers with lower risk. In this paper, a robust bi-level optimization model is developed for a supply–distribution relief network under uncertainty in demand and supply parameters. It optimizes the relief operating costs as well as considering a penalty term for unsatisfied victims’ demands. Moreover, the proposed framework optimizes the relief commodity flow in a relief chain along with the supply risk minimization by identifying the suppliers with a lower risk. This paper proposes an integrated optimization method in which the supply risk value for each supplier is obtained via the TOPSIS method. Next, these values are utilized in a robust bi-level model to select appropriate suppliers and allocate orders. Finally, the robustness and effectiveness of the proposed model are demonstrated by a case of flood disaster.

Decision support framework for location selection and disaster relief network design

Purpose The purpose of this paper is to provide a decision support framework for locations identification to address network design in the domain of disaster relief supply chains. The solution approach is then applied to a real-life case about Indonesia. Design/methodology/approach An approach integrating geographic information system technology and fuzzy analytical hierarchy process has been used. Findings For the Indonesian case, distribution centers should be located in Pekanbaru, Surabaya, Banjarmasin, Ambon, Timika, and Manado. Research limitations/implications The main limitation of this work is that facilities being sited are incapacitated. Inclusion of constraints over capacity would elevate the framework to a further level of sophistication, enabling virtual pool of inventory that can be used to adsorb fluctuation in the demand due to disasters. Practical implications The use case provided in this paper shows a practical example of applicability for the proposed framework. This study is able to support worldwide decision makers facing challenges related with disaster relief chains resilience. In order to achieve efficiency and effectiveness in relief operations, strategic logistics planning in preparedness is key. Hence, initiatives in disaster preparedness should be enhanced. Originality/value It adds value to the previous literature on humanitarian logistics by providing a real-life case study as use case for the proposed methodology. It can guide decision makers in designing resilient humanitarian response, worldwide. Moreover, a combination of recommendations from humanitarian logistics practitioners with established models in facility location sciences provides an interdisciplinary solution to this complex exercise.

Similarities between disaster supply chains and commercial supply chains: a SCM process view

The purpose of this paper is to demonstrate conceptual and theoretical similarities between disaster relief chains (DRCs) and commercial supply chains using the customer relationship management process model (CRM) of the seminal global supply chain forum framework (GSCF) as a lens of analysis (Croxton et al. in Int J Logist Manag 12(2):13–36, 2001). A range of empirical data from a case study of the 2006 relief and recovery response to the Cyclone Larry disaster is analysed using the CRM process model of the GSCF framework as a lens of analysis. We find that there are unexpected conceptual and theoretical similarities between DRCs in the Cyclone Larry disaster response and commercial supply chains. The study demonstrates that core commercial SCM concepts such as integration and integrative process management can also be found empirically in the domain of disaster response operations and allied management of supply chains for disaster relief and recovery (DROSCM). The study also shows that research in the DROSCM domain can develop in a range of directions unfettered by a paradigm focused on differences between DRCs and commercial supply chains. This is the first study to conceptually, theoretically, and empirically demonstrate similarities between DRCs and commercial supply chains.

Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty

The accidental and unpredictable nature of disasters such as earthquake brings about some plans to deal with critical problems in order to reduce the dangers at the time of their occurrence. Effective distribution of relief goods and supplies plays an important role in the rescue operation after an earthquake. Therefore, a two-phase, multi-objective mixed integer, multi-period and multi-commodity mathematical modeling in the three-level relief chain was offered, in which, locating of the distribution centers and warehouses with various levels of capacity, related decisions to the stored goods in the warehouses and established distribution centers were considered in the first phase, and considering the limited hard time windows, in the second phase, operational programming was performed for vehicle routing and distribution of goods to the affected areas, so minimizing the total cost and travel time also increased the reliability of the route. In addition to the features considered in this model, in special cases, it is possible that each critical area receives service more than once; to consider split delivery assumption in the problem, a different model will be presented for this purpose. Since some parameters are uncertain during the crisis, in order to let the model approach the reality, using a robust optimization approach, the model was developed in an uncertain condition. Two meta-heuristic algorithms of NSGAII and MOPSO were used to solve the given problem, in which the accuracy of the mathematical model and the proposed algorithms efficiency were assessed through numerical examples. The results of algorithms were presented for 35 various problems.

