Job Sequencing in a Miniload System

More and more warehouses shift towards the use of automated systems. One such system is a miniload system. In this system, a crane retrieves bins from their storage locations and brings the bins to the picking station, where a worker takes the requested products from the retrieved bins. After a product has been picked, the bin is put back by the crane in its original location. A buffer is present at the picking station to absorb fluctuations in speed between the picker and the crane. The problem of scheduling jobs in this system has received little attention in the literature. We describe system properties and give insight in the performance of a number of heuristics

Layout and Routing Methods for Warehouses

Layout and Routing Methods for Warehouses discusses aspects of order picking in warehouses. Order picking is the process by which products are retrieved from storage to meet customer demand. Various new routing methods are introduced to determine efficient sequences in which products have to be retrieved from storage. Furthermore, a new method is given to determine a layout for the order picking area. The objective is to minimize the average distance traveled per route by the order pickers

Integrating neighborhood delivery services into parcel delivery networks

Problem definition: Leveraging developments in the sharing economy, several innovative delivery models have been adopted in the parcel delivery industry. One such innovation is the neighborhood delivery service model, where local residents receive parcels and deliver them within their neighborhood. We study the integration of neighborhood delivery services into a parcel delivery network. Methodology and results: We combine distributionally robust optimization with continuous approximation to build a model that captures the interaction between demand volatility, neighborhood delivery service capacities, and the delivery performance of a parcel delivery company. Efficient lower bounding procedures determine fleet size requirements that guarantee meeting service level targets for individual neighborhoods. An algorithm based on column generation solves the problem for a network of multiple neighborhoods. Analytical results show that average demand profiles, demand volatility levels, and service level targets determine the ability of neighborhood delivery services to reduce the fleet size and save total network cost. Numerical analyses with empirical data from a case study emphasize the important role of neighborhood delivery service capacity. Even with a modest capacity of 3.3% of the peak demand, the fleet size can be reduced by 4.0%. Larger fleet size reductions, of up to 24.9%, can be achieved when capacity of a neighborhood delivery service is 33.3% of the peak demand in the neighborhood. Interestingly, these fleet size reductions also translate into total network cost savings of 1.3% and 8.6%, respectively. Managerial implications: Our study reveals two key levers for managers to recruit and retain neighborhood delivery services and negotiate for higher capacities. Of those, making minimum compensation agreements is new to the literature and can be used more liberally than the other lever: increasing unit outsourcing cost. Furthermore, we show how managers should consider the average demand, demand volatility, and service level targets in recruiting neighborhood delivery services.<br/

Stochastic Cyclic Inventory Routing with Supply Uncertainty: A Case in Green-Hydrogen Logistics

Hydrogen can be produced from water, using electricity. The hydrogen can subsequently be kept in inventory in large quantities, unlike the electricity itself. This enables solar and wind energy generation to occur asynchronously from its usage. For this reason, hydrogen is expected to be a key ingredient for reaching a climate-neutral economy. However, the logistics for hydrogen are complex. Inventory policies must be determined for multiple locations in the network, and transportation of hydrogen from the production location to customers must be scheduled. At the same time, production patterns of hydrogen are intermittent, which affects the possibilities to realize the planned transportation and inventory levels. To provide policies for efficient transportation and storage of hydrogen, this paper proposes a parameterized cost function approximation approach to the stochastic cyclic inventory routing problem. Firstly, our approach includes a parameterized mixed integer programming (MIP) model which yields fixed and repetitive schedules for vehicle transportation of hydrogen. Secondly, buying and selling decisions in case of underproduction or overproduction are optimized further via a Markov decision process (MDP) model, taking into account the uncertainties in production and demand quantities. To jointly optimize the parameterized MIP and the MDP model, our approach includes an algorithm that searches the parameter space by iteratively solving the MIP and MDP models. We conduct computational experiments to validate our model in various problem settings and show that it provides near-optimal solutions. Moreover, we test our approach on an expert-reviewed case study at two hydrogen production locations in the Netherlands. We offer insights for the stakeholders in the region and analyze the impact of various problem elements in these case studies.<br/

Multi-period Stochastic Network Design for Combined Natural Gas and Hydrogen Distribution

