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1.1 Introduction

With the rapidly developing global economy, today’s companies face greater challenges than ever to employ manufacturing systems capable of dealing with unexpected events and meeting customers’ specific requirements. In overcoming these challenges, flexibility is the key concept in the development of manufacturing systems. The issue of flexibility in manufacturing systems is not new and has attracted significant attention by researchers since the development of flexible manufacturing systems (FMS) four decades ago. The introduction of FMS in industry has provided significant potential benefit both for reducing production time and for responding to unpredictable market demands (Udhayakumar and Kumanan 2010; Joseph and Sridharan 2011).

FMS are typically classified into two main subsets: flexible assembly systems (FAS) and flexible machining systems (FMS) (Browne et al. 1984; Maccarthy and Liu 1993). Much of the research work has focused on FMS, whereas FAS have received less attention from researchers. Both FAS and FMS can be generally described as computer integrated manufacturing systems. Nevertheless, they differ in several respects (Lee et al. 2006): first, the number of different tasks performed in FAS is much more than that in FMS. In FAS, several components are jointed at the same time, while FMS involve operating only one part at a time; second, the processing time of each operation in FAS is much less than the time required in FMS. Therefore, the ratio of the transfer time to the processing time in FAS is high compared with that in FMS; third, the material handling system in FAS is more complicated in comparison with that in FMS. These differences make the decision problems in FAS more difficult compared with those in FMS. The purpose of this introductory chapter is:

  • To describe flexible assembly systems (FAS) and the two main types of FAS, robotic assembly line (RAL) and robotic assembly cell (RAC), and to highlight the advantageous features of the RAC.

  • To briefly outline the important issues in the design, planning, scheduling and controlling of RAC, and then clarify the significance of studying the scheduling problems in RAC.

  • To summarize the motivations that led to this research.

  • To identify the scope of the research and the main aim of this thesis.

1.2 Flexible Assembly Systems

A flexible assembly system (FAS) is a completely integrated system which consists of a number of stations that can assemble different part types and are connected by an automated material handling system and controlled by a central computer (Sawik 1998; Zhanga et al. 2005). The main components of FAS can be classified into two types: robots and peripheral equipment. These components are presented in the following sub-sections.

1.2.1 Robots

Robots are considered as crucial components in the assembly systems (Levitin et al. 2006). In general, robots fetch the assembled parts and move them from one station to another. Different types of assembly robots have different efficiencies and capabilities for the various elements of assembly tasks (Gao et al. 2009). Assembly robots can be categorized into two main types: Selective Compliant Assembly Robot Arm (SCARA) and Articulated Robots (Groover 2008).

SCARA is considered as one of the most popular robots for automatic assembly. A SCARA is rigid in the Z-axis and has full range of motion on its X and Y axes. This robot design gives four axes freedom of movement, as shown in Fig. 1.1. SCARA has high speed for vertical assembly and is suitable for assembling small parts such as electromechanical components (Taylan and Canan 2005; Salman et al. 2009) in one set direction (along Z axis).

Fig. 1.1
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Four-axis SCARA robots

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Articulated robots or industrial robots have six axes and consequently more freedom of movement, as shown in Fig. 1.2. For this reason, articulated robots are the key solution for improved flexibility and increased productivity in industrial systems (Pan et al. 2012). Due to the increasing trend of using this robot, recent research has been devoted to making articulated robots easy to use (Qi et al. 2008; Zhang and Qi 2008).

Fig. 1.2
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Six-axis articulated robot

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1.2.2 Peripheral Equipment

FAS generally consist of different peripheral equipment for assembly operations. Peripheral equipment can be divided into five essential types (Sawik 1999; Delchambre 1992):

  1. (1)

    Assembly stations where the parts are assembled.

  2. (2)

    A gripper changing station where the grippers are changed.

  3. (3)

    Material handling devices such as input conveyors for supplying the base parts, and output conveyors for conveying out a final product when assembly processes are completed.

  4. (4)

    Storage areas such as tables for subassemblies and part feeders for supplying parts to the cells.

  5. (5)

    Accessories for assembly operations such as tools for executing a fastening process, grippers for transferring and positioning the parts or subassemblies, and fixtures for holding the components in an assembly station during construction of a product.

1.3 Classification of Flexible Assembly Systems

The different FAS can be categorized according to many characteristics such as material flow configuration, system layout, capacity and capability of assembly machines related to large volume/limited variety and limited volume/large variety production (Sawik 2004; Rosati et al. 2013). FAS can be divided into two main types (Sawik 1999): robotic assembly line (RAL) and robotic assembly cells (RAC). These types and their characteristics are explained in the following sub-sections.

