Generalized CAPP and synthetic evaluation strategy for production line of accuracy welding

This paper presents generalized CAPP （G-CAPP） method which deals with macro process planning for multiobjective in the planning stage of production line of accuracy welding （PLAW） based on the features of accuracy welding production （ AWP ）. G-CAPP offers foundations for prototype design and general equipment sorting, production capacity predication and production analysis by means of simulation and optimization. A synthetic hierarchy evaluation （SHE） model for G-CAPP established according to the planning objective is utilized to estimate the alternate processing plans by using membership function and analytic hierarchy process （AHP） of operational theory. The assembly welding line of hydraulic torque converter （HTC） is as an example of typical A WP to explicate G-CAPP and synthetic evaluating strategy of PLAW. The feasible and rational process configuration strategies of HTC assembly welding line are pointed oat under different planning objective.

The application of projection pursuit classification in the process of strategy selection and evaluation based on the real coded accelerating genetic algorithm

During the process of enterprises＇ strategy evaluation and selection, there are many evaluating indicators, and among them there are some potential correlations and conflicts. Thus it poses the problems to the decision-makers how to conduct correct evaluation on a business and how to make strategy adjustment and selection according to the evaluation. Based on the qualitative and quantitative method, the paper introduces the Projection Pursuit Classification （PPC） model based on the Real-coded Accelerating Genetic Algorithm （RAGA） into the process of enterprises＇ strategy evaluation and selection. The characteristic of PPC model is that it ultimately overcomes the influence of the proportion of subjectivity and avoids precocious convergence, thus providing a new objective method for strategy evaluation and selection by pursuing the most objective strategy evaluation to make the relatively sensible strategy portfolio and action.

Evaluation of Three Harvest Control Rules for Bigeye Tuna(Thunnus obesus) Fisheries in the Indian Ocean

The stock of Bigeye tuna(Thunnus obesus) in the Indian Ocean supports an important international fishery and is considered to be fully exploited. The responsible management agency, the Indian Ocean Tuna Commission(IOTC), does not have an explicit management decision-making framework in place to prevent over-fishing. In this study, we evaluated three harvest control rules, i) constant fishing mortality(CF), from 0.2 to 0.6, ii) constant catch(CC), from 60000 to 140000 t, and iii) constant escapement(CE), from 0.3 to 0.7. The population dynamics simulated by the operating model was based on the most recent stock assessment using Stock Synthesis version Ⅲ(SS3). Three simulation scenarios(low, medium and high productivity) were designed to cover possible uncertainty in the stock assessment and biological parameters. Performances of three harvest control rules were compared on the basis of three management objectives(over 3, 10 and 25 years): i) the probability of maintaining spawning stock biomass above a level that can sustain maximum sustainable yield(MSY) on average, ii) the probability of achieving average catches between 0.8 MSY and 1.0 MSY, and iii) inter-annual variability in catches. The constant escapement strategy(CE=0.5), constant fishing mortality strategy(F=0.4) and constant catch(CC=80000) were the most rational among the respective management scenarios. It is concluded that the short-term annual catch is suggested at 80000 t, and the potential total allowable catch for a stable yield could be set at 120000 t once the stock had recovered successfully. All the strategies considered in this study to achieve a ‘tolerable' balance between resource conservation and utilization have been based around the management objectives of the IOTC.

Acute optic neuritis: a clinical paradigm for evaluation of neuroprotective and restorative strategies?

What makes the acute optic neuritis model unique in an era of trials for neuroprotective and myelin repair agents？Acute optic neuritis（AON）is a common,and often the earliest manifestation of central nervous system（CNS）inflammatory demyelinating disorders like multiple sclerosis （MS） and neuromyelitis optica （NMO）.

The Clinical Possibility to Make Use of P++ Strategy to Obtain Refined Biological Optimization for the Treatment of Locally Advanced Nasopharyngeal Carcinoma

It has been believed that the better optimization can be achieved by tile P＋＋ optimization strategy since the P＋ curve shows a rather shallow maximum. In this paper, the P＋＋ optimization strategy has been verified by evaluating （intensity-modulated radiation therapy, IMRT） plans for a T4 stage NPC patient in the situation where diffieuh compromise has to be made between probabilities for tumor control and normal tissue injuh＇y. The results showed that including the biological objective gEUD into the plan optimization couht decrease the mean dose of OAR. Theoretically, P＋＋ optimization strategy could be helpfnl to find the refined optimization solution for radiation therapy planning. However, in clinical radiotherapy practice, disease situations will form restrictions to use the biological evaluation only. More factors including both physical and biological models should be considered in a planning evaluation process to aehieve a best clinical solution.

An Improved Multi-Objective Hybrid Genetic-Simulated Annealing Algorithm for AGV Scheduling under Composite Operation Mode

This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time.

Multi-agent evaluation for energy management by practically scalingα-rank

Currently,decarbonization has become an emerging trend in the power system arena.However,the increasing number of photovoltaic units distributed into a distribution network may result in voltage issues,providing challenges for voltage regulation across a large-scale power grid network.Reinforcement learning based intelligent control of smart inverters and other smart building energy management(EM)systems can be leveraged to alleviate these issues.To achieve the best EM strategy for building microgrids in a power system,this paper presents two large-scale multi-agent strategy evaluation methods to preserve building occupants’comfort while pursuing systemlevel objectives.The EM problem is formulated as a general-sum game to optimize the benefits at both the system and building levels.Theα-rank algorithm can solve the general-sum game and guarantee the ranking theoretically,but it is limited by the interaction complexity and hardly applies to the practical power system.A new evaluation algorithm(TcEval)is proposed by practically scaling theα-rank algorithm through a tensor complement to reduce the interaction complexity.Then,considering the noise prevalent in practice,a noise processing model with domain knowledge is built to calculate the strategy payoffs,and thus the TcEval-AS algorithm is proposed when noise exists.Both evaluation algorithms developed in this paper greatly reduce the interaction complexity compared with existing approaches,including ResponseGraphUCB(RG-UCB)andαInformationGain(α-IG).Finally,the effectiveness of the proposed algorithms is verified in the EM case with realistic data.

