automated assembly scheduling: a review

Computer Integrated Manufacturing Systems 1994 7 (1) 514~1

Automated assembly scheduling: A review

DAVID LITTLE and ANDREW HEMMINGS

Abstract: This paper is concerned with the scheduling of" automated assembly systems, and is based on a brief survey of current industrial practice and work undertaken within one of the case study sites. It begins with a review of the categories of assembly systems found in industry, and examines the main characteristics of each main type before going on to examine approaches adopted for scheduling and the role of simulation as a scheduling tool. Two short case studies are then presented, one concerning an engine plant and the other a plant manufacturing axles. These studies emphasize the difficulties associated with the automated assembly of large and relatively complex products, and the practical problems associated with assembly scheduling within an AMT environment.

Keywords: automated assembly, scheduling, assembly scheduling

s ince the 1960s when plant capacity finally caught up with industrial and consumer demand, all manufacturing companies have been fighting to

improve their competitive position. This has usually been achieved by productivity increases arising from reduced manning levels made possible by automation, and by associated quality improvements brought about by improved design and processing.

The 1970s were characterized for many companies by the automation of material control systems. Managers worked on the development and implementation of material requirements planning (MRP) software and, later, on manufacturing resource planning (MRPII) in an attempt to improve material availability, reduce inventory and improve machine and labour utilization 1 . These attempts were, in the main, successful, and today few plants have survived wi'thout some form of complex manufacturing control system.

Overlapping these developments were significant advancements in the automation of the design and processing of products which led to computer aided design (CAD) and computer aided manufacture (CAM). These advances culminated in the installation of a large number of flexible manufacturing systems (FMS) around the world in the 1980s 2. Although many

Department of Industrial Studies, University of Liverpool, PO Box 147, Liverpool L69 3BX, UK

0951-5240/94/010051-11 (~) 1994

Vol 7 No 1 February 1994

of these systems met with mixed success, the lessons, learned have been valuable and are now being carried over to the automation of assembly.

A recent CBI report, Competing with the World's Best, indicates that despite this recent heavy emphasis upon the automation of design, processing and material control, Britain's industrial productivity over the last ten years has not changed much in relative terms. The report gives a lag of 45% with the US and 30% with Germany; figures not significantly different from those established by Pratten and Atkinson in 1976.

The current trend is towards lean production with its emphasis upon fast new product introduction based upon a simultaneous engineering approach. This approach is typified by the car industry, as illustrated by Womack, Jones and Roos 3. Lean production allows greater product variety with an inevitably shorter product life cycle, and forces manufacturing industry concerned with volume production to switch from the mass production of a limited range of products to lower volume production of a wider range.

Shorter product life cycles and greater product variety have placed emphasis upon 'time to market' with its requirement for flexible processes which respond quickly to product changes. To an extent, this requirement has been met by the automation of drafting and design through computer-aided design, but has not impacted adequately upon the reduction of the complete design to manufacture process. Similarly, the shopfloor automation which has taken place, and has brought both flexibility and process control relates mainly to manufacture and does not include the assembly process.

That the focus of many companies should now turn to assembly is hardly surprising when review of many products reveals an assembly contribution of up to 53% of total production time and 22% of labour cost 4. This high proportion of assembly has only recently received the same scrutiny as design and machining systems, and is being viewed as having the potential to provide considerable productivity improvement.

A strategy for the automation of assembly based upon a similar approach to that successfully applied in

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the introduction of advanced manufacturing technology to the shop floor is now required to give a company the requisite competitive advantage.

Categories of assembly systems

In the design of modern assembly systems flexibility is viewed as an important criterion in the search for improved plant effectiveness. Indeed, increased flexibility is of such value that the higher capital cost of automated flexible assembly systems (FAS) can now be amortized over successive product generations.

Potential benefits from assembly automation are not restricted to increased productivity and reduced cost but also include the possibility of automated inspection and improved quality. In addition, in parallel with the experience of FMS, the opportunity exists to automate many ancillary tasks such as materials handling, transport and storage. Many such benefits provide in- tangible as well as tangible advantages.

Trends in design methodology, including design for assembly (DFA) and modular product design have greatly assisted the automation of the assembly process. In high volume production, assembly automation has now moved from modification of the inflexible conventional single model assembly line into the complex mixed-model assembly line, often accompanied by complex line-balancing problems due to differences between products. In this way, as the flexibility of assembly increases so do the problems of line scheduling.

Initially these adapted mixed-model assembly lines were limited to narrow product families based on a standard item with a small range of optional features and where the demand is relatively high and predict- able 5. The first industry move in this direction was the motor industry where conventional assembly lines were modified to cope with an expanded product range produced in smaller product volumes - the first move towards lean production.

