Forced displacement crises have become a pressing humanitarian concern. Refugee movements expose individuals to dire living conditions with severe inaccessibility to essential resources. Humanitarian organizations play a vital role in alleviating these hardships through relief aid interventions. This study aims to optimize the fulfillment of recurring needs for geographically dispersed refugee groups en route to safe destinations. Here, capacitated mobile facilities are tasked with delivering relief aid to refugee groups periodically to ensure equitable service frequency. We formulate the problem as a Markov decision process with multinomial state-transition distributions, shaped by external migration pull factors such as safety conditions, road accessibility, and spatial proximity. The objective is to minimize the relocation and replenishment costs of mobile facilities, along with the deprivation costs faced by underserved refugees. We develop an approximate dynamic programming algorithm featuring a novel policy replication routine. To complement this offline method, we introduce a state-dependent variable threshold policy that enables high-quality, real-time relief provision. Using instances inspired by the Syrian refugee crisis, our results demonstrate the substantial value of stochastic modeling, yielding a 25% reduction in expected total costs compared to deterministic baselines and up to 12% savings through coordinated planning among humanitarian actors. The proposed methods remain effective under dispersed and cohesive refugee group dynamics and multi-destination migration scenarios. Furthermore, we uncover high-frequency traversal and service hotspots along migration paths to provide tactical insights for parameter calibration and resource prepositioning. Collectively, our findings offer practical insights for managing ongoing and future refugee migration crises.

This article addresses the dynamic multi-skill workforce scheduling and routing problem with time windows and synchronization constraints (DWSRP-TW-SC) inherent in the on-demand home services sector. In this problem, new service requests (tasks) emerge in real-time, necessitating a constant reevaluation of service team task plans. This reevaluation involves maintaining a portion of the plan unaltered, ensuring team-task compatibility, addressing task priorities, and managing synchronization when task demands exceed a team's capabilities. To address the problem, we introduce a real-time optimization framework triggered upon the arrival of new tasks or the elapse of a set time. This framework redesigns the routes of teams with the goal of minimizing the cumulative weighted throughput time for all tasks. For the route redesign phase of this framework, we develop both a mathematical model and an Adaptive Large Neighborhood Search (ALNS) algorithm. We conduct a comprehensive computational study to assess the performance of our proposed ALNS-based reoptimization framework and to examine the impact of reoptimization strategies, frozen period lengths, and varying degrees of dynamism. Our contributions provide practical insights and solutions for effective dynamic workforce management in on-demand home services.

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.

The decade-long Syrian civil war has triggered a significant migration wave in the Middle East, with Turkey hosting the largest number of Syrian refugees. Our study introduces an agent-based model (ABM) designed to simulate and predict migration paths in potential future refugee crises. The primary goal is to support aid organizations in planning the delivery of essential aid services during migration movements, offering insights that can be applied to various geographical areas and migration scenarios. While we use the Syrian refugee movement to Turkey as a case study, the model is intended as a flexible tool for analyzing migration patterns in future crises. The proposed ABM considers two characteristics of refugee groups: level of risk sensitivity and level of information. To enhance the model’s functionality, we have extended the A* algorithm with a cost metric to calculate the weighted average of distance and risk to a destination point. Our case study examines the crisis in southern Idlib through six scenarios, offering insights into refugee numbers, migration paths, camp occupancy rates, and heat maps of densely populated regions for each scenario. Validation is performed by comparing model outcomes with situation reports and official statements from the relevant period, demonstrating the proposed ABM’s potential for adaptation to other migration instances and further analysis under different parameters.

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.

