Scheduling (production processes)

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Scheduling is the process of arranging, controlling and optimizing work and workloads in a production process or manufacturing process. Scheduling is used to allocate plant and machinery resources, plan human resources, plan production processes and purchase materials.


It is an important tool for manufacturing and engineering, where it can have a major impact on the productivity of a process. In manufacturing, the purpose of scheduling is to minimize the production time and costs, by telling a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce costs.

Manufacturing industrial activity producing goods for sale using labor and machines

Manufacturing is the production of products for use or sale using labour and machines, tools, chemical and biological processing, or formulation. The term may refer to a range of human activity, from handicraft to high tech, but is most commonly applied to industrial design, in which raw materials are transformed into finished goods on a large scale. Such finished goods may be sold to other manufacturers for the production of other, more complex products, such as aircraft, household appliances, furniture, sports equipment or automobiles, or sold to wholesalers, who in turn sell them to retailers, who then sell them to end users and consumers.

Engineering applied science

Engineering is the use of scientific principles to design and build machines, structures, and other things, including bridges, roads, vehicles, and buildings. The discipline of engineering encompasses a broad range of more specialized fields of engineering, each with a more specific emphasis on particular areas of applied mathematics, applied science, and types of application. See glossary of engineering.

In some situations, scheduling can involve random attributes, such as random processing times, random due dates, random weights, and stochastic machine breakdowns. In this case, the scheduling problems are referred to as Stochastic scheduling.

Stochastic scheduling concerns scheduling problems involving random attributes, such as random processing times, random due dates, random weights, and stochastic machine breakdowns. Major applications arise in manufacturing systems, computer systems, communication systems, logistics and transportation, machine learning, etc.


Scheduling is the process of arranging, controlling and optimizing work and workloads in a production process. Companies use backward and forward scheduling to allocate plant and machinery resources, plan human resources, plan production processes and purchase materials.

The benefits of production scheduling include:

Production scheduling tools greatly outperform older manual scheduling methods. These provide the production scheduler with powerful graphical interfaces which can be used to visually optimize real-time work loads in various stages of production, and pattern recognition allows the software to automatically create scheduling opportunities which might not be apparent without this view into the data. For example, an airline might wish to minimize the number of airport gates required for its aircraft, in order to reduce costs, and scheduling software can allow the planners to see how this can be done, by analyzing time tables, aircraft usage, or the flow of passengers.

Automated planning and scheduling, sometimes denoted as simply AI planning, is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles. Unlike classical control and classification problems, the solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to decision theory.

Key concepts in scheduling

A key character of scheduling is the productivity, the relation between quantity of inputs and quantity of output. Key concepts here are:

In economics and related disciplines, a transaction cost is a cost in making any economic trade when participating in a market.

Scheduling algorithms

Production scheduling can take a significant amount of computing power if there are a large number of tasks. Therefore, a range of short-cut algorithms (heuristics) (a.k.a. dispatching rules) are used:

The economic production quantity model determines the quantity a company or retailer should order to minimize the total inventory costs by balancing the inventory holding cost and average fixed ordering cost. The EPQ model was developed by E.W. Taft in 1918. This method is an extension of the economic order quantity model. The difference between these two methods is that the EPQ model assumes the company will produce its own quantity or the parts are going to be shipped to the company while they are being produced, therefore the orders are available or received in an incremental manner while the products are being produced. While the EOQ model assumes the order quantity arrives complete and immediately after ordering, meaning that the parts are produced by another company and are ready to be shipped when the order is placed.

The Shifting Bottleneck Heuristic is a procedure intended to minimize the time it takes to do work, or specifically, the makespan in a job shop. The makespan is defined as the amount of time, from start to finish, to complete a set of multi-machine jobs where machine order is pre-set for each job. Assuming that the jobs are actually competing for the same resources (machines) then there will always be one or more resources that act as a 'bottleneck' in the processing. This heuristic, or 'rule of thumb' procedure minimises the effect of the bottleneck. The Shifting Bottleneck Heuristic is intended for job shops with a finite number of jobs and a finite number of machines.

Batch production scheduling


Batch production scheduling is the practice of planning and scheduling of batch manufacturing processes. See Batch production. Although scheduling may apply to traditionally continuous processes such as refining, [1] [2] it is especially important for batch processes such as those for pharmaceutical active ingredients, biotechnology processes and many specialty chemical processes. [3] [4] Batch production scheduling shares some concepts and techniques with finite capacity scheduling which has been applied to many manufacturing problems. [5] The specific issues of scheduling batch manufacturing processes have generated considerable industrial and academic interest.

