Explore the vast range of titles available.

Purpose Part building orientation (PBO) is an important factor affecting the quality of laser powder bed fusion (L-PBF), which can affect the surface quality and manufacturing cost. The purpose of this paper is to propose a PBO optimization method to optimize the surface roughness and molding time of parts at the same time on the premise of small calculation scale and arbitrary resolution. Design/methodology/approach Efficient and accurate evaluation is an important index of PBO optimization method. In this paper, a PBO optimization method based on scaling enumeration method is proposed, and the surface roughness and molding time of L-PBF parts are modeled as the objective evaluation function of PBO optimization process. To realize multi-objective optimization, an expert system is established, and the fuzzy multiple-attribute group decision-making theory is used to provide weights for each objective evaluation function. Findings Research shows that the scaling-enumeration method can optimize the surface roughness and molding time at the same time and get the best PBO. Compared with the traditional method, the surface roughness and molding time are reduced by 1.1% and 0.58%, respectively, and the operation scale of the scaling-enumeration method is reduced by 99% compared with the traditional method. PBO with arbitrary angular resolution can be achieved. Originality/value This paper presents a new method to optimize the forming direction of L-PBF parts. This method has small operation scale and accurate results, so it is meaningful for industrial application.

Optimizing the locations of sewage treatment plants has enormous practical significance. In this study, a large-system mathematical model was developed for optimizing the locations of sewage treatment plants within a system and designing the associated pumping station pipe network. Head loss of pipe segments in the pipe network was the coupling constraint, the economic flow rate of pipe segments was determined by the feasible region constraints of decision variables, and the design variables were the sewage treatment plant locations, the design head of the pumping stations, the pipeline economic life, and the pipe diameter of divided pipe segments. The minimum total annual cost of the sewage treatment plant(s) and the pumping station pipe network was the objective function. A large-system quadratic orthogonal test-based selection method was used with a discrete enumeration comparison and selection method to determine pipeline economic life. A dynamic programming method was used to determine the pipe diameter of the divided pipe segments. By comparing the total annual cost of the sewage treatment plants and the associated pumping station pipe network corresponding to different pipeline economic lifetimes, the optimal solution that generates the minimum total annual cost can be identified. The sewage treatment plant and pumping station pipe network in Taizhou, China, was used as an example to compare and analyze optimization results. The new optimization method would have produced much lower annual cost than that of the existing system. This study provides valuable theoretical references for probing the layout design of urban sewage treatment plants corresponding to different pipeline economic lifetimes.

It is strategically important to optimize the pipe network used for reclaimed urban water reuse. A large and complex system model is developed, with the minimum total annual cost of the pumping station and pipe engineering as objective functions, the head loss in the divided pipe segment as the coupling constraint, the economic flow velocity and the optimal diameter of a standard pipe as feasible domain constraints, and the design head and diameter of the pipe as decision variables. The problem is solved by combining the ratio of the discrete enumeration of the design head to the decomposition-dynamic programming aggregation of the large-scale system. This strategy can solve the problem of the optimal pipe diameter in each section of the pipe network engineering under a fixed design head of the pressurized pumping station. On this basis, the annual cost of reclaimed water reuse pipe network engineering is compared under the different design heads of the pressurized pumping stations, allowing the optimal design scheme for reclaimed water reuse pipe network engineering to be obtained. At the same time, the optimal design head of the pressurized pumping station and the optimal pipe diameter of each section can be identified. This can provide a theoretical reference for the optimal design of urban and rural water pipe networks.

This study attempts to propose an availability optimization decision support design system for repairable n-stage mixed systems, in which different combinations of subsystems, such as parallel, standby, and k-out-of-q, are connected in series configuration. The enumeration method, tabu search, simulated annealing, non-equilibrium simulated annealing, and the modified redundancy allocation heuristic combined with a modified genetic algorithm will be proposed to solve the system availability optimization problem and further determine the appropriate system configuration design. Several simulated cases are conducted by following the procedural flow of the proposed availability optimization decision support design system to reach the optimal allocations of the component redundancy amount, the optimal repair rates, and the optimal failure rates of all subsystems to minimize the total system cost under several configuration constraints for different repairable n-stage mixed systems. Simulated results display that the proposed availability optimization decision support design system can definitely take advantage of different component redundancy system designs, including the parallel-series system, n-stage standby system, n-stage k-out-of-q system, and n-stage mixed system, to save a lot of cost and meet the high level of the system availability requirement compared to the n-stage single component series system. Additionally, the results for all proposed combined methods also show that the parallel-series system can obviously reach the same level of system availability requirement with less system total cost, in contrast to the n-stage standby system, by presuming the identical deteriorating probability for both the operating components and the standby components. The performance comparisons of five proposed combined methods for four proposed system configurations are analyzed comprehensively. It can be concluded that the performances of the modified redundancy allocation heuristic method, combined with a modified genetic algorithm on the criteria of the optimal system costs for four proposed system configurations, are not only superior to the other four combined methods, but also to the performances on the criteria of CPU running time for four proposed system configurations.

