International Journal of Advanced Operations Management (5 papers in press)

Regular Issues

• Mathematical modelling and nature-inspired metaheuristics for solving the football team selection problem  
  by Soukaina Laabadi, Mohamed Nezar Abourraja 
  Abstract: The football team selection (FTS) problem consists of selecting performant players from a squad under given constraints to form an optimal team. Many relevant solutions have been developed; however, the literature review revealed that metaheuristic-based solutions are scarce despite their well-known effectiveness in solving selection problems. To fill this gap, first, this paper develops a 0/1 linear programming model incorporating financial limitations, age considerations, and injury status as constraints. Second, to solve it, CPLEX optimisation tool and two nature-inspired algorithms are employed, namely the binary particle swarm optimisation (BPSO) and genetic algorithms (GAs). Then, experiments are conducted using data from the 2022 FIFA World Cup, specifically focusing on the big four. The results demonstrate that both BPSO and GAs yield promising outcomes that outperform those of CPLEX, closely aligning with real-world data, particularly when considering team performance as a key factor in achieving victory. The proposed approaches offer valuable insights into optimising FTS; ultimately it can be generalised to other multi-player sports.
  Keywords: football; player selection; team formation; 0/1 linear programming; genetic algorithms; binary particle swarm optimisation; BPSO.
  DOI: 10.1504/IJAOM.2025.10072025
   
  
• An adaptive pandemic inventory model for time-constrained goods with income and price-sensitive demand with neutrosophic uncertainty  
  by Suchitra Pattnaik, Jitendra Kaushik, Mitali Madhusmita Nayak 
  Abstract: This paper presents an economic order quantity model considering income-dependent end-item demand and cost-price-dependent input product supply. The COVID-19 pandemic has heightened consumer awareness of budget constraints when making purchases. In this context, we focus on demand based on customers' income and unit sale price. Additionally, the materials in question are assumed to have a time-varying degradation rate with a maximum lifespan. We develop a precise model to minimise average inventory costs. This model is classified into three fuzzy environments: general, intuitionistic, and neutrosophic. We utilise the signed distance process to defuzzify the demand and supply rates as triangular neutrosophic quantities. The neutrosophic optimum order quantity and overall cost are derived from these. Additionally, we compare the numerical results obtained under various conditions. Finally, the model is validated through sensitivity analysis and graphical representation.
  Keywords: degradation; neutrosophic fuzzy; NF; pandemic; inventory.
  DOI: 10.1504/IJAOM.2025.10072385
   
  
• A two-warehouse inventory model of deteriorating products with preservation technology investment and carbon emission  
  by Nipa Biswas, Shubham Priyadarshi, Om Prakash 
  Abstract: This paper presents a two-warehouse inventory model for deteriorating items with time-dependent nonlinear demand and preservation technology. Investing in preservation technology helps reduce the deterioration effects. This model considers that inventory holding, ordering, and deterioration activities contribute to carbon emissions, which are regulated through the implementation of a carbon tax policy. Shortages are assumed to be fully backlogged. The objective of this model is to maximise overall profit by determining the best ordering strategy and replenishment time. An algorithm is presented to find the optimal solutions. Some numerical examples are provided to support the established model, and a sensitivity analysis is conducted on the key parameters to examine the impact on the decision variables. The proposed approach is further illustrated through a graphical depiction of the optimal solution. The findings show that reducing carbon emissions from holding, transporting, and deteriorating items boosts the retailer's order quantity and total profit.
  Keywords: inventory; time-dependent demand; preservation technology; carbon emission.
  DOI: 10.1504/IJAOM.2025.10072251
   
  
• Analysis of drivers enabling integration of big data analytics with Industry 4.0 in automotive component manufacturing scenario  
  by Vigneshvaran Regupathy, S. Vinodh 
  Abstract: Industry 4.0 (I4.0) is associated with technical advancement; yet numerous I4.0 technologies rely significantly on data and interdependence for their operation. The data is accessible in multiple formats; businesses that effectively utilise this data can fully leverage the benefits of Industry 4.0 manufacturing. Big data and I4.0 technologies are interdependent. The interdependent nature of big data renders its integration a more complex and involved procedure. The main objective of this study is to identify the enablers of big data analytics within I4.0 context. The complex characteristics of big data necessitated the utilisation of a multi-criteria decision-making approach to evaluate and prioritise the enablers according to their significance. Sixteen enablers are identified and evaluated with fuzzy TOPSIS approach. Key enablers such as data management, enhanced technical infrastructure, and real-time processing are identified as the most significant facilitators for the integration of big data in the I4.0 production environment.
  Keywords: drivers; enablers; big data analytics; integration; Industry 4.0; fuzzy TOPSIS; internet of things; IoT; cyber physical system; CPS.
  DOI: 10.1504/IJAOM.2025.10072400
   
  
• Eco-packaging of the millet foods: unveiling consumer willingness to pay for sustainable solutions through perceived values and environmental concern  
  by Uttam Kaur, Prashant Kumar Siddhey 
  Abstract: This study investigates the factors influencing consumers' willingness to pay a premium for eco-friendly packaged products, specifically focusing on millet-based food items. Despite increasing environmental awareness, the higher cost of sustainable packaging remains a barrier for many consumers, and limited research has explored the underlying drivers of purchase intentions in this context. To bridge this gap, a survey was conducted with 365 consumers of eco-packaged products. Using partial least squares structural equation modelling (PLS-SEM), the study analysed the impact of perceived value, environmental concern, and willingness to pay on consumers' purchase intentions. The findings reveal that consumers are more likely to invest in eco-packaged products when they perceive high value, exhibit strong environmental concern, and show readiness to pay extra for sustainable options. These insights offer valuable implications for marketers and policymakers promoting sustainable consumption.
  Keywords: eco-packaging; consumers; millet food; perceived values; willingness to pay; WTP; PLS-SEM; environmental concern; premium price.
  DOI: 10.1504/IJAOM.2025.10072313