A Bi-objective Stochastic Optimization Model for Humanitarian Relief Chain by Using Evolutionary Algorithms

Due to the increasing amount of natural disasters such as earthquakes and floods and unnatural disasters such as war and terrorist attacks, Humanitarian Relief Chain (HRC) is taken into consideration of most countries. Besides, this paper aims to contribute humanitarian relief chains under uncertainty. In this paper, we address a humanitarian logistics network design problem including local distribution centers (LDCs) and multiple central warehouses (CWs) and develop a scenario-based stochastic programming (SBSP) approach. Also, the uncertainty associated with demand and supply information as well as the availability of the transportation network's routes level after an earthquake are considered by employing stochastic optimization. While the proposed model attempts to minimize the total costs of the relief chain, it implicitly minimize the maximum travel time between each pair of facility and the demand point of the items. Additionally, a data set derived from a real disaster case study in the Iran area, and to solve the proposed model a exact method called ɛ-constraint in low dimension along with some well-known evolutionary algorithms are applied. Also, to achieve good performance, the parameters of these algorithms are tuned by using Taguchi method. In addition, the proposed algorithms are compared via four multi-objective metrics and statistically method. Based on the results, it was shown that: NSGA-II shows better performances in terms of SNS and CPU time, meanwhile, for NPS and MID, MRGA has better performances. Finally, some comments for future researches are suggested.

Dispatching policies for last-mile distribution with stochastic supply and demand

Relief distribution has received considerable attention in the disaster operations management literature. However, the majority of this literature assumes that supply is always available. In reality, a significant portion of the materials that flow through the humanitarian relief chain are donations, which represent an uncertain supply source in terms of both quantity and timing. This paper investigates a two-stage relief chain consisting of a single staging area (SA) where donations arrive over time in uncertain quantities, which are periodically distributed to random numbers of disaster survivors located at a point of distribution (POD). A single vehicle travels back and forth between the SA and POD transporting relief supplies during a finite horizon. The goal of this study is to identify dispatching policies for the vehicle with the sole purpose of minimizing unsatisfied demand at the POD. To this end, we examine the effectiveness of two common-sense heuristic policies relative to the optimal dispatching policy, the latter of which is determined via stochastic dynamic programming. Our findings indicate that although continuously dispatching the vehicle between the SA and POD is not an optimal policy, it is either optimal or close to optimal in most situations.

Humanitarian relief supply chain: a multi-objective model and solution

This paper models a humanitarian relief chain that includes a relief goods supply chain and an evacuation chain in case of a natural disaster. Optimum network flow is studied for both the chains by considering three conflicting objectives, namely demand satisfaction in relief chain, demand satisfaction in evacuation chain and overall logistics cost. The relief goods supply chain consists of three echelons: suppliers, relief camps and affected areas. The evacuation chain consists of two echelons: evacuation camps and affected areas. The model has been made more resilient by considering multiple paths between any two locations and disruption of camps and paths due to natural factors. The Mixed Integer Programming problem has been solved using NSGA-III and results have been compared to those from benchmark algorithms. The model has been successfully tested on generated real-life-like data.

Warehouse location determination for humanitarian relief distribution in Nepal

Abstract The use of warehouse for storing emergency relief items has been proven to improve overall responsiveness, efficiency and effectiveness of the humanitarian supply chain while decreasing the cost incurred in the process. Driven by the same goals, this study determines the optimal number and locations of warehouses to be placed in different parts of Nepal for a humanitarian relief chain that would respond to sudden-onset disasters. The study utilizes a modified version of the maximal covering location problem which introduces additional constraints that reflect the real scenario of Nepal. The problem is solved using simplex algorithm with branch and bound applied to the relaxed integer. The novelty of this study lies in the introduction of indexes for development, disaster safety and transportation accessibility constraints to reflect socioeconomic, geo-climatic and topographical features of Nepal respectively. Three scenarios are chosen based on coverage distance. The results show the number and spatial locations of warehouses for each scenario, as well as their maximum and minimum coverages.

