Order picking has been recognized as one of the most challenging activities in terms of time, labor, and cost for most warehouses. Order fulfillment has a definite time constraint that is predefined by the customer and any deficit in the process of order picking at the warehouse level will impact the entire supply chain. This paper addresses the problem of routing optimization for order picking in a warehouse to minimize the travel time and distance. In particular, we propose an easy-to-implement vehicle routing based approach in conjunction with the distance matrix for obtaining an optimal route for order picking, which is solved using the off-the-shelf solver Gurobi with the Julia programming language. The optimal route is then compared with the routes obtained by other traditional approaches such as S-shape, return, mid-point, and largest gap methods by means of simulation. Two order picking demand scenarios are considered: one with uniform pick locations throughout the warehouse and the other with differentiated pick locations. We show that the proposed vehicle routing based approach outperforms the S-shape, return, mid-point, largest gap methods by 36.55%, 44.89%, 46.64%, and 36.9 %, respectively, for scenario 1, and by 25.12%, 24.01%, 27.57%, and 26.77%, respectively, for scenario 2. The detailed discussion about the routing policy implications on management is also provided.

This paper focuses on developing a predictive model for estimating the shipment lead time using machine learning methods for ocean import freight considering interests of different stakeholders such as shipper, carrier, freight forwarder, and consignee. Two different terminal criteria for calculating shipment lead time are defined considering different milestones: one with empty container return and the other with delivery confirmation at the destination. Real data obtained from an industry partner are used for implementation, and multinomial logistic regression is identified as the best classifier with the highest accuracy in each binning method, which is followed by a decision tree method. Additionally, commonly used classifiers such as multinomial logistic regression, decision tree, K-nearest neighbors, and support vector machine perform better than Naïve Bayes when the categorical variables are binarized, and vice versa when the categorical variables are converted into ordinal values. The proposed model has the significant potential to benefit different parties in the supply chain by providing improved visibility and predictability for shipment lead times.