On the Benefits of Flexible Customer-to-Depot Assignments in Attended Home Delivery

This paper investigates the value of flexibility in customer-to-depot assignments in last-mile distribution operations. In current practice, many companies use fixed delivery regions for their fulfillment warehouses. This allows a decomposition of delivery operations by ware- house, thereby simplifying the planning process. However, this approach foregoes some optimization potential, relative to a holistic planning of the delivery operations across the network. In this paper, we assess the value of this flexibility. We first identify and characterize two specific types of benefits, namely a routing benefit and a pooling benefit. We then proceed to quantify these benefits in an extensive numerical study. Our results disentangle how the benefits depend on key environmental parameters. They also show that a flexible customer-to-depot assignment can add significant value in various relevant conditions

Shunting operations at flat yards : retrieving freight railcars from storage tracks

In this paper, we study the railcar retrieval problem (RRT) where specified numbers of certain types of railcars have to be withdrawn from the storage tracks of a flat yard. This task arises in the daily operations of workshop yards for railcar maintenance. The objective is to minimize the total cost of shunting via methods such as minimizing the usage of shunting engines. We describe the RRT formally, present a mixed-integer program formulation, and prove the general case to be NP-hard. For some special cases, exact algorithms with polynomial runtimes are proposed. We also analyze several intuitive heuristic solution approaches motivated by observed real-world planning routines. We evaluate their average performances in simulations with different scenarios and provide their worst-case performance guarantee. We show that although the analyzed heuristics result in much better solutions than the naive planning approach, they are still on average 30%-50% from the optimal objective value and may result in up to 14 times higher costs in the worst case. Therefore, we conclude that optimization should be implemented in practice in order to save valuable resources. Furthermore, we analyze the impacts of yard layout and the widespread organizational routine of presorting on the railcar retrieval cost

Trends and challenges in E-grocery

The e-grocery business, i.e. the online purchase and delivery of perishable food items, has many challenges that sets it apart from both, traditional retailers and other e-commerce businesses. Especially, the last-mile distribution has proven to be a major challenge for the service providers since here individual orders consists of many different items which might require special handling and the margins are very thin. This calls for an efficient design of the delivery process. On the other hand, customers usually ask for specific and short delivery windows. This leads to a tradeoff between offering the best possible service to customers and logistics costs. Ongoing trends like same-day delivery push the fulfillment strategy to being more responsive, making the tradeoff faced by e-grocers even more challenging. We investigate different aspects of the field in order to identify opportunities for future research that arise from the above mentioned challenges and trends

Trends and challenges in E-grocery

The e-grocery business, i.e. the online purchase and delivery of perishable food items, has many challenges that sets it apart from both, traditional retailers and other e-commerce businesses. Especially, the last-mile distribution has proven to be a major challenge for the service providers since here individual orders consists of many different items which might require special handling and the margins are very thin. This calls for an efficient design of the delivery process. On the other hand, customers usually ask for specific and short delivery windows. This leads to a tradeoff between offering the best possible service to customers and logistics costs. Ongoing trends like same-day delivery push the fulfillment strategy to being more responsive, making the tradeoff faced by e-grocers even more challenging. We investigate different aspects of the field in order to identify opportunities for future research that arise from the above mentioned challenges and trends

Shunting operations at flat yards : Retrieving freight railcars from storage tracks

In this paper, we study the railcar retrieval problem (RRT) where a specified number of certain types of railcars has to be withdrawn from the storage tracks of a flat yard. This task arises in the daily operations of yards belonging to a workshop for railcar maintenance. The objective is to minimize the total costs of shunting, i.e. minimizing the usage of shunting engines. We describe the RRT formally as a mixed-integer program and prove it to be NP-hard in the general case. In addition, we analyze several heuristics for solving the problem and provide their worst case performance guarantee. Furthermore, computational experiments are conducted that show the average performance of the introduced heuristics and compare them to the optimal solution provided by the mixedinteger program. The results show that both, the heuristics and the exact formulation come to solutions for realistically-sized instances in running times acceptable to practitioners, leading to the advice that optimization should be implemented in practice in order to save valuable resources

Shunting operations at flat yards : retrieving freight railcars from storage tracks

In this paper, we study the railcar retrieval problem (RRT) where specified numbers of certain types of railcars have to be withdrawn from the storage tracks of a flat yard. This task arises in the daily operations of workshop yards for railcar maintenance. The objective is to minimize the total cost of shunting via methods such as minimizing the usage of shunting engines. We describe the RRT formally, present a mixed-integer program formulation, and prove the general case to be NP-hard. For some special cases, exact algorithms with polynomial runtimes are proposed. We also analyze several intuitive heuristic solution approaches motivated by observed real-world planning routines. We evaluate their average performances in simulations with different scenarios and provide their worst-case performance guarantee. We show that although the analyzed heuristics result in much better solutions than the naive planning approach, they are still on average 30%-50% from the optimal objective value and may result in up to 14 times higher costs in the worst case. Therefore, we conclude that optimization should be implemented in practice in order to save valuable resources. Furthermore, we analyze the impacts of yard layout and the widespread organizational routine of presorting on the railcar retrieval cost

Attended-home delivery from multiple depots

Attended-Home Delivery (AHD) is the prevailing method for bridging the last-mile between e-grocers, online retailers that sell food, and their customers. There are two major strategies: picking and delivering from dedicated fulfillment centers or picking items in existing stores. Here, we will concentrate on e-grocers that are servicing larger areas from more than one dedicated fulfillment center and investigate the role of flexibility in assigning customers to depots. Since time windows play an important role in the AHD context, the e-grocer is facing a multi-depot vehicle routing problem with time windows (MDVRPTW). The MDVRPTW is a topic relatively unexplored in academic literature. We present an exact mixed-integer programming formulation and use this as well as an implementation of a heuristic in order to analyze the benefit of a MDVRPTW solution approach in contrast to a simplistic decomposition by depots. In addition, we investigate the impact of different factors on this benefit as well as on the absolute solution value