A hybrid decision support system for managing humanitarian relief chains

Decisions regarding location, allocation and distribution of relief items are among the main concerns of the humanitarian relief chain (HRC) managers in response to no-notice large-scale disasters such as earthquakes. In this paper, a hybrid decision support system (HDSS) consisting of a simulator, a rule-based inference engine, and a knowledge-based system (KBS) is developed to configure a three level HRC. Three main performance measures including the coverage, total cost, and response time are considered to make an explicit trade-off analysis between the cost efficiency and responsiveness of the designed HRC. In the first step, the simulator calculates the performance measures of the different configurations of the HRC under a number of generated disaster scenarios. Then, the rule-based inference engine attempts to build the best configuration of the HRC including facilities' locations, relief items' allocation and distribution plan of the scenario under investigation based on the calculated performance measures. Finally, the best configuration for each scenario is stored in the KBS as the extracted knowledge from the above analyses. In this way, the HRC managers can retrieve the most appropriate HRC configuration in accordance with the realized post-disaster scenario in an effective and timely manner. The results of a real case study in Tehran demonstrate that the developed HDSS is an effective tool for fast configuration of HRCs using stochastic data.

Pre-positioning of relief items in humanitarian logistics considering lateral transhipment opportunities

The main objective of this study is to investigate the inclusion of lateral transhipment opportunities into the humanitarian relief chain and to examine the effect of different parameters on minimizing the average distance travelled per item while serving the beneficiaries. Direct shipment model (DT), lateral transhipment model (LTSP) and maritime lateral transhipment model (MLTSP) are developed and compared between each other by using a real life earthquake scenario prepared for the city of Istanbul by JICA (Japanese International Cooperation Agency). Developed mathematical models decide on the locations and number of disaster relief facilities, quantity of relief items to hold at those facilities, and quantity of lateral transhipment between the facilities. Vulnerability of the roads and heterogeneous capacitated facilities are also considered. It can be concluded that both LTSP and MLTSP models gave better results than DT model and lateral transhipment option helps beneficiaries to obtain relief items faster and with higher service level.

A robust optimization model for humanitarian relief chain design under uncertainty

Due to the importance of relief operations in disasters, this paper aims to contribute humanitarian logistics under uncertainty. In this paper, a three-level relief chain model consisting of suppliers, relief distribution centers, and affected areas is considered. The uncertainty associated with demand, supply, and all of the cost parameters is addressed by employing robust optimization, where the uncertain parameters are independent and bounded random variables. While the proposed model attempts to minimize the total costs of the relief chain, it implicitly maximizes people's satisfaction level in the affected areas through applying a penalty to shortages of relief commodities. Additionally, a data set derived from a real disaster case study in the Alborz area, which is vulnerable to earthquakes, is applied to test the efficiency of the proposed robust relief chain model compared to its deterministic form. The study analyzes the degree to which each uncertain parameter affects the solution of the relief chain model and consequently helps the decision maker to tune the parameter values more accurately.

Drivers and Indicators of Performance in Relief Chain: An Empirical Study

Relief and rehabilitation is an important component of disaster management. Success of this humanitarian intervention is dependent upon a combination of factors; logistics is among the important ones. The purpose of this article is to discuss key drivers of performance while managing disaster relief logistics. This research uses two-stage research designs. The first stage incorporates exploratory research through interviews with experts and focus group discussions. In the second stage, a survey was conducted among those affected by the Kosi floods (2008) in Bihar. A non-random sampling plan was adopted. Results show that there is empirical evidence that the key drivers identified in the present research positively influence the performance of relief operations. This work can also help practitioners and policy makers identify important parameters related to relief logistics. The study was confined to India and only focuses on sudden onset disasters.

