To effectively implement order fulfillment, we present an integrated framework model focusing on the whole process of order fulfillment. Firstly, five aims of the OFS （order fulfillment system） are built. Then after...To effectively implement order fulfillment, we present an integrated framework model focusing on the whole process of order fulfillment. Firstly, five aims of the OFS （order fulfillment system） are built. Then after discussing three major processes of order fulfillment, we summarize functional and quality attributes of the OFS. Subsequently, we investigate SOA （Service Oriented Architecture） and present a SOA meta-model to be an integrated framework and to fulfill quality requirements. Moreover,based on the SOA meta-model, we construct a conceptual framework model that aims to conveniently integrate other functions fiom different systems into the order fulfillment system. This model offers enterprises a new approach to implementing order fulfillment.展开更多
This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse function...This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.展开更多
We study the Transport and Pick Robots Task Scheduling(TPS)problem,in which two teams of specialized robots,transport robots and pick robots,collaborate to execute multi-station order fulfillment tasks in logistic env...We study the Transport and Pick Robots Task Scheduling(TPS)problem,in which two teams of specialized robots,transport robots and pick robots,collaborate to execute multi-station order fulfillment tasks in logistic environments.The objective is to plan a collective time-extended task schedule with the minimization of makespan.However,for this recently formulated problem,it is still unclear how to obtain satisfying results efficiently.In this research,we design several constructive heuristics to solve this problem based on the introduced sequence models.Theoretically,we give time complexity analysis or feasibility guarantees of these heuristics;empirically,we evaluate the makespan performance criteria and computation time on designed dataset.Computational results demonstrate that coupled append heuristic works better for the most cases within reasonable computation time.Coupled heuristics work better than decoupled heuristics prominently on instances with relative few pick robot numbers and large work zones.The law of diminishing marginal utility is also observed concerning the overall system performance and different transport-pick robot numbers.展开更多
文摘To effectively implement order fulfillment, we present an integrated framework model focusing on the whole process of order fulfillment. Firstly, five aims of the OFS （order fulfillment system） are built. Then after discussing three major processes of order fulfillment, we summarize functional and quality attributes of the OFS. Subsequently, we investigate SOA （Service Oriented Architecture） and present a SOA meta-model to be an integrated framework and to fulfill quality requirements. Moreover,based on the SOA meta-model, we construct a conceptual framework model that aims to conveniently integrate other functions fiom different systems into the order fulfillment system. This model offers enterprises a new approach to implementing order fulfillment.
文摘This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.
基金This work is supported by the National Natural Science Foundation of China(Grant U1813206)the National Key R&D Program of China(Grant 2020YFC2007500)the Science and Technology Commission of Shanghai Municipality(Grant 20DZ2220400).
文摘We study the Transport and Pick Robots Task Scheduling(TPS)problem,in which two teams of specialized robots,transport robots and pick robots,collaborate to execute multi-station order fulfillment tasks in logistic environments.The objective is to plan a collective time-extended task schedule with the minimization of makespan.However,for this recently formulated problem,it is still unclear how to obtain satisfying results efficiently.In this research,we design several constructive heuristics to solve this problem based on the introduced sequence models.Theoretically,we give time complexity analysis or feasibility guarantees of these heuristics;empirically,we evaluate the makespan performance criteria and computation time on designed dataset.Computational results demonstrate that coupled append heuristic works better for the most cases within reasonable computation time.Coupled heuristics work better than decoupled heuristics prominently on instances with relative few pick robot numbers and large work zones.The law of diminishing marginal utility is also observed concerning the overall system performance and different transport-pick robot numbers.
