### Proposed Approach
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Article**} & \multirow{2}{*}{**Optimization**} & \multirow{2}{*}{**Decision**} & \multicolumn{3}{c}{**Objective**} & \multicolumn{3}{c}{**Storage Type**} & \multirow{2}{*}{**Data-**} \\  & & & **Variables** & & & **Holding** & **Shorage** & **Sustainability** & **Cold Storage** & **Wirehouse** & **Tanks** & **Driven** \\ \hline [PERSON] and Rajendran [19] & Bi & Installation base-stock levels & X & X & & & X & \\ [PERSON] [38] & Many & Order size and safety factor & X & X & & & X & \\ [PERSON] et al. [39] & Many & Order quantities & X & & & & X & \\ [PERSON] and Minner [31] & Single & Inventory levels, satisfied & X & & & X & & x \\  & & demands & & & & & X & \\ [PERSON] et al. [40] & Bi & Batch quantity and binary & & & & X & & \\  & & decision & & & & & X & \\ [PERSON] and [PERSON] [33] & Many & Reorder point and order & & & & & & \\  & & & & & & & & \\  & & & Cycle time, selling price, & & & & & \\ [PERSON] et al. [30] & Single & & preservation and environmental & & & & & & \\  & & emission cost, ordering cost per & & & & & & \\  & & cycle and per order & & & & & \\ [PERSON] et al. [32] & Bi & & Order quantity & & & & & \\ [PERSON] et al. [41] & Single & Cycle length, credit period, & & & & & & \\  & & production rate & & & & & & \\  & & Production run time, selling & & & & & \\ [PERSON] et al. [42] & Single & price, and two investment & & & & & & \\ [PERSON] and [PERSON] [27] & Bi & Binary routing decisions & & & & & & \\  & & & Routing decisions & & & & & \\  & & [PERSON] et al. [29] & Single & Routing decisions and order & & & & & \\  & & & quantities & & & & & \\ Proposed Approach & Many & Stored quantities & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of inventory management state-of-the-art research regarding the type of optimization used, the decision variables considered, whether or not holding, shortage, and sustainability are considered objectives; whether or not cold storage, general warehousing, or tanks are addressed and modelled; and whether or not the approach has any data-driven component. Publications are ordered according to publication date (older publications first). Publications classified as single-objective approaches but with multiple objectives simply mean that those objectives are considered in the objective function, e.g., by adding holding and shortage costs. different types, as the implication in terms of energy consumptions changes drastically. Finally, not many publications have data-driven considerations--however, with the advent of big data, it becomes easier and more necessary to include data as a driving aspect of optimization problems. This work is also within the scope of the advent Industry 5.0, as the pillars of this new evolution of industrial technology are based on sustainability, human centricity, and resilience [43]. To this end, this research is greatly centred on the pillars of Industry 5.0: human-centric, as the solution outputted by the many-objective optimization model requires a human decision-maker with field expertise; sustainable, as one of the objectives is the reduction in energy consumption; resilient, as the risk of shortage is also minimized, and the optimization is data-driven, based on the production schedule of the company. This study was inspired by the pharmaceutical industry. However, the methodology detailed ahead is perfectly applicable to any other industries with dynamic production schedules, as the balancing act of storing raw materials is a complex process that has to take into account many considerations. In this problem, a single warehouse, a single cold storage, and multiple tanks were considered; other industries may feature a different configuration of storage locations. Some may not require, e.g., cold storage, depending on the industry. These changes should be easily implemented as there is large flexibility to adapt to different companies.