Heuristic approaches for the multiperiod location-transportation problem with reuse of vehicles in emergency logistics

The coordination among the different actors in relief chains is crucial to provide effective and efficient response in emergency logistics. By recognizing this fact, we have developed two stochastic mixed-integer programming models to integrate and coordinate facility location, transportation and fleet sizing decisions in a multi-period, multi-commodity, and multi-modal context under uncertainty. One model even considers the option of reusing vehicles to cover extra routes within the same time period in an attempt to save overall resources and improve service levels. Typical uncertainty in victims׳ needs, incoming supply, inventory conditions, and roads availability are modeled through a set of scenarios representing plausible disaster impacts. To solve instances of practical size, we have devised relax-and-fix and fix-and-optimize heuristics based on decompositions by time, scenario, and stage. The proposed instances entail characteristics of the megadisaster in the Mountain Region of Rio de Janeiro State in Brazil. The results suggest that the integration of decisions in a multiperiod context and the option of reusing vehicles reduce total costs, thus improving the overall performance of the relief operations. Also, the time-decomposition fix-and-optimize heuristic outperforms the CPLEX solver in terms of elapsed times and optimality gaps, mainly in moderate-size instances. Finally, we show the importance to explicitly consider randomness instead of using simpler worst-case scenario approaches.

Hybrid simulation modeling for humanitarian relief chain coordination

Purpose – The purpose of this paper is to present a conceptual framework for a hybrid simulation model that can be used to study the decision making and behaviors of humanitarian logistics actors to determine how/whether certain coordination mechanisms enable better relief chain efficiency and effectiveness over time. Design/methodology/approach – The agent-based portion of the model is used to represent human decision making and interactions in a more realistic way than has been done previously, and the discrete-event simulation (DES) portion of the model allows the movement of vehicles, materials, and information throughout a supply network to be represented in a way that allows for dynamic and stochastic behavior. Findings – Coordinated efforts by actors in humanitarian logistics operations involve complex interactions and adaptations over time, which can be capture and explored via hybrid agent-based model (ABM)-DES modeling. Research limitations/implications – This paper describes a framework for a hybrid ABM-DES model. The actual development and implementation of the model, including input data collection and analysis, model development, experimentation, and output data collection and analysis, will be the subject of future work. Practical implications – The hybrid model framework provides other researchers with a starting point for model development. Social implications – This paper provides a basis for future modeling and assessment of coordination in humanitarian logistics, an area that is in need of research. Originality/value – The hybrid simulation modeling framework presented in this paper is a novel application of a new modeling methodology to the problem of coordination in humanitarian logistics.

Special issue on humanitarian relief chains in honor of Professor Benita Beamon

Special issue on humanitarian relief chains in honor of Professor Benita Beamon

Research in Humanitarian Supply Chain Management and a New Framework

With the frequency and magnitude of disasters on the rise, millions of people suffer huge losses every year. Scholars have recently proposed various frameworks in disaster relief management in order to guide the research in this field. Although successful disaster relief requires the entire humanitarian supply chain to respond in harmony, it is surprising that there exists no humanitarian relief framework drawn from the perspective of supply chain management. In this article, we create a new research framework for Humanitarian Supply Chain Management (HSCM) that is complimentary but distinct from commercial supply chain management (CSCM) frameworks. The framework we developed offers a new lens for humanitarian researchers. We also conduct a systematic literature review in this field and identify some opportunities for future research. The results strongly suggest the need for additional empirical research to test the existing concepts and models. Second, there is evidence that research focusing on “upstream” relief chain has been neglected relative to “downstream”. Additionally, due to its rapid advancement, information technology related research opportunities in this field would always be there.

Humanitarian relief chain: Rapid response under uncertainty

The recent worldwide devastations have reemphasized the importance of rapid response for saving life. Relief supplies must arrive on time and in adequate quantities. Coordination in a relief chain is complex because of the uncertainties associated with disaster intensity, strike probability, infrastructure-disruption, and the actual damage. The relief effort must weigh the expected social value against delivery delays, and costs of logistics. We explore a 2-stage proactive/reactive approach where response time and relief amounts are decided ex post (after disaster occurs), and the prepositioned inventory is determined ex ante (before disaster occurs). Our major findings are: (i) the response quantity and time must be adjusted to imputed social value, but differently at different disaster intensities and cost structures, and in different communities, (ii) disaster intensity can be categorized into ranges that reveal whether rapid response and/or large relief quantity would be appropriate in each range, (iii) effectiveness of the relief strategy decreases as the disaster intensity increases, and (iv) in scenarios with limited budget, it is possible to earmark a unique budget for real time relief operations, that ensures maximization of social value.