Scheduling in the batch processing environment

A batch process can be described in terms of a recipe which comprises a bill of materials and operating instructions which describe how to make the product. [6] The ISA S88 batch process control standard [7] provides a framework for describing a batch process recipe. The standard provides a procedural hierarchy for a recipe. A recipe may be organized into a series of unit-procedures or major steps. Unit-procedures are organized into operations, and operations may be further organized into phases.

The following text-book recipe [8] illustrates the organization.


A simplified S88-style procedural organization of the recipe might appear as follows:

  • Unit Procedure 1: Reaction
    • Operation 1: Charge A & B (0.5 hours)
    • Operation 2: Blend / Heat (1 hour)
    • Operation 3: Hold at 80C for 4 hours
    • Operation 4: Pump solution through cooler to blend tank (0.5 hours)
    • Operation 5: Clean (1 hour)
  • Unit Procedure 2: Blending Precipitation
    • Operation 1: Receive solution from reactor
    • Operation 2: Add solvent, D (0.5 hours)
    • Operation 3: Blend for 2 hours
    • Operation 4: Pump to centrifuge for 2 hours
    • Operation 5: Clean up (1 hour)
  • Unit Procedure 3: Centrifugation
    • Operation 1: Centrifuge solution for 2 hours
    • Operation 2: Clean
  • Unit Procedure 4: Tote
    • Operation 1: Receive material from centrifuge
    • Operation 2: Load dryer (15 min)
  • Unit Procedure 5: Dry
    • Operation 1: Load
    • Operation 2: Dry (1 hour)

Note that the organization here is intended to capture the entire process for scheduling. A recipe for process-control purposes may have a more narrow scope.

Most of the constraints and restrictions described by Pinedo [9] are applicable in batch processing. The various operations in a recipe are subject to timing or precedence constraints that describe when they start and or end with respect to each other. Furthermore, because materials may be perishable or unstable, waiting between successive operations may be limited or impossible. Operation durations may be fixed or they may depend on the durations of other operations.

In addition to process equipment, batch process activities may require labor, materials, utilities and extra equipment.

Cycle-time analysis

In some simple cases, an analysis of the recipe can reveal the maximum production rate and the rate limiting unit. In the process example above if a number of batches or lots of Product C are to be produced, it is useful to calculate the minimum time between consecutive batch starts (cycle-time). If a batch is allowed to start before the end of the prior batch the minimum cycle-time is given by the following relationship: [10]

Where CTmin is the shortest possible cycle time for a process with M unit-procedures and τj is the total duration for the jth unit-procedure. The unit-procedure with the maximum duration is sometimes referred to as the bottleneck. This relationship applies when each unit-procedure has a single dedicated equipment unit.

If redundant equipment units are available for at least one unit-procedure, the minimum cycle-time becomes:

Where Nj is the number of redundant equipment for unit procedure j.


If equipment is reused within a process, the minimum cycle-time becomes more dependent on particular process details. For example, if the drying procedure in the current example is replaced with another reaction in the reactor, the minimum cycle time depends on the operating policy and on the relative durations of other procedures. In the cases below, an increase in the hold time in the tote can decrease the average minimum cycle time.


Various charts are used to help schedulers visually manage schedules and constraints. The Gantt chart is a display that shows activities on a horizontal bar graph in which the bars represent the time of the activity. Below is an example of a Gantt chart for the process in the example described above.
Another time chart which is also sometimes called a Gantt chart [11] shows the time during which key resources, e.g. equipment, are occupied. The previous figures show this occupancy-style Gantt chart.

Resources that are consumed on a rate basis, e.g. electrical power, steam or labor, are generally displayed as consumption rate vs time plots.

Algorithmic methods

When scheduling situations become more complicated, for example when two or more processes share resources, it may be difficult to find the best schedule. A number of common scheduling problems, including variations on the example described above, fall into a class of problems that become very difficult to solve as their size (number of procedures and operations) grows. [12]

A wide variety of algorithms and approaches have been applied to batch process scheduling. Early methods, which were implemented in some MRP systems assumed infinite capacity and depended only on the batch time. Such methods did not account for any resources, and would produce infeasible schedules. [13]

Mathematical programming methods involve formulating the scheduling problem as an optimization problem where some objective, e.g. total duration, must be minimized (or maximized) subject to a series of constraints which are generally stated as a set of inequalities and equalities. The objective and constraints may involve zero-or-one (integer) variables as well as nonlinear relationships. An appropriate solver is applied for the resulting mixed-integer linear or nonlinear programming (MILP/MINLP) problem. The approach is theoretically guaranteed to find an optimal solution if one exists. The disadvantage is that the solver algorithm may take an unreasonable amount of time. Practitioners may use problem-specific simplifications in the formulation to get faster solutions without eliminating critical components of the scheduling model. [14]

