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The purpose of this paper is to develop an attitude control strategy for the reusable boosted vehicle with large angle of attack, and to remove the cross coupling among roll, pitch and yaw channels.

The coordinated gain scheduling control strategy consists mainly of two parts. First, initially ignoring dynamic coupling, single channel gain scheduling controller is designed based on linearized models, respectively. Second, with respect to main channel gain scheduling controller, coordinated scheduling controller is used to generate intentionally cross coupling to partly cancel inter‐channel cross coupling of reusable boosted vehicle.

A coordinated gain scheduling control strategy is presented, and no such analytical solution can be found for the reusable boosted vehicle.

The design idea of coordinated gain scheduling strategy is straightforward in physical concepts and has great value for engineering applications.

Coordinated gain scheduling control strategy is novel in that single channel gain scheduling design does not involve small perturbation linearization and coordinated channel is scheduled.

Three‐dimensional (3‐D) computer aided design (CAD) and its integration with project schedules (4‐D CAD) are promising technologies in design and construction. This paper represents a second study on the effectiveness of visualization on project planning. While the initial study investigated 2‐D, 3‐D and walk‐thru technologies’ impact on project schedule development, the subsequent study reported herein, focuses on the impact of 3‐D/4‐D visualization on project schedule review. Experimental results provide quantitative evidence of the advantages of 3‐D/4‐D representations for schedule review for construction projects.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

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.

In this paper we consider the use of ontologies as the basis for structuring and simplifying the process of constructing real‐time problem‐solving tools, focusing specifically on the task of production scheduling. In spite of the commonality in production scheduling system requirements and design, different scheduling environments invariably present different challenges (e.g. different constraints, different objectives, different domain structure, etc.). The proposed methodology for building ontologies used for production scheduling systems represents a synthesis of extensive work in developing constraint‐based scheduling models for a wide range of applications in manufacturing and production planning. Since the effective modeling is one of the most important and difficult steps in the development of reliable information systems, and taking into consideration the fact that the general problem of the production scheduling in the industries is very difficult and still unsolved, one can easily estimate the merit of this methodology.

Constructs a knowledge‐based simulation model for job shop scheduling simulation techniques to build the initial status of the knowledge base. Uses the simulation experiments to study the effect of technological factors, such as machine loading conditions and criteria of scheduling performance requirements, on the selection of scheduling priority rules. Implements the model with real workshop data to test its validity to cater for real‐world situations such as machine group centres, more than one machine and the availability of operators. The proposed model yields satisfactory results.

Service operations managers often attempt to improve labour‐scheduling efficiency through the use of one or more types of scheduling flexibility. Part‐time labour, for example, is one type of scheduling flexibility which is used in many service operations. The use of part‐time workers increases productivity by providing a closer match between the forecast labour requirements and the amount of labour actually scheduled. Demonstrates the feasibility of implementing a microcomputer‐based approach to workforce‐scheduling problems which contain a substantial degree of scheduling flexibility. This approach has particular application to small service firms which may have, or can obtain, microcomputers to schedule their operating personnel. The actual scheduling procedure is designed to produce daily and weekly work schedules simultaneously (i.e. tour schedules) for workers assumed to be of homogeneous skill and ability, such as those employed in a fast‐food restaurant. The application of such an approach in the microcomputer environment would have a significant impact on the ability of small service establishments further to enhance labour utilization through the increased use of workforce‐scheduling flexibility. Clearly demonstrates that a microcomputer‐based workforce‐scheduling model which incorporates a substantial amount of scheduling flexibility, can and should be developed for use in today′s small service organizations.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?