The impact of natural mortality variations on the performance of management procedures for Spanish mackerel(Scomberomorus niphonius) in the Yellow Sea, China

Natural mortality rate（M） is one of the essential parameters in fishery stock assessment, however, the estimation of M is commonly rough and the changes of M due to natural and anthropogenic impacts have long been ignored.The simplification of M estimation and the influence of M variations on the assessment and management of fisheries stocks have been less well understood. This study evaluated the impacts of the changes in natural mortality of Spanish mackerel（Scomberomorus niphonius） on their management strategies with data-limited methods. We tested the performances of a variety of management procedures（MPs） with the variations of M in mackerel stock using diverse estimation methods. The results of management strategies evaluation showed that four management procedures DCAC, SPMSY, cur E75 and minlen Lopt1 were more robust to the changes of M than others; however, their performance were substantially influenced by the significant decrease of M from the 1970s to 2017. Relative population biomass（measure as the probability of B〉0.5 BMSY） increased significantly with the decrease of M, whereas the possibility of overfishing showed remarkable variations across MPs. The decrease of M had minor effects on the long-term yield of cur E75 and minlen Lopt1, and reduced the fluctuation of yield（measure as the probability of AAVY〈15%） for DCAC, SPMSY. In general, the different methods for M estimation showed minor effects on the performance of MPs, whereas the temporal changes of M showed substantial influences. Considering the fishery status of Spanish mackerel in China, we recommended that cur E75 has the best trade-off between fishery resources exploitation and conservation, and we also proposed the potentials and issues in their implementations.

Modelling the potential consequences of adaptive closure management in a penaeid trawl fishery

Spatial management of fishing effort can be used to avoid catching undesirable size classes for target species,and improve yield-per-recruit for the exploited stock.Adaptive closure management has been proposed as a means to more effectively utilise spatial management,however these management provisions often lack quantitative evaluation which constrains the information available to inform decisions.We demonstrate the use of a spatially and size structured population dynamics model to evaluate the potential impact of spatial management on a multijurisdictional fishery for a highly migratory species(eastern king prawn,Penaeus[Melicertus]plebejus).Under current conditions in the fishery,the overall effect of closures on harvest was estimated to be comparatively minor,regardless of assumptions about how effort or fisher behavior are affected by spatial management.Alternative assumptions about the movement patterns of eastern king prawn had little influence on the impact of closures on overall harvest.However,when effort was increased to historic levels similar to those observed when the closures were implemented,a much greater impact on overall harvest was observed.The approach taken and simulation outcomes are discussed in the context of spatial management for both eastern king prawn,and penaeid fisheries more broadly.

Evaluating effectiveness of biological reference points for bigeye tuna (Thunnus obesus) and yellowfin tuna (Thunnus albacares) fisheries in the Indian Ocean

Biological reference point(BRP)is one of the essential components in the management strategy evaluation that is used to determine the status of fishery stock and set management regulations.However,as BRPs can be derived from different models and many different BRPs are available,the effectiveness and consistency of different BRPs should be evaluated before being applied to fisheries management.In this study,we used a computation-intensive approach to identify optimal BRPs.We systematically evaluated 1500 combinations of alternative BRPs in managing the bigeye tuna(Thunnus obesus)and yellowfin tuna(Thunnus albacares)fisheries in the Indian Ocean.The effectiveness and consistency of these BRPs were evaluated using four performance measures related to fisheries landing performance and biomass conservation.Monte Carlo simulation was used to evaluate various uncertainties.The results suggest that the proposed computation-intensive approach can be effective in identifying optimal BRPs with respect to a set of defined performance measures.We found that the current maximum sustainable yield(MSY)-based BRP combinations are effective target BRPs to manage the bigeye and yellowfin tuna fisheries with the“linear”harvest control rule(HCR).However,using the“knife-edge”HCR,better BRPs could be found for both the bigeye and yellowfin tuna fisheries management with improved fisheries and conservation performance.The framework developed in this study can be used to identify suitable BRPs based on a set of defined performance measures for other fisheries.

Consumers can learn and can forget-Modeling the dynamic decision procedure when watching TV

Facing the challenge of attracting consumers and winning market share under the pro-liferation of TV stations and channels,the traditional TV stations often make some mar-keting strategies.However,how to evaluate the effectiveness of different strategies and select the best one is a key issue.This study proposes to resolve this problem.We develop an innovative structural model to simulate the dynamic choices consumers make under two interactive behaviors:learning and forgetting.Learning behavior refers to updating programme quality assessment by using experience,while forgetting behavior prevents the use of previous experience.The Bayesian rules are employed to model learning behavior,and they are extended by incorporating an exponential decay function to mea-sure the effect of forgetting behavior.The structural model is tested and validated by using Hong Kong television viewing data.The empirical results show that when modeling consumer choice decisions,considering learning and forgetting behavior significantly improves the performance of the model in regard to rating prediction and marketing strategy evaluation.Five cases are simulated to show how the model is used to evaluate marketing strategies.Managerial implications are then discussed to guide the decision-making of traditional TV broadcasters and advertisers.