Scheduling is the allocation of resources over time to perform a collection of tasks, while taking all constraints into account, so that input demands are met in a timely and cost-effective manner. Most typically, this involves determining a set of activity start and end times, together with resource assignments, which satisfy all temporal constraints on activity execution, satisfy resource capacity constraints, and optimize some set of performance objectives to the extent possible. The result of scheduling is a schedule, which can be defined as a plan with reference to the sequence of and time allocated for each item or operation necessary to its completion. For example, time is the scarcest resource and scheduling could help us making the most of a limited amount of time. Effective scheduling can lead to due date performance that results in meeting the company’s customer service goals, and reducing work in process inventories and production lead times.
Scheduling has been primarily studied from a mathematical point of view in a discipline called operations research. In the last 40 years, operations research has produced over 20,000 publications about scheduling problems. From this body of research, we now understand that it is often difficult to make a single schedule for the whole task system of an organization. Therefore, the task systems are often decomposed into a hierarchically organized planning and control structure to reduce the complexity of the scheduling problem. This approach is also known as hierarchical production planning (HPP), which is characterized by detailed, direct, restricted, and sustained control on the tasks, available materials, and capacity. Besides HPP, some other useful scheduling tools include the Gantt chart, Excel, heuristics, constraint programming, (integer) linear programming, and Advanced Planning System.
There are three general models in scheduling: time-line model, order-centric model, and resource centric model. A time-line model slices time and duration for each task, using the greatest common divisor of the activities’ duration as a base. It is generally used when a resource can be described in simple terms. An order-centric model studies a chain of activities per order by assigning resources to these activities. It considers three constraints: the supplier/consumer dependencies, the resource constraints, and the transition constraints. It is generally used for an order-driven production with a small number of alternatives and simple resource constraints. A resource-centric model deals with a sequence of activities per resource, with the focus on “what the resource can process” rather than “how to satisfy the order.” It is generally used when the resource constraints are complex, with nonordered production.
The classic view on scheduling is to treat it as a problem-solving activity, that is, given problem constraints and objective criteria, scheduling is to figure out how to best cover the capacity over time surface with operations. The research goal of the classic view is to specify new problems and provide new optimum solutions. However, current researchers realized that practical problems can rarely be formulated as static optimization tasks. In reality, scheduling is an ongoing iterative process situated in a larger problem-solving context in a dynamic, unpredictable environment. Thus, scheduling is an ongoing process of responding to change. Based on this observation, the research goal of the modern view is to build schedules that retain flexibility and promote localized recovery with incremental rescheduling techniques and self-scheduling control systems.
In practice, scheduling is tightly coupled with an individual, that is, the scheduler. Although there is a large variety in responsibilities and roles that are fulfilled by schedulers, it is possible to mention some common characteristics. Often, a scheduler is someone who almost naturally assumes responsibility for the progress and timeliness of tasks, regardless of formal responsibilities. This feeling of being responsible is strengthened by the fact that the scheduler is an essential source of information for many colleagues, customers, and suppliers. Usually, only a small portion of the time of the scheduler is spent on constructing an initial schedule, whereas a large portion of the time is spent on monitoring the execution of the schedule. The aim of the monitoring activities is to identify problems, which are often solved by the scheduler, using a variety of skills such as communication, negotiation, and intuition. Schedulers also try to anticipate possible problems, with varying success. Thus, many practitioners in operations management are convinced of the fact that manual scheduling is to a great extent subject to improvement.
- Aarup, Monte Zweben, and Mark Fox, Intelligent Scheduling (Morgan Kaufmann Publishers, 1994);
- Kenneth R. Baker, Introduction to Scheduling and Sequencing (Wiley, 1974);
- I. Dessouky, N. Moray, and B. Kijowski, “Strategic Behavior and Scheduling Theory,” Human Factors (v.37/3, 1995);
- B. Gargeya and R. H. Deane, “Scheduling Research in Multiple Resource Constrained Job Shops: A Review and a Critique,” International Journal of Production Research (v.34/8, 1996);
- Nicholas George Hall, Lei Lei, and Michael Pinedo, Supply Chain Coordination and Scheduling (Springer, 2008);
- Joanna Jozefowska, Justin-Time Scheduling Models and Algorithms for Computer and Manufacturing Systems (Springer Science+Business Media, 2007);
- Pamela M. Sanderson, “Towards the Model Human Scheduler,” International Journal of Human Factors in Manufacturing (v.1/3, 1991);
- Elizabeth Szelke and Roger M. Kerr, “Knowledge-based Reactive Scheduling,” Production Planning & Control (v.5/2, 1994).
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