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您的检索式：作者名="Daryl L.Essam"
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| 1 | The implications of blockchain-coordinated information sharing within a supply chain: A simulation study显示文摘The profitability of a supply chain(SC)is proportional to the stability of all its stakeholders as well as their consistent information sharing with an effective and efficient communication mechanism.Various inefficiencies,such as the bullwhip effect(BWE)and product unavailability,may be caused by a lack of coordination in an SC.The importance of sharing consumer demand has been quantified by comprehensive studies under the assumption that all SC participants will access the same information.However,only a few studies have studied the effect of minimal coordination or limited visibility of information while considering their effect on the overall efficiency of an SC.This work primarily leverages blockchain technology(BCT)to create a simulation model.To do this,an SC BWE-based model is initially developed.Following that,a blockchain-based robust information sharing system is simulated.Furthermore,information sharing is challenging,and SC stakeholders may not really trust each other and hence be reluctant to share sensitive information.Considering that,this paper propose an improved proof-ofauthority(PoA)consensus algorithm that will increase trust in a decentralized SC model.Multiple experiments are carried out to demonstrate the effectiveness of our approach,and the simulation results clearly demonstrate the effectiveness of information sharing in a supply chain via blockchain,as well as that trust between partners tends to increase overall SC efficiency and reduce BWE. | Aaliya Sarfaraz Ripon K.Chakrabortty Daryl L.Essam | 2023 | Blockchain(Research and Applications)2023，4，1： | 1 |
| 2 | Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments显示文摘Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time.In such problems,it is commonly assumed that all problem instances are feasible.In reality some instances can be infeasible due to various practical issues,such as a sudden change in resource requirements or a big change in the availability of resources.Decision-makers have to determine whether a particular instance is feasible or not,as infeasible instances cannot be solved as there are no solutions to implement.In this case,locating the nearest feasible solution would be valuable information for the decision-makers.In this paper,a differential evolution algorithm is proposed for solving dynamic constrained problems that learns from past environments and transfers important knowledge from them to use in solving the current instance and includes a mechanism for suggesting a good feasible solution when an instance is infeasible.To judge the performance of the proposed algorithm,13 well-known dynamic test problems were solved.The results indicate that the proposed algorithm outperforms existing recent algorithms with a margin of 79.40%over all the environments and it can also find a good,but infeasible solution,when an instance is infeasible. | Mohamed A.Meselhi Saber M.Elsayed Daryl L.Essam Ruhul A.Sarker | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 3 | Analyzing the Simple Ranking and Selection Process for Constrained Evolutionary Optimization显示文摘Many optimization problems that involve practical applications have functional constraints,and some of these constraints are active,meaning that they prevent any solution from improving the objective function value to the one that is better than any solution lying beyond the constraint limits.Therefore,the optimal solution usually lies on the boundary of the feasible region.In order to converge faster when solving such problems,a new ranking and selection scheme is introduced which exploits this feature of constrained problems.In conjunction with selection, a new crossover method is also presented based on three parents.When comparing the results of this new algorithm with six other evolutionary based methods,using 12 benchmark problems from the literature,it shows very encouraging performance.T-tests have been applied in this research to show if there is any statistically significance differences between the algorithms.A study has also been carried out in order to show the effect of each component of the proposed algorithm. | Ehab Z.Elfeky Ruhul A.Sarker Daryl L.Essam | 2008 | Journal of Computer Science & Technology2008，23，1： | 0 |
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