In high volume batch industries, for example the manufacture of small domestic appliances and car components, the design of systems has been towards cellular manufacture. Here the products are usually completed within the cell. Products are less complex and work balancing is more easily achieved than in assembly lines because of operator flexibility. Auto- mated cellular assembly has developed systems with a number of workstations. Each will be equipped with a robotic arm or mechanical assembly device, to give the flexibility to complete a range of tasks. These workstations are usually linked by conventional materials handling conveyors and parts feeders. This can allow a degree of routing flexibility between each workstation but with increased scheduling difficulty as the penalty.

Assembly systems can be divided into two main groups, flow lines where the product moves along the line and the components are incrementally added and flexible cells where in-cell movement is restricted or non-existent. These two groups can be broken down

further to categorize the main assembly systems. These main categories and their characteristics are set out below:

• Dedicated flow line Single machine workstations dedicated to a specific product

• Mixed model flow line Single machine workstations with limited flexibility within the product group

• Flexible flow line Multiple workstations with flexibility within product group

• Multi-stage cell Multiple workstations with fixed tasks but with operational flexibility matched to product range

• Multi-station cell Multiple assembly workstations each with some product and operation flexibility. Stations linked for flexible transfer

• Single station cell Single workstation with tool changing capability. Complete product and operational flexibility.

Assembly flow lines

The categories above have been explained below. In the first two categories of flow line, there is no routing flexibility whilst in the third case some degree of routing flexibility is available.

Dedicated flowlines A flow line when each workstation receives jobs in the same sequence and carries out identical operations on all parts. Where the workstations can be adapted to perform those operations on different product types (usually within the same product family), the flow line becomes a mixed-model flow line. An additional complexity with the mixed-model flow line is the additional material control necessitated where different components are required to be fed to the assembly process for different products.

Dedicated flow lines are inflexible because the only routing flexibility is via the omission of one or more operations. Workstations are generally connected by a stepped conveyor programmed to release a job from a workstation only when the downstream workstation signals that it is ready to receive it. Jobs can only enter such systems when the first workstation is empty.

Because of the high degree of repetitivity, scheduling is not a problem with dedicated flow lines. See Figure 1 for a generic layout of a flow line.

Mixed model f l ow lines Finding a sequence for production that optimizes the utilization of each workstation without causing over- load at some point during a working shift provides the main difficulty in operating a mixed-model assembly line. The use of labour flexibility is commonly used to overcome such problems, even though this is a form of overmanning of the line. The excess capacity to cope with peak demands caused by the least optimal sequence can be considerable 6. Line scheduling becomes important in the resolution of this issue.

A similar effect often arises where an optimal work sequence has to be quickly altered to accommodate

52 Computer Integrated Manufacturing Systems

Workstation 1 Workstation 2 Workstation ,t

,n , o u t

Conveyor

; L__ Parts Buffer Parts Buffer Parts Buffer

Figure 1. Generic flow line layout

parts delays or machine breakdown. In the mixed- model flow line additional problems result from the need to re-sequence sub-assemblies and components in line with the adjusted schedule.

Flexible flow line The flexible flow line is a more recent development of the flow line which utilizes workstations equipped with a number of identical assembly machines. These machines are identical in terms of speed and capability, and can perform a range of operations on a variety of products.

These workstations are connected by a non- synchronous materials handling system that permits some degree of routing flexibility between the assembly machines at each workstation, as shown in Figure 2. In more complex versions, the routings allow a particular workstation to be skipped or revisited. The non- synchronous transfer necessitates workpiece buffering between workstations to maintain a satisfactorily high degree of machine utilization.

The need to balance the work content at each work station is an important requirement of the assembly flow line. A workstation can only release work if the next input buffer has a free position available, if this is not the case, the work must be held. This phenomenon is known as 'blocking'. The converse, when a downstream workcentre is idle because of lack of work from upstream, is known as 'starvation'. Additional equipment and expense is required for the control logic necessary for such sequencing.

D LrrrLE AND A HEMM1NGS

Flexible assembly cells

In this category, the assembly cell consists of several automated assembly stations, linked by automatic material handling devices. The ability to process different subassemblies simultaneously gives the system its flexibility. These subassemblies are processed at different workstations until they are brought together at some later stage for assembly. This reduces change- over times between operations. Small to medium-sized products are produced in assembly cells of this type where volumes are medium and products are assembled on a mixed-model basis.

