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Augmenting staffing strategies using mathematical programming

As a response to the call for papers at the Machine Learning Developers Summit 2023, organized by Analytics India Magazine where among the overwhelming 376 submissions we landed in the top 10%. The purpose of the Machine Learning Developers Summit is to promote research in artificial intelligence (AI) and encourages work that cuts across technical areas in the context of important application domains, such as healthcare, sustainability, transportation, and commerce. 

In this paper, we describe our evaluation of alternate organizational policies on payroll cost to optimise retail staff in the most efficient way without sacrificing quality of service. 

Preparing an efficient and effective staffing roster is a challenging task for retailers. An efficient staffing schedule optimises payroll costs while meeting desired customer service levels. Doing this, while complying with labour laws and organizational policies makes the schedule effective. While compliance with legal requirements is mandatory, evaluating the impact of alternate organizational policies helps formulate a suitable staffing strategy. These policies present themselves in a combination of factors around shift duration, standardized shift start times, number and length of breaks during the day, off-days, etc.  

After submitting the paper, we were invited for a speaking slot and got published in the ML journal Lattice by the Association of Data Scientists – a global professional body of data science & machine learning professionals. 

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By addressing inefficiencies via optimisation, businesses can overcome the challenge of relying on manual processes and inefficient systems. This leads to improved resource utilisation, reduced waste, and increased productivity, and the ability to maintain a profitable business