Single Distribution Center Research Articles

Optimization of emergency logistics delivery path based on guided local search algorithm

Nowadays, the demand for risk response is increasing in countries worldwide, leading to the development of emergency-related industries as strategic emerging sectors. However, the emergency logistics industry is facing increasingly critical distribution issues. This study applies K-means clustering analysis to convert multiple distribution centers into multiple single distribution center problems. It then compares and analyzes the vehicle routing model with time windows for emergency logistics delivery in multiple distribution centers using guided local search (GLS), taboo search (TS), and simulated annealing (SA) algorithm. The results demonstrate that the GLS algorithm outperformed both the SA and TS algorithm in optimizing emergency logistics delivery paths for multiple distribution centers. The GLS algorithm proved to be more effective in solving this problem. This study confirms the contemporary value of emergency logistics distribution problems and offers practical insights into optimizing emergency logistics distribution paths in multiple distribution centers.

An application of deep reinforcement learning and vendor-managed inventory in perishable supply chain management

This article delves into the challenging supply chain management domain, explicitly addressing the intricate issue of perishable inventory allocation within a two-echelon supply chain. The approach outlined here leverages deep reinforcement learning with a keen understanding of the inherent stochasticity arising from uncertain demands and variable supply conditions. The examined supply chain encompasses two retailers and a central distribution center operating under a vendor-managed inventory system. The primary goal of this research is to combat the prevalent problems of wastage and shortages frequently encountered at the retail level in such supply chains. The study employs the Advantage Actor-Critic (A2C) algorithm, tailored to handle the continuous action space inherent in inventory allocation. To rigorously evaluate this approach, empirical data from a real-world blood supply chain in Tabriz is used for numerical experiments. This practical case study involves a single distribution center and two hospitals. The outcomes of these experiments affirm the effectiveness of the A2C algorithm, showcasing its ability to address the complex inventory allocation problem successfully. Furthermore, the research highlights that the algorithm outperforms existing supply chain policies, underscoring the pivotal role of optimal allocation in enhancing efficiency and operational excellence in perishable supply chains.

Logistics Pure Electric Vehicle Routing Based on GA-PSO Algorithm

Abstract:Based on current energy storage technologies such as batteries and fuel cells, the inherent battery capacity of electric vehicles puts constraints on their driving range and requires charging in the process of completing driving tasks. In this paper, with the current practical application in logistics industry as the background, from electric vehicle charging scheduling and path planning, a hybrid algorithm combining genetic-particle swarm algorithm is proposed to plan the best driving route for a group of electric logistics vehicles with vehicle load, vehicle battery life, charging facility location and customer time window as constraints and the total cost as the objective function. Based on the single distribution center, a more complex multi-distribution center electric vehicle path planning problem is considered. In this paper, multiple sets of Solomon VRPTW data sets are selected to test the prepared algorithm, and the results show that the algorithm can effectively plan the best distribution scheme.

A cost-based tool for the comparison of different e-grocery supply chain strategies

E-commerce is always a more diffused sales channel around the whole world market. The grocery market has been interested in the expansion of this phenomenon, especially during the COVID-19 pandemic emergency, when electronic grocery (e-grocery) shopping increased considerably. Moreover, it has remained a diffused selling channel also later, in the non-emergency state. To satisfy this specific market demand, grocery chains are facing the need for a redesign with a new logistic perspective. A grocer can carry out online orders in several ways; it can process them directly in stores using internal staff to shop from the shelves during off-peak hours. Alternatively, some local stores can be closed to customers and dedicated to online orders (dark stores). Another strategy is to carry out online orders from a single distribution centre (e-hub), using stores to complete orders with very fresh products and from which to carry out deliveries. Finally, online orders can be wholly managed by multi e-hubs. Each solution has different logistics costs and performances, influenced by online demand.For this reason, this work aims to present a cost-based function for grocery chains that compares four strategies to respond to e-grocery shopping. The cost function considers picking, refilling, and transport costs by varying orders and articles quantity. Further, we aim to minimise costs according to online order characteristics and volumes. We identify five decision variables to select the most suitable strategy for the design of the e-grocery network. Finally, a decision support system (DSS) is developed to define the best strategy based on the decision variables.

