A Large Neighbourhood Search Metaheuristic for the Contagious Disease Testing Problem.

In late 2019 a new coronavirus disease (COVID-19) emerged, causing a global pandemic within only a few weeks. A crucial factor in the public health response to pandemics is achieving a short turnaround time between a potential case becoming known, specimen collection and availability of a test result. In this article we address a logistics problem that arises in the context of testing potential cases. We assume that specimens can be collected in two ways: either by means of a mobile test-team or by means of a stationary test-team in a test-centre. After the specimens have been collected they must be delivered to a laboratory in order to be analysed. The problem we address aims at deciding how many test-centres to open and where, how many mobile test-teams to use, which suspected cases to assign to a test-centre and which to visit with a mobile test-team, which specimen to assign to which laboratory, and planning the routes of the mobile test-teams. The objective is to minimise the total cost of opening test-centres and routing mobile test-teams. We introduce this new problem, which we call the contagious disease testing problem (CDTP), and present a mixed-integer linear-programming formulation for it. We propose a large neighbourhood search metaheuristic for solving the CDTP and present an extensive computational study to illustrate its performance. Furthermore, we give managerial insights regarding COVID-19 test logistics, derived from problem instances based on real world data.

Safe and secure vehicle routing: a survey on minimization of risk exposure.

Safe and secure vehicle routing problems refer to the transportation of dangerous (e.g., flammable liquids) or valuable goods (e.g., cash), the surveillance of streets (e.g., police patrols) or other areas (e.g., those within a factory or building), and the response to sudden incidents (e.g., robberies or street disruptions). It thus covers a multitude of models and methods with each having its own objective and constraints, such as unpredictability or risk. We review and classify literature in this field and thereby identify a starting point for researchers in this evolving and practically relevant field. Our study reveals that there are 82 articles that cover aspects related to safe and secure routing, a majority of which were published in the last five years. We classify the articles into five main categories: (i) transportation of hazardous materials, (ii) patrol routing, (iii) cash-in-transit, (iv) dissimilar routing problems, and (v) modeling of multi-graphs. Categories (i)-(iv) elaborate on the problem studied, while (v) provides a general concept based on road network characteristics most commonly found in safe and secure routing problems. Relevant methods and instances, along with their similarities and dissimilarities, have also been discussed in the paper. Furthermore, specific problem characteristics and future research directions are identified.

Third dose of SARS-CoV-2 vaccination in hemato-oncological patients and health care workers: immune responses and adverse events - a retrospective cohort study.

BACKGROUND: Due to potentially immune-escaping virus variants and waning immunity, a third SARS-CoV-2 vaccination dose is increasingly recommended. However, data in patients with cancer are limited. PATIENTS AND METHODS: We measured anti-SARS-CoV-2 spike protein antibody levels after the third vaccination dose in 439 patients with cancer and 41 health care workers (HCW) at an academic centre in Austria and a rural community hospital in Italy. Adverse events were retrieved from questionnaires. RESULTS: Overall, 439 patients and 41 HCW were included. SARS-CoV-2 infections were observed in 62/439 (14.1%) patients before vaccination and in 5/439 (1.1%) patients after ≥1 dose. Longitudinal analysis revealed a decrease of antibody levels between 3 and 6 months after second vaccination in patients with solid tumours (p < 0.001) and haematological malignancies without anti-B cell therapies (p < 0.001). After the third dose, anti-S levels increased compared to the first/second dose. Patients receiving B cell-targeted agents had lower antibody levels than patients with haematological malignancies undergoing other treatments (p < 0.001) or patients with solid tumours (p < 0.001). Moreover, anti-S levels correlated with CD19+ (B cell) and CD56+ (NK cell) counts in peripheral blood. The most frequent adverse events after the third dose were local pain (75/160, 46.9%), fatigue (25/160, 15.6%) and fever/chills (16/160, 10.0%). Patients with cancer had lower anti-S levels than HCW (p = 0.015). CONCLUSIONS: This study in patients with cancer shows improved antibody levels after the third vaccination dose at an acceptable side-effect profile. Lower antibody levels than in controls underline the need for further follow-up studies and dedicated trials.

