1. A time-evolving optimization model for an intermodal distribution supply chain network : a case study at a healthcare companySara Johansson, My Westberg, 2016, master's thesis Abstract: Enticed by the promise of larger sales and better access to customers, consumer goods compa- nies (CGCs) are increasingly looking to evade traditional retailers and reach their customers directly–with direct-to-customer (DTC) policy. DTC trend has emerged to have major im- pact on logistics operations and distribution channels. It oers significant opportunities for CGCs and wholesale brands to better control their supply chain network by circumventing the middlemen or retailers. However, to do so, CGCs may need to develop their omni-channel strategies and fortify their supply chains parameters, such as fulfillment, inventory flow, and goods distribution. This may give rise to changes in the supply chain network at all strategic, tactical and operational levels.
Motivated by recent interests in DTC trend, this master thesis considers the time-evolving supply chain system of an international healthcare company with preordained configuration. The input is bottleneck part of the company’s distribution network and involves 20% ≠ 25% of its total market. A mixed-integer linear programming (MILP) multiperiod optimization model is developed aiming to make tactical decisions for designing the distribution network, or more specifically, for determining the best strategy for distributing the products from manufacturing plant to primary distribution center and/or regional distribution centers and from them to customers. The company has got one manufacturing site (Mfg), one primary distribution center (PDP) and three dierent regional distribution centers (RDPs) worldwide, and the customers can be supplied from dierent plants with various transportation modes on dierent costs and lead times. The company’s motivation is to investigate the possibility of reduction in distribution costs by in-time supplying most of their demand directly from the plants. The model selects the best option for each customer by making trade-os among criteria involving distribution costs and lead times. Due to the seasonal variability and to account the market fluctuability, the model considers the full time horizon of one year.
The model is analyzed and developed step by step, and its functionality is demonstrated by conducting experiments on the distribution network from our case study. In addition, the case study distribution network topology is utilized to create random instances with random parameters and the model is also evaluated on these instances. The computational experiments on instances show that the model finds good quality solutions, and demonstrate that significant cost reduction and modality improvement can be achieved in the distribution network. Using one-year actual data, it has been shown that the ratio of direct shipments could substantially improve. However, there may be many factors that can impact the results, such as short-term decisions at operational level (like scheduling) as well as demand fluctuability, taxes, business rules etc. Based on the results and managerial considerations, some possible extensions and final recommendations for distribution chain are oered.
Furthermore, an extensive sensitivity analysis is conducted to show the eect of the model’s parameters on its performance. The sensitivity analysis employs a set of data from our case study and randomly generated data to highlight certain features of the model and provide some insights regarding its behaviour.
Keywords: optimization, mixed-integer linear programming, supply chain, distribution network, sensitivity analysis
Published in RUNG: 14.04.2025; Views: 146; Downloads: 1
 Link to file
This document has many files! More... |
2. Designing the intermodal multiperiod transportation network of a logistic service provider company for container managementTobias Sahlin, 2016, master's thesis Abstract: Lured by the promise of bigger sales, companies are increasingly looking to raise the volume of international trade. Consequently, the amount of bulk products carried in containers and transported overseas exploded because of the flexibility and reliability of this type of transportation. However, minimizing the logistics costs arising from the container flow management across different terminals has emerged asa major problem that companies and affiliated third-party logistics firms face routinely.
The empty tankcontainer allocation problem occurs in the context of intermodal distribution systems management and transportation operations carried out by logistic service provider companies. This paper considers the time-evolving supply chain system of an international logistic service provider company that transports bulk products loaded in tank containers via road, rail and sea. In such system, unbalanced movements of loaded tank containers forces the company to reposition empty tank containers. The purpose of this paper is to develop a mathematical model that supports tactical decisions for flow management of empty tank containers. The problem involves dispatching empty tank containers of various types to the meet on-time delivery requirements and repositioning the other tank containers to storage facilities, depots and cleaning stations. To this aim, a mixed-integer linear programming (MILP) multiperiod optimization model is developed.
The model is analyzed and developed step by step, and its functionality is demonstrated by conducting experiments on the network from our case study problem, within the boarders of Europe. The case study constitutes three different scenarios of empty tank container allocation. The computational experiments show that the model finds good quality solutions, and demonstrate that cost and modality improvements can be achieved in the network The sensitivity analysis employs a set of data from our case study and randomly selected data to highlight certain features of the model and provide some insights regarding the model’s behavior.
Keywords: supply chain, distribution network, repositioning, intermodal transport, sensitivity analysis
Published in RUNG: 14.04.2025; Views: 142; Downloads: 0
Link to file
This document has many files! More... |
3. |
4. |
5. The ǂcircular economy : the ǂbutterfly diagram, systems theory and the economic pluriverseKeith R. Skene, Andreea Oarga-Mulec, 2024, original scientific article Keywords: earth system, non-linearity, emergence, regeneration, resilience, restoration, supply chain network
Published in RUNG: 12.08.2024; Views: 1492; Downloads: 5
 Full text (758,34 KB)
This document has many files! More... |
6. |
7. Investigations of a novel energy estimator using deep learning for the surface detector of the Pierre Auger ObservatoryFiona Ellwanger, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2023, published scientific conference contribution Abstract: Exploring physics at energies beyond the reach of human-built accelerators by studying cosmic rays requires an accurate reconstruction of their energy. At the highest energies, cosmic rays are indirectly measured by observing a shower of secondary particles produced by their interaction in the atmosphere. At the Pierre Auger Observatory, the energy of the primary particle is either
reconstructed from measurements of the emitted fluorescence light, produced when secondary particles travel through the atmosphere, or shower particles detected with the surface detector at the ground. The surface detector comprises a triangular grid of water-Cherenkov detectors that measure the shower footprint at the ground level. With deep learning, large simulation data sets can be used to train neural networks for reconstruction purposes.
