The Specifics of Production Scheduling in Process

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ucts (materialised in a distributed hierarchically organized computer ... factories have general-purpose production equipment that can perform a wide ... Job shops are gener- .... is in close correlation to the efficiency of all management levels.

Elektrotehniˇski vestnik 69(5): 305–310, 2002 Electrotechnical Review, Ljubljana, Slovenija

The Specifics of Production Scheduling in Process Manufacturing Vladimir Jovan Jozef Stefan Institute, Department of Systems and Control Jamova 39, 1000 Ljubljana, Slovenia E-mail: [email protected] Abstract. Process manufacturing has some characteristics that make it different from other types of industry. They are reflected in the design of a process factory management system. This paper addresses some features of process manufacturing that have to be taken into account during the implementation of a production scheduling system. Key words: computer integrated manufacturing, production management, process manufacturing, production scheduling

Posebnosti razporejanja proizvodnje v procesni industriji Povzetek. Procesna proizvodnja ima nekatere znaˇcilnosti, ki jo loˇcijo od drugih tipov industrije. Te znaˇcilnosti se odraˇzajo tudi pri naˇcrtovanju vodenja proizvodnje. V cˇ lanku obravnavamo nekatere lastnosti procesne proizvodnje, ki jih moramo upoˇstevati pri naˇcrtovanju in izbiri sistemov za razporejanje proizvodnje. Kljuˇcne besede: raˇcunalniˇsko podprto vodenje proizvodnje, upravljanje proizvodnje, procesna proizvodnja, razporejanje proizvodnje

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Introduction

The current business environment demands an instant reply to customers’ needs, short product lifecycle, product diversification, minimal inventories, extremely short lead times, concurrent processing of different products and short delivery times, as well as compliance with national government regulations, environmental constraints, safe and reliable production maesures, energy and material criteria, social pressures, changes in workforce, etc. Advanced manufacturing requires fast and accurate decisions and actions at all management levels in a factory, and a high degree of decision-making autonomy within particular business and production processes in a factory. In order to satisfy the above demands, companies have been forced to integrate all business and production management activities into a unified management system that ensures flexible and efficient production. This concept of manufacturing management demands an adequate technical and organisational support, and is often termed as the Computer Integrated Manufacturing (CIM) concept [3]. Received 22 April 2002 Accepted 14 August 2002

This concept can be discussed from two aspects: technical and organizational. From the technical point of view, the term CIM stands for a concept of a coordinated and sensible application of a contemporary HW&SW IT products (materialised in a distributed hierarchically organized computer system, where on a particular hierarchical level certain business or production activities are located, see [6]), to be applied in a company with the aim of improving the business performance. From the organizational point of view, CIM is an integration of process, plant and business operations (from order entry and scheduling through production, quality control, maintenance, shipping and accounting), made possible through computerized information networking [8]. Keeping in mind the two definitions, it can be stated that the implementation of the CIM concept assures efficient overall management of a company based on current and accurate data collected, processed and displayed using modern IT tools and products. In both cases it serves as a tool to integrate the management levels of a company. The CIM concept was meant to be applied in both discrete or assembly industries (e.g. car, domestic appliance, semiconductor and furniture industries) and continuous or process industries (e.g. chemical, oil refining, pulp&paper and pharmaceutical industries). However, nowadays it is in discrete manufacturing production that the CIM concept has demonstrated its usefulness in full; in process industries by contrast, the implementation of the classic CIM concept is not as straightforward, owing to a number of specific properties of this kind of industry [1, 10, 4]. One of the activities that differ to a certain extent between discrete and process manufacturing is produc-

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tion scheduling. The aim of this paper is to present the specifics that should be often taken into account in designing production scheduling in process industries. The first part of the paper deals with the classification of different types of manufacturing operations. The general features of discrete and process types of industry are elaborated in terms of relations with the market, production process, quality assurance, planning and management, etc. The specifics of process manufacturing that affect the scheduling process are listed and discussed in the second part of the paper.

