## 1 Introduction  
Evolutionary algorithms (EAs) are used to yield better approximate solutions to complex problems. Finding exact solutions is often difficult, and sub-optimal solutions may be proven better. EAs are applied in evacuation planning, disaster management, decision making, etc. Genetic algorithms (GA) is the most popular among EAs and is greatly used in optimization problems. It is a search algorithm that is based on natural selection where the fittest solution would survive. Such algorithms are robust, simple and powerful. It is observed from literature that these algorithms are extensively used in layout optimization problems.  
Outpatient clinics (OPCs) are systems that require a lot of continuous optimizations. To improve the OPC's performance, parameters such as waiting times, cycle times and resource utilization need to be optimized. For this, OPCs should plan, schedule and control patients and resources effectively [1; 2; 3]. Generally, an OPC is a collection of different service-providing departments (physical locations) that are put together. The way the rooms are allocated to the departments (referred as OPC layouts) affects the patient workflows and also the operations management/workflow management, either positively or negatively. It is observed from literature and practice that layout optimization is a complex problem and has been addressed in settings such as facility layouts, chemical plants, nuclear reactors and manufacturing plants [4]. When these kinds of layouts are compared with OPC layouts, OPCs stand apart as there are several constraints (like variability and uncertainty in patient demand and workflow pathways) involved in the design of OPC layouts.  
Outpatient clinics have many processes/services that are interconnected and interrelated, making an OPC a complex network. The locations of the services are based on functional structure (organized depending on the type of work and not on the care pathway or disease) as patients are required to move from one location to another in order to complete their treatment processes. This travelling of patients increases their waiting time and the cycle time. Generally, layout designs are strategic decisions that are, or should be, evaluated every 7-10 years. The layout problem can also be viewed as a capacity planning problem [5]. Over a period of time, demand increases, its mix changes and offered services change. With the addition of multiple specialties, the resources get shared between them. Also, there could be a few changes that are based on short-term requirements. However, the buildings remain in use even after these changes, and this may result in congestion and chaos in workflows, eventually leading to compromise in efficiency and experience quality. There is a relationship between operations management and OPC design [6].  
It is at the operational level that the layout influences workflow management and the quality of healthcare services [7]. Therefore, the OPC layout should be designed carefully so that it can adapt to the future changes in the workflows. As the layout design is a strategic decision with a long-term impact, it cannot be very easily adapted to the changing scenarios because it involves high cost. It is a challenging task to design or redesign the OPC layout when it is associated with uncertainties about future patient demand and related technologies. The goal of this study is to redesign a layout of an OPC that is adaptable in future to the changing patient demand and their mixes and aide in decision making. In this study, the layout redesigns are obtained with the use of a genetic algorithm, and layouts obtained are compared based on waiting times.  
This paper is organized as follows: Section 2 presents the background of the study, Section 3 presents the materials and methods--data collection and analysis, model development and validation, and experimental designs followed by the problem formulation. Section 4 compares the results and discussion of both the algorithms. Section 5 presents the conclusion.