The steadily growing popularity of grocery home-delivery services is most likely based on the convenience experienced by its customers. However, the perishable nature of the products imposes certain requirements during the delivery process. The customer must be present when the delivery arrives so that the delivery process can be completed without interrupting the cold chain. Therefore, the grocery retailer and the customer must mutually agree on a time window during which the delivery can be guaranteed. This concept is referred to as the attended home delivery (AHD) problem in the scientific literature. The phase during which customers place orders, usually through a web service, constitutes the computationally most challenging part of the logistical processes behind such services. The system must determine potential delivery time windows that can be offered to incoming customers and incrementally build the delivery schedule as new orders are placed. Typically, the underlying optimization problem is a vehicle routing problem with a time windows. This work is concerned with a case given by an international grocery retailer’s online shopping service. We present an analysis of several efficient solution methods that can be employed to AHD services. A framework for the operational planning tools required to tackle the order placement process is provided. However, the basic framework can easily be adapted to be used for many similar vehicle routing applications. We provide a comprehensive computational study comparing several algorithmic strategies, combining heuristics utilizing local search operations and mixed-integer linear programs, tackling the booking process. Finally, we analyze the scalability and suitability of the approaches.
Rail freight transportation is involved with highly complex logistical processes and requires a lot of resources such as locomotives or wagons. Thus, cost-efficient strategies for routing freight cars in a cargo network are of great interest for railway companies. When it comes to single wagon load traffic, trains are usually formed by collecting individual freight cars into batches at shunting yards, in order to transport them jointly to their destinations. The problem of finding optimal routes and schedules for single freight cars is typically solved in two steps: (i) determining routes for the freight cars in the railway network by solving the Single-freight car routing problem (), and (ii) deciding on time schedules for trains by solving the freight train scheduling problem (). Since train departure and arrival times, as well as freight car routes are highly interdependent, one aims to solve the and the simultaneously. For smooth and convenient operational processes many railway companies apply the concept of a routing matrix. This matrix defines unique routes between all shunting yards that are used for all shipments. In this work, we present an integrated mathematical model based on time discretization, that jointly solves the and and enforces the routing matrix concept. To the best of our knowledge, this is the first work that combines all three aspects. The approach is tailored for Rail Cargo Austria’s (RCA) needs, incorporating train capacities, yard capacities, and restrictions regarding travel times. We perform an extensive computational study based on real-world data provided by RCA. Besides the performance we analyze the utilization of trains, waiting times of freight cars, and the number of shunting processes.
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