Scheduling theory is proposed as a normative model for strategic behavior when operators are confronted by several tasks, all of which should be completed within a fixed time span, and when they are free to choose the order in which the tasks should be done. Three experiments are described to investigate the effect of knowing the correct scheduling rule on the efficiency of performance, subjective workload, and choice of strategy under different conditions of time pressure. The most potent effects are from time pressure. The reasons for the weak effect of knowing the rules are discussed, and implications for strategic behavior, displays, and decision aids are indicated.
Strategic behavior is frequently characterized by the need to decide among several courses of action, each of which may lead to a desired goal, subject to time constraints. Often strategic behavior can be regarded as a series of answers to the question, "In what sequence should I perform the set of actions required, and when should I start and stop each of them?" Scheduling theory, which is usually used to determine the sequencing of operations in such situations as transportation and manufacturing, provides normative answers to such a question. We introduce the concepts and terminology of scheduling theory and show how these can be identified with aspects of human operator behavior. Scheduling theory can provide a systematic conceptual framework for planning research on behavior in complex human-machine settings, both in and beyond laboratory contexts. It can be used to discover optimal or satisficing strategies and to provide norms against which to measure the quality of strategic decision making and performance in complex systems. The use of scheduling theory is one example of the many well-developed quantitative models available in operations research that are applicable to the analysis of behavior, well beyond the discrete trials paradigm that often characterizes human factors laboratory research.
We address the problem of scheduling a single-stage multi-product batch chemical process with fixed batch sizes. We present a mixed-integer nonlinear programming model to determine the schedule of batches, the batch size, and the number of overtime shifts that satisfy the demand at minimum cost for this process. We introduce a polynomial-time algorithm to solve the problem when the processing times of an batches are identical and the setup and cleaning times are sequence-independent. The solution procedure is based on recognizing that the optimal fixed batch size is a member of a set whose cardinality is polynomial. Given a batch size, the problem may be formulated as an assignment problem. Thus, an optimal solution may be found by iteratively solving a polynomial number of assignment problems. This work was motivated by a pesticide manufacturing company in the design of a new plant where the assumptions of a single bottleneck machine, fixed batch sizes, sequence-independent setup times, and identical batch processing times are all valid. An example is developed for this application.
We consider the problem of determining the allocation of demand from different customer orders to production batches and the schedule of resulting batches to minimize the total weighted earliness and tardiness penalties in context of batch chemical processing. The problem is formulated as a mixed-integer nonlinear programming model. An iterative heuristic procedure that makes use of the network nature of the problem formulation is presented to approximate an optimal solution. An algorithm polynomial in the number of batches to produce is also presented that optimally solves the problem under special cost structures.
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