Cell configurations can have different assembly characteristics. Some cells have multiple workstations that can carry out all, or a subset of, the required assembly operations on a variety of parts, as shown in Figure 3.

Different configurations operate akin to the typical flexible manufacturing cell, with assembly as a multi- stage process where parts move in a prescribed route through the different workstations for assembly (see Figure 4). Multi-stage cells use a common buffer with the workstations arranged so that pick-up and deposit areas are in reach of a single transfer robot.

It is a logical step from this configuration to a cell where provision is made for automated end-effector exchange, as illustrated in Figure 5. End effector exchange increases flexibility considerably allowing more complex assembly tasks. Here, the assembly processing time is likely to be of the order of a few seconds so that cell fast control systems are required.

All systems must be capable of storing the multiple program sets required for the complete range of products assembled in the cell and have the facility to cope with frequent new product introduction and modifications to existing products.

Scheduling of assembly systems

As highlighted earlier, as assembly capability becomes more flexible and the assembly task more complex, in general the complexity and importance of effective scheduling to process productivity increases. The scheduling issues that apply to all types of manufacturing situation also apply to the scheduling of

Workstation 1

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Figure 2. Flexible flow line

Part or Parts Kit Part or Parts Kit

Vol 7 No I February 1994 53

Automated assembly scheduling." A review

Cell Conveyors

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Figure 3. Flexible assembly cell having multiple workstations

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Figure 4. Multi-stage assembly cell

automated assembly systems. In simple terms these may be considered as the process of deciding the operations to be performed, their sequence and on which workstations the assembly should take place.

To develop an effective schedule one needs to know:

• the operational sequence • the constraints upon different assembly sequences • the cycle time for all operations • the move times between workstations • the capacities of all workstations.

For simpler flow lines, schedules are rate based and rely upon the sequencing of input to the line. Line balancing is important here. With more complex lines and cells, schedules tend to be based upon a 'pull' orientation such that schedules move through the product structure. The scheduling process starts with

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F-q Workstation °ut I

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Figure 5. Flexible assembly cell with automatic end- effector exchange

component assembly to meet the requirements of the sub-assembly schedule which, in turn, has been established to support the subsequent assembly of end-items to meet the master production schedule (MPS). Where the product mix is wide, because of the volumes involved, it is likely that material requirements planning (MRP) will be used to breakdown the sub-assembly and component requirements from the assembly pro- gramme.

For a modern plant, final assembly governs both plant output (and thereby cash flow) and work in process (WIP). The machine shop is increasingly being viewed as a feeder shop to assembly, with assembly as the 'customer'. Therefore the effective scheduling of the assembly system is critical to overall manufacturing performance. Poor final assembly schedules do not meet customer delivery dates, and lead to low throughput and high WIP.

The ability to handle frequent engineering changes quickly is an additional measure of scheduling effectiveness. Fast change handling allows for responsiveness to customer demands and increases the competitiveness of the plant and 'reactivity' is now becoming an important scheduling criterion as the trend is towards a decrease in batch size and an increase in product variety.

Effective scheduling is more difficult in a fully automated assembly environment than in plants where human intervention is expected to compensate to overcome scheduling errors or to increase assembly optimization. This is seen as a major limit to the full automation of the assembly of complex products with high product variety. Such assembly places very high demands upon parts flow, component recognition and scheduling algorithms and is judged to be beyond current capability, this is mainly restricted to the assembly groups of medium complexity parts.


Measurement of schedule performance There are four primary objectives which are commonly used to improve the performance of any assembly system and each supports the effectiveness of the assembly scheduling procedure:

• Maximization of workcentre utilization • Minimization of assembly cycle time • Achievement of customer due dates • Increased flexibility of the assembly system.

The first objective leads to decreased assembly cost. The second objective, by the minimization of cycle time, contributes to the maximization of throughput and the minimization of inventory. The third supports the effectiveness of the scheduling procedure by the avoidance of late job completion. This also preserves cash flow and controls WlP. The fourth objective allows scheduling to encompass a greater range of products and increases the ability of the assembly system to meet the requirements of successive generations of products.

A means of assessing schedule performance against such objectives is an important tool for improving the effectiveness of assembly scheduling. Simulation models are often used to evaluate the performance of specific configurations. However, care needs to be exercised in the selection of objectives for a given assembly system. Objectives may be mutually exclusive, high utilization and due date achievement for example, and some prioritization of objectives may become necessary for a specific configuration.

Where the volume of items is high and the variety is low, minimization of assembly cost is frequently the key issue and workstation utilization must be monitored. Conversely, where product variety is high and volumes are not, the flexibility of the system is critical and the overall reduction of assembly time is more important than the utilization of a particular workstation - flexibility demands spare capacity. This phenomenon is closely identified with Just-in-Time systems.

