Szczegóły publikacji
Opis bibliograficzny
Integrating simulation and optimization: a case study in Pamplona for self-collection delivery points network design / Irene Izco, Adrian Serrano-Hernandez, Bartosz SAWIK, Javier Faulin // W: Proceedings of the 35th European Modeling & Simulation Symposium, EMSS [Dokument elektroniczny] : 20th international multidisciplinary Modeling & simulation multiconference : Athens, Greece, Septmeber 18–20, 2023 / eds. Michael Affenzeller, [et al.]. — Wersja do Windows. — Dane tekstowe. — [Athens : Cal-Tek], cop. 2023. — (The ...European modeling & simulation symposium ; ISSN 2724-0029). — e-ISBN: 978-88-85741-87-4. — S. [1–6]. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://www.cal-tek.eu/proceedings/i3m/2023/emss/030/pdf.pdf [2023-09-19]. — Bibliogr. s. [5–6], Abstr. — B. Sawik - dod. afiliacja: Public University of Navarra, Spain
Autorzy (4)
- Izco Irene
- Serrano-Hernandez Adrian
- AGHSawik Bartosz
- Faulin Javier
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 148690 |
|---|---|
| Data dodania do BaDAP | 2023-10-02 |
| DOI | 10.46354/i3m.2023.emss.030 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | The ...European modeling & simulation symposium |
Abstract
The disruptions experienced by the processes in the last mile delivery during the SARS-CoV-2 pandemic raised the dilemma of up-to-date last mile approaches for Urban Logistics (UL) issues. Self-Collection Delivery Systems (SCDS) have been proved to be an improvement for all the players of the SC, providing flexibility of time-windows and reducing overall mileage, delivery time and, consequently, gas emissions. Differing from previous works involving hybrid modeling for automated parcel lockers (APL) network design, this paper brings a System Dynamics Simulation Model (SDSM) to forecast online shopping demand in the Spanish city of Pamplona. A bi-criteria Facility Location Problem (FLP) is solved by means of an ε-constraint method, where ε is defined as the level of coverage of the total demand. The experiment run considers 90% of demand coverage, in order to obtain the most complex network possible. The simulation and demand forecast was carried out using Anylogic simulation software and the optimization procedure makes use of the Java-based CPLEX API solver.