Assignment constraints in shared transportation services.

Competitive markets, increased fuel costs, and underutilized vehicle fleets are characteristics that currently define the logistics sector. Given an increasing pressure to act in a manner that is economically and ecologically efficient, mechanisms that help to benefit from idle capacities are on the rise. In the Sharing Economy, collaborative usage is typically organized through platforms that facilitate the exchange of goods or services. Our study examines a collaborative pickup and delivery problem where carriers can exchange customer requests. The aim is to quantify the potential of horizontal collaborations under a centralized framework. An Adaptive Large Neighborhood Search is developed to solve yet unsolved test instances. A computational study confirms the results of past studies which have reported cost savings between 20 and 30%. In addition, the numerical results indicate an even greater potential for settings with a high degree of regional customer overlap. Unfortunately, these high collaborative gains typically come at the cost of an uneven customer distribution, which is known to be one of the main barriers that prevent companies from entering into horizontal collaborations. To generate acceptable solutions for all participants, several constraints are included in the model. The introduction of these constraints to single-vehicle instances, decreases the potential collaborative gain considerably. Surprisingly, this does not happen in more realistic settings of carriers operating multiple vehicles. Overall, the computational study shows that centralized collaborative frameworks have the potential to generate considerable cost savings, while at the same time limiting customer or profit share losses and enabling carriers to keep some of their most valued customers.

SARS-CoV-2 screening in cancer outpatients during the second wave of the COVID-19 pandemic : Conclusions for crisis response at a high-volume oncology center.

BACKGROUND: During the second wave of the coronavirus disease 2019 (COVID-19) pandemic Austria suffered one of the highest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rates worldwide. We report performance parameters of a SARS-CoV­2 screening program established for cancer outpatients at our center. METHODS: Institutional policy recommended routine biweekly SARS-CoV­2 testing. Adherence to the testing recommendation during the second wave of the COVID-19 pandemic between 1 October and 30 November 2020 was analyzed. The SARS-CoV­2 infection rate during first wave period (21 March to 4 May 2020) was compared to the one during second wave. RESULTS: A total of 1577 cancer patients were seen at our outpatient clinic during the second wave. In 1079/1577 (68.4%) patients, at least 1 SARS-CoV2 test was performed. Overall 2833 tests were performed, 23/1577 (1.5%, 95% confidence interval, CI 1.0-2.2%) patients were tested positive for SARS-CoV­2, which indicates a significant increase compared to the first wave (4/1016; 0.4%, 95% CI 0.1-1.0%) with an odds ratio of 3.9 (95% CI 1.5-10.1; p < 0.005). Patients undergoing active anticancer treatment (172/960; 17.9% not tested) were more likely to have undergone a SARS-CoV­2 test than patients in follow-up or best supportive care (326/617; 52.8% not tested p < 0.001). Furthermore, patients with only 1 visit within 4 weeks were more likely to not have undergone a SARS-CoV­2 test (386/598; 64.5%) compared to patients with 2 or more visits (112/979; 11.4%; p < 0.001). The projected number of patients with undetected SARS-CoV­2 infection during the study period was 5. CONCLUSION: We identified clinical patient parameters influencing SARS-CoV­2 testing coverage in cancer outpatients. Our data can provide information on generation of standard operating procedures and resource allocation during subsequent infection waves.

Secure and efficient routing on nodes, edges, and arcs of simple-graphs and of multi-graphs.

