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8 results on '"Gansterer M"'

1. Locker box location planning under uncertainty in demand and capacity availability

Author

Mancini, S., Gansterer, M., Triki, C., Mancini, S., Gansterer, M., and Triki, C.

Abstract

In this paper, we address the location of locker boxes in the last-mile delivery context under uncertainty in demand and capacity. The problem is modelled as an extension of the capacitated facility location problem, in which a fixed number of facilities has to be opened, choosing among a set of potential locations. Facilities are characterized by a homogeneous capacity, but a capacity reduction may occur with a given probability. The uncertainty in demand and capacity is incorporated through a set of discrete scenarios. Each customer can be assigned only to compatible facilities, i.e., to facilities located within a given radius from the individual location. The goal is to first maximize the total number of customers assigned to locker boxes, while, in case of a tie on this primary objective, a secondary objective intervenes aiming at minimizing the average distance covered by customers to reach their assigned locker box. A stochastic mathematical model as well as three matheuristics are presented. We provide an extensive computational study in order to analyze the impact of different parameters on the complexity of the problem. The importance of considering uncertainty in input data is discussed through the usage of general stochastic indicators from the literature as well as of problem specific indicators. A real-world case related to the City of Turin in Italy is analyzed in detail. The benefit achievable by optimizing locker box locations is discussed and a comparison with the current configuration is provided.

Published
2023

3. Bundle generation for last-mile delivery with occasional drivers

Author

Mancini S., Gansterer M., Mancini S., and Gansterer M.

Subjects
Information Systems and Management, Last-mile delivery, Matheuristic, Occasional drivers, Routing, Strategy and Management, Settore MAT/09 - Ricerca Operativa, Management Science and Operations Research
Abstract

In this paper, we present the vehicle routing problem (VRP) with occasional drivers (OD) and order bundles (OB). The problem VRP-OD-OB is an extension of the VRP-OD, where instead of assigning one customer per driver, drivers are assigned bundles of customers. To deal with the bundle-to-driver assignment, a bidding system is exploited, in which a company offers a set of bundles and the drivers raise their bids. These bids depend on features such as the drivers’ destination, flexibility in deviating from the shortest path, and willingness to offer service. To generate valuable bundles of customers, we propose two strategies: (i) an innovative approach based on the creation of corridors, and (ii) a traditional approach based on clustering. Through an experimental study, carried out on randomly generated instances and on a real road network, we show that the innovative corridor-based approach strongly outperforms the clustering-based approach. Given a set of bundles and a corresponding set of bids, we provide a mathematical formulation and valid inequalities to solve the VRP-OD-OB. To address larger instances, we design an efficient large neighborhood search-based matheuristic. The results of an extensive computational study show that this method provides near-optimal solutions within very short run times. An analysis of the impact of drivers’ flexibility and willingness levels on the percentage of customers assigned to ODs is presented. Moreover, the case in which ODs dynamically appear at regular time intervals is investigated. Also in this dynamic setting, considerable total cost reductions are shown. Moreover, we derive several important managerial insights, which include the observation that it is not necessary to provide a high number of bundles to achieve good quality solutions. Companies should rather focus on generating fewer but more attractive bundles.

Published
2022

4. 1692P SARS-CoV-2 RNA testing in cancer patients treated at a Department of Medical Oncology in Vienna, Austria

Author

Berghoff, A.S., primary, Gansterer, M., additional, Bathke, A.C., additional, Trutschnig, W., additional, Hungerländer, P., additional, Berger, J.M., additional, Kreminger, J., additional, Starzer, A., additional, Schmidt, R., additional, Lamm, W., additional, Raderer, M., additional, Gottlieb, A.D., additional, Mauser, N.J., additional, and Preusser, M., additional

Published
2020
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5. The on-demand warehousing problem

Author

Sara Ceschia, Margaretha Gansterer, Simona Mancini, Antonella Meneghetti, Ceschia S, Gansterer M, Mancini S, and Meneghetti A.

Subjects
combinatorial optimisation, Sharing economy, demand–supply matching, warehouse, combinatorial optimisation, matheuristic, matheuristic, Strategy and Management, demand–supply matching, warehouse, Settore MAT/09 - Ricerca Operativa, Management Science and Operations Research, Sharing economy, Industrial and Manufacturing Engineering
Abstract

Warehouses are key elements of supply chain networks, and great attention is paid to increase their efficiency. Highly volatile space requirements are enablers of innovative resource sharing concepts, where warehouse capacities are traded on online platforms. In this context, our paper introduces the on-demand warehousing problem from the perspective of platform providers. The objective prioritises demand–supply matching with maximisation of the number of transactions. If there is a tie, the secondary objective maximises the number of suppliers matched with at least one customer and the number of customers that have matches within a specific threshold with respect to the minimum achievable cost. Besides the mathematical integer programming formulation, a myopic list-based heuristic and an efficient matheuristic approach are presented and benchmarked against the performance of a commercial optimisation solver. The impact of several parameters on the platform’s objective is analysed. A particularly relevant finding is that the pricing flexibility on the demand side does not necessarily imply higher payments to the supply side. All data instances are made available publicly to encourage more researchers to work on this timely and challenging topic.

Published
2022

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

Author

Wolfinger D, Gansterer M, Doerner KF, and Popper N

Abstract

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., (© 2021 The Author(s).)

Published
2023
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7. Enhanced SARS-CoV-2 breakthrough infections in patients with hematologic and solid cancers due to Omicron.

Author

Mair MJ, Mitterer M, Gattinger P, Berger JM, Trutschnig W, Bathke AC, Gansterer M, Berghoff AS, Laengle S, Gottmann L, Buratti T, Haslacher H, Lamm WW, Raderer M, Tobudic S, Fuereder T, Valenta R, Fong D, and Preusser M

Subjects
Antibodies, Viral, Humans, SARS-CoV-2, COVID-19 complications, Neoplasms complications
Abstract

Competing Interests: Declaration of interests A.S.B. has received research support from Daiichi Sankyo and Roche; honoraria for lectures; consultation or advisory board participation from Roche, Bristol-Meyers Squibb, Merck, and Daiichi Sankyo; and travel support from Roche, Amgen, and AbbVie. T.F. has received honoraria for lectures and consultation or advisory board participation from the for-profit companies Merck Sharp & Dohme (MSD), Merck Darmstadt, Roche, Bristol-Myers Squibb, Accord, Sanofi, and Boehringer Ingelheim as well as travel support from Roche, MSD, and Bristol-Myers Squibb. The following for-profit companies have supported clinical trials and contracted research conducted by T.F. with payments made to his institution: MSD, Merck Darmstadt, Bristol-Myers Squibb. R.V. has received research grants from HVD Life-Sciences, Vienna, Austria; WORG Pharmaceuticals, Hangzhou, China; and Viravaxx AG, Vienna, Austria. He serves as consultant for Viravaxx AG and WORG. M.P. has received honoraria for lectures and consultation or advisory board participation from the following for-profit companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group (GLG), CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, BMJ Journals, MedMedia, Astra Zeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo, Sanofi, Merck Sharp & Dome, and Tocagen. The following for-profit companies have supported clinical trials and contracted research conducted by M.P. with payments made to his institution: Boehringer-Ingelheim, Bristol-Myers Squibb, Roche, Daiichi Sankyo, Merck Sharp & Dome, Novocure, GlaxoSmithKline, and AbbVie. All other authors declare that they have no conflict of interest related to the present study.

Published
2022
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8. Assignment constraints in shared transportation services.

Author

Gansterer M, Hartl RF, and Wieser S

Abstract

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., (© The Author(s) 2020.)

Published
2021
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