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The air‐breathing hypersonic vehicle (AHV) includes intricate inherent coupling between the propulsion system and the airframe dynamics, which results in an intractable nonlinear system for the controller design. The purpose of this paper is to propose an H∞ control method for AHV based on the online simultaneous policy update algorithm (SPUA).

Initially, the H∞ state feedback control problem of the AHV is converted to the problem of solving the Hamilton‐Jacobi‐Isaacs (HJI) equation, which is notoriously difficult to solve both numerically and analytically. To overcome this difficulty, the online SPUA is introduced to solve the HJI equation without requiring the accurate knowledge of the internal system dynamics. Subsequently, the online SPUA is implemented on the basis of an actor‐critic structure, in which neural network (NN) is employed for approximating the cost function and a least‐square method is used to calculate the NN weight parameters.

Simulation study on the AHV demonstrates the effectiveness of the proposed H∞ control method.

The paper presents an interesting method for the H∞ state feedback control design problem of the AHV based on online SPUA.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

In order to reduce logistics transportation costs and respond to low-carbon economy, the purpose of this paper is to study the more practical and common simultaneous pickup and delivery vehicle routing problem, which considers the carbon tax policy. A low-carbon simultaneous pickup and delivery vehicle routing problem model is constructed with the minimum total costs as the objective function.

This study develops a mathematical optimization model with the minimum total costs, including the carbon emissions costs as the objective function. An adaptive genetic hill-climbing algorithm is designed to solve the model.

First, the effectiveness of the algorithm is verified by numerical experiments. Second, the research results prove that carbon tax mechanism can effectively reduce carbon emissions within effective carbon tax interval. Finally, the research results also show that, under the carbon tax mechanism, the effect of vehicle speed on total costs will become more obvious with the increase of carbon tax.

This paper only considers the weight of the cargo, but it does not consider the volume of the cargo.

Few studies focus on environmental issues in the simultaneous pickup and delivery problem. Thus, this paper constructs a green path optimization model, combining the carbon tax mechanism for the problem. This paper further analyzes the impact of carbon tax value on total costs and carbon emission; at the same time, the effect of vehicle speed on total cost is also analyzed.

This paper proposes a new simultaneous optimization model of the industrial systems design and maintenance. This model aims to help the designer in searching for technical solutions and the product architecture by integrating the maintenance issues from the design stage. The goal is to reduce the life-cycle cost (LCC) of the studied system.

Literature indicates that the different approaches used in the design for maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and the maintainability of a multicomponent system as well as the modeling of the dynamic maintenance. This article proposes to go further in the optimization of the product, by simultaneously characterizing the design, in terms of reliability and maintainability, as well as the dynamic planning of the maintenance operations. This combinatorial characterization is performed by a two-level hybrid algorithm based on the genetic algorithms.

The proposed tool offers, depending on the life-cycle expectation, the desired availability, the desired business model (sales or rental), simulations in terms of the LCCs, and so an optimal product architecture.

In this article, the term “design” is limited to reliability properties, possible redundancies, component accessibility (maintainability), and levels of monitoring information.

This work is distinguished by the use of a hybrid optimization algorithm (two-level computation) using genetic algorithms. The first level is to identify an optimal design configuration that takes into account the LCC criterion. The second level consists in proposing a dynamic and optimal maintenance plan based on the maintenance-free operating period (MFOP) concept that takes into account certain criteria, such as replacement costs or the reliability of the system.

The paper's purpose is to provide a method of reducing inventory costs in multi‐product and multi‐nodes supply chains (SC).

The proposed approach is based on applying the classical inventory control models and simulation. This is a two‐stage approach in which inventory cost reduction in the SC occurs as a result of the appropriate selection of inventory control policies by its members.

Cost reduction in the whole SC may occur as a result of an appropriate choice of inventory control policies in every node. SC members can learn which inventory control policies should be applied. Results of the learning process are closely connected with a kind of collaboration between chain nodes. The best results of the whole system can happen with a simultaneous deterioration of operation of its component elements. Therefore, losses which occur in some chain members as a result of collaboration should be compensated.

