http://dspace.iimk.ac.in:80/xmlui/handle/2259/1184 2026-09-15T07:19:12Z http://dspace.iimk.ac.in:80/xmlui/handle/2259/1193 Reliable and flexible supplier selection problem: a genetic algorithm inspired simulation approach Ram Kumar, P.N; Sridharan, R.; Anish, M.N.; Vishnu, C.R.; Sangeeth P. Das Strategic decision making in a supply chain commences with a proper sourcing plan. Disruptions in the supply side will certainly inflict cascading effects throughout the chain. To minimise this vulnerability, the procurement system should be incorporated with strategic risk mitigation capabilities like reliability and flexibility. However, the financial implications for improving risk management capabilities and the absence of a suitable decision support system to handle the inherent computational complexities restrained enterprises from upgrading their systems. The present research intends to meet this requirement by proposing a novel multi-objective mathematical model to prepare a procurement plan that optimises reliability and flexibility together with different cost components. Since the proposed model contains nonlinear constraints and mixed-integer variables, we present a simulation-based optimisation methodology inspired by genetic algorithm to solve the model. An illustrative problem is solved, and the managerial implications are discussed to exhibit the scope of the proposed model. International Journal of Logistics Systems and Management- pp 185-209, Ram Kumar P.n (Quantitative Methods and Operations Management, Indian Institute of Management Kozhikode, IIMK Campus P.O., Calicut-673570, Kerala, India), R. Sridharan (Department of Mechanical Engineering, National Institute of Technology Calicut, NIT Campus PO, Calicut-673601, Kerala, India), M.N. Anish (Department of Instrumentation and Control Engineering, NSS College of Engineering, Palakkad, NSS College Rdk, NSS Nagar, Akathethara, Palakkad-678008, Kerala, India), C.R. Vishnu (Production and Operations Management Division, Xavier Institute of Management & Entrepreneurship, Kochi, HMT PO, Kochi-683503, Kerala, India), Sangeeth P. Das (Department of Mechanical Engineering, National Institute of Technology Calicut, NIT Campus PO, Calicut-673601, Kerala, India) DOI: https://doi.org/10.1504/IJLSM.2024.136487 2024-02-05T00:00:00Z http://dspace.iimk.ac.in:80/xmlui/handle/2259/1186 Intelligent crop management system for improving yield in maize production: evidence from India Jinil Persis; Sakshi Vishnoi Purpose – Managing weeds and pests in cropland is one of the major concerns in agriculture that greatly affects the quantity and quality of the produce. While the success of preventing potential weeds and pests is not guaranteed, early detection and diagnosis help manage them effectively to ensure crops’ growth and health Design/methodology/approach – We propose a diagnostic framework for crop management with automatic weed and pest detection and identification in maize crops using residual neural networks. We train two models, one for weed detection with a labeled image dataset of maize and commonly occurring weed plants, and another for leaf disease detection using a labeled image dataset of healthy and infected maize leaves. The global and local explanations of image classification are obtained and presented Findings – Weed and disease detection and identification can be accurately performed using deep-learning neural networks. Weed detection is accurate up to 97%, and disease detection up to 95% is made on average and the results are presented. Further, using this crop management system, we can detect the presence of weeds and pests in the maize crop early, and the annual yield of the maize crop can potentially increase by 90% theoretically with suitable control actions Practical implications –The proposed diagnostic models can be further used on farms to monitor the health of maize crops. Images obtained from drones and robots can be fed to these models, which can then automatically detect and identify weed and disease attacks on maize farms. This offers early diagnosis, which enables necessary treatment and control of crops at the early stages without affecting the yield of the maize crop Social implications – The proposed crop management framework allows treatment and control of weeds and pests only in the affected regions of the farms and hence minimizes the use of harmful pesticides and herbicides and their related health effects on consumers and farmers. Originality/value – This study presents an integrated weed and disease diagnostic framework, which is scarcely reported in the literature International Journal of Productivity and Performance Management-pp. 3319-3334,Jinil Persis (Indian Institute of Management Kozhikode, Kozhikode, India), Sakshi Vishnoi (Indian Institute of Management Mumbai, Mumbai, India) 2024-04-01T00:00:00Z