# A Simulation-Based Optimization Method for Warehouse Worker Assignment
## 2 Literature Review  
Customer satisfaction via effective resource utilization, the shipment of the right product in good condition and within the target shipment time, is the key objective of warehousing [13, 14, 15, 16]. Warehousing aims to address the differences in time and space between suppliers and customers, while adapting to the fluctuating market conditions [17]. Warehouses execute a broad range of process-based functions, including temporary storage, protecting goods, service support in customer order fulfillment, goods packaging, after sales service support, quality inspections, testing, assembly, and repairs [18, 19]. In the current global economic environment, the warehouses face an unprecedented level of competitive and economic pressures [20], including a high level of uncertainty and risk of supply chain disruptions due to the COVID-19 pandemic [21, 22]. The pressures from competitors and customers result in the reduction of profit margins [23, 24]. Under this situation, efficient resource utilization becomes more critical than ever. Humans are the central and most critical elements of the resource base in warehousing [25]. Due to the high fluctuations (both predictable and unexpected) in workload demand [18], research has advocated the need for the implementation of manpower planning strategies [20, 26]. For example, [PERSON] et al. [27] distinguishes three phases of manpower planning, which include the prediction of manpower demand, the prediction of the future supply of manpower, and reconciliation of the discrepancies between supply and demand via workforce scheduling and staffing. Consideringthe above, workforce scheduling--and specifically personnel work assignments that deal with the allocation of personnel to tasks and work stations--is crucial to warehouse operational efficiency [6; 28; 29]. Optimal staffing, or allocation of workers to tasks, is the key to tackling the challenges of high demand fluctuations on a daily basis. In the warehouse, this problem is dependent on workers; qualifications, i.e., skill sets, which are very specific to each employee. The most relevant problem to our topic is the mixed-model assembly line balancing problem (MALBP), which tries to assign the tasks of different models to the workstations. It is called a mixed-model as multiple models are assembled on the same assembly line. As summarized in [PERSON] et al. [8], there are three types of MALBP problems, categorized according to the constraint and objective:  
1. MALBP-I: given the cycle time to minimize the number of workstations;
2. MALBP-II: given the number of workstations to minimize the cycle time;
3. MALBP-III: minimize both the cycle time and the number of workstations. However, our target is to maximize the service level, i.e., the ratio between the number of completed tasks and the number of arriving tasks, given a fixed number of workers. Our research problem is different from the above-mentioned three problems by [PERSON] et al. [8], except for the MALBP-II which can be considered the most relevant to our problem. The major points that differentiate our problem are:  
* In our problem, each worker is only capable of performing a subset of operations. * The processing time of each task is stochastic. * The objective is to maximize the service level. In [PERSON] et al. [30], a machine deployment problem is considered, where the objective is to minimize the cost of setting up all the machines, while satisfying the precedence and space constraints. This involves also assignments of the machines to the tasks, and a GA-based optimization approach was designed to identify a set of best solutions. The machines in [PERSON] et al. 's [30] model can be considered as workers in our model. However, in our problem the number of workers is fixed, while the objective is to maximize the service level. In contrast, in [PERSON] et al. 's [30] model the number of machines in different stages (tasks) are independent. Simulation-based optimization has been used for a number of decades for problem solving in logistics.