https://bura.brunel.ac.uk/handle/2438/32878 2026-08-21T14:28:05Z https://bura.brunel.ac.uk/handle/2438/33695 Title: Sustainable Steel Supply Chain Network Design under Export Taxes and Import Tariffs: A Multi-objective Stochastic Programming Framework Authors: Sahebjamnia, Navid; Verter, Vedat; Azadnia, Amir Hossein Abstract: This paper introduces a two-stage stochastic programming model featuring multi-objective recourse functions to address the economic, environmental, and social dimensions of sustainability in steel supply chain networks. The model integrates strategic decisions (facility location, capacity acquisition, and technology selection) with tactical material flow coordination. The dynamics of steel industry-specific characteristics are captured through multi-scale time periods, technology compatibility requirements, and sustainability metrics based on location-technology-flow configurations. Furthermore, the model incorporates export taxes and import tariffs to optimise material flows between nationwide and global networks, enabling comprehensive analysis of how these policy tools influence strategic and tactical decisions whilst impacting sustainability objectives. A solution approach is developed using the ε-constraint and Sample Average Approximation methods to address demand uncertainty and the trade-offs among multiple recourse objectives. Novel algorithms are introduced to determine the upper and lower bounds of the constrained objective functions and to form the loops of the ε-constraint method. The model's application to a real-world case study demonstrates its effectiveness in achieving balanced material flows across the steel SCN after four strategic periods. Through sustainability assessment and analysis of various tax and tariff policies, we derive eight policy insights regarding their impacts on strategic and tactical decisions. The results confirm the model's capability to generate robust solutions that effectively balance network capacity against demand patterns while achieving sustainability objectives. Description: Data availability: Data will be made available on request. 2026-07-24T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33624 Title: The online election campaign planning problem: Optimizing election campaign strategies with inaccurate information Authors: Shiri, Davood; Shahmanzari, Masoud; Tanrisever, Fehmi Abstract: Effective management of election campaigns involves dynamic decision-making under uncertainty. Traditional approaches rely heavily on pre-planned strategies that often fail to adapt to real-time changes in voter sentiment and external factors. This paper introduces the Online Election Campaign Planning Problem (OECPP) to optimize the scheduling of campaign activities in the context of U.S. presidential elections. OECPP incorporates sequentially updated predictions that represent assessments of the impact of campaign activities over the course of the campaign. Since these predictions evolve in response to new information and their accuracy cannot be fully assessed without perfect information, we develop deterministic and randomized online algorithms for OECPP that can operate effectively under unreliable and evolving predictions. We evaluate the performance of our algorithms using the competitive ratio (CR), a metric particularly useful when probabilistic modeling is impractical. We begin by establishing a tight upper bound on the CR of the online algorithms for the OECPP under unreliable reward predictions. We then introduce a sequential setup-based CR metric to capture the value of reoptimization as new predictions arrive, and we design deterministic and randomized algorithms that are optimal under this metric. Using data from U.S. presidential elections, we show that randomized online algorithms can significantly outperform their deterministic counterparts in terms of empirical CR. We also find that the effectiveness of randomized algorithms is driven by two factors: the selection of prediction samples for generating activity scenarios and the randomization cut-off, which determines the scenarios to be randomized. The benefit of randomization is non-monotonic, and the best empirical CR is achieved by selectively adding prediction samples to the randomization set. Description: Supplementary Material is available online at: https://journals.sagepub.com/doi/suppl/10.1177/10591478261438365/suppl_file/sj-pdf-1-pao-10.1177_10591478261438365.pdf . 2026-03-24T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33539 Title: Untangling indigenous leadership competences in sustainability challenged firms: A Sustainable Indigenous Network Leadership commitment toward emission mitigation in Bahrain energy industry Authors: AlGhanem, N; Braganza, A; Harrison, C Abstract: This study examines how indigenous leadership competences can be integrated into network leadership frameworks to support emission mitigation in Bahrain's energy sector. Given the lack of culturally aligned leadership models in sustainability-challenged firms, this research addresses a theoretical and practical gap. Drawing on qualitative data from eight firms, the study proposes a Sustainable Indigenous Network Leadership (SINLA) framework comprising four competence dimensions: socio-cultural, socio-political, socio-economic, and socio-knowledge. The findings reveal that embedding indigenous values into network leadership enhances organisational change capacity and supports organisational transformation addressing climate change. This contributes to leadership theory by expanding the applicability of network leadership to non-Western, emission-intensive contexts. Description: Data availability: Data will be made available on request. 2026-06-12T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33535 Title: The Economic Effects of Artificial Intelligence Adoption in Small and Medium-Sized Enterprises Authors: Bolfek, M; Rajko, M; Bolfek, B Abstract: Artificial intelligence is one of the key technologies of the Fourth Industrial Revolution and is increasingly significant for companies’ economic performance. Small and medium-sized enterprises (SMEs), the foundation of economic development in most national economies, face numerous challenges and opportunities in applying artificial intelligence in business. This paper aims to examine the economic effects of applying artificial intelligence in SMEs, with a special emphasis on labor productivity, business process efficiency, and reduced operating costs. Empirical research was conducted on a sample of 228 SMEs using a questionnaire, with the data analyzed using multiple linear regression. The research results show that different applications of artificial intelligence have a statistically significant, positive impact on labor productivity and on reducing operating costs. In contrast, their impact on business process efficiency is moderate and partially limited. The operational application of artificial intelligence, such as automation and data analysis, has proven to be the most important factor in economic effects. At the same time, its application in managerial decision-making also has a significant, but somewhat weaker impact. On the other hand, the mere growth of AI applications over time does not necessarily lead to increased efficiency without targeted and concrete implementation. The paper’s results contribute to understanding the role of AI in transforming SMEs and highlight the importance of targeted investments in operational and management applications of AI. The paper provides practical implications for entrepreneurs and economic policymakers in fostering sustainable, competitive development of SMEs. Description: Data Availability Statement: The data presented in this study are available from the corresponding author upon reasonable request. Data are not publicly available due to confidentiality and anonymity requirements.; JEL Classification: D24; O33 2026-06-18T00:00:00Z