This paper studies the problem of heterogeneous electric vehicles, fast chargers, and synchronized jobs that have time windows in home healthcare routing and scheduling. We consider a problem that aims to establish daily routes and schedules for healthcare nurses to provide a variety of services to patients located in a scattered area. Each nurse should be assigned to an electric vehicle (EV) from a heterogeneous fleet of EVs to perform the assigned jobs within working hours. We consider three different types of EVs in terms of battery capacity and energy consumption. We aim to minimize the total cost of energy consumption, fixed nurse cost, and costs arising from the patients that cannot be served within the working day. We model the problem as a mixed integer programming formulation. We develop a hybrid metaheuristic based on a greedy random adaptive search procedure heuristic, to generate good quality initial solutions quickly, and an adaptive variable neighborhood search algorithm to generate high quality solutions in reasonable time. The hybrid metaheuristic employs a set of new advanced efficient procedures designed to handle the complex structure of the problem. Through extensive computational experiments, the performance of the mathematical model and the hybrid metaheuristic are evaluated. We conduct analyses on the robustness of the metaheuristic and the performance contribution of employing adaptive probabilities. We analyze the impact of problem parameters such as competency requirements, job duration, and synchronized jobs.

This paper studies the multi-period home healthcare routing and scheduling problem with homogeneous electric vehicles and time windows. The problem aims to construct the weekly routes of healthcare nurses, which provide service to the patients located at a scattered geographic area. Some patients may require to be visited more than once in the same workday and/or in the same workweek. We consider three charging technologies; normal, fast, and super-fast. The vehicles might be charged during the working day at a charging station or at the end of the working day at the depot. Charging a vehicle at a depot at the end of a working day requires the transfer of the corresponding nurse from the depot to her/his home. The objective is to minimize the total cost that comprises the fixed cost of utilizing healthcare nurses, the energy charging costs, the costs associated with depot-to-nurse home transfer services, and the costs of a patient left unserved. We formulate a mathematical model and develop an adaptive large neighborhood search metaheuristic that has been efficiently crafted to handle specific problem features. We conduct extensive computational experiments on benchmark instances to assess the competitiveness of the heuristic and to deeply analyze the problem. Our analysis shows the importance of competency level matching as mismatching competency levels could increase the costs of home healthcare providers.

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 opti-mize 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.

This paper introduces a multi-period, two-dimensional vehicle loading and dispatching problem, called Two-Dimensional Vehicle Loading and Dispatching Problem with Incompatibility Constraints (VLDP). The problem concerns preparing a single-origin single-destination transportation plan of loading required orders to vehicles at the origin and dispatching the vehicles to deliver the orders to the destination within their due dates. The decision maker uses their own fleet of vehicles, with each vehicle having a fixed transportation cost per trip, and may outsource additional vehicles at a higher cost. VLDP involves constraints regarding the due dates of the orders, pairwise incompatibility of orders packed in the same vehicle, incompatibility of orders and vehicles, as well as area and weight capacity of the vehicles. An order can be delivered earlier than its due date, incurring an earliness penalty due to storage requirements at the destination. The objective is to minimize the total vehicle usage and earliness penalty costs. A Mixed-Integer Linear Programming model (MILP) is provided, as well as an Adaptive Large Neighbourhood Search (ALNS) algorithm. Results of computational experiments on instances derived from real-world data show the effectiveness of the ALNS algorithm. (C) 2022 Elsevier B.V. All rights reserved.

Many humanitarian organizations aid en route refugee groups who are on their journey to cross borders using mobile facilities and need to decide the number and routes of the facilities. We define a multi period facility location problem in which both the facilities and demand are mobile on a network. Refugee groups may enter and exit the network in different periods and follow various paths. In each period, a refugee group moves from one node to an adjacent one in their predetermined path. Each facility should be located at a node in each period and provides service to the refugees at that node. Each refugee should be served at least once in a predetermined number of consecutive periods. The problem is to locate the facilities in each period to minimize the total setup and travel costs of the mobile facilities, while ensuring the service requirement. We call this problem the multi-period mobile facility location problem with mobile demand (MM-FLP-MD) and prove its NP-hardness. We formulate a mixed integer linear programming (MILP) model and develop an adaptive large neighborhood search algorithm (ALNS) to solve large-size instances. We tested the computational performance of the MILP and the metaheuristic algorithm by extracting data from the 2018 Honduras Migration Crisis. For instances solved to optimality by the MILP model, the proposed ALNS determines the optimal solutions faster and provides better solutions for the remaining instances. By analyzing the sensitivity to different parameters, we provide insights to decision-makers.(c) 2021 Elsevier B.V. All rights reserved.

