Sequencing and scheduling in disassembly shops: a review of problem formulations and solution approaches

Stochastic terminal flight arrival and departure scheduling problem under performance-based navigation environment

Military operations are turning to more complex and advanced automation technologies forminimumriskandmaximumefficiency.Acriticalpiecetothisstrategyisunmannedaerial vehicles. Unmanned aerial vehicles require the intelligence to safely maneuver along a path to an intended target and avoiding obstacles such as other aircrafts or enemy threats. This paper presents a unique three-dimensional path planning problem formulation and solution approach using particle swarm optimization. The problem formulation was designed with three objectives: 1) minimize risk owing to enemy threats, 2) minimize fuel consumption incurred by deviating from the original path, and 3) fly over defined reconnaissance targets. The initial design point is defined as the original path of the unmanned aerial vehicles. Using particle swarm optimization, alternate paths are generated using B-spline curves, optimized based on the three defined objectives. The resulting paths can be optimized with a preference toward maximum safety, minimum fuel consumption, or target reconnaissance. This method has been implemented in a virtual environment where the generated alternate paths can be visualized interactively to better facilitate the decision-making process. The problem formulation and solution implementation is described along with the results from several simulated scenarios demonstrating the effectiveness of the method.

Purpose of ReviewTask allocation using a team or coalition of robots is one of the most important problems in robotics, computer science, operational research, and artificial intelligence. We present a survey of multi-robot task allocation covering many problem variants and solution approaches, both centralized and decentralized.Recent FindingsIn recent work, research has focused on handling complex objectives and feasibility constraints amongst other variations of the multi-robot task allocation problem. There are many examples of important research and recent progress in these directions, which are captured in this survey, along with similar examples for the various solutions that have been developed to solve such problems.SummaryWe first present a general formulation of the task allocation problem that generalizes several versions that are well-studied. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate, and we describe how the problem can be varied depending on the feasibility constraints, objective functions, and the level of dynamically changing information. In addition, we discuss existing solution approaches for the problem including optimization-based approaches, and market-based approaches.

Delivering the right information at the right time to the right persons is one of the most important requirements of today’s business world. Nevertheless, enterprise systems do not always provide the information in a way suitable for the individual information needs and working practices of business users. Due to the complexity of enterprise systems, business users are not able to adapt these systems to their needs by themselves. The adoption of End-User Development (EUD) approaches, supporting end-users to create individual software artifacts for information access and retrieval, could enable better utilization of existing information and better support of the long tail of end-users’ needs. In this paper, we assess possibilities for improving information management through EUD, by analyzing relevant problem domains and solution approaches considering fundamental aspects of technology acceptance theories. The analysis is based on a questionnaire survey, conducted in three midsized German companies. We investigate the domains of information access and the flexible post-processing of enterprise data. Therefore we assess the importance of the respective domain for the work of end-users, perceived pain points, the willingness to engage in related EUD activities and the perceived usefulness of concrete EUD approaches we developed to address the respective domains.

This paper presents a framework to determine the feasible dispatch limits of solar photovoltaic (PV) generation in the unbalanced distribution network considering the interconnection of electric vehicles (EVs) and associated uncertainties. The proposed framework determines the lower and upper dispatch limits of PV generation considering the worst-case realization of a) the minimum and maximum storage capacity of EVs, b) the minimum and maximum power dispatch of EVs, c) the lower and upper bounds for the arrival and departure times, and d) the available energy at arrival and departure times. The unbalanced operation of the distribution network as well as the uncertainty in the maximum PV generation and demand forecasts were considered. The problem formulation and solution approach are validated using the modified IEEE 34-bus and IEEE 123-bus distribution systems. The impacts of the budget of uncertainty and the vehicle-to-grid operation mode of EV clusters were addressed in the case studies. It is shown that integrating EVs with charging capability will increase the lower dispatch limit or increase the upper dispatch limit of the PV generation. Moreover, the increase in the budget of uncertainty will reduce the difference between the upper and lower dispatch limits by increasing the lower dispatch or decreasing the upper dispatch limit of the PV generation. Finally, it is shown that the vehicle-to-grid capability will reduce the total lower dispatch limit or increase the total upper dispatch limit of the PV generation in the operation horizon.

Stereolithography apparatus (SLA) is capable of in situ fabrication of complex parts, as well as mechanisms and complex devices with embedded components. In this paper, a series of example devices are presented to illustrate the power of building around embedded components (inserts). The problem formulation, solution approach, and specific rules and procedures are presented using these examples and experimental results. A case study approach is used for presentation. These procedures and results lend insight into promising new applications of SLA technology, as well as novel methods of implementing additional functionality into SLA and other rapid prototyping technologies.

We study the design of linear precoders for secure transmission in the two-user multiple-input single-output (MISO) broadcast channel with confidential messages (BC-CM). The transmitter has multiple antennas, and each user has a single receive antenna. Two independent messages are simultaneously transmitted, one intended for each user, and each message should be kept confidential from the other user. Assuming real-valued transmitted signals, we design the linear precoders subject to total and per-antenna average power constraints, and also subject to amplitude constraints. In both cases, we tackle the design problem via weighted secrecy sum rate maximization. The resulting problem, however, involves a fractional objective, making it nonconvex and difficult to solve. Nevertheless, we show that this difficult problem can be transformed into a more tractable problem, for which a solution can be obtained by an iterative search algorithm. In addition, we characterize a condition under which the obtained solution is guaranteed to be optimal. Furthermore, we show that the problem formulation and solution approach can be easily extended to handle the robust version of the design problem with uncertain channel information. We provide numerical examples to demonstrate the performance of the proposed precoder in terms of the achievable secrecy rate regions subject to the aforementioned constraints. We also demonstrate the performance of the robust precoder under different channel uncertainty levels.