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Manufacturing types

Generally speaking, production enterprises can be classified into three types by manner of their operation (Figure 1) [2]: job shops, repetitive manufacturing and batch manufacturing. These three basic types differ greatly in the degree of flexibility of the production line, the variety of their products and the amount (rate) of production. A job shop manufacturing business contracts to make to order custom products in accordance with designs supplied by the customer. The volume of each product is typically low, so these companies must produce a wide variety of products. To maintain their volume of business, these factories have general-purpose production equipment that can perform a wide variety of operations and staff with a broad range of skills. An inventory of only certain raw materials frequently used exists; often the largest part of the inventory is work in process that accumulates between production process stages. Job shops are generally classified as high-variety, low volume manufacturers. The scheduling process is oriented towards utilisation of resources, elimination of bottlenecks and frequent rescheduling due to new or cancelled orders. Mass production facilities that produce a high volume of the same or similar products fall into the category of repetitive manufacturing. Typically, material is moved along the production path in small lots, often one at a time. The products are usually made to stock, and the items are not identified as belonging to a particular order during their production. The production equipment in repetitive manufacturing performs the same operations repeatedly, may be custom de-signed and is often fully automated. To maintain stable production, inventories of raw materials are often held to ensure supply and a reasonable quantity of finished products are kept in stock to smooth fluctuations in demand. Sequencing and timing are often designed into the production process, so the task of scheduling is in assuring a uniform production rate and minimising set-up times by appropriate job scheduling of similar products together. Batch manufacturing factories produce an intermediate variety of products in intermediate volumes. The volume of each product is not sufficient to justify a specially

designed and fully automated production line, so a few or several products share the production resources. The company will make a batch (a production run) of one product and then switch over the equipment to make a batch of another product. More than one production line is possible and they share common resources. The production of a particular item is repeated over a certain period of time. After a batch is completed, the equipment must be set up anew to produce another item. The ability to set up the production line quickly is important in this type of production system. A batch manufacturing factory is generally more complex to schedule than a repetitive facility of the same size. The variety of products is greater and the routing of particular products requires more work. Much of the complexity arises in determining the priority of jobs since often the production of similar items on parallel routes requires common resources. Assembly and process manufacturing are two other terms that are often used to describe the nature of manufacturing operations. Some production facilities produce a product that blends together in bulk rather than being in discrete units. The industries that produce these types of products are often called process industries particularly if some physical or chemical reaction is used. Examples of such industries are the chemical industry, oil refining, the pulp and paper industry, the pharmaceutical industry, cement factories and flour mills. While discrete or assembly industries (e.g. the car, domestic appliance, semiconductor and furniture industries) cover all three types of manufacturing mentioned above, process industry is mostly representative of mass production repetitive manufacturing or, in some cases, of the batch type of manufacturing as seen from Figure 1. Variety of products HIGH

JOB SHOP BATCH

MEDIUM

PROCESS MANUFACTURING

REPETITIVE LOW

LOW

MEDIUM

HIGH Volume of production

Figure 1. Classification of manufacturing types

The process industry has several specifics compared with a discrete one. These specifics make process manufacturing complex and uncertain [10, 6, 5]. The complexity of the production process arises primarily from the required linking of various sub-processes, each of which affects the quality of the final product. Each sub-process

The Specifics of Production Scheduling in Process Manufacturing requires the maintenance of a certain number of process parameters (pressure, temperature, flow, viscosity, etc.), which leads to a large number of operation-level sensors, actuators, controllers and programmable controllers, which have to operate safely and reliably. The uncertainty of the process industry is expressed above all in product quality. It is desirable to achieve high and uniform product quality in every industrial process plant, but the nonuniformity of the quality of basic raw materials, the poor performance of control systems, deviations in process parameters, failures of technological equipment, outages in energy supply, and often also combinations of various undetermined reasons render any prediction of the level of both quality and production rate a risky business. Both uncertainty and complexity have a great effect on the management of the production process. Thus in this type of industry it is difficult to establish both effective planning and a link (production scheduling) between planning and production. The following tables (Tables 1 – 5, based on the work of [12]) present the general features of both discrete and process manufacturing in terms of relations with the market, production process, quality assurance, planning and management, etc.