In this work, we present an application of a neural network to estimate the energy of the primary particle from the surface detector data by exploiting the time structure of the particle footprint.
When evaluating the precision of the method on air shower simulations, we find the potential to significantly reduce the composition bias compared to methods based on fitting the lateral signal distribution. Furthermore, we investigate possible biases arising from systematic differences between simulations and data.
Keywords: ultra-high energy cosmic rays, Pierre Auger Observatory, surface detector, neural network
Published in RUNG: 22.01.2024; Views: 2191; Downloads: 5
Full text (1,78 MB)
This document has many files! More... |
8. Max-type reliability in uncertain post-disaster networks through the lens of sensitivity and stability analysisAhmad Hosseini, 2024, original scientific article Abstract: The functionality of infrastructures, particularly in densely populated areas, is greatly impacted by natural disasters, resulting in uncertain networks. Thus, it is important for crisis management professionals and computer-based systems for transportation networks (such as expert systems) to utilize trustworthy data and robust computational methodologies when addressing convoluted decision-making predicaments concerning the design of transportation networks and optimal routes. This study aims to evaluate the vulnerability of paths in post-disaster transportation networks, with the aim of facilitating rescue operations and ensuring the safe delivery of supplies to affected regions. To investigate the problem of links' tolerances in uncertain networks and the resiliency and reliability of paths, an uncertainty theory-based model that employs minmax optimization with a bottleneck objective function is used. The model addresses the uncertain maximum reliable paths problem, which takes into account uncertain risk variables associated with links. Rather than using conventional methods for calculating the deterministic tolerances of a single element in combinatorial optimization, this study introduces a generalization of stability analysis based on tolerances while the perturbations in a group of links are involved. The analysis defines set tolerances that specify the minimum and maximum values that a designated group of links could simultaneously fluctuate while maintaining the optimality of the max-type reliable paths. The study shows that set tolerances can be considered as well-defined and proposes computational methods to calculate or bound such quantities - which were previously unresearched and difficult to measure. The model and methods are demonstrated to be both theoretically and numerically efficient by applying them to four subnetworks from our case study. In conclusion, this study provides a comprehensive approach to addressing uncertainty in reliability problems in networks, with potential applications in various fields.
Keywords: Disaster Management, Network Reliability, Stability Analysis, Transportation, Uncertainty
Published in RUNG: 24.11.2023; Views: 2182; Downloads: 9
Link to file
This document has many files! More... |
9. Search for EeV photon-induced events at the Telescope ArrayI. Kharuk, R. U. Abbasi, Y. Abe, T. Abu-Zayyad, M. Allen, Yasuhiko Arai, R. Arimura, E. Barcikowski, J. W. Belz, Douglas R. Bergman, 2023, published scientific conference contribution Abstract: We report on the updated results on the search for photon-like-induced events in the data, collected by Telescope Array's Surface Detectors during the last 14 years. In order to search for photon-like-induced events, we trained a neural network on Monte-Carlo simulated data to distinguish between the proton-induced and photon-induced air showers. Both reconstructed composition-sensitive parameters and raw signals registered by the Surface Detectors are used as input data for the neural network. The classification threshold was optimized to provide the strongest possible constraint on the photons' flux.
Keywords: Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, photons, neural network, machine learning
Published in RUNG: 09.10.2023; Views: 2573; Downloads: 9
Full text (543,46 KB)
This document has many files! More... |
10. Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural NetworksJ.M. Carceller, Andrej Filipčič, Jon Paul Lundquist, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2022, published scientific conference contribution Abstract: We present a method based on the use of Recurrent Neural Networks to
extract the muon component from the time traces registered with
water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre
Auger Observatory. The design of the WCDs does not allow to separate the
contribution of muons to the time traces obtained from the WCDs from those of
photons, electrons and positrons for all events. Separating the muon and
electromagnetic components is crucial for the determination of the nature of
the primary cosmic rays and properties of the hadronic interactions at
ultra-high energies.
We trained a neural network to extract the muon and the
electromagnetic components from the WCD traces using a large set
of simulated air showers, with around 450 000 simulated events.
For training and evaluating the performance of the neural network,
simulated events with energies between 10^18.5 eV and 10^20 eV
and zenith angles below 60 degrees were used. We also study the
performance of this method on experimental data of the Pierre
Auger Observatory and show that our predicted muon lateral
distributions agree with the parameterizations obtained by the
AGASA collaboration.
Keywords: Pierre Auger Observatory, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, muons, machine learning, recurrent neural network
Published in RUNG: 04.10.2023; Views: 2642; Downloads: 9
Full text (1,08 MB)
This document has many files! More... |