We survey recent research and practice in the area of chemical-mechanical polishing (CMP) fill synthesis, in terms of both problem formulations and solution approaches. We review the CMP as the planarization technique of choice for multilevel very large-scale integration metallization processes. Post-CMP wafer topography varies according to pattern density. We review density-analysis methods and density-control objectives that are used in today's fill-synthesis algorithms. In addition, we discuss the concept of design-driven fill synthesis that seeks to optimize CMP fill with respect to objectives beyond mere density uniformity. Design-driven fill synthesis minimizes the impact of CMP fill on design performance and parametric yield while still satisfying the density criteria that are motivated by manufacturing models. We conclude with a discussion of where CMP fill synthesis may be naturally integrated within future design and manufacturing flows.

The theory of singular perturbations and time scales has played an important role in aircraft performance analysis and in the development of feedback guidance laws for aerospace vehicles. This paper presents a survey of the applications of the theory of singular perturbations and time scales in guidance, navigation, and control of air and space transportation systems. In particular, emphasis will be placed on problem formulation and solution approaches that are useful in applying the theory for various types of problems arising in aerospace systems. Fhrther, the survey e m braces the related fields such as fluid dynamics, space structures and space robotics.

Future wide-area measurement and control applications in large electric power systems will require a new decentralized architecture that scales up with the rapidly growing deployment of Phasor Measurement Units (PMUs). The emerging cloud computing paradigm that allows dynamic creation of virtual machines to form virtual data centers would help better support this new architecture through more efficient and flexible use of the networking and computing resources. However, this paradigm shift poses new technical challenges to the underneath communication and computing infrastructure leading to new problem formulations and solution approaches. Given that the primary communication pattern in the decentralized system will consist of various types of real-time group communication methods, in this paper we present a preliminary study on two problems, namely communication group formation and routing, that are fundamental to the envisioned new communication architecture.

Recent directions in netlist partitioning: a survey

The reconstruction of clonal haplotypes and their evolutionary history in evolving populations is a common problem in both microbial evolutionary biology and cancer biology. The clonal theory of evolution provides a theoretical framework for modeling the evolution of clones. In this paper, we review the theoretical framework and assumptions over which the clonal reconstruction problem is formulated. We formally define the problem and then discuss the complexity and solution space of the problem. Various methods have been proposed to find the phylogeny that best explains the observed data. We categorize these methods based on the type of input data that they use (space-resolved or time-resolved), and also based on their computational formulation as either combinatorial or probabilistic. It is crucial to understand the different types of input data because each provides essential but distinct information for drastically reducing the solution space of the clonal reconstruction problem. Complementary information provided by single cell sequencing or from whole genome sequencing of randomly isolated clones can also improve the accuracy of clonal reconstruction. We briefly review the existing algorithms and their relationships. Finally we summarize the tools that are developed for either directly solving the clonal reconstruction problem or a related computational problem. In this review, we discuss the various formulations of the problem of inferring the clonal evolutionary history from allele frequeny data, review existing algorithms and catergorize them according to their problem formulation and solution approaches. We note that most of the available clonal inference algorithms were developed for elucidating tumor evolution whereas clonal reconstruction for unicellular genomes are less addressed. We conclude the review by discussing more open problems such as the lack of benchmark datasets and comparison of performance between available tools.

A solution approach for contact problems based on the dual interpolation boundary face method

PurposeThe purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and mean-variance optimisation of multiple “quality characteristics” (or “responses”), considering predictive uncertainties. The second objective is comparing the solution qualities of the proposed approach with those of existing approaches. The third objective is the proposal of a modified non-dominated sorting genetic algorithm-II (NSGA-II), which improves the solution quality for multiple response optimisation (MRO) problems.Design/methodology/approachThe proposed solution approach integrates empirical response surface (RS) models, a simultaneous prediction interval-based MOO iterative search, and the multi-criteria decision-making (MCDM) technique to select the best implementable efficient solutions.FindingsImplementation of the proposed approach in varied MRO problems demonstrates a significant improvement in the solution quality in worst-case scenarios. Moreover, the results indicate that the solution quality of the modified NSGA-II largely outperforms those of two existing MOO solution strategies.Research limitations/implicationsThe enhanced MOO solution approach is limited to parametric RS prediction models and continuous search spaces.Practical implicationsThe best-ranked solutions according to the proposed approach are derived considering the model predictive uncertainties and MCDM technique. These solutions (or process setting conditions) are expected to be more reliable for satisfying customer specification compared to point estimate-based MOO solutions in real-life implementation.Originality/valueNo evidence exists of earlier research that has demonstrated the suitability and superiority of an MOO solution approach for both mean and mean-variance MRO problems, considering RS uncertainties. Furthermore, this work illustrates the step-by-step implementation results of the proposed approach for the six selected MRO problems.

Rescheduling in railway networks is a challenging problem in both practice and theory. It requires good quality solutions in reasonable computation time to resolve unexpected situations, involving different problem scales, railway network infrastructures, objectives, and constraints. This paper presents a comprehensive survey on different problem models for rescheduling in railway networks by a clear classification. Some frequently used models are described in detail through reviewing their variables and constraints. This paper also focuses on the solution approaches proposed in the literature. The main ideas of the solution approaches with the objectives are described. Based on our review results, the analysis of the problem models used in various problem types and the solution approaches used in different problem models are presented. Conclusion and suggestions for further research to rescheduling in railway networks are drawn toward the end of the paper.