Scheduling for mixed model flow lines The main issues for mixed model flow line scheduling are the sequencing of the launch of assemblies into the system and the synchronization of component and subassembly feed to the line. Inventory is minimized by pulling products through the assembly system in a just- in-time fashion so that parts are drawn from a pre- ceding process, n, by a subsequent process, n + 1. To avoid delay, process n must have sufficient capacity and resource availability to supply the parts requirement of process n + 1 at all times.

Process n must therefore be capable of meeting the maximum variation in either time interval between withdrawals or the quantity of parts withdrawn. Vari- ation is minimized by smoothing the production of each model over the duration of the production run and fixing either the launch time interval between each

D LITTLE AND A HEMMINGS

model produced, or the quantity produced per hour. This enables production to respond to changes in customer demand by simply adjusting the unit cycle time without altering the lot size for each process.

This prompt and timely production of the various product types demands short product lead times. This, in turn, requires production systems having the flexibility to produce the full product range, with set up times that can be minimized or preferably eliminated. Optimizing the launch sequence for mixed model production is difficult if a constant withdrawal interval and quantity are required. Either the total assembly time for each process on the line shold be levelled (this is more applicable to paced assembly lines) or the rate of usage of every part should be kept constant.

These issues are addressed by two goal chasing heuristics developed by the Toyota Motor Corpora- tion 7. Algorithms and heuristics have also been developed by Miltenburg 8 to optimise production lot size. These algorithms schedule the final stage of multi- stage production to minimize variation between the actual production and the required production in terms of product mix and units produced (component usage rate variability, as identified in the Goal Chasing 2 heuristic). This is considered to be of more importance than levelling the assembly time.

Sumichrast and Russell 9 evaluate the two Toyota Goal Chasing heuristics and Miltenburg's heuristic based on their ability to minimize the mean absolute deviation from uniform production of each model.

Scheduling for flexible flow lines This is more complicated than for mixed model flow lines to optimize a given performance criterion because of the greater routing flexibility required. Because of differences in processing times, the order in which jobs reach subsequent stations can differ from their original input sequence. Poor scheduling can result in the line stopping because of blocking, where downstream assembly must wait for an upstream machine to become available or starvation, where upstream assembly must wait for downstream machines to supply jobs.

In this category, scheduling needs to consider the five main variables:

• Part launch sequence to the first workstation • Which job to allocate when a machine becomes

available • Parts routing to individual machines in each machine

bank • WIP control to prevent either blocking or starvation • Part entry & transfer timing from butter to machine.

A static 'local search approach' proposed by Kochhar et al. m relies on a simulation of the flow line to generate production schedules. These are based on optimizing the entry point sequence, whilst controlling part entry and transfer timing by a system using vacant buffer positions as kanbans.

Gantt charts for the multiple entry point sequences

Vol 7 No 1 February 1994 55


are generated and the costs associated with each solution evaluated. From this the optimum solution can be selected. A dynamic scheduling algorithm (for use on-line) has been developed by Wittrock ~1 This considers the variables in terms of three sub-problems each of which is solved heuristically:

1. Machine allocation, which determines which parts will visit each individual machine in each machine bank.

2. Entry sequencing. 3. Timing, which determines the times at which parts

enter the line.

Machine allocation minimizes the bottleneck workload by minimizing the maximum workload for each machine in each machine bank. Parts are then sequenced to achieve minimum makespan without any queuing. The algorithm proceeds by computing an initial set of loading times, computing the resulting schedule and then modifying the loading times to improve the schedule.

An important consideration is the implication of component size on flexible flow line scheduling. This can be a physical constraint both on buffer configuration and on assembly parts kit delivery control, particularly where large subassemblies are involved (Yano, Lee and Srinivasan6).

Parts kit delivery is best simplified by strictly main- taining the original input sequence at each buffer and workstation, as the sequence of kits arriving to their storage banks would match the jobs sequence. How- ever this can result in unacceptably low utilization because of blocking and starvation.

Processing parts on a First-Come-First-Served (FCFS) basis or using a scheduling algorithm, requires the matching of jobs to their respective parts kits. This requires some form of real-time updating to inform the kit delivery system of the sequence and times at which jobs arrive at workstations. Higher throughputs are achieved, but need relatively large kit banks if considerable kit expediting is to be avoided.