1.3.1 Robotic Assembly Line

A robotic assembly line (RAL) is a flow type system consisting of a series of special purpose robotic assembly stations connected by an automated material handling system, as shown in Fig. 1.3. A RAL is used for high volume/low variety assembly of a few products that have stable designs and demand requirements (Levitin et al. 2006; Daoud et al. 2014; Sawik 1999). A RAL can be compared with a conventional transfer line; it uses special purpose machines, and hence has the ability to achieve high productivity (Yoosefelahi et al. 2012; Gao et al. 2009; Bukchin and Tzur 2000).

Fig. 1.3
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Robotic assembly line simulation model (Cheng 2000)

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1.3.2 Robotic Assembly Cell

Robotic assembly cell (RAC) is a highly modern system, structured with industrial robot(s), assembly stations and an automated material handling system, all monitored by computer numerical control (Manivannan 1993; Marian et al. 2003). RAC are capable of assembling a large variety of products in small to medium batch sizes (Mohamed et al. 2001).

The design of RAC with multi-robots has three key advantages for industrial companies. First, the RAC layout with multi-robots is very flexible and combines the productivity of product-flow layout with the flexibility of process-based layout. In the RAC, the multi-robots are used for the rapid transfer of parts and partial assemblies between highly productive assembly stations. The second advantage comes from the need of the assembly process for robots with different characteristics such as end effectors, payload, repeatability, degrees of freedom and accuracy. The third advantage is the ability of robots to employ end effectors as fixtures to allow reduction of complex orienting, because the assembly processes of products could require more than one direction of part insertion. With these advantages, employing multi-robots in the assembly cells obviously allows for increased flexibility and reconfiguration capacity (Gilbert et al. 1990) (Fig. 1.4).

Fig. 1.4
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Robotic flexible assembly cell made by ABB software (ABB 2013)

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1.3.3 Simple Comparison Between RAL and RAC

Based on the characteristics of the RAL and RAC, as shown in Table 1.1, RAC can be considered to have higher dexterity and flexibility than RAL due to the following reasons: (1) In RAC, the sequence of the assembly process is unconstrained, while in RAL, only one way of assembling the product is possible; (2) RAC are easier to modify and reconfigure and also may need less space compared with RAL; (3) RAC are more adaptable to assembling a variety of products using the same resources; (4) In RAC, the equipment and machines are multi-purpose, whilst in RAL equipment is dedicated to the specific products. For the above reasons, it can be concluded that RAC have a higher degree of flexibility than RAL. Hence, this research will focus on the RAC. The acronym (RFAC) for Robotic Flexible Assembly Cell will be used in the rest of this research, as used in other relevant research.

Table 1.1 The difference between RAL and RAC (Makino 1989)
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1.4 Scheduling of Robotic Flexible Assembly Cell

Flexible assembly systems are typically fully integrated production systems. They consist of more than one robot and much peripheral equipment. For this reason, the use of such a system is extremely complex. In order to employ FAS as effectively as possible due to their complexity and the high cost of the robots, the four main stages of decision making (design, planning, scheduling and control) in the FAS should work effectively (Kazerooni 1997; Udhayakumar and Kumanan 2012). Taking into account the time horizon in flexible assembly systems, the four stages can be redescribed as three interconnected decision levels (Stecke 1985; Schneeweiss 1995), as shown in Fig. 1.5 and explained below:

Fig. 1.5
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Decision levels in FASs

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  • FAS design problems involve layout design, assembly machines and material handling selection, and product design for automated assembly. They also involve assembly planning (Bukchin and Tzur 2000; Park et al. 2001; Nakase et al. 2002). These problems generally have long-term implications (Schneeweiss 1995).

  • FAS planning problems are medium-term problems including resource allocation, and machine loading (Schneeweiss 1995; Sawik 1999). The planning problems also include assembly planning which is associated with the sequence planning to be done in order to assemble the final products (Rosell 2004).

  • FAS scheduling and control are major short-term problems. The objective of scheduling problems is to address the detailed sequencing of all the assembly tasks that are required to assemble a product (Sawik 2004). The control problems are considered as part of the production planning and scheduling (Kazerooni 1997; Chan et al. 2002). The objective of control problems is to monitor the system performance and decide whether or not the system status needs corrective actions (Schneeweiss 1995; Sawik 1999).

Clossen and Malstrom (1982) stated that “hundreds of robots and millions of dollars’ worth of computer-controlled equipment are worthless if they are under-utilized or if they spend their time working on the wrong part because of poor planning and scheduling”. Additionally, many researchers confirm that scheduling problems play a crucial role in determining the system’s performance compared with design and planning problems (Joseph and Sridharan 2011; Udhayakumar and Kumanan 2012; Burnwal and Deb 2013). Consequently, this research has mainly focused on the scheduling problems of robotic flexible assembly cells (RFAC), which are considered one type of FAS.