Constraint programming is a similar approach except that the problem is formulated only as a set of constraints and the goal is to arrive at a feasible solution rapidly. Multiple solutions are possible with this method. [15] [16]

See also

Related Research Articles

Computerized batch processing, since the 1964 introduction of the IBM System/360, has primarily referred to the scripted running of one or more programs, as directed by Job Control Language, with no human interaction other than, if JCL-requested, the mounting of one or more pre-determined input and/or output computer tapes.

Workflow defined sequence of activities in a working system of an organization

A workflow consists of an orchestrated and repeatable pattern of activity, enabled by the systematic organization of resources into processes that transform materials, provide services, or process information. It can be depicted as a sequence of operations, the work of a person or group, the work of an organization of staff, or one or more simple or complex mechanisms.

Automatic process control in continuous production processes is a combination of control engineering and chemical engineering disciplines that uses industrial control systems to achieve a production level of consistency, economy and safety which could not be achieved purely by human manual control. It is implemented widely in industries such as oil refining, pulp and paper manufacturing, chemical processing and power generating plants.

MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster.

Operations management An area of management concerned with designing and controlling the process of production and redesigning business operations

Operations management is an area of management concerned with designing and controlling the process of production and redesigning business operations in the production of goods or services. It involves the responsibility of ensuring that business operations are efficient in terms of using as few resources as needed and effective in terms of meeting customer requirements. Operations management is primarily concerned with planning, organizing and supervising in the contexts of production, manufacturing or the provision of services.

Quality, cost, delivery (QCD), sometimes expanded to QCDMS, is a management approach originally developed to help companies within the British automobile sector. Make it work. QCD analysis is used to assess different components of the production process. It also provides feedback in the form of facts and figures that help managers make logical decisions. By using the gathered data it is easier for organizations to prioritize their future goals.

Single-minute Digit exchange of die (SMED) is one of the many lean production methods for reducing waste in a manufacturing process. It provides a rapid and efficient way of converting a manufacturing process from running the current product to running the next product. This rapid changeover is key to reducing production lot sizes and thereby improving flow (Mura), reducing production loss and output variability.

Process optimization is the discipline of adjusting a process so as to optimize some specified set of parameters without violating some constraint. The most common goals are minimizing cost and maximizing throughput and/or efficiency. This is one of the major quantitative tools in industrial decision making.

Design structure matrix

The design structure matrix (DSM; also referred to as dependency structure matrix, dependency structure method, dependency source matrix, problem solving matrix (PSM), incidence matrix, N2 matrix, interaction matrix, dependency map or design precedence matrix) is a simple, compact and visual representation of a system or project in the form of a square matrix.

Schedule time management tool listing times when events are intended to take place

A schedule or a timetable, as a basic time-management tool, consists of a list of times at which possible tasks, events, or actions are intended to take place, or of a sequence of events in the chronological order in which such things are intended to take place. The process of creating a schedule — deciding how to order these tasks and how to commit resources between the variety of possible tasks — is called scheduling, and a person responsible for making a particular schedule may be called a scheduler. Making and following schedules is an ancient human activity.

S88, shorthand for ANSI/ISA-88, is a standard addressing batch process control. It is a design philosophy for describing equipment, and procedures. It is not a standard for software, it is equally applicable to manual processes. It was approved by the ISA in 1995 and updated in 2010. Its original version was adopted by the IEC in 1997 as IEC 61512-1.

Copy Exactly! is a factory strategy model developed by the computer chip manufacturer, Intel, to build new manufacturing facilities with high capacity practices already in place. The Copy Exactly! model allows factories that successfully design and manufacture chips to be replicated in locations globally,

The genetic algorithm is an operational research method that may be used to solve scheduling problems in production planning.

A master production schedule (MPS) is a plan for individual commodities to be produced in each time period such as production, staffing, inventory, etc. It is usually linked to manufacturing where the plan indicates when and how much of each product will be demanded. This plan quantifies significant processes, parts, and other resources in order to optimize production, to identify bottlenecks, and to anticipate needs and completed goods. Since an MPS drives much factory activity, its accuracy and viability dramatically affect profitability. Typical MPSs are created by software with user tweaking.