The Syrian civil war, which started in 2011, has caused a great wave of forced migration in the Middle East.One of the most popular destination points for Syrian refugees has been Turkey.The purpose of this study is to predict the routes of refugees who leave the conflict areas in Syria to reach the refugee camps located in Turkey during a crisis.The study proposes an agent-based model to simulate the decision mechanisms of refugees in a highly uncertain environment.The model employs the A* algorithm to calculate the cost of each available destination point (refugee camp) for each agent, based on their risk preferences and starting locations, and allows agents to choose the camp with the minimum cost as the destination point.By use of the model, we simulate a moment of crisis namely the South Idlib bombardment (from December 2019 to January 2020) under four different scenarios that are generated considering the real-life data gathered from the newspapers of December 2019 and various other sources.The simulation results show the main pathways of Syrian refugees and give insights on the required camp capacities.The results are compared with the gathered secondary data to validate the proposed model.

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.

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 clinics and coverage of potential patients.

Airlines serve complimentary or for-purchase in-flight meals that vary depending on flight duration. These meals are prepared by airline catering companies and are ideally loaded immediately before the flight. However, as the loading process takes time and effort and it is costly to have the required amount of meals at the departure airport immediately before each flight, airline companies conduct catering loading at predetermined airports. In general, the catering loading sites, i.e. airports, can be classified into two types: normal or cross-loading sites. At the normal loading sites, the catering can be directly loaded to the aircraft with a fixed loading cost and a variable handling cost that depends on the loaded amount and personnel cost at the corresponding location. At the cross-loading sites, the catering is transported from a catering facility before the loading operation, incurring an additional transportation cost. During a flight, an aircraft may carry the catering demand for the next flights. The total amount of catering carried during a flight depends on the shelf life of the catering and the aircraft capacity and affects the fuel consumption during the flight. Although the flight plan might dynamically change, airlines determine catering loading sites before each flight season based on the established flight plan and estimated amount of catering consumed during each flight. In this study, given the flight plan of an airline for a specified planning horizon with the estimated demand for each catering type at each flight, we address the problem of determining the locations of normal and cross-loading sites. The objective is to minimize total operational costs that include the fixed costs of opening normal or cross-loading sites, fixed and variable costs of loading, transportation costs for cross-loading, and additional aircraft fuel costs that depend on the catering load of the aircraft such that the estimated catering demand for each flight is fully met. The aircraft catering capacity limits and lifetime for each catering type should be considered. We first develop a mixed integer programming formulation for the problem. As the planning horizon increases, it is not possible to obtain good solutions via the mathematical formulation over a reasonable time. Therefore, we propose a hybrid solution approach based on a tabu search algorithm and dynamic programming approach for realistic planning horizons. We analyze the performance of the proposed approaches on realistic problem instances obtained from an airline company based in Turkey. (C) 2020 Elsevier Ltd. All rights reserved.

We study the multi-skill workforce scheduling and routing problem in field service operations. It is motivated by a real-life problem faced by electricity distribution companies on a daily basis. Given a set of technicians with different skills and a set of geographically dispersed tasks with different skill requirements and priorities, the aim is to form teams of technicians and to assign a sequence of tasks to each team according to their skills. There are two objectives: completing higher priority tasks earlier and minimizing total operational costs. We propose a mixed integer programming model to find Pareto optimal solutions. Because the computational effort considerably increases for real life problem instances, we propose a two-stage matheuristic to obtain a good approximation of the Pareto frontier. We demonstrate the performance of the proposed matheuristic in real life problem instances and instances from the literature. (C) 2020 Elsevier Ltd. All rights reserved.