Resequencing jobs into their original input sequence between stations facilitates kit delivery, but necessitates random access to the input buffers. Such buffers are more expensive in terms of equipment space and control software than sequential access buffers, thus there is a trade off between the capacity required to resequence parts and that required to resequence jobs.

Many of the above problems illustrate the complexity of scheduling for fully automated assembly by flexible flow lines and indicate why much plant scheduling using this format still requires operator involvement.

Schedul ing o f f lexible assembly cell systems The effective utilization of flexible assembly cells (FAC) requires methods for scheduling both workstations and assemblies within the cell, controlling the handling or transportation device, and monitoring

system performance. These methods need to be considered in conjunction with assembly system characteristics in that:

1. Workstation designs are usually complex and customer specific rather than off the shelf.

2. Short processing/assembly times at a workstation can put considerable demands on the material handling/transport systems and on the FAC control system itself.

3. Assembly involves the joining of 2 or more components and can present a large number of part handling/delivery options, further complicating control system design.

In addition there are the main system characteristics that need consideration:

• the FAC consists of a number of workstations • the FAC assembles a number of products each

requiring one or more operations on each of its parts • operations may be performed in one or more

possible sequences • a specific operation performed at any one station

occupies that workstation for a given period of time • each workstation can only carry out one operation at

a time • parts can only be processed by one station at a time.

The problem is to develop a schedule that will assign parts to workstations so that completed products are produced, whilst meeting the required system objectives for delivery and throughput. Off-line solutions are inappropriate for FAC because of the unpredictability of the environment (due to, amongst other things, machine breakdowns, variability in processing times and material shortages) and the relatively short cycle times involved.

Schedul ing o f multi-stage assembly cells Work within a multi-stage assembly cell generally follows a pre-set route through the different workstations. Where cycle times are short, the ratio of transfer to assembly time can be relatively high. Cell performance in such circumstances can, therefore, be restricted by the transfer sequence due to speed limitations of the transfer device. This, when combined with the unpredictability of the environment, requires systems having on-line control with fast decision- making capabilities.

Suliman et al.~2 developed a scheduling procedure that considers cell performance and decision lead-time. This is based on a part sequencing approach that controls three elements:

• Part entry - which part to launch into the cell • Part allocation - which part to allocate to an idle

workstation • Transfer sequencing - which transfer request to

execute first.


The part having the highest ratio of operation time remaining to be completed outside the cell compared to its original requirement at the start of the production period is given entry priority. Work is then allocated to workstations according to user specified priority rules for resolving part and workstation operations conflict. These allow the user to select a set of rules that satisfy a selected performance strategy. The transfer function then generates all possible loaded sequences which are then prioritised using the shortest time rule for the unloaded movements of the robot or other transfer mechanism.

Scheduling of cells with multiple independent workstations A paper by Donath et al. 13 considers the scheduling of a cell comprising four identical assembly stations, each capable of performing all of the required operations on a variety of parts. Although Donath considered manual assembly stations, the same reasoning applies to automated assembly. The flexibility of the system lies in its capability to produce different products simultaneously using different operation sequences.

Two important problems concerning the scheduling process are generated by this that need to be solved. The first is that of the operations sequence, the determination of which requires a complete breakdown of all operations that can be performed next for each stage of the assembly process. The second problem is that of determining which station should execute a particular operation. Mathematically, modelling the system as a static problem (to minimise makespan) solved periodically for near real time control is too complicated. It is concluded that an heuristical approach is required. This solves the problem by treating it as three sub-problems: determining the operations sequence that leads to the product comple- tions; assignment of part to workstations; sequencing of parts waiting to be processed by the same workstation. This becomes inconsequential if job queues are limited to zero or one. The heuristic dispatching and routing procedure determines a sequence of part/operation pairs for subsequent execution by each workstation 14.

A trigger event initiates a set of part/operation/ workplace assignment decisions for all available stations and all parts with unstarted operations. The 'cost' of each part/operation/workstation assignment is determined, based on a user specified performance measure, and the assignment set that minimizes cost is selected.

The method therefore provides a near real-time decision-making capability for the scheduling and part routing of the multiple products being simultaneously assembled in the system. In each case the variables and scheduling techniques are different depending on cell configuration, operating procedure and performance measures.

Simulation as a scheduling tool The organization of a schedule, although complicated, can be achieved using purely manual rules and tech-

D LITTLE AND A HEMMINGS

niques such as shortest processing time, longest processing time and nearest due-date rules. The schedule effectiveness using a particular rule, however, may vary depending on the product mix and the type of production facility.