This approach does not offer an optimal solution. The authors do not know how far they are from an optimal solution. The approach should be tested for more complicated SC. The authors have not studied the strategy in which SC cells negotiate the level of their inventories.

SC cells should apply a local cooperation strategy. When the cost of the whole SC decreases, the cost in particular nodes may significantly increase. The start of collaboration in the SC can cause a deterioration of results in some companies; therefore this loss should be compensated.

The paper presents the development of a framework in which an application of different policies of inventory control and application of different collaboration strategies in the SC are studied. The framework enables the use of classical inventory control models in a coordinated manner, and SC members decide which policies should be applied only on the basis of earlier collected experiences.

Cycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.

A stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.

First, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.

A novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.

The purpose of the study is to fill a gap in the literature on mathematical production planning (joint balancing and sequencing) in the fashion industry. It considers in particular situations of mass customization, made-to-measure or small lot sizes.

The paper develops a mathematical model based on product options and attributes instead of fixed variants. It proposes an easy-to-use genetic algorithm to solve the resulting optimization problem. Functionality and performance of the algorithm are illustrated via a computational study.

An easy-to-implement, yet efficient algorithm to solve the multi-objective implementation of a problem structure that becomes increasingly relevant in the fashion industry is proposed. Implementation of the algorithm revealed that the algorithm is ideally suited to generate significant savings and that these savings are impervious to problem and thus company size.

The solutions from the algorithm (Pareto-efficient frontier) offer decision-makers more flexibility in selecting those solutions they deem most fitting for their situation. The computational study illustrates the significant monetary savings possible by implementing the proposed algorithm to practical situations.

In contrast to existing papers, for the first time, to the best of the author’s knowledge, the focus of the joint balancing and sequencing approach has been applied in the fashion instead of the automotive industry. The applicability of the approach to specific fields of the fashion industry is discussed. An option and attributes-based model, rarely used in general assembly line sequencing per se, is used for more flexibility in representing a diverse set of model types.

The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.

The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

This paper provides a new Digital Library architecture that supports polyhierarchic ontology structure where a child concept representing an interdisciplinary subject area can have multiple parent concepts. The paper further proposes an access control mechanism for controlled access to different concepts by different users depending on the authorizations available to each such user. The proposed model thus provides a better knowledge representation and faster searching possibility of documents for modern Digital Libraries with controlled access to the system.

Since the proposed Digital Library Architecture considers polyhierarchy, the underlying hierarchical structure becomes a Directed Acyclic Graph instead of a tree. A new access control model has been developed for such a polyhierarchic ontology structure. It has been shown that such model may give rise to undecidability problem. A client specific view generation mechanism has been developed to solve the problem.

The paper has three major contributions. First, it provides better knowledge representation for present-day digital libraries, as new interdisciplinary subject areas are getting introduced. Concepts representing interdisciplinary subject areas will have multiple parents, and consequently, the library ontology introduces a new set of nodes representing document classes. This concept also provides faster search mechanism. Secondly, a new access control model has been introduced for the ontology structure where a user gets authorizations to access a concept node only if its credential supports it. Lastly, a client-based view generation algorithm has been developed so that a client’s access remains limited to its view and avoids any possibility of undecidability in authorization specification.

The proposed model, in its present form, supports only read and browse facilities. It would later be extended for addition and update of documents. Moreover, the paper explains the model in a single user environment. It will be augmented later to consider simultaneous access from multiple users.

The paper emphasizes the need for changing the present digital library ontology to a polyhierarchic structure to provide proper representation of knowledge related to the concepts covering interdisciplinary subject areas. Possible implementation strategies have also been mentioned. This design method can also be extended for other semantic web applications.

This paper offers a new knowledge management strategy to cover the gradual proliferation of interdisciplinary subject areas along with a suitable access control model for a digital library ontology. This methodology can also be extended for other semantic web applications.