In this research, we study the trip-ferry assigment and ferry routing problem that arises in public ferry transportation services. The problem is motivated by the real problem that Istanbul Şehir Hatları Inc. needs to solve periodically while assigning ferries to ferry lines having predetermined tarrifs. Tariffs differ according to the season and day of the week. The trip-ferry assignment should consider passenger demand of ferry lines, line-ferry restrictions, ferry passenger capacities, ferry capacities of piers and ferry-pier restrictions. Additionally, working regulations of ferry personnel restrict ferry working hours. The objective is to minimize total cost of fuel consumption and outsourcing. First, a mathematical model was developed. As the mathematical model is inadequate for solving realistic size problem instances, a tabu search-based heuristic method is proposed. The effectiveness of the heuristic is analayzed on realistic data instances. Şehir İçi Deniz Yolu Toplu Taşımacılığında Vapur Atama ve Rotalama Optimizasyonu: İstanbul Şehir Hatları Uygulaması Ferry Assignment and Routing Optimization for Public Ferry Transportation: A Case Study for Istanbul Şehir Hatları

Bu çalışmada, şehir içi deniz yolu toplu taşımacılığında seferlere vapur atama ve rotalama problemi ele alınmıştır. Problem, gerçek hayatta İstanbul Şehir Hatları A.Ş.’nin periyodik olarak karşılaştığı, tarifelerde saatleri belirlenen seferlere vapur atama probleminden yola çıkılarak tanımlanmıştır. Sefer tarifeleri, yaz ve kış dönemine ve haftanın günlerine göre farklılık göstermektedir. Atamada; seferlerin yolcu talepleri, hat-vapur kısıtları, vapurların yolcu kapasiteleri, iskelelerin vapur kapasiteleri, vapurların iskelelere bağlanabilme kısıtları dikkate alınmalıdır. Ayrıca, personellerin günlük çalışma saatini belirleyen kanuni düzenlemeler vapurların çalışma saatlerini kısıtlamaktadır. Amaç, yakıt tüketimi ve dış kaynak kullanımı maliyetlerinden oluşan toplam maliyetleri en küçüklenmektir. Öncelikle, problem için bir matematiksel model geliştirilmiştir. Gerçek boyutlu problem örnekleri için matematiksel modelin yetersiz kalması nedeniyle tabu arama yöntemine dayalı bir sezgisel yöntem geliştirilmiştir. Geliştirilen yöntemin etkinliği gerçekçi veri kümeleri üzerinde gösterilmiştir.

We study the delivery of mobile medical services and in particular, the optimization of the joint stop location selection and routing of the mobile vehicles over a repetitive schedule consisting of multiple days. Considering the problem from the perspective of a mobile service provider company, we aim to provide the most revenue to the company by bringing the services closer to potential customers. Each customer location is associated with a score, which can be fully or partially covered based on the proximity of the mobile facility during the planning horizon. The problem is a variant of the team orienteering problem with prizes coming from covered scores. In addition to maximizing total covered score, a secondary criterion involves minimizing total travel distance/cost. We propose a data-driven optimization approach for this problem in which data analyses feed a mathematical programming model. We utilize a year-long transaction data originating from the customer banking activities of a major bank in Turkey. We analyze this dataset to first determine the potential service and customer locations in Istanbul by an unsupervised learning approach. We assign a score to each representative potential customer location based on the distances that the residents have taken for their past medical expenses. We set the coverage parameters by a spatial analysis. We formulate a mixed integer linear programming model and solve it to near-optimality using Cplex. We quantify the trade-off between capacity and service level. We also compare the results of several models differing in their coverage parameters to demonstrate the flexibility of our model and show the impact of accounting for full and partial coverage.