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Production scheduling in process industries

Taking into account the given characteristics of both types of industries some conclusions regarding the production scheduling become more evident. Discrete manufacturing usually comprises component fabrication and assembly, with the focus on work queues at machines, balancing throughput, and having all the component parts ready together for sub-assembly and final assembly. Production scheduling in discrete manufacturing is focused mainly on the effective utilisation of resources and tracking of assembly parts. The specifics of process manufacturing usually have a great influence on a schedule, and the focus here is to maintain stable production within given constraints. In the framework of production scheduling, special attention should also be given to the following characteristics of process manufacturing: • Unlike in discrete industries, energy consumption in process industries is an important limiting factor that can seriously influence the production schedule. This is especially true in a parallel batch production line where the variations in energy consumption can be extremely large and the demand can easily become greater than can be guaranteed by energy sources. A scheduling strategy must take into account the need for uniform energy consumption. • Process manufacturing frequently takes relatively few raw materials, transforming these into many different finished goods with many packaging variants. Unlike discrete industries, many process indus-

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tries have continuous (or batch or semi-batch) manufacturing processes, where batching rules and synchronisation are extremely important. In chemicals and pharmaceuticals for example, recipe formulation management is important, and there are often very complex (regulated) equipment allocation rules. • Once started, most chemical processes must run to completion. There is usually no intermediate storage option other than the processing vessel itself, and all materials (raws and intermediates) must be available in the right quantity and quality at the right time. Sometimes the intermediate products are unstable and no slack time is allowed between particular phases of a batch. • In some process industries (e.g. food and beverages), products have a limited duration time. For that reason, a due-date scheduling strategy needs to be implemented. Similar problems arise when there are no storage capacities or where just-in-time production strategy is implemented. • Some process industries must follow strict standards and regulations concerning the traceability of a product. In order to guarantee the traceability of a product, both intermediate and final products must not be mixed into common storage reservoirs; neither can the storage capacity be used before it is cleaned or sterilised. • Unlike discrete manufacturing, a production process in process industries is not flexible, and change times for starting a new product on a technological line are usually high. On the other hand, the programme assortment is usually small and a lot of final products are merely variations of a certain number of basic products. In order to avoid large set-up times, jobs using similar technological processes should be sequenced one after another. In the paint industry for example, you may minimise set-up times by selecting jobs in colour sequence. • Finally, it should be stressed that the exact production rate in process industries is difficult to predict. This uncertainty arises from the non-uniformity of quality of basic raw materials, deviations of process parameters, failures of technological equipment, outages in energy supply, and often also combinations of various indeterminate reasons. As the duration of a particular order is difficult to define exactly, the use of specific scheduling algorithms (like the minimisation of processing times) is sometimes unsuitable. In the context of scheduling in process manufacturing, it is usually more sensible to combine different scheduling strategies (e.g. uniform employment of resources with dynamic bottleneck elimination, priority-based algorithms and the sequencing of

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Process industries

Discrete flow industries

Type of product Product assortment Demand per product Cost per product Winning advantages

Standard Small Large Low Price, delivery guarantee

Transport costs New products

High Few

Customised Large Small High Delivery speed, product characteristics Low Many

Table 1. Specifics of process industries - Relations with the market

Process sheets Layout Flexibility Production equipment Labour value Capital value Change time In-progress Energy consumption Intermediate products and byproducts Recycling of raw materials or products Volumes

Process industries

Assembly industries

Fixed Per product Low Specialised Low High High Low Often high Yes

Variable with substitute Per function High All-purpose High Low (changes) Low High (changes) Low No

Often

No

High

Low

Table 2. Specifics of process industries - Production process

Environmental constraints Danger Quality measurements Raw materials quality Product quality

Process industries

Assembly industries

Yes Sometimes Sometimes long Varying Often non-uniform

Rarely Almost never Short Uniform Uniform

Table 3. Specifics of process industries - Quality

The Specifics of Production Scheduling in Process Manufacturing

Production Long term planning Short term planning Planning of launching Material flow Output variability Management system breakdown Co-products Batch traceability

Process industries

Discrete flow industries

On stock Capacity Use of capacity Availability of capacity

On order Product design Use of personnel Availability of material

Divergent+convergent Sometimes high

Convergent Often low

Via formulas Sometimes, often required

Via bills of materials Sometimes unnecessary

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Table 4. Specifics of process industries: Planning and management

Type of data Number of signals Architecture Centralisation Time scale Type of control Complexity of control Human Impact

Process industries

Assembly industries

Analog, Digital Often medium Distributed tightly coupled system High ms-s-min-hour-day-week Closed loop High Sometimes high

Digital High Distributed loosely coupled system Sometimes low s-min Sequential Sometimes low Often low

