Determining the type of vehicles to transport goods between multiple factories and numerous distributors with different demands is one of the major logistic decisions that have to be made by industry players to reduce...Determining the type of vehicles to transport goods between multiple factories and numerous distributors with different demands is one of the major logistic decisions that have to be made by industry players to reduce the cost of operations. A Mixed-Integer Quadratic Programming (MIQP) model was used to optimally distribute goods to 105 distributors from two factories across Ghana. The formulated model and analysis show that the existence of multiple vehicles in a fleet purposely for long hauling of goods also renders an optimal minimum cost as compared to a single-vehicle fleet. This optimum minimum cost accounts for 0.2066 of the total cost incurred by the two factories. This resulted in a 25% reduction in transportation cost. Again, a single-vehicle fleet with loading capacity within the mean value of all individual demands gave a minimum cost next to the optimal minimum.展开更多
Extension multi-factorial evaluation method was used in water quality early-earning in Yincungang River based on MATLAB. The results showed that water quality in summer was safe,while that in other three seasons were ...Extension multi-factorial evaluation method was used in water quality early-earning in Yincungang River based on MATLAB. The results showed that water quality in summer was safe,while that in other three seasons were in pre-warning state with the order of winter > spring > autumn.展开更多
Background: This paper reports findings from a literature review undertaken to assess the current evidence base for clinical medication review and falls in older people. This forms part of a larger, organisational sup...Background: This paper reports findings from a literature review undertaken to assess the current evidence base for clinical medication review and falls in older people. This forms part of a larger, organisational supported project design work-stream, where the objectives are to define the operational details for clinical medication review as part of multi-factorial assessment for elderly fallers in the community. Patients will be identified and targeted through an integrated care pathway mapping and elderly patient care screening service. Objective: A review of national and best practice guidance to help our understanding of how clinical medication review could be optimised. Methods: A PubMed database search was undertaken with search terms including “elderly” and “falls” and “medicines” followed by study of relevant publications in English and including cited referenced publications within selected papers. Results: Our findings were that both medication over-use and under-use in the elderly occur frequently and can be harmful. Many drugs commonly used by older persons have not been systematically studied as risk factors for falls. The screening tool of older people’s prescriptions (STOPP) and screening tool to alert to right treatment (START), validated for assessment of potentially inappropriate prescribing in the elderly, offer the possibility of provision of a structured clinical medication review to patients, with a need for more research on the impact of the STOPP START interventions on both the rates of falls and risk of falls in the elderly.展开更多
Flexible manufacturing faces the challenge of increasing productivity and conserving resources,especially in complex production environments with dynamic event.This paper addresses a dynamic Hybrid Flow-shop Schedulin...Flexible manufacturing faces the challenge of increasing productivity and conserving resources,especially in complex production environments with dynamic event.This paper addresses a dynamic Hybrid Flow-shop Scheduling Problem(HFSP)with unrelated parallel machines using a Deep Reinforcement Learning(DRL)approach to intelligently allocate continuous new job arrivals while minimizing the total weighted tardiness cost.In this paper,Evolution Strategies-guided Deep Reinforcement Learning(ES-DRL)scheduling model is proposed by designing appropriate state features,scheduling actions,and training strategies.In addition,goal-directed composite rules are proposed to provide effective scheduling actions.Meanwhile,the state transition in the environment is adjusted by introducing key state.The ES-DRL model is then trained to make decisions,indicating the reasoning behind the system design.Experimental results show that ES-DRL outperforms the other comparison algorithms regarding significance.In addition,the experiments are extended to the multi-factories system to further validate the scalability and adaptability of the scheduling model,and this extension also yields encouraging results.These results affirm the universal applicability of ES-DRL for dynamic HFSP.展开更多
文摘Determining the type of vehicles to transport goods between multiple factories and numerous distributors with different demands is one of the major logistic decisions that have to be made by industry players to reduce the cost of operations. A Mixed-Integer Quadratic Programming (MIQP) model was used to optimally distribute goods to 105 distributors from two factories across Ghana. The formulated model and analysis show that the existence of multiple vehicles in a fleet purposely for long hauling of goods also renders an optimal minimum cost as compared to a single-vehicle fleet. This optimum minimum cost accounts for 0.2066 of the total cost incurred by the two factories. This resulted in a 25% reduction in transportation cost. Again, a single-vehicle fleet with loading capacity within the mean value of all individual demands gave a minimum cost next to the optimal minimum.
基金Supported by Jiangsu Taihu Treatment Scientific Research Program(TH2010101)
文摘Extension multi-factorial evaluation method was used in water quality early-earning in Yincungang River based on MATLAB. The results showed that water quality in summer was safe,while that in other three seasons were in pre-warning state with the order of winter > spring > autumn.
文摘Background: This paper reports findings from a literature review undertaken to assess the current evidence base for clinical medication review and falls in older people. This forms part of a larger, organisational supported project design work-stream, where the objectives are to define the operational details for clinical medication review as part of multi-factorial assessment for elderly fallers in the community. Patients will be identified and targeted through an integrated care pathway mapping and elderly patient care screening service. Objective: A review of national and best practice guidance to help our understanding of how clinical medication review could be optimised. Methods: A PubMed database search was undertaken with search terms including “elderly” and “falls” and “medicines” followed by study of relevant publications in English and including cited referenced publications within selected papers. Results: Our findings were that both medication over-use and under-use in the elderly occur frequently and can be harmful. Many drugs commonly used by older persons have not been systematically studied as risk factors for falls. The screening tool of older people’s prescriptions (STOPP) and screening tool to alert to right treatment (START), validated for assessment of potentially inappropriate prescribing in the elderly, offer the possibility of provision of a structured clinical medication review to patients, with a need for more research on the impact of the STOPP START interventions on both the rates of falls and risk of falls in the elderly.
基金supported by the National Key Research and Development Program of China(No.2022YFB4501402)the Key Research and Development Program of Hubei Province(No.2023BAB065)the National Natural Science Foundation of China(No.62073300).
文摘Flexible manufacturing faces the challenge of increasing productivity and conserving resources,especially in complex production environments with dynamic event.This paper addresses a dynamic Hybrid Flow-shop Scheduling Problem(HFSP)with unrelated parallel machines using a Deep Reinforcement Learning(DRL)approach to intelligently allocate continuous new job arrivals while minimizing the total weighted tardiness cost.In this paper,Evolution Strategies-guided Deep Reinforcement Learning(ES-DRL)scheduling model is proposed by designing appropriate state features,scheduling actions,and training strategies.In addition,goal-directed composite rules are proposed to provide effective scheduling actions.Meanwhile,the state transition in the environment is adjusted by introducing key state.The ES-DRL model is then trained to make decisions,indicating the reasoning behind the system design.Experimental results show that ES-DRL outperforms the other comparison algorithms regarding significance.In addition,the experiments are extended to the multi-factories system to further validate the scalability and adaptability of the scheduling model,and this extension also yields encouraging results.These results affirm the universal applicability of ES-DRL for dynamic HFSP.