This paper presents a simple and quick control strategy for a class of first-order nonholonomic manipulator: planar n -link manipulator with a passive first joint (no gravity). The control target is driving its endpoint from any initial position to any target position. First, we reduce the planar n -link manipulator to a planar three-link one by maintaining the states (angles and angular velocities) of n -3 active links in the initial value all the time. That is, we only adjust the states of the remaining two active links in the whole control process, and these two adjusted active links are chosen to guarantee that the target position is in the reachable region of the planar n -link manipulator by using the enumeration method. Then, we divide the whole control process into two stages for the reduced planar three-link manipulator. In each stage, the manipulator becomes a planar two-link one by maintaining the states of one adjusted active link to be the constant value. The state constraint existing between the passive first link and the adjusted active link is obtained by using the integral characteristics of a planar two-link manipulator. Meantime, the geometric constraint between the position of the endpoint and angles of all joints is obtained based on the homogeneous coordinate transformation method. According to the above two kinds of constraint, the target angles of the two adjusted active links are calculated by employing the particle swarm optimization algorithm. When the two adjusted active links are controlled to their target angles in turn, the control target of the planar n -link manipulator is completed. Finally, simulation results demonstrate that the proposed control strategy is valid and rapid.

With continued development of novel molecular-based technologies for rapid, high-throughput detection of foodborne pathogenic bacteria, the future of conventional microbiological methods such as viable cell enumeration, selective isolation of bacteria on commercial media, and immunoassays seems tenuous. In fact, a number of unique approaches and variations on existing techniques are currently on the market or are being implemented that offer ease of use, reliability, and low cost compared with molecular tools. Approaches that enhance recovery of sublethally injured bacteria, differentiation among species using fluorogenics or chromogenics, dry plate culturing, differentiation among bacteria of interest using biochemical profiling, enumeration using impedence technology, techniques to confirm the presence of target pathogens using immunological methods, and bioluminescence applications for hygiene monitoring are summarized here and discussed in relation to their specific advantages or disadvantages when implemented in a food microbiology setting.Key words: food pathogen, detection, enumeration methods, food safety.

A new approach to solving a large class of factorable nonlinear programming (NLP) problems to global optimality is presented in this paper. Unlike the traditional strategy of partitioning the decision-variable space employed in many branch-and-bound methods, the proposed approach approximates the NLP problem by a reverse-convex programming (RCP) problem to a controlled precision, with the latter then solved by an enumerative search. To establish the theoretical guarantees of the method, the notion of “RCP regularity” is introduced and it is proven that enumeration is guaranteed to yield a global optimum when the RCP problem is regular. An extended RCP algorithmic framework is then presented and its performance is examined for a small set of test problems.

This study considers the team recommendation problem as a generalized assignment problem. Firstly, a formal description of the team recommendation problem is given; secondly, a team-recommended generalized assignment model (TRGAM) is established based on the work ability value of alternative members, the comprehensive work ability value of the team as the core concern index, the importance weight of team tasks and the energy allocation weight of team members as the fusion strategy of the data; thirdly, a solution method for the standard case of TRGAM is designed using the enumeration method and Hungarian algorithms (BEM⊕HM–TRGAMs) as local computational tools; fourthly, the alternative member set refinement methods and standardization measures for TRGAM are given; finally, BEM⊕HM–TRGAMs are analyzed using specific arithmetic examples. The theoretical analysis and experimental results show that TRGAM has good structural features and interpretability and BEM⊕HM–TRGAMs can effectively solve the TRGAM solving problem.

As the stages of emergency response shift, it is an important task in the early stage of emergency response to handle emergency supply (ES) collection with fuzzy demand and multi-hub ES collection network. Taking into consideration the constraints such as no limit to collection time, capacity constraint to single hubs and different demand levels to be met by collection, this paper aims at establishing a mathematic planning model with two objectives, namely the shortest collection time and the optimal collection cost. In terms of algorithm design, the sequential enumeration method (SEM) was employed to solve the model, for higher iteration efficiency and lower difficulty level of the algorithm. The first step was to find the optimal ES collection time, collection route and allocation program in the direct shipment mode, the hub-and-spoke mode and the mixed mode. Then, the three modes were compared in terms of the collection route planning, the shortest collection time, the shortest collection route and the optimal collection cost of the entire ES collection network. The numerical example showed that compared with the simulated annealing (SA) algorithm, the result of SEM is basically the same with that of SA algorithm; however, SEM is simpler, more practical and easier to operate, so that it is of better promotional value.

The target of this work is the determination of the optimal solution in transmission expansion planning in the particular case of isolated systems involving dc interconnections. Therefore special attention is given to dc-links modelling aspects for steady state analysis, as well as security constraints relevant to small sized systems operation. Therein lies the first contribution of this work, namely in the incorporation of operating reserve related considerations and simplified dc-links models in isolated systems planning. The proposed method combines a Monte-Carlo simulation, which enables the probabilistic assessment of power system operation, with an adaptive enumeration technique which recurrently determines and evaluates investment scenarios of interest, based on suitable probabilistic indices. This constitutes the second contribution of this work, as a map of all meaningful solutions to the planning problem may be generated, which in turn allows not only the determination of the optimal solution, but also the evaluation of the effects of over- or under-investment in certain parts of the grid.