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As a humanity crisis, the tragedy of forced displacement entails relief aid distribution efforts among en route refugees to alleviate their migration hardships. This study aims to assist humanitarian organizations in cost-efficiently optimizing the logistics of capacitated mobile facilities utilized to deliver relief aid to transiting refugees in a multi-period setting. The problem is referred to as the Capacitated Mobile Facility Location Problem with Mobile Demands (CMFLP-MD). In CMFLP-MD, refugee groups follow specific paths, and meanwhile, they receive relief aid at least once every fixed number of consecutive periods, maintaining continuity of service. To this end, the overall costs associated with capacitated mobile facilities, including fixed, service provision, and relocation costs, are minimized. We formulate a mixed integer linear programming (MILP) model and propose two solution methods to solve this complex problem: an accelerated Benders decomposition approach as an exact solution method and a matheuristic algorithm that relies on an enhanced fix-and-optimize agenda. We evaluate our methodologies by designing realistic instances based on the Honduras migration crisis that commenced in 2018. Our numerical results reveal that the accelerated Benders decomposition excels MILP with a 46% run time improvement on average while acquiring solutions at least as good as the MILP across all instances. Moreover, our matheuristic acquires high-quality solutions with a 2.4% average gap compared to best-incumbents rapidly. An in-depth exploration of the solution properties underscores the robustness of our relief distribution plans under varying migration circumstances. Across several metrics, our sensitivity analyses also highlight the managerial advantages of implementing CMFLP-MD solutions. © 2024 Elsevier Ltd

This paper presents a novel model for the Vaccine Allocation Problem (VAP), which aims to allocate the available vaccines to population locations over multiple periods during a pandemic. We model the disease progression and the impact of vaccination on the spread of the disease and mortality to minimise total expected mortality and location inequity in terms of mortality ratios under total vaccine supply and hospital and vaccination centre capacity limitations at the locations. The spread of the disease is modelled through an extension of the well-established Susceptible–Infected–Recovered (SIR) epidemiological model that accounts for multiple vaccine doses. The VAP is modelled as a nonlinear mixed-integer programming model and solved to optimality using the Gurobi solver. A set of scenarios with parameters regarding the COVID-19 pandemic in the UK over 12 weeks are constructed using a hypercube experimental design on varying disease spread, vaccine availability, hospital capacity, and vaccination capacity factors. The results indicate the statistical significance of vaccine availability and the parameters regarding the spread of the disease. © 2024 Elsevier Ltd, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: TR 220N017, 220N017; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK; Newton Fund, NF: 623795194; Newton Fund, NF

10th International Network Optimization Conference, INOC 2022 -- 7 June 2022 through 10 June 2022 -- 187074, In this paper, we help humanitarian organizations provide service via mobile facilities (MFs) to migrating refugees, who attempt to cross international borders. Over a planning horizon, we aim to optimize number and routes and relocations of the MFs over a planning horizon. The problem is represented on a network where several refugee groups relocate in their predetermined paths throughout the periods. To incorporate continuity of service, each refugee group should be served at least once every fixed consecutive periods via capacitated MFs. We aim to minimize the total cost, consisting of fixed, service provision, and MF relocation costs, while ensuring the service continuity requirement. We formulate a mixed integer linear programming (MILP) model for this problem. We develop a matheuristic and an accelerated Benders decomposition algorithm as an exact solution method. The proposed model and solution methods are investigated over instances we extracted from the 2020 Honduras migration crisis. © 2022 Copyright held by the owner/authors(s)., Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 119M229, Acknowledgment: This research is supported by TUBITAK [Grant number 119M229].