Alternatively, computerized techniques including some advanced MRPII and finite scheduling tools (a considerable number of the latter have become available within the last five years) can be used as a means of generating production schedules. These techniques can work well on a global level but fall down when highly interactive components and frequent change are present. They are generally used to produce weekly 'work to' lists rather than detailed schedules for assembly.

Simulation-based scheduling can however provide an effective tool for the scheduling of shop floor assembly, generating schedules which are based on an accurate and realistic model of the production facility 15. This enables information to be gained by experimentation, providing a means of assessing the performance of alternative schedules against established performance criteria prior to the selection and implementation of a particular schedule.

As simulation models can accurately predict system behaviour, it is a natural extension to apply simulation on a day-to-day basis to assess schedule performance and identify problems before they occur. Simulation is particularly suited to this type of analysis and can be effectively used to generate and evaluate different production schedules quickly. Work undertaken by the authors within one of the case study companies is a case in point. Here, a simulation model of the assembly system is updated on a real-time basis and 'mimics' actual system status. This can then be 'run ahead' to evaluate alternative schedules to optimize system performance and so provides management with a powerful tool which not only monitors shop floor performance, but also assists in evaluating alternative scheduling decisions.

Simulation can be used to evaluate the effect of interruptions to the schedule and to resolve the problems identified. A typical application could be in selecting the most appropriate batch size in a production order. Either the entire order could be processed at each operation as one batch, or the order processed as two or more batches moving independently through the system. Simulation could be used as a means of comparing the options available to enable selection of the optimum batch size. This is particularly useful where time can be lost due to tool changeovers or to identify, locate and deliver components.

More sophisticated software packages are now available which use animation add-ons which provide the user with the ability to see the system in action. Siman- Cinema and Witness are examples of these. This enhances understanding of machine or product mix interactions and their effects on individual cells or groups of cells 16. Animation can provide a better understanding of the implications resulting from

Vol 7 No ! February 1994 57


disturbances, both upstream and downstream, than that obtained from a tabulated numeric output. Such disturbances, in reality, result from sudden events such as machine breakdowns and component defects and visual aids greatly assist management to understand the implications of the problem by seeing how the system operates, it also finds an important use as a training tool for new systems.

The use of computer simulation has become increasingly popular over the past few years. Advances in computer hardware and the expanding availability and functionality of software packages, particularly those designed for PCs, have accelerated a growth in interest. It is quite probable that this, combined with the increasing use of PCs on the shop floor and the decrease in the price of local area networks for factories, will result in a rapid increase in the use of simulation for scheduling purposes.

Case studies in assembly scheduling

The results of a brief survey of industrial automated assembly systems suggests that flow line systems having mixed model capability are generally the most common in use. Cellular systems are uncommon as yet and are usually limited to production of small assemblies typified by hand-held domestic appliances, alternators and personal computers. Two case studies are presented which are typical of the assembly systems found in use. Both give a number of insights into current industrial practice. One is concerned with the production of engines and the second with the assembly of axles.

For mixed model flow lines, different model designs and assembly component variations can require the line to cope with considerable variation. Both case studies exhibit such extremes, which for the first amounts to a product specification variability of up to 350 types at a line output of 16 units per hour.

Both assembly plant configurations are based upon groups of mixed model flow lines which produce subassemblies that are then brought together for final assembly. Lines combine a mix of separate manual, semi-automated and automated workstations linked by a stepped conveyor, which carries subassemblies on individual platens that index from one station to the next. Workstation functions are load, assembly, set, test or a combination. Set and test stations provide the best opportunity for automation, whereas assembly and load stations can be either manual or automated, depending upon whether there are component selection options, feeding or manipulation is awkward or judgement is required.

Operations are performed on each model type without the need for tooling changeovers. Changes in platen fixturing may be incorporated to accommodate different product design although components are still assembled in the same fixed sequence.

Subassembly production and assembly process control The subassemblies and assemblies produced can poten-

tially combine any number of variants in a random sequence. Therefore, to produce finished products according to the master build list, the system must both:

1. Initiate and produce the subassembly variants for each product on the list.

2. Co-ordinate the production of each subassembly to ensure they are available in the correct sequence and in time for the final assembly.

This may need to be accomplished without the facility to buffer subassemblies, such that each subassembly flow line must produce subassembly variants to the correct specification and in the order required to produce complete products. Failure to achieve this would prevent final assembly. To achieve this, subassembly production and final, assembly in one case is PLC controlled and utilises an electromagnetic tagging system. This enables specific assembly data regarding an individual product type to be recorded on an electromagnetic tag attached to each platen.