Table 5. Specifics of process industries – Industrial management & Process control system

minimal set-up times, etc.) instead of forcing only one of them. Due to the mentioned uncertain nature of process manufacturing, the plant behaviour cannot be perfectly predicted. The schedule needs to be adapted on-line in response to different disturbances and it has to react promptly to unexpected events. Therefore, the current state of the plant should be explicitly incorporated in the scheduling problem formulation and the calculation time is limited [11]. Such schedulers are known as reactive schedulers. Many process plants have technological constraints whose exceeding leads to very costly effects, so they must not be violated under any conditions. In these cases scheduler must be robust enough so that normal uncertainty in production does not reflect in violation of such critical constraints.

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Conclusions

In a manufacturing facility, the efficiency of production is in close correlation to the efficiency of all management levels. Today IT technologies have reached a state of maturity and their products are widely used throughout the manufacturing management system. One such product,

appearing on the market in the last decade, is the finite capacity scheduler. An appropriate implementation of a production scheduler can successfully bridge functional and informational gaps between business and process management levels [5, 10] in a manufacturing enterprise, thus improving overall production efficiency. Discrete industries share a number of manufacturing characteristics. Process industries also share common manufacturing characteristics but these are often distinctly different to those in discrete industries. The aim of this paper is to outline the specifics of process-type industries. These specifics influence the design of production management system and should be taken into account above all during the implementation of finite-capacity scheduling in a process manufacturing enterprise.

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References [1] T. Allweyer, A.-W. Scheer, Integrating Scheduling in Logistic Systems for Batch Scheduling, Proceedings of IFAC Symposium on Automated Systems Based on Human Skills, Berlin, Germany, pp. 97-102, 1995. [2] J. B. Dilworth, Operations Management, McGraw – Hill, 1996.

[3] H. L. Hales, CIMPLAN The Systematic Approach to Factory Automation, Cutter Information Corp., Arlington, USA, 1989.

[9] MESA International, White Paper Number 2: MES Functionalities and MRP to MES Data Flow Possibilities, http://www.mesa.org/html

[4] V. Jovan, G. Dolanc, Process control as an element in a CIM concept – a case study, Proceedings of the 9th Mediterranean Electrotechnical Conference Melecon ’98, Tel-Aviv, Israel, pp. 226-230, May 18-20, 1998.

[10] E. Scherer, Approaches to Complexity and Uncertainty of Scheduling in Process Industries: Process Regulation in Highly Automated Systems, Proceedings of IFAC Symposium on Automated Systems Based on Human Skills, Berlin, Germany, pp. 91-95, 1995.

ˇ [5] V. Jovan, J. Cernetiˇ c, G. Dolanc, Integration of business and production levels in process Industries, INCOM’98 Advances in industrial engineering: preprints of the 9th IFAC Symposium on Information Control in Manufacturing, Nancy and Metz, France, vol. 3, pp. 275-280, June 24-26, 1998. [6] V. Jovan, The Relationship between Planning and Production Activities in Process Industries, Proceedings of the 7 th Mediterranean Conference on Control and Automation, Haifa, Israel, pp. 1982-1989, June 28-30, 1999. [7] V. Jovan, The integration of management levels in process industries, Proceedings of the Fifth Italian Conference on Chemical and Process Engineering, ICheaP-5, Florence, Italy, vol. 1, pp. 453-458, May 20-23, 2001. [8] J. R. Leigh, Applied Digital Control Theory, Design & Implementation, Prentice Hall International, 1992.

[11] V. Terpstra, Batch Scheduling, Dissertation, Technische Universiteit Delft, The Netherlands, 1996. [12] A. Thomas, S. Lamouri, Industrial Management in the Process Industry, Preprints of the 9th Symposium on Information Control in Manufacturing, Nancy and Metz, France, pp. 327-332, 1998.

Vladimir Jovan studied Informatics and Computer Science at the Faculty of Electrical Engineering in Ljubljana, Slovenia, where he also completed his PhD in Automation in 1992. Currently he is a senior researcher at the Department of Systems and Control at IJS, Ljubljana and the Director of the Technology Centre for Industrial Automation, Robotics and Informatics. His research interests include computer control of industrial processes and integrated computer-based management of industrial enterprises. He has been involved in several industrial projects in computer-based process control and production management.

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