Many security companies offer patrolling services, such that guards inspect facilities or streets on a regular basis. Patrolling routes should be cost efficient, but the inspection patterns should not be predictable for offenders. We introduce this setting as a multi-objective periodic mixed capacitated general routing problem with objectives being cost minimization and route inconsistency maximization. The problem is transformed into an asymmetric capacitated vehicle routing problem, on both a simple-graph and a multi-graph; and three multi-objective frameworks using adaptive large neighborhood search are implemented to solve it. As tests with both artificial and real-world instances show that some frameworks perform better for some indicators, a hybrid search procedure, combining two of them, is developed and benchmarked against the individual solution methods. Generally, results indicate that considering more than one shortest path between nodes, can significantly increase solution quality for smaller instances, but is quickly becoming a detriment for larger instances.

SARS-CoV-2 Testing in Patients With Cancer Treated at a Tertiary Care Hospital During the COVID-19 Pandemic.

PURPOSE: To analyze the prevalence of SARS-CoV-2 infection in patients with cancer in hospital care after implementation of institutional and governmental safety measurements. METHODS: Patients with cancer routinely tested for SARS-CoV-2 RNA by nasal swab and real-time polymerase chain reaction between March 21 and May 4, 2020, were included. The results of this cancer cohort were statistically compared with the SARS-CoV-2 prevalence in the Austrian population as determined by a representative nationwide random sample study (control cohort 1) and a cohort of patients without cancer presenting to our hospital (control cohort 2). RESULTS: A total of 1,688 SARS-CoV-2 tests in 1,016 consecutive patients with cancer were performed. A total of 270 of 1,016 (26.6%) of the patients were undergoing active anticancer treatment in a neoadjuvant/adjuvant and 560 of 1,016 (55.1%) in a palliative setting. A total of 53 of 1,016 (5.2%) patients self-reported symptoms potentially associated with COVID-19. In 4 of 1,016 (0.4%) patients, SARS-CoV-2 was detected. At the time of testing at our department, all four SARS-CoV-2-positive patients were asymptomatic, and two of them had recovered from symptomatic COVID-19. Viral clearance was achieved in three of the four patients 14-56 days after testing positive. The estimated odds ratio of SARS-CoV-2 prevalence between the cancer cohort and control cohort 1 was 1.013 (95% CI, 0.209 to 4.272; P = 1), and between control cohort 2 and the cancer cohort it was 18.333 (95% CI, 6.056 to 74.157). CONCLUSION: Our data indicate that continuation of active anticancer therapy and follow-up visits in a large tertiary care hospital are feasible and safe after implementation of strict population-wide and institutional safety measures during the current COVID-19 pandemic. Routine SARS-CoV-2 testing of patients with cancer seems advisable to detect asymptomatic virus carriers and avoid uncontrolled viral spread.

The collaborative multi-level lot-sizing problem with cost synergies.

Collaborative operations planning is a key element of modern supply chains. We introduce the collaborative multi-level lot-sizing problem with cost synergies. This arises if producers can realise reductions of their costs by providing more than one product in a specific time horizon. Since producers are typically not willing to reveal critical information, we propose a decentralised mechanism, where producers do not have to reveal their individual items costs. Additionally, a Genetic Algorithms-based centralised approach is developed, which we use for benchmarking. Our study shows that this approach comes very close to the a central plan, while in the decentralised one no critical information has to be shared. We compare the results to a myopic upstream planning approach, and show that these results are almost 12% worse than the centralised ones. All solution approaches are assessed on available test instances for problems without cost synergies. For the biggest available instances, the proposed centralised mechanism improves the best known solutions on average by 10.8%. The proposed decentralised mechanism can be applied to other problem classes, where collaborative decision makers aim for good plans under incomplete information.

The cost of incentive compatibility in auction-based mechanisms for carrier collaboration.

