Today with the rapid improvement of new technologies, people tend to buy various products from online retailers which facilitate the purchasing process and save their valuable limited time. Two important and interconn...
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Today with the rapid improvement of new technologies, people tend to buy various products from online retailers which facilitate the purchasing process and save their valuable limited time. Two important and interconnected operations of each online retailing system are order picking and delivery planning. In an online system, lots of small orders including different products arrive dynamically and must be delivered on time, so there is a limited time to retrieve products from their storage locations, pack them, load onto trucks, and deliver to the destinations. In this study, we deal with these two problems of an online retailer that stores a variety of products in a warehouse and sells them online through their website. A rule-based heuristic algorithm is proposed which integrates decisions of order batching, picking schedule of batches, and assigning orders to trucks as well as, scheduling and routing of trucks. Three different batching methods including two well- known heuristics and a genetic algorithm have been used. An extensive numerical experiment is carried out to show the efficiency of the rule-basedalgorithm and investigate the results of using each batching method for different problem sizes. It is demonstrated that while the algorithm has efficient performance with three used batching methods, the genetic algorithm can lead to less system cost and more order pickers productivity.
The uninterrupted operation of the quay crane(QC)ensures that the large container ship can depart port within laytime,which effectively reduces the handling cost for the container terminal and ship *** QC waiting caus...
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The uninterrupted operation of the quay crane(QC)ensures that the large container ship can depart port within laytime,which effectively reduces the handling cost for the container terminal and ship *** QC waiting caused by automated guided vehicles(AGVs)delay in the uncertain environment can be alleviated by dynamic scheduling optimization.A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems,in which the scheduling scheme determines the starting and ending nodes of paths,and the choice of paths between nodes affects the scheduling of subsequent *** work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime.A dynamic optimization algorithm,including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm,is designed to solve the optimal AGV scheduling and path schemes.A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between *** experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.
Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail *** this paper,we propose a rescheduling model that incorporates constrain...
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Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail *** this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer *** approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train *** dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction *** introduce six interfaces to dynamically construct constraints and objectives that capture human *** summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristicalgorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire ***,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous *** proposed interaction method complements existing literature on rescheduling methods involving human-computer *** serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.
A well-maintained catenary system is crucial to efficient and safe railway operations. Schedule catenary maintenance tasks with consideration of reliability and cost is vital for annual maintenance planning. Since pas...
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A well-maintained catenary system is crucial to efficient and safe railway operations. Schedule catenary maintenance tasks with consideration of reliability and cost is vital for annual maintenance planning. Since past research on catenary maintenance planning mainly adopt the preventive maintenance (PM) policy with fixed maintenance intervals, this study considers practical concerns of railway operators and applies predictive maintenance (PdM) policy in the annual catenary maintenance planning. A decision support model is proposed by using both mixed integer programming and heuristic methods to identify and assign catenary maintenance tasks with the objective of minimizing maintenance cost and labor cost. The numerical results show that the cost can be improved by 25% compared to the current PM-only practice. The proposed model assists planners to determine and schedule maintenance tasks effectively and ensures the required reliability.
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