## 2 Literature Review  
The existing research on the HFLP is mainly based on QAP (quadratic assignment problem) models and MIP (mixed integer problem) models. One of the earlier researches on the HFLP was proposed by [PERSON] [29] as a QAP model. [PERSON] and [PERSON] [30] proposed a QAP model to solve the problem of different department sizes. [PERSON] et al. [31] presented a QAP model that can be used for the overall facility layout of a large general hospital. [PERSON] and [PERSON] [4] applied process-mining technology to analyze the pathway of patients and discover the patient flows between departments. They developed a multi-objective model which took distance, design preference, and relocation cost into consideration to improve the efficiency of an emergency department. [PERSON] and [PERSON] [32] also applied a process-mining method and built a QAP model to find the optimal layout. QAP models are NP-hard and it is difficult to obtain optimal solutions by exact procedure when the problem scale is large. Therefore, some papers have developed different meta heuristics to solve large-scale QAP models. [PERSON] and [PERSON] [33] proposed a tabu search algorithm to solve the QAP model. [PERSON] et al. [34] integrated the whale algorithm with tabu search to solve the QAP model and determine the location of each department of a hospital. [PERSON] and [PERSON] [35] applied big data analysis to find out factors that have the greatest impact on the layout design. Then they used a hybrid firefly simulated annealing algorithm to solve the QAP model. [PERSON] et al. [36] used three different algorithms, namely migrating bird optimization, simulated annealing, and tabu search, to solve a large-scale hospital layout problem. Because of the discreteness of the solutions, QAP models can only be used for problems where all the potential locations are determined prior to the calculation. MIP models are used in a continuous layout representation that is an extension of the discrete QAP model [11]. However, research in this field is not abundant. In MIP models, departments can be located anywhere as long as all identified constraints are satisfied. In practice, many hospitals arrange their departments into rows [1; 24; 26; 27; 37; 38; 39]. Therefore, the most relative research in this area considers how to arrange departments into several rows more reasonably, so as to improve the efficiency of the hospital. [PERSON] and [PERSON] [24] addressed a multi-period layout planning problem of hospital wards, which aims at minimizing either the number of demand violations or costs for installing fixed patient rooms. In their model, the wards are arranged in two rows in each period. A series of layout problems of operating theaters was discussed by [PERSON] et al. [1; 2; 26; 27; 38]. In these researches, all considered departments were arranged into either two rows [1; 2; 26; 27] on one floor or three rows on several floors [38]. Among the above researches, no fixed facility is considered, and all floor spaces are available for planning layout. The models of [PERSON] et al. [37] and [PERSON] et al. [39] both considered the layout problem of emergency departments similar to ours, as they also arrange departments into rows while considering some fixed facilities. MOEA/D (multi-objective evolutionary algorithm based on decomposition) [37] and tabu search [39] are used to search for optimal solutions. However, their models and algorithms are based on a single-floor problem. Additionally, even in researches where some facilities are fixed they have little influence on the layout planning process. In this paper, fixed facilities play an important role and can be regarded as obstacles that limit the potential locations of the departments. There are previous studies regarding general double-row models, however, this paper is also different from them.