Robust Two-Stage Location Allocation for Emergency Temporary Blood Supply in Postdisaster

Disaster medical rescue in China mainly adopts the “on-site rescue” model. Whether the location of emergency temporary blood supply sites is reasonable or not directly affects the rescue efficiency. The paper studies the robust location-allocation for emergency temporary blood supply after disaster. First, the factors of several candidate sites were quantified by the entropy-based TOPSIS method, and 12 candidate blood supply sites with higher priority were selected according to the evaluation indicators. At the same time, the uncertainty of blood demand at each disaster site increased the difficulty of decision-making, and then, a robust location model (MIRP) was constructed with minimum cost with time window constraints. It is also constrained by the uncertain demand for blood in three scenarios. Second, the survival probability function was introduced, and the time window limit was given at the minimum cost to maximize the survival probability of the suffered people. Finally, the numerical example experiments demonstrate that the increase in demand uncertainty and survival probability cause the MIRP model to generate more costs. Compared with the three MIRP models, the MIRP-ellipsoid set model gained better robustness. Also, given the necessary restrictions on the time window, the cost can be reduced by about 13% with the highest survival probability. Decision-makers can select different combinations of uncertainty levels and demand disturbance ratios and necessary time constraints to obtain the optimal location-allocation solution according to risk preference and actual conditions.

Location and hierarchical allocation disaster with combined ε-constraint and simulation-based optimization approach

The increasing frequency of natural disasters and the necessity of proper planning to minimize the impact and casualties of such crises have always been matters of great concern to human societies. In this study, a hybrid mathematical-simulative location-allocation model is proposed to carry out disaster management (DM) efforts with maximum coverage in the immediate aftermath of an earthquake. The proposed model consists of two phases: determining the optimal location of the temporary emergency stations (TECs), followed by optimal and hierarchical allocation of casualties to said temporary medical centers (TMCs). Given the contradictory nature of the model’s two objectives, that is, minimizing the cost of setting up TMCs and the time taken to transfer casualties to TMC. In the second phase, a simulation-based optimization approach is employed to simulate casualties’ behavior at the onset of the disaster and to determine the optimal capacity of the medical centers. The findings indicate that the costs and distance traveled by casualties during the earthquake have been reduced by 15%.

Spatial Decision Support System for Efficient School Location-Allocation

In Greece, a lack of a planning strategy was identified in the context of allocating students to schools. Particularly, the Secondary Educational Management of Lesvos Prefecture along with school Principals decide upon student allocation based on empirical knowledge and approximation techniques. As a consequence, during the school season of 2018-2019 capacity and proximity limitations were violated. This study introduces a Spatial Decision Support System (SDSS) to assist school location-allocation decisions in future seasons. The objective of the proposed SDSS is to minimize commute-to-school distance concerning capacity and proximity limitations. For this purpose, a capacitated P-median approach is adopted and formulated as a mixed-integer linear problem. The SDSS is further evaluated using actual data for students' transition from primary to secondary education in the city of Mytilene, Greece. Evaluation of current allocation practices carried out and further compared to those obtained by the SDSS. The results indicate a decrease of 8% in total distance whereas proximity and capacity constraints were respected accordingly. The results may be potentially useful for school planners to assist the allocation decisions in the city of Mytilene.

Designing a Resilient and Sustainable Logistics Network under Epidemic Disruptions and Demand Uncertainty

To face the new challenges caused by modern industry, logistics operations managers need to focus more on integrating sustainability goals, adapt to unexpected disruptions and find new strategies and models for logistics management. The COVID-19 pandemic has proven that unforeseen fragilities, negatively affecting the supply chain performance, can arise rapidly, and logistics systems may confront unprecedented vulnerabilities regarding network structure disruption and high demand fluctuations. The existing studies on a resilient logistics network design did not sufficiently consider sustainability aspects. In fact, they mainly addressed the independent planning of decision-making problems with economic objectives. To fill this research gap, this paper concentrates on the design of resilient and sustainable logistics networks under epidemic disruption and demand uncertainty. A two-stage stochastic mixed integer programming model is proposed to integrate key decisions of location–allocation, inventory and routing planning. Moreover, epidemic disruptions and demand uncertainty are incorporated through plausible scenarios using a Monte Carlo simulation. In addition, two resiliency strategies, namely, capacity augmentation and logistics collaboration, are included into the basic model in order to improve the resilience and the sustainability of a logistics chain network. Finally, numerical examples are presented to validate the proposed approach, evaluate the performance of the different design models and provide managerial insights. The obtained results show that the integration of two design strategies improves resilience and sustainability.

Geospatial Analysis of Road Traffic Accidents and Emergency Response Optimization in Kano Metropolis, Nigeria

