Robotic Cell Scheduling Problems and Their Solution Procedures: A Survey and Future Research Directions

Introduction

Now a day’s incorporation of automation and repetitive processing in manufacturing systems is very much essential to fulfill increasing market requirements. Due to this, modern manufacturing systems consist of human workers and robots and automated material handling systems to produce products efficiently. Many industries apply automated material handling systems for transporting materials through various stages. One of the most common forms of automated material handling systems is the implementation of robotic cells in modern manufacturing systems.

A robotic cell contains single or multi robots to convey the parts within the cell, an input buffer, several processing stages, and an output buffer. Each stage consists of single or multiple machines to complete the processing of that stage. Suppose the robotic cell has only one robot which can handle one part at once, one machine per stage, and no buffer between two adjacent stages for intermediate storage. In that case, the configuration of that robotic cell is treated as the default configuration. As in such robotic cells, a machine can process a part at once; therefore, these can be considered a flowshop with blocking. In robotic cells, all the processes are carried out by computer-controlled machines; hence, there is no involvement of a human-tended workstation. In most cases, the robotic cells have one of the two following layouts: circular (see figure 1) and linear (see figure 2). For circular robotic cells, the machines are placed in a circular path, while in a linear layout, the robots are placed in a straight line.

Figure 1.

Circular layout robotic cell

Figure 2.

Inline robotic cell layout

Today several industries use robots to move parts conveniently from one stage to another. According to Browne et al. (1996), in industries, robots are installed to (i) achieve consistent quality, (ii) compensate shortage of skilled labor, (iii) substitute labor working in hazardous environments, (iv) provide production system with more flexibility (v) increase production rate, and (vi) reduce labor cost. From the previous literature, it has been seen that a maximum number of authors addressed the application of robotic cells in semiconductor manufacturing industries. In addition, some studies reported other industrial implementations of robotic cells, including engine block manufacturing (Shang, 1988; Matsuo et al., 1991), textile mills, crane scheduling, testing and inspection of mainframe computer boards (Armstrong et al.,1986), electroplating of printed circuit boards (Lei & Wang, 1994; Chen et al., 1998; Che et al., 2002) and truck differential assemblies (Asfahl, 1992). Despite such industrial applications, robotic cells are also used for fiber-optics manufacturing and molding battery components (Dawande et al., 2005). Moreover, robotic cells have now found their utility in the medical field, especially for producing testing components in relevant biological screens (Rudge, 1997), automation in pharmacy compounding (Dawande et al., 2005), and making components of magnetic resonance imaging systems (Dawande et al., 2005).