## 2 Background  
Layout optimization is commonly seen and addressed in manufacturing industries, VLSI chip designs and chemical plants. Facility layout problems (FLP) relate to allocation problems with constraints and to solve these, heuristic and meta-heuristic algorithms are used. The layout optimization has been commonly modelled as a quadratic assignment problem (QAP) [8; 9]. The distance correlations between the facilities and locations are analyzed and to achieve this, branch and bound techniques are used. The optimization of the new layout differs from that of the existing layouts. The existing layouts have more constraints compared to that of the new layouts. Over a period of time, patient demand increases and also the specialties offered change. In such scenarios, OPCs should review their layouts for possible changes. Designing optimal layout can easily be implemented in the new OPC, in which the workflow dependencies and complexities can be addressed during design. As any change in layout results in additional costs, simulation is an important tool to identify and analyze the optimal layout. Simulation aids the study and analysis of different scenarios, also with the purpose cost and or quality of work and service in altering the layout [10; 11]. Efficient and effective planning during layout design is most important to achieve better performance [12]. Hospital/OPC buildings were shaped to fit as many patients as possible [13]. Evidence-based practices are used to cut down the travel time of staff and patients [14]. For example: centralized nurse stations surrounded by patient rooms will increase the efficiency. The authors in [15] show the effect of the changes in layout and process improvement on the performance of emergency room operations. Based on before and after implementation it is observedthat combining both structure-oriented and process-oriented strategies is beneficial in maximizing the performance. A parametric model of layout optimization is developed as a geometric approach that helps in handling the complexities that arise because of predefined rules and parameters and precedencies [16]. A clinical pathway mining approach was used in [17] where current pathways were collected. Based on this data, the future clinical pathways were predicted using a machine learning algorithm. A mathematical model and expert knowledge were used in hospital layout planning decisions. It is observed that layout problems are solved using methodologies such as exact procedure, heuristics and meta heuristics. Currently, meta-heuristics such as simulated annealing and genetic algorithm are extensively used. These solve large scale facility layout problems. Other approaches for layout optimization are neural network, fuzzy logic, ant colony optimization (ACO) and expert systems [9; 11; 18]. ACO is a combinatorial algorithm and it is good for discrete problems whereas genetic algorithm is good for continuous and discrete. Ant agent algorithm has been used in OPC layout optimization. The authors in [19] have modified the ant agent algorithm to incorporate the sequential flow constraints associated with the OPC. It provided an optimal layout design (single best solution) that considered the workflow constraints and improved the waiting time and cycle time. But, having only one solution to redesign the existing layout makes it difficult for implementing the changes suggested in the optimal solution. The layout provided by ant agent algorithm, however, was the best layout that improved the performance of the OPC, and it is suitable for new-buildings as the changes/modifications can be easily accommodated. The OPCs that already have constraints cannot adapt this solution/design. Therefore, for such situations multiple layout redesigns are needed in relation to whichever layout design suits the OPC that could be used. Evolutionary algorithms generate multiple solutions, optimal (best) and sub-optimal solutions (next/close to best, but not best) to the problems using the techniques that are inspired by natural evolution.