10th International Network Optimization Conference, INOC 2022 -- 7 June 2022 through 10 June 2022 -- 187074, In this paper, we help humanitarian organizations provide service via mobile facilities (MFs) to migrating refugees, who attempt to cross international borders. Over a planning horizon, we aim to optimize number and routes and relocations of the MFs over a planning horizon. The problem is represented on a network where several refugee groups relocate in their predetermined paths throughout the periods. To incorporate continuity of service, each refugee group should be served at least once every fixed consecutive periods via capacitated MFs. We aim to minimize the total cost, consisting of fixed, service provision, and MF relocation costs, while ensuring the service continuity requirement. We formulate a mixed integer linear programming (MILP) model for this problem. We develop a matheuristic and an accelerated Benders decomposition algorithm as an exact solution method. The proposed model and solution methods are investigated over instances we extracted from the 2020 Honduras migration crisis. © 2022 Copyright held by the owner/authors(s)., Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 119M229, Acknowledgment: This research is supported by TUBITAK [Grant number 119M229].

The European Marine Board Communications Panel (EMBCP), a pan-European platform for marine science communicators, decided in 2019 to write a policy document, under the auspices and with the support of the European Marine Board (EMB). This document would sketch and analyse the state of the art in the field of marine science communication, aiming to highlight marine science communication as a growing and needed field of activity, and providing recommendations towards its future and developing best practices.

This paper introduces the electric home health care routing and scheduling problem with time windows and fast chargers. The problem aims to construct the daily routes of health care nurses so as to provide a series of services to the patients located at a scattered area. The problem minimizes the total cost, which comprises of total traveling cost of electric vehicles, total cost of uncovered jobs, and total costs of recharged energy. We develop an adaptive large neighborhood search heuristic, which contains a number of advanced efficient procedures tailored to handle specific features of the problem. The paper conducts extensive computational experiments on generated benchmark instances and assesses the competitiveness of the heuristic. Results show that the heuristic is highly effective on the problem. Our analyses quantify the advantages of considering all charger technologies, i.e., normal, fast- and super-fast. We show that the downgrading of competence levels of jobs yields an improvement in total cost. © 2022 Elsevier Ltd, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 119M007, This work was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under grant number 119M007 . This support is gratefully acknowledged.

This paper introduces the electric home health care routing and scheduling problem with time windows and fast chargers. The problem aims to construct the daily routes of health care nurses so as to provide a series of services to the patients located at a scattered area. The problem minimizes the total cost, which comprises of total traveling cost of electric vehicles, total cost of uncovered jobs, and total costs of recharged energy. We develop an adaptive large neighborhood search heuristic, which contains a number of advanced efficient procedures tailored to handle specific features of the problem. The paper conducts extensive computational experiments on generated benchmark instances and assesses the competitiveness of the heuristic. Results show that the heuristic is highly effective on the problem. Our analyses quantify the advantages of considering all charger technologies, i.e., normal, fast- and super-fast. We show that the downgrading of competence levels of jobs yields an improvement in total cost. © 2022 Elsevier Ltd, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 119M007, This work was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under grant number 119M007 . This support is gratefully acknowledged.

A significant number of Syrian refugees under temporary protection in Turkey work in agriculture seasonally in various rural areas during several months a year. These migrant farm workers and their families are deprived of access to the regular health care system and preventive services due to their remote locations. The government supports the delivery of different types of mobile health care services, such as vaccination for children, reproductive health and screening services. While planning the mobile health care service delivery, it is critical to know where the refugees will work during what time frame; hence the demand for the services. By analyzing the call record data of a major mobile network operator in Turkey, we quantify the increase in the volume of calls made by Syrian refugees in various agricultural areas during the harvesting season of local crops. This information helps us to forecast spatial and temporal distribution of demand for mobile health care services at a fine granularity. Taking demand over multiple periods as input into a mathematical programming model, we optimize the routing of mobile clinics that visit locations close to where refugees are concentrated over the given planning horizon. We consider three hierarchical objectives. Given the availability of a number of mobile clinics at community health centers in the districts, the first objective aims to maximize the percentage of refugees that can benefit from each service type within pre-defined close distances. The second objective minimizes the number of clinics needed while covering the maximum percentage of refugees. The third objective minimizes the total travel distance of the clinics, while keeping the maximum coverage level using a minimum number of clinics to achieve this level. We quantify the benefits of centralized planning (by the province directorate) over decentralized planning (by each district separately). We also show the trade-off between the required number of clin

