New Variations of the Online <em>k</em>-Canadian Traveler Problem: Uncertain Costs at Known Locations

In this chapter, we study new variations of the online k-Canadian Traveler Problem (k-CTP) in which there is an input graph with a given source node O and a destination node D. For a specified set consisting of k edges, the edge costs are unknown (we call these uncertain edges). Costs of the remaining edges are known and given. The objective is to find an online strategy such that the traveling agent finds a route from O to D with minimum total travel cost. The agent learns the cost of an uncertain edge, when she arrives at one of its end-nodes and decides on her travel path based on the discovered cost. We call this problem the online k-Canadian Traveler Problem with uncertain edges. We analyze both the single-agent and the multi-agent versions of the problem. We propose a tight lower bound on the competitive ratio of deterministic online strategies together with an optimal online strategy for the single-agent version. We consider the multi-agent version with two different objectives. We suggest lower bounds on the competitive ratio of deterministic online strategies to these two problems

Analysis of multimedian problems on time dependent networks

Ankara : The Department of Industrial Engineering and the Institute of Enginering and Science of Bilkent Univ., 1994.Thesis (Master's) -- Bilkent University, 1994.Includes bibliographical references leaves 81-85.Time dependency arises in transportation and computer-communication networks due to factors such as time varying demand, traffic intensity, and road conditions. This necessitates a locational decision to be based on an analysis involving a time horizon. In this study, we analyze multi-median problems with linear demand functions on both tree and cyclic networks in a continuous time domain. The trajectory of the optimal solution is a piecewise linear concave function. We develop an algorithm that constructs the trajectory by solving 0{q) static problems, where q is the number of linear pieces in the trajectory. The properties of the optimal solution over the time horizon are also analyzed for various randomly generated problem instances.Salman, F SibelM.S

Investing in the Links of a Stochastic Network to Minimize Expected Shortest Path. Length

We consider a network whose links are subject to independent, random failures due to a disruptive event. The survival probability of a link is increased, if it is strengthened by investment. A given budget is to be allocated among the links with the objective of optimizing the post-event performances of the network. Specifically, we seek to minimize the expected shortest path Length between a specified origin node and destination node in the network. This criterion is defined through the use of a fixed penalty cost for those network realizations in the expectation, that do not have a path connecting the origin node to the destination node. This problem type arises in the pre-disasters, by upgrading its weakest elements. We model the problem as a two-stage stochastic program in which the underlying probability distribution of the random variables is dependent on the first stage decision variables. Using a path-based approach we construct its equivalent deterministic program and derive structural results for the objective function. We then propose an approximate solution procedure based on a first order approximation the objective function. The procedure is tested by numerical experiments on a small-size network. The test results show that it yields very good performance on the instances solved.network vulnerability ; decision dependent probability distribution ; two-stage stochastic program ; multilinear function

Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity

© 2016 Elsevier B.V. After a natural disaster roads can be damaged or blocked by debris, while bridges and viaducts may collapse. This commonly observed hazard causes some road sections to be closed and may even disconnect the road network. In the immediate disaster response phase work teams are dispatched to open a subset of roads to reconnect the network. Closed roads are traversable only after they are unblocked/cleared by one of the teams. The main objective of this research is to provide an efficient solution method to generate a synchronized work schedule for the road clearing teams. The solution should specify the synchronized routes of each clearing team so that: 1) connectivity of the network is regained, and 2) none of the closed roads are traversed unless their unblocking/clearing procedure is finished. In this study we develop an exact Mixed Integer Programming (MIP) formulation to solve this problem. Furthermore, we propose a matheuristic that is based on an MIP-relaxation and a local search algorithm. We prove that the optimality gap of the relaxation solution is bounded by K times the lower bound obtained from the relaxed model, where K is the number of teams. We show computationally that the matheuristic obtains optimal or near-optimal solutions

Minimizing latency in post-disaster road clearance operations

After a natural disaster, roads and bridges can be damaged or blocked by debris, causing inaccessibility between critical locations such as hospitals, disaster response centers, shelters and disaster-struck areas. We study the post-disaster road clearing problem with the aim of providing a fast and effective method to determine the route of a work troop responsible for clearing blocked roads. The problem is to find a route for the troop that starts at the depot and visits all of the critical locations. The objective is to minimize the total latency of critical nodes, where the latency of a node is defined as the travel time from the depot to that node. A mathematical model for this problem has already been developed in the literature. However, for real-life instances with more than seven critical nodes, this exact formulation cannot solve the problem optimally in a 3-hour limit. To find a near-optimal solution in a short running time, we develop a heuristic that solves a mixed integer program on a transformed network and a lower bounding method to evaluate the optimality gaps. Alternatively, we develop a metaheuristic based on a combination of Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Search (VNS). We test both the matheuristic and the metaheuristic on Istanbul data and show that optimal or near-optimal solutions are obtained within seconds. We also compare our algorithms with existing work in the literature. Finally, we conduct an analysis to observe the trade-off between total and maximum latency

Online routing and scheduling of search-and-rescue teams

We study how to allocate and route search-and-rescue (SAR) teams to areas with trapped victims in a coordinated manner after a disaster. We propose two online strategies for these time-critical decisions considering the uncertainty about the operation times required to rescue the victims and the condition of the roads that may delay the operations. First, we follow the theoretical competitive analysis approach that takes a worst-case perspective and prove lower bounds on the competitive ratio of the two variants of the defined online problem with makespan and weighted latency objectives. Then, we test the proposed online strategies and observe their good performance against the offline optimal solutions on randomly generated instances

Minimizing total weighted latency in home healthcare routing and scheduling with patient prioritization

We study a home healthcare routing and scheduling problem, where multiple healthcare service provider teams should visit a given set of patients at their homes. The problem involves assigning each patient to a team and generating the routes of the teams such that each patient is visited once. When patients are prioritized according to the severity of their condition or their service urgency, the problem minimizes the total weighted waiting time of the patients, where the weights represent the triage levels. In this form, the problem generalizes the multiple traveling repairman problem. To obtain optimal solutions for small to moderate-size instances, we propose a level-based Integer Programming (IP) model on a transformed input network. To solve larger instances, we develop a metaheuristic algorithm that relies on a customized saving procedure and a General Variable Neighborhood Search algorithm. We evaluate the IP model and the metaheuristic on various small, medium, and large-sized instances coming from the vehicle routing literature. While the IP model finds the optimal solutions to all the small and medium-sized instances within three hours of run time, the metaheuristic algorithm achieves the optimal solutions to all instances within merely a few seconds. We also provide a case study involving Covid-19 patients in a district of Istanbul and derive insights for the planners by means of several analyses

The global impact of the COVID-19 pandemic on the management and course of chronic urticaria

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

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice

Multi-item dynamic lot-sizing with delayed transportation policy

We optimize ordering and inbound shipment decisions for a manufacturer that sources multiple items from a single supplier. The objective is to satisfy the requirements in the production plan with minimum transportation and inventory holding costs over a multi-period planning horizon. Transportation costs are charged to the manufacturer on a per truck shipment basis. We investigate the option of delaying a less-than-full truckload shipment to the next period, by utilizing the safety stocks as needed. We analyze the impact of delaying shipments on both cost and service levels in stochastic environments through experiments with data from a bus manufacturer. The results indicate that the proposed policy reduces both holding and transportation costs without creating much stock-out risk.Multi-item dynamic lot-sizing Inbound shipments Single sourcing Delayed transportation policy