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.

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.