جستجوی مقالات مرتبط با کلیدواژه "data-driven approach" در نشریات گروه "صنایع"

This study addresses the Hub Location-Routing Problem (HLRP) in transportation networks, considering the inherent uncertainty in travel times between nodes. We employed a method centered on data-driven robust optimization, utilizing Support Vector Clustering (SVC) to form an uncertainty set grounded in empirical data. The proposed methodology is compared against traditional uncertainty sets, showcasing its superior performance in providing robust solutions. A comprehensive case study on a retail store's transportation network in Tehran is presented, demonstrating significant differences in hub locations, allocations, and vehicle routes between deterministic and robust models. The SVC-based model proves to be particularly effective, yielding substantially improved objective function values compared to polyhedral and box uncertainty sets. The study concludes by highlighting the practical significance of this research and suggesting future directions for advancing transportation network optimization under uncertainty.

This study addresses the Hub Location-Routing Problem (HLRP) in transportation networks, considering the inherent uncertainty in travel times between nodes. We employed a method centered on data-driven robust optimization, utilizing Support Vector Clustering (SVC) to form an uncertainty set grounded in empirical data. The proposed methodology is compared against traditional uncertainty sets, showcasing its superior performance in providing robust solutions. A comprehensive case study on a retail store's transportation network in Tehran is presented, demonstrating significant differences in hub locations, allocations, and vehicle routes between deterministic and robust models. The SVC-based model proves to be particularly effective, yielding substantially improved objective function values compared to polyhedral and box uncertainty sets. The study concludes by highlighting the practical significance of this research and suggesting future directions for advancing transportation network optimization under uncertainty.

Novel marketing theories that focus on service dominant approaches require to deeply consider customer specifications and needs within using products and services by customers. In this way, data driven approaches that focus on analyzing customer behavior are critically important to realize service dominant logic of marketing. Although previous studies have proposed different approaches to enhance dynamic and customer centric value propositions, there is not a comprehensive view on data-driven approaches that can be used within this context. The main research question that is addressed in this paper is "what are the data-driven approaches, concepts, and practical domains that are addressed for customer centric value propositions to enable service ecosystems to co-create value with customers”. To answer this research question, a systematic literature review is conducted. Based on the relevant evidence extracted from 124 papers, the approaches, core concepts, and key practical domains of customer centric value propositions are described. The paper aims to systematically bridge between prescriptive approaches and tools that have emerged in the field of data analytics and descriptive concepts that have introduced by novel marketing theories.