BackgroundIncrease in occurrence of road traffic accidents in Kano metropolis have resulted into continuous loss of lives, injuries and increased people’s exposure to risk. This study examined road traffic accidents emergency response within Kano metropolis with a view to enhancing its efficiency through establishing communication and synergy between Emergency Healthcare Facilities (EHCF), ambulances and accident hotspots. MethodsGPS surveying was conducted to obtain the location and attributes of the major EHCF, accident hotspots along the junctions of the highways and the 2 existing ambulances at Kano State Fire Service and Federal Road Safety Corp head offices (KSFS and FRSC). Road traffic data (vector format) was digitized from Worldview 3 satellite image (2018, 30cm spatial resolution) from which two major road classes were identified (highways and minor roads) along with their speed limits of 50km/hr and 30km/hr respectively. Time distances were determined based on length and speed limits. Nearest Neighbor and Network analysis (closest facility, shortest route and location-allocation) analyses were conducted. ResultsThe result revealed a variation in the distribution patterns of EHCH, ambulances and accident spots. Closest ambulance facility analysis shows that it takes the FRSC ambulance about 9.41 minutes to reach to accident spot 18 (Maiduguri Road, after NNPC), and 7.52 minutes to travel to AKTH as the closest EHCF. On the other hand, it takes the same ambulance about 3 times the time taken to spot 18 and 4 times the time taken to AKTH to reach to Court road incident spot (spot 16) and IRPH as the closest EHCF. This signifies greater chances of death of almost all victims across the metropolis due to inability to provide CPS within the first 4 minutes before reaching to the hospital. However, in case of Pediatric emergencies, the analysis of closest EHCF from accident spots revealed that it takes less than one minute to travel from accident spots 13, 14 and 15 to IRPH as the closest Pediatric EHCF. Equally, similar time is taken to travel from incident spots 20 and 23 to Sir Sunusi and MMS hospitals respectively. Location-allocation analysis identified eight new locations based on maximum of 4 minutes impedance cutoff from all directions towards the incidents spots. ConclusionIt is concluded that the prevailing road traffic accident emergency response system within the metropolis is inefficient. Therefore, more ambulances should be strategically positioned to fasten emergency response.

A MCDM Based on Goal Programming and GIS-Based Risk Assessment for the Evaluation of Educational Locations in Conflict Areas

Conflict is recognized as a major barrier in socio-economic development. In conflict situations, most sectors such as health, food, shelter and education are adversely affected. The provision of education services to conflict-affected children saves them from becoming a lost generation and contributes to community building. Thus, we conducted this research to investigate the potential of a GIS (Geographic Information Systems) approach and risk assessment based multi-criteria decision making (MCDM) for the allocation of displaced dropped-out children to the most appropriate educational centres, taking into account multiple goals related to cost, distance, risk, etc. A two-stage approach was adopted, utilizing a risk assessment approach, and a location-allocation approach. The risk assessment approach was carried out using GIS and F-AHP (Fuzzy Analytic Hierarchy Process) to determine the risk value of each candidate educational centre in the conflict area. In the location-allocation stage, a mathematical model was developed to allocate all demands to the chosen centres. All presented methods were computationally conducted on real case data provided by direct beneficiaries and stakeholders in the 26 sub-districts in the Idleb governorate, Syria. The computational results demonstrate that the proposed approaches ensure practical and theoretical impacts.

Analysis of Spatial Accessibility and Capacity of Multi-Level Healthcare Facilities in Greater Irbid Municipality, Jordan

Measuring the spatial accessibility and capacity of healthcare facilities is an important task to improve the quality of health services and reduce the pressure on them. This research assesses the current spatial accessibility and capacity of two-level of healthcare facilities (comprehensive healthcare centers and hospitals) in the Greater Irbid Municipality using the enhanced two-step floating catchment area (E2SFCA) method. To do this, Network analysis techniques including original-destination matrix (OD), service area, and location-allocation were employed for determining the travel time from residents' points towards every healthcare facility, the service coverage and capacity within travel time zones, and the number of served areas by every healthcare facility. Then, optimum locations for new healthcare facilities that improve the accessibility and capacity rates were determined. The results show that while all areas in the study area are located within a 30-minute drive from the hospital's locations, 18 out of 23 areas are within 15 minutes drive towards the comprehensive health centers. This means that 28.80% of the population needs more than 15 minutes of driving time to access the second level of healthcare services. In addition, the annual average of the actual patient-doctor ratio ranges from 1338 to 2900 patients per doctor in the hospitals, and 2676 to 8524 patients per doctor in the comprehensive healthcare centers, and thus, the health services are inadequate in the study area. Furthermore, the suggested new healthcare facilities in terms of the numbers and optimum location would improve the spatial accessibility and the capacity ratio.

Guiding placement of health facilities using multiple malaria criteria and an interactive tool

Background Access to healthcare is important in controlling malaria burden and, as a result, distance or travel time to health facilities is often a significant predictor in modelling malaria prevalence. Adding new health facilities may reduce overall travel time to health facilities and may decrease malaria transmission. To help guide local decision-makers as they scale up community-based accessibility, the influence of the spatial allocation of new health facilities on malaria prevalence is evaluated in Bunkpurugu-Yunyoo district in northern Ghana. A location-allocation analysis is performed to find optimal locations of new health facilities by separately minimizing three district-wide objectives: malaria prevalence, malaria incidence, and average travel time to health facilities. Methods Generalized additive models was used to estimate the relationship between malaria prevalence and travel time to the nearest health facility and other geospatial covariates. The model predictions are then used to calculate the optimisation criteria for the location-allocation analysis. This analysis was performed for two scenarios: adding new health facilities to the existing ones, and a hypothetical scenario in which the community-based healthcare facilities would be allocated anew. An interactive web application was created to facilitate efficient presentation of this analysis and allow users to experiment with their choice of health facility location and optimisation criteria. Results Using malaria prevalence and travel time as optimisation criteria, two locations that would benefit from new health facilities were identified, regardless of scenarios. Due to the non-linear relationship between malaria incidence and prevalence, the optimal locations chosen based on the incidence criterion tended to be inequitable and was different from those based on the other optimisation criteria. Conclusions This study findings underscore the importance of using multiple optimisation criteria in the decision-making process. This analysis and the interactive application can be repurposed for other regions and criteria, bridging the gap between science, models and decisions.