1.5 Motivation for Research in Scheduling of RFAC

The design of RFAC with multi-robots leads to increased productivity in a shorter cycle time and with lower production costs. However, there are certain difficulties that have arisen with this design concept. For example, a system with more than one robot operating simultaneously in the same work environment requires complex scheduling and control to prevent collisions between robots, and also to prevent deadlock problems (Lee and Lee 2002). Moreover, industrial robots must be employed as effectively as possible due to the high cost of the robots. Thus, the first obvious motivation of this research is to propose a new solution to the scheduling problems in RFAC in order to overcome the above difficulties and improve the system’s performance.

Due to the flexibility of RFAC and the resulting advantages, such as increased productivity with shorter cycle time, decreased labor and production costs, and increased assembling flexibility (Sect. 1.3.3), the second focus of this research has been the ability of these cells to not only assemble one type of product, but also to be adapted to assemble new products and more than one product at a time, using the same hardware, without the need for reconfiguration of the cell layout (Marian et al. 2003). This adaptation has been achieved using group technology rules when the resources of the system deal with similar parts that have the same geometrical and physical characteristics.

In real industrial situations, manufacturing systems are dynamic, due to facing unexpected events such as order cancellation, arrival of urgent orders, due date change and temporary unavailability of tools or materials. These dynamic events may cause deviations from the generated schedules, and the schedule plan may become impractical to implement when it is released to the system (Ouelhadj and Petrovic 2009). Another important motivation was to study the dynamic job shop scheduling problems of RFAC. The dynamic scheduling of RFAC to assemble more than one product is a relatively unexplored problem which is more complicated compared with static scheduling. In summary, robotic flexible assembly cell (RFAC) is a new and promising concept, which require expensive investment. In existing RFAC, scheduling decisions are the vital issues in trying to improve system utilization.

1.6 Research Gap and Scope

As mentioned earlier in this chapter, employing multi-robots in RFAC offers many advantages over RAL such as reduced surface of robotic cell, increased productivity in a shorter cycle time with lower production costs, and the unique possibility to assemble a variety of products simultaneously using the same resources. On the other hand, two robots operating simultaneously in the same workplace need a complex system to prevent collisions between them (see Appendix A). Therefore, a sophisticated scheduling system is required.

Studies on how to use the RFAC more effectively to assemble products remain limited. In the existing literature of RFAC, three vital limitations were identified. First, scheduling of RFAC in just a single-product assembly environment was considered. Second, scheduling of RFAC only in a static situation was investigated, without considering dynamic status, which reflects real world problems. Third, scheduling of RFAC only in single-objective optimization problems was examined in order to improve the RFAC performance. Therefore, the primary goal of this research is to cover the above research gaps by proposing new strategies that will allow decision-makers to model, simulate and optimize the scheduling of RFAC in complex environments in the most effective way. In order to achieve this goal, the scope of this thesis is divided into the following four main tasks:

  • To critically review the relevant literature regarding the use of different approaches for scheduling problems in RFAC, and then highlight the major limitations that must be considered when developing a new approach (Chap. 2).

  • To develop a new methodology for the static scheduling problems in RFAC based on a combination of advanced solution approaches such as simulation modelling with an artificial intelligence technique (Chap. 3). Subsequently, the developed methodology will be applied via a scenario-based case study of RFAC to demonstrate the effectiveness of this methodology (Chap. 4).

  • To expand the developed methodology by considering the dynamic scheduling problems of RFAC. In this task, the important factors which influence the scheduling of RFAC will be examined. The applicability of the proposed solution will be demonstrated via a realistic case study. Then statistical analysis tools will be applied to determine the most significant factors which affect the system performance (Chap. 5).

  • To develop an optimization approach to deal with multi-objective problems for the dynamic scheduling of RFAC. In this approach, a hybrid intelligent technique will be used in order to deal with the problems which arise when the information is uncertain and ambiguous (Chap. 6). The developed approach will be verified and validated, using a realistic case study and results will be analyzed (Chap. 7).

1.7 Concluding Remarks

In this chapter, the characteristic features of the robotic flexible assembly cell (RFAC) were described and it was shown that this type of assembly system has a higher degree of dexterity and flexibility than the robotic assembly line (RAL), due to the following main reasons: the sequence of the assembly process to produce a product in RFAC is unconstrained; the design of RFAC is easy to modify and also may waste less space compared with RAL; the equipment and machines are multi-purpose. Nevertheless, the main problem of the RFAC is that more than one robot operating simultaneously in the same workplace needs a complex scheduling and control system to prevent collisions between them. To overcome the fundamental problem in RFAC, a sophisticated scheduling approach is required to guarantee higher system utilisation and ensure that the robots will move without collision.

Due to the complexity of the scheduling problems in RFAC, the procedure of the developed approach will be divided into the following four steps: (1) identifying the current research limitations for scheduling problems in RFAC; (2) developing a new optimization procedure to handle the complexity of scheduling in RFAC; (3) scheduling RFAC in a dynamic situation, which takes into consideration the significant factors influencing the system utilisation; (4) scheduling RFAC in multi-objective optimization problems in order to fully handle the uncertainty and imprecision in real world problems.