Collaboration has been one of the important trends in vehicle routing. A typical mechanism to enable carrier collaboration is to use combinatorial auctions, where requests are not traded individually but are combined into bundles. Previous literature on carrier collaboration has focused on issues such as bundle formation or winner determination, typically assuming truthfulness of all agents and absence of any strategic behavior. This article considers the interdependencies and problems that arise from bidders acting as buyers and sellers of requests at the same time. From standard auction theory, desirable properties of exchange mechanisms are identified as efficiency, incentive compatibility, individual rationality, and budget balance. It is shown that these desirable properties cannot be fulfilled at the same time. In particular, the properties efficiency and incentive compatibility induce that budget balance is violated, that is, an outside subsidy is required. We propose two incentive compatible exchange mechanisms. One is more closely related to the classical VCG approach, while the other one uses a more complicated concept for computing payments to participants. A numerical study investigates how frequently desired properties are violated. We show that both mechanisms can be acceptable in practical situations, but none of them can satisfy all desired properties.

Exact solutions for the collaborative pickup and delivery problem.

In this study we investigate the decision problem of a central authority in pickup and delivery carrier collaborations. Customer requests are to be redistributed among participants, such that the total cost is minimized. We formulate the problem as multi-depot traveling salesman problem with pickups and deliveries. We apply three well-established exact solution approaches and compare their performance in terms of computational time. To avoid unrealistic solutions with unevenly distributed workload, we extend the problem by introducing minimum workload constraints. Our computational results show that, while for the original problem Benders decomposition is the method of choice, for the newly formulated problem this method is clearly dominated by the proposed column generation approach. The obtained results can be used as benchmarks for decentralized mechanisms in collaborative pickup and delivery problems.

Centralized bundle generation in auction-based collaborative transportation.

In horizontal collaborations, carriers form coalitions in order to perform parts of their logistics operations jointly. By exchanging transportation requests among each other, they can operate more efficiently and in a more sustainable way. This exchange of requests can be organized through combinatorial auctions, where collaborators submit requests for exchange to a common pool. The requests in the pool are grouped into bundles, and these are offered to participating carriers. From a practical point of view, offering all possible bundles is not manageable, since the number of bundles grows exponentially with the number of traded requests. We show how the complete set of bundles can be efficiently reduced to a subset of attractive ones. For this we define the Bundle Generation Problem (BuGP). The aim is to provide a reduced set of offered bundles that maximizes the total coalition profit, while a feasible assignment of bundles to carriers is guaranteed. The objective function, however, could only be evaluated whether carriers reveal sensitive information, which would be unrealistic. Thus, we develop a proxy for the objective function for assessing the attractiveness of bundles under incomplete information. This is used in a genetic algorithms-based framework that aims at producing attractive and feasible bundles, such that all requirements of the BuGP are met. We achieve very good solution quality, while reducing the computational time for the auction procedure significantly. This is an important step towards running combinatorial auctions of real-world size, which were previously intractable due to their computational complexity. The strengths but also the limitations of the proposed approach are discussed.

The two-region multi-depot pickup and delivery problem.

Logistics networks are constantly evolving such that new and more varied structures arise and need to be studied. Carriers are aiming for opportunities to save costs by efficient planning. Motivated by this, we define the two-region multi-depot pickup and delivery problem. A region in this setting refers to an area where customers and depots are located. We differentiate two kinds of requests depending on whether their customers are located in the same region or not. Due to geographical characteristics, direct transportation between different regions is considered inefficient and a long-distance transportation mode needs to be used to connect them. Hence, we face a complex problem where interrelated decisions are to be made. We propose a decomposition into three subproblems, which relate to well-known problems in the literature. For solving the global problem, an adaptive large neighborhood search (ALNS) algorithm is developed. The algorithm mixes operators tailored to each of the different decisions of each subproblem. We demonstrate that these operators are efficient when applied to problems of their primal nature. In an extensive computational study, we show that the proposed ALNS dominates alternative ALNS schemes, where subproblems are treated sequentially. A detailed analysis of the solution convergence is provided. The proposed approach is a powerful tool to tackle complex decision problems in large distribution networks.