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A common problem that many universities face, especially with their specialized programs, is coordinating faculty availability and class offerings. The schedule is usually developed using paper and pencil after numerous iterations. As a result, the objectives of the program, such as course integration, length of course, and student workload, are most likely compromised in lieu of faculty availability. This chapter describes a multiple objective approach to this class assignment problem that considers the program’s objectives and faculty preferences. The results of applying this class assignment model to an Executive MBA (EMBA) program are presented.

Addresses the problem of maintenance planning and scheduling and reviews pertinent literature. Discusses the characteristics and the complexity of the problem. Advocates mathematical programming approaches for addressing the maintenance scheduling problem. Gives examples to demonstrate the utility of these approaches. Proposes expansion of the state‐of‐the‐art maintenance management information system to utilize the mathematical programming approaches and to have better control over the maintenance scheduling problem.

The purpose of this paper is to develop a methodology for automatically determining the optimal allocation of police officers in accordance with the division and organization of labor.

The problem is defined as the problem of the goal programming for which the mathematical model of mixed integer programming was developed. In modeling of the scheduling problem the approach police officer/scheme, based on predefined scheduling patterns, was used. The approach is applied to real data of a police station in Bosnia and Herzegovina.

This study indicates that the determination of monthly scheduling policemen is complex and challenging problem, which is usually performed without the aid of software (self-rostering), and that it can be significantly facilitated by the introduction of scheduling optimization approach.

The developed mathematical model, in its current form, can directly be applied only to the scheduling of police officers at police stations which have the same or a similar organization of work.

Optimization of scheduling significantly reduces the time to obtain a monthly schedule. In addition, it allows the police stations to experiment with different forms of organization work of police officers and to obtain an optimal schedule for each of them in a short time.

The problem of optimal scheduling of employees is often resolved in other fields. To the authors knowledge, this is the first time that the approach of goal programming is applied in the field of policing.

Although line‐of‐balance (LOB) scheduling can be superior to bar charts and networks in repetitive‐unit construction, there are indications that its use is not widespread. In this study, the major limitations of the existing LOB methodology are identified and then eliminated by developing a computer program called repetitive unit scheduling system (RUSS). An effective algorithm that facilitates the implementation of LOB scheduling is developed. A tool that handles logical and strategic limitations caused by the particular characteristics of repetitive activities is provided. A learning model is developed and incorporated into LOB calculations. The program is designed to optimize resource allocation by using multiples of the natural rhythm of activities. An optimum crew size that guarantees maximum productivity in an activity is used throughout the LOB calculations to achieve cost‐optimized schedules. Non‐linear and discrete activities are incorporated into the LOB calculations. RUSS displays the LOB diagram of every individual path in the unit network. It is believed that a system such as RUSS will make the LOB method more appealing to contractors of repetitive projects.

Scheduling is defined by Baker as, “the allocation of resources over time to perform a collection of tasks”. The term facilities is often used instead of resources and the tasks to be performed may involve a variety of different operations.

Learner control is a widely touted and popular element of e-learning, both in the educational and organizational training domains. In this chapter, we explore the concept of learner control, highlighting its multidimensional and psychological nature. We examine the theoretical basis for the effects of learner control on learning and engagement. Next, we provide the reader with empirically based recommendations for designing learner-controlled training. We conclude by discussing how learner control research may be adapted to accommodate a variety of instructional methods, such as textbooks, mobile learning, and Massive Open Online Courses (MOOCs).

purpose of this paper is providing a solution for flexible flow shop scheduling problem with uncertain processing time in aeronautical composite lay-up workshop.

A flexible flow scheduling model and algorithm with interval grey processing time is established. First, according to actual needs of composite laminate shop scheduling process, interval grey number is used to represent uncertain processing time, and interval grey processing time measurement method, grey number calculation and comparison rules, grey Gantt chart, and other methods are further applied. Then a flexible flow shop scheduling model with interval grey processing time (G-FFSP) is established, and an artificial bee colony algorithm based on an adaptive neighbourhood search strategy is designed to solve the model. Finally, six examples are generated for simulation scheduling, and the efficiency and performance of the model and algorithm are evaluated by comparing the results.

Results show that flexible flow shop scheduling model and algorithm with interval grey processing time can provide an optimal solution for composite lay-up shop scheduling problems and other similar flow shop scheduling problems.

Uncertain processing time is common in flexible workshop manufacturing, and manual scheduling greatly restricts the production efficiency of workshop. In this paper, combined with grey system theory, an intelligent algorithm is used to solve flexible flow shop scheduling problem to promote intelligent and efficient production of enterprises.

This paper applies and perfects interval grey processing time measurement method, grey number calculation and comparison rules, grey Gantt chart and other methods. A flexible flow shop scheduling model with interval grey processing time is established, and an artificial bee colony algorithm with an adaptive domain search strategy is designed. It provides a comprehensive solution for flexible flow shop scheduling with uncertain processing time.

Presents a framework for deciding on a good execution strategy for a given program based on the available data and task parallelism in the program on PC laboratory clusters. Proposes a virtual cluster scheduling scheme to take account of the relationships between tasks for task parallelism, and also processor speed, processor load and network environment to balance load for data parallelism in a PC cluster environment. The approach is very effective in terms of the overall execution time, and demonstrates the feasibility of automatic cluster assignment, processor set selection and data partition functions for data and task parallel programs.

Maintenance control plays a key role in achieving the stated objective of effectiveness and efficiency of the maintenance system. In a recent paper, Gits proposed a reference framework that guides in the design and structuring of maintenance control. The framework is conceptual in nature and its use in practice is limited. Poses Gits’ framework as a set of mathematical programming models. Extends some of Gits’ procedure for maintenance control, then outlines the required expansion in the maintenance management information system (MMIS) in order to provide the needed data to execute the models. The models provide operational plans and schedules ready for implementation.

Presents a new approach to working set generation for personnel scheduling problems. In full‐time (FT) and mixed‐workforce (MW) experiments, generates the schedules in the working sets from the use of two‐phase heuristic labour scheduling solution procedures. The solution procedures were implemented on a 386 microcomputer and did not require the specification of the size of the working sets in advance. In the FT experiment, the general set‐covering formulations (GSCFs) associated with the produced working sets were solved with integer programming. The new working set procedure yielded optimal integer solutions for all 36 test problems in the FT experiment. Owing to the size and complexity of the problem data in the MW experiment, the GSCFs associated with the working sets were solved with linear programming, and heuristic rounding procedures were applied to obtain feasible integer solutions. The mean labour costs of these solutions averaged 0.69 per cent less than the mean cost of solutions obtained via the application of heuristic rounding procedures applied to the linear programme solutions for the GSCFs associated with the master sets. Compares solution costs for the new working set method with those associated with other working set generation/refinement procedures. Results indicate that the new method produces lower solution costs in less control processing unit time.