The data regarding individual products is downloaded from the PLC to the tags in the required build sequence so that each station equipped with a read/ write head can determine what subassembly is mounted on the platen and its build history. The tag also provides information regarding:

I. The component to be fitted at that station. 2. Whether the assembly has been operated on by all

the previous stations. 3. Whether it is classed as a pass or reject.

This information is used to directly control automatic stations, or provide electronic display data at manual and semi-automated stations.

After each operation data associated with either the component or the operation at that station is written to the tag, including whether an assembly has been classed as a reject. On completion of all operations, the read/ write buffer is cleared, allowing the tag to be updated with new data for the next product.

Reject subassemblies Where there are no buffering facilities, any reject subassembly deficit must be made up if the intended assembly is to be completed. This will require a stock of replacement subassemblies held off-line.

Where rectification spurs exist, associated subassemblies will need to be held in buffering until rectification and remaining assembly is completed. It is therefore imortant that component selection and assembly is correct. The use of Poka Yoke devices incorporated into the platen release mechanism of each station are a particularly useful, especially at manual stations.

Case study 1

In the first case study, the build requirements for three


days engine production are downloaded from the Master Production Schedule (MPS) to an automated inventory management system which holds Bill of Materials and inventory data. The order in which the three different engine types are to be built is determined by production controllers, who sequence production so that engines are produced in a controlled mixed-model sequence. The production sequence is smoothed, mainly to level the total assembly time for each process on the line (this has required extensive line balancing in order to optimize workload distribution). The sequence, along with engine assembly data, is entered into the inventory management systems, downloaded from this to the 'line set' station in each cell and then subsequently written to the Statec tags on each platen. The layout of the engine assembly area is given in Figure 6.

On completion of each workstation assembly operations, information regarding the quantity and type of components assembled is transmitted back to the inventory management system, enabling the shop floor data to be updated accordingly.

Quantities of components delivered from suppliers are also entered into the automated inventory management system at the warehouse receiving station. This combination of information regarding both materials used and received not only supports strict inventory control but, combined with the MPS and BOM data, enables the system to 'flag up' a schedule that cannot be met because of component shortages. The inventory management system is also used to perform a splitting routine on received parts consignments, so that the delivery routing can be split between the warehouse and the shop floor, as required.

Materials delivery from the warehouse to individual shop floor stations is controlled in several ways:

1. Milk Run - the buffers for parts withdrawn on a regular basis are refilled from stores on a regular milk run around the factory.

2. V i s u a l - the buffers for large components, for example cylinder blocks, are replenished when the buffer is seen to be running low. This is noted by the stores delivery man on the milk run.

Final assembly area Cylinder head ~ ~sb~nCkl 4 r~ Icy linderhead

Y ~ I [subassembly I I

I Short block subassembly [ ~ I I

Figure 6. Layout o f engine assembly area

D L~TrLE AND A HEMM1NGS

3. Build - components are replaced according to how many have been assembled in the production process.

4. Signal - manual stations are directly linked to the stores via an electronic signalling system. If there is a part shortage at a station, then the operator can press a display board button corresponding to that part number. Acknowledgement at the stores cancels an illuminated light on the operators display board.

Case study 2

The second case study is a mixed model flow line which has to cope with different designs and component variations to produce subassemblies that are then combined into a final assembly. The lines comprise a mix of manual, semi-automated and automated workstations fed by a stepped conveyor which transports individual plantens carrying subassemblies from one workstation to the next. Although the term cell is used locally, the cells are, effectively, small flow lines. The layout of the automated axle assembly area is given in Figure 7.

Each cell contains separate workstations linked by the conveyors and perform the functions of load, unload, assembly, set/test, or a combination of these. Axles are automatically delivered to workstations in kits where they are manually assembled. The size and weight of the axles are such that an automated delivery system is essential to effective assembly. The same fixed assembly sequence is followed for each type of axle so routing flexibility is not required. Each workstation can produce each axle variant without tooling changes except for a platen change required by one cell. Labour flexibility is used to cope with variations in assembly load between variants and considerable care is taken to balance the line.

A Poka Yoke approach to component design prevents the assembly of wrong components by fitters and the use of manual assembly provides a considerably higher degree of flexibility over a fully automated system. Indeed, the high variety of axle types and the aggressive new product introduction policy creates an environment where volumes are too small and axle life cycles Ioo short for full automation to have been a consideration in the system design. At the time of writing, 80% of products had been in existence for less than two years.

Axle assembly scheduling is carried out on a daily basis corresponding to a build list issued each morning. This shows the axle types and quantities and is driven by an MPS according to customer orders. The launch sequence of different axles to the assembly line is at the discretion of the production supervisor. This can depend both upon the components immediately available and the expected component supply sequence from upstream machining centres.

