# Towards Productive and Ergonomic Order Picking: Multi-Objective Modeling Approach
## 1 Introduction
### Manual Man-to-Goods Order Picking  
Over the last decade, we have witnessed the growth of e-commerce driven by digitalization, and more recently, a pandemic. Accordingly, the need for picking items listed on customers' orders from locations in storage racks is growing and has become omnipresent.  
This activity is in 80% of warehouse environments performed manually by order pickers [23; 24]. Most warehouses and distribution centers use a man-to-goods picking model according to which order pickers travel around the warehouse and collect items sequentially. The costs of these processes are relatively high. Some authors [25; 26] report that they can account for more than 50% of warehouse operating costs. Today, order picking is mainly recognized as a cost, not adding value for the final customers. The key checked daily performance indicator for order picking is the cost per line ordered. This mindset is reflected in the managerial treatment of order picking, where concern for efficiency and searching for new workers to replace the absentees and those with burnout are at the forefront. With rising labor costs, an aging population, increasing sick leave costs and increasing efforts to motivate for quality work, the focus from the economy is slowly shifting to caring for the physical health of employees.  
The research stream in the field of order picking optimization is mainly concerned with time optimization. It is generally acknowledged that the amount of time spent depends mainly on factors related to the work environment (job factors) and factors related to the individual worker (order picker's factors). Figure 1 highlights the most frequently mentioned factors, which, however, also affect energy expenditure and health risk. The use of time optimization benefits in the increase in picks per time unit, as the same time order pickers spend more energy and repeat movements more frequently. Time optimization without built-in restrictions leads to workers' burnout and/or MSDs occurrence. Energy consumption must not exceed the permanent capacity limit or the load at which even at 8 h of exposure, no harmful conditions occur. No postures should represent a high health risk for MSDs occurrence.  
The turnaround occurs in 2016, when [9] integrated energy expenditure into the bi-objective optimization model in addition to time [27]. New scientific contributions [28; 29] in solving the storage assignment problem in the last five years propose, justify, and take into account order picking time and additionally also energy expenditure and health risk. Adding energy expenditure and health risk results from the fact that they are dependent on many interrelated factors occurring during order picking. On the other hand, they are relatively measurable and resultant. The number of published scientific works including only order picking time in modeling leads before works combining order picking time and energy expenditure, which, however, are also no longer rare. Since 2016, a growing trend of scientific contributions that have added health risk to order picking time and energy expenditure in decision support models for storage assignment is emerging.