Introduction: The COVID-19 pandemic dramatically disrupts health care around the globe. The impact of the pandemic on chronic urticaria (CU) and its management are largely unknown. Aim: To understand how CU patients are affected by the COVID-19 pandemic; how specialists alter CU patient management; and the course of CU in patients with COVID-19. Materials and Methods: Our cross-sectional, international, questionnaire-based, multicenter UCARE COVID-CU study assessed the impact of the pandemic on patient consultations, remote treatment, changes in medications, and clinical consequences. Results: The COVID-19 pandemic severely impairs CU patient care, with less than 50% of the weekly numbers of patients treated as compared to before the pandemic. Reduced patient referrals and clinic hours were the major reasons. Almost half of responding UCARE physicians were involved in COVID-19 patient care, which negatively impacted on the care of urticaria patients. The rate of face-to-face consultations decreased by 62%, from 90% to less than half, whereas the rate of remote consultations increased by more than 600%, from one in 10 to more than two thirds. Cyclosporine and systemic corticosteroids, but not antihistamines or omalizumab, are used less during the pandemic. CU does not affect the course of COVID-19, but COVID-19 results in CU exacerbation in one of three pat

Introduction: the COVID-19 pandemic dramatically disrupts health care around the globe. The impact of the pandemic on chronic urticaria (CU) and its management are largely unknown. Aim: to understand how CU patients are affected by the COVID-19 pandemic; how specialists alter CU patient management; and the course of CU in patients with COVID-19. Materials and methods: our cross-sectional, international, questionnaire-based, multicenter UCARE COVID-CU study assessed the impact of the pandemic on patient consultations, remote treatment, changes in medications, and clinical consequences. Results: the COVID-19 pandemic severely impairs CU patient care, with less than 50% of the weekly numbers of patients treated as compared to before the pandemic. Reduced patient referrals and clinic hours were the major reasons. Almost half of responding UCARE physicians were involved in COVID-19 patient care, which negatively impacted on the care of urticaria patients. The rate of face-to-face consultations decreased by 62%, from 90% to less than half, whereas the rate of remote consultations increased by more than 600%, from one in 10 to more than two thirds. Cyclosporine and systemic corticosteroids, but not antihistamines or omalizumab, are used less during the pandemic. CU does not affect the course of COVID-19, but COVID-19 results in CU exacerbation in one of three patients, with higher rates in patients with severe COVID-19. Conclusions: the COVID-19 pandemic brings major changes and challenges for CU patients and their physicians. The long-term consequences of these changes, especially the increased use of remote consultations, require careful evaluation., Novartis; Sanofi; Menarini Universidad Espiritu Santo; Takeda; Allakos; AstraZeneca; CSL Behring; Genentech; Pharming; GSK; Shire/Takada; BioCryst; ResTORbio; Pearl Therapeutics, CVS Health; Law offices of Levin; Riback; Adelman; Flangel; Vedder Price; Fresenius; Taiho; Kyowa Kirin; Tanabe; Korin; Uriach Pharma; Instituto Carlos III FEDER; Menarini; Amgen; Thermo Fisher; Avene; ALK‐Abello; Bencard/Allergy Therapeutics; Celgene; Allergopharma; Faes Farma; AbbVie; Janssen; Leo Pharma; Lilly; Roche; Genesis; Menlo Therapeutics; UCB; Pfizer; Almirall; Galderma; Allergika; Beiersdorf; Biocryst; Biogen Idec; BMS; Boehringer‐Ingelheim; Eli‐Lilly; Galderma; Hexal; Klosterfrau; LEO‐Pharma; LETI‐Pharma; L´Oreal; Medice; Octapharma; Pflüger; Pharming; Regeneron; Shire; ALK‐Abello; Fraunhofer‐IZI Leipzig; Hautnetz Leipzig/Westsachsen; MSD; HAL‐Allergy; Bencard; Nestle; Nutricia; Bayer Health Care; FAES; Henkel; Allakos; Argenx; Genentech Menarini; Moxie; Aralez; Celldex