Hydrogen is produced from water using renewable electricity. Unlike electricity, hydrogen can be stored in large quantities for long periods. This storage ability acts as a \textit{green battery}, allowing solar and wind energy to be generated and used at different times. As a result, green hydrogen plays a central role in facilitating a climate-neutral economy. However, the logistics for hydrogen are complex. As new hydrogen pipelines are developed for hydrogen, there is a trend toward repurposing the natural gas network for hydrogen, due to its economic and environmental benefits. Yet, a rapid conversion could disrupt the balance of natural gas supply and demand. Furthermore, technical and economic developments surrounding the transition contribute additional complexity, which introduces uncertainty in future supply and demand levels for both commodities. To address these challenges, we introduce a multi-period stochastic network design problem for the transition of a natural gas pipeline network into a green hydrogen pipeline network. We develop a progressive hedging based matheuristic to solve the problem. Results demonstrate our matheuristic is efficient, both in computation time and in solution quality. We show that factoring in uncertainty avoids premature expansion and ensures the development of an adequate pipeline network meeting long-term needs. In a case study in the Northern Netherlands for \textit{Hydrogen Energy Applications in Valley Environments for Northern Netherlands} initiative, we focus on two key scenarios: local production and importation, exploring their impacts on performance indicators. Our case insights exemplify the solid foundation for strategic decision-making in energy transitions through our approach

Multi-period Stochastic Network Design for Combined Natural Gas and Hydrogen Distribution

Hydrogen is produced from water using renewable electricity. Unlike electricity, hydrogen can be stored in large quantities for long periods. This storage ability acts as a \textit{green battery}, allowing solar and wind energy to be generated and used at different times. As a result, green hydrogen plays a central role in facilitating a climate-neutral economy. However, the logistics for hydrogen are complex. As new hydrogen pipelines are developed for hydrogen, there is a trend toward repurposing the natural gas network for hydrogen, due to its economic and environmental benefits. Yet, a rapid conversion could disrupt the balance of natural gas supply and demand. Furthermore, technical and economic developments surrounding the transition contribute additional complexity, which introduces uncertainty in future supply and demand levels for both commodities. To address these challenges, we introduce a multi-period stochastic network design problem for the transition of a natural gas pipeline network into a green hydrogen pipeline network. We develop a progressive hedging based matheuristic to solve the problem. Results demonstrate our matheuristic is efficient, both in computation time and in solution quality. We show that factoring in uncertainty avoids premature expansion and ensures the development of an adequate pipeline network meeting long-term needs. In a case study in the Northern Netherlands for \textit{Hydrogen Energy Applications in Valley Environments for Northern Netherlands} initiative, we focus on two key scenarios: local production and importation, exploring their impacts on performance indicators. Our case insights exemplify the solid foundation for strategic decision-making in energy transitions through our approach

Design of Cross-chain Internet Order Fulfillment Centres

Many consumers have embraced the option of ordering via the Internet, which has resulted in an enormous increase in direct orders compared to the times when direct ordering was done by catalogue and phone. The fulfillment process in the supply chain is an important factor for these consumers impacting how long they must wait between ordering and delivery. This fact has significantly increased the importance of the back-end fulfillment process. We present a novel supply chain design to enable cross-chain coordination of order fulfillment operations for internet sales. Shared warehousing facilities are used more and more to achieve competitive advantage. This situation asks for new models to enable a smooth warehousing process for each web shop, but at the same time to ensure overall efficiency and effectiveness. This paper introduces a layout model for shared operations under one roof by simultaneously optimizing the overall facility layout and the area layout

Mathematical models for the warehouse reassignment problem

For several decades, researchers have developed optimization techniques forwarehouse operations. These techniques are related in particular to the materialhandling, the order picking and storage assignment strategies for a myriad of warehouse configurations. It is often neglected that these strategies need to be regularlyadjusted in order to adapt to changes in technology, in the demand and/or product offers. Most research on storage assignment provide excellent methods to determine where products should be located. However, the handling part of the problem is often set aside. Moving from one setup to another requires a large amount of work and disturbs regular order-picking operations. This chapter presents the warehouse reassignment problem in order to minimize the total workload to reassign the products to their new locations. We demonstrate how one can move from an out-of-date storage assignment to a better one, in a minimum of working time. We introduce three different mathematical formulations and compare them through extensive computational experiments in order to identify the best one.<br/