Axles are assembled in discrete batches or runs subject to component availability, which can result in

Vol 7 No l February 1994 59


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Figure 7. Layout of automated axle assembly area

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the axle type launch sequence being adapted to the components fed to the subassembly cells. There is no levelling of the assembly sequence to minimize variation in the parts requirement from upstream processes, so batch size optimization is not required. Upstream machining centres also produce in batches or runs. The result is an extremely cost effective and reactive assembly system.

C o n c l u s i o n s

The literature and the two case studies show that flexibility is a key criterion in the design of assembly systems for all but high volume products and that, at the current state of development, the necessary flexibility is usually provided by operator manned workstations within an automated delivery system.

The main limitations to the full automation of assembly, particularly the case in the large engineered

products of the case studies, is the ability of the system to recognise the delivered sub-assembly, to marshal the components to be assembled in the correct sequence (a particular problem when they are supplied to order on a JIT basis) and the ability of the system to change jigs and tools to match the configuration to be built.

To meet these requirements flexibly demands a high level of intelligence at each workstation or a level of control of sub-assembly and component sequencing which is not only difficult but may, in fact, mitigate against overall system responsiveness, one of the objectives of much of current plant design.

The procedure for scheduling mixed-model automated assembly depends on the type of assembly system, i.e. flow line, flexible flow line, or cell. For flow lines, the same scheduling methodology can be applied to both manual and automated systems. Schedules can be developed off-line using heuristic techniques or simulation.


Flexible flow lines and automated assembly cells require on-line scheduling. Because of the routing flexibility and differing processing times for each model, with flexible flow lines heuristics techniques or simulators can be used. The different cell configurations and workstation capabilities associated with automated assembly cells require the scheduling methodology to be cell-specific.

The assembly systems seen in the case studies were either autonomous machine groups or flow lines having a combination of both automated and manual assembly stations. Few companies were found that used cellular manufacturing systems. The reasons for this are not clear, but may either be because of financial cost, the concept being relatively new and not yet fully developed, or because there is a preference to apply automation to the more familiar flow line systems.

In the future, the rapidity of product changes and the likely halving of product life cycles will demand mixed model lines with the capability to match changing customer needs and maintain competitive prices through high workstation utilization. More effective shop floor control systems, particularly for component supply and inventory control, need to be developed to support assembly since the marshalling and sequencing of component supply is critical within a responsive system. To provide such responsiveness for the assembly of large engineering assemblies, it is likely that many plants will rely upon an FMS approach for component supply and the assembly control system will drive the FMS (and other) support systems to provide components on a 'pull' basis against incoming orders or an assembly schedule.

Fully automated lines are likely to be restricted to those where the volumes are sufficient to justify the high capital equipment cost. Virtually fully automated lines have been developed for automobile engine assembly in the US, and are currently in use at the Saturn Assembly Plant. Where assembly volumes are lower (the more common scenario) then hybrid systems which include significant manual elements are likely to be used, as in the case study plants.

Discussions with a number of research groups and a major project involving the researchers in one of the case study companies indicates a trend towards the use of on-line simulation models for scheduling. Such models are updated continuously from the assembly line so that they 'mimic' current status. When required, the model may be run in time-compressed mode to evaluate alternative schedule performance.

References

1 Keable, D A and Youell, N D 'Design and implementation of a manufacturing control system in a multi-location company', Proc. 16th Euro. Tech-

D LITrLE AND A HEMM1NGS

nical Conf. on Production and Inventory Control, BPICS, Birmingham, (1 November 1981)

2 Dunn, J G 'FMS in the small batch manufacturing environment - a case study', Proc. 7th Int. Conf. on FMS, IFS, Stuttgart, Germany (13-14 September 1988)

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11 Wittrock, R J 'An adaptable scheduling algorithm for flexible flow lines', Operat. Res., Vol 36 No 3 (1988) pp 445-452

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13 Donath, M, Graves, R J and Carlson, D A 'Flexible assembly systems: The scheduling problem for multiple products', J. Manuf. Syst., Vol 8 No 1 (1991) pp 27-33

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15 Grant, H and Clapp, C 'Making production scheduling more efficient helps control manufacturing costs and improve productivity', Ind. Eng., Vol 20 No 6 (1988) pp 54q52

16 Welke, H A and Overbeeke, J 'Cellular manufacturing: A good technique for implementing Just- in-Time and total quality control', Ind. Eng., Vol 20 No 1l (1988) pp 36-31

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automated assembly scheduling: a review

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