Manufacturing execution systems (MES) are computerized systems used in manufacturing, to track and document the transformation of raw materials to finished goods. MES provides information that helps manufacturing decision makers understand how current conditions on the plant floor can be optimized to improve production output. MES works in real time to enable the control of multiple elements of the production process.

Project management triangle

The Project Management Triangle is a model of the constraints of project management. While its origins are unclear, it has been used since at least the 1950s. It contends that:

  1. The quality of work is constrained by the project's budget, deadlines and scope (features).
  2. The project manager can trade between constraints.
  3. Changes in one constraint necessitate changes in others to compensate or quality will suffer.

Industrial engineering is an engineering profession that is concerned with the optimization of complex processes, systems, or organizations by developing, improving and implementing integrated systems of people, money, knowledge, information, equipment, energy and materials.

Kimeme is an open platform for multi-objective optimization and multidisciplinary design optimization. It is intended to be coupled with external numerical software such as Computer Aided Design (CAD), Finite Element Analysis (FEM), Structural analysis and Computational Fluid Dynamics tools. It was developed by Cyber Dyne Srl and provides both a design environment for problem definition and analysis and a software network infrastructure to distribute the computational load.

In production and project management, a bottleneck is one process in a chain of processes, such that its limited capacity reduces the capacity of the whole chain. The result of having a bottleneck are stalls in production, supply overstock, pressure from customers and low employee morale. There are both short and long-term bottlenecks. Short-term bottlenecks are temporary and are not normally a significant problem. An example of a short-term bottleneck would be a skilled employee taking a few days off. Long-term bottlenecks occur all the time and can cumulatively significantly slow down production. An example of a long-term bottleneck is when a machine is not efficient enough and as a result has a long queue.

Production planning border area between business management, engineering, industrial engineering, and computer science in particular, the economic

Production planning is the planning of production and manufacturing modules in a company or industry. It utilizes the resource allocation of activities of employees, materials and production capacity, in order to serve different customers.


  1. Marcus V. Magalhaes and Nilay Shah, “Crude Oil Scheduling,” Foundations of Computer-Aided Operations (FOCAPO) 2003,pp 323-325.
  2. Zhenya Jia and Marianthi Ierapetritou, “Efficient Short-Term Scheduling of Refinery Operation Based on a Continuous Time Formulation,” Foundations of Computer-Aided Operations (FOCAPO) 2003, pp 327-330
  3. Toumi, A., Jurgens, C., Jungo, C., MAier, B.A., Papavasileiou, V., and Petrides, D., “Design and Optimization of a Large Scale Biopharmaceutical Facility using Process Simulation and Scheduling Tools,” Pharmaceutical Engineering (the ISPE magazine) 2010, vol 30, no 2, pp 1-9.
  4. Papavasileiou, V., Koulouris, A., Siletti, C., and Petrides, D., “Optimize Manufacturing of Pharmaceutical Products with Process Simulation and Production Scheduling Tools,” Chemical Engineering Research and Design (IChemE publication) 2007, vol 87, pp 1086-1097
  5. Michael Pinedo, Scheduling Theory, Algorithms, and Systems,Prentice Hall, 2002,pp 1-6.
  6. T. F. Edgar, C.L. Smith, F. G. Shinskey, G. W. Gassman, P. J. Schafbuch, T. J. McAvoy, D. E. Seborg, Process control, Perry’s Chemical Engineer’s Handbook, R. Perry and D. W. Green eds.,McGraw Hill, 1997,p 8-41.
  7. Charlotta Johnsson, S88 for Beginners, World Batch Forum, 2004.
  8. L.T. Biegler, I. E. Grossman and A. W. Westerberg, Systematic Methods of Chemical Process Design, Prentice Hall, 1999 p181.
  9. M. Pinedo, 2002, pp 14-22.
  10. Biegler et al. 1999, p187
  11. M. Pinedo, 2002, p430
  12. M. Pinedo, 2002, p28
  13. G. Plenert and G/ Kirchmier, 2000, pp38-41
  14. C. Mendez, J. Cerda, I. Grossman, I. Harjunkoski, M. Fahl, State of the art Review of Optimization Methods for Short Term Scheduling of Batch Processes, Computers and Chemical Engineering, 30 (2006), pp 913-946
  15. I. Lustig, Progress in Linear and Integer Programming and Emergence of Constraint Programming, Foundations of Computer-Aided Operations (FOCAPO) 2003, 133-151
  16. L. Zeballos and G.P. Henning, A Constraint Programming Approach to the Multi-Stage Batch Scheduling Problem, Foundations of Computer-Aided Operations (FOCAPO), 2003, 343-346

Further reading