Two-stage optimization scheme of routing scheduling from a single distribution center to multiple customers

This paper proposes a solution scheme for the problem of scheduling deliveries from distribution centers. The solution scheme is developed in two phases. The first phase corresponds to the collection and processing of data, and the second phase includes the design of a customer assignment model based on the data of the first phase. The first phase provides the characteristics of the customers, the number of orders to be dispatched, and the number of visited customers. In the second phase, optimization techniques are used, considering scheduling and assignment problem-solving methodologies. The optimization model provides the correct assignment of customer orders to vehicles, reducing the number of trips and the fleet's size and occupation percentage. The proposed solution scheme is developed with a company operating a distribution center responsible for dispatching vehicles to all retail customers, wholesalers, and distributors in the Province of Concepción, Province of Arauco, and Province of Itata in Chile. The obtained results are prominent and show that the proposed methodology can be extended to companies performing storage, transportation, and distribution tasks with similar characteristics.

A case study of the creation of a common depot for several postal operators to ensure the last mile in Slovakia

In recent years, green delivery is mainly understood as the replacement of the fleet of vehicles with conventional propulsion (diesel, petrol) with alternative propulsion, whether in the form of CNG or the use of electric vehicles. Some authors already see green delivery in the use of packaging materials, which should be environmentally friendly and biodegradable. In our case, we have decided to consider as part of green delivery in last mile for postal operators the creation of a single distribution center (depot) providing the last mile for all participating postal enterprises. The first mile would be provided by the postal enterprises themselves, while the sorting of the postal items and their actual delivery would already be carried out from the depot. To assess the strategy of an enterprise that would decide to provide the service in the Slovak Republic, we decided to perform a SWOT analysis. The solution is understood only as a case study. The case study on the creation of distribution centers does not discuss the exact number of distribution centers needed to ensure delivery availability, nor does it specify the quality that would need to be met for delivery. The case study focuses only on aspects of the external and internal environment that could influence a developer in creating such a depot.

Personal Shopper Systems in Last-Mile Logistics

This paper explores the logistics operations of instant grocery delivery services. Therefore, we introduce the instant delivery problem (IDP) to replicate and examine two widely adopted strategies in the rapid delivery market: the personal shopper system (PSS) and the inventory owned delivery (IOD) system. In the PSS, couriers visit affiliated brick-and-mortar stores in the delivery area to pick up and purchase ordered products and then deliver them to customers. In the IOD system, couriers collect products from a single distribution center, or so-called dark store, in which the platform manages the inventory. Even though a PSS strategy is asset light because of the utilization of existing retailers in the area, maintaining a good level of on-time instant deliveries with the PSS is more complex than with IOD. This is because the PSS requires deciding which store to purchase ordered goods from, and picking and shopping at stores needs to be considered in the real-time decision process. We propose a tailored rolling horizon framework that utilizes column generation to browse updated delivery plans for arriving customer orders. Computational studies both in real life–inspired settings and in case studies on selected urban areas show that the PSS is a highly competitive strategy compared with IOD, particularly when dealing with small-sized customer orders. We observe that the performance of the PSS is robust when varying the delivery service time frame. The case studies also suggest that the PSS becomes even more competitive in areas where the retail store density is high. History: This paper has been accepted for the Service Science Special Issue on Innovation in Transportation-Enabled Urban Services. Funding: This work was supported by National Natural Science Foundation of China [Grant 72150410444] to J.P. van der Gaast. Supplemental Material: The e-companion is available at https://doi.org/10.1287/serv.2022.0310 .

Omnichannel Distribution to Fulfill Retail Orders

Problem definition: Although the analytical literature extensively studies distribution channels, empirical evidence on the value of omnichannel distribution is limited, especially for the omnichannel used by manufacturing companies to fulfill retail orders. I empirically evaluate the extent to which the dual–distribution center (dual-DC) distribution channel and the factory-direct distribution channel contribute to fulfilling orders from retail stores compared with the traditional single–distribution center (single-DC) channel. Academic/practical relevance: Many manufacturing companies develop their distribution omnichannel to fulfill retail orders. To make proper decisions on various channels, they need to understand the trade-offs between different order-fulfillment measures and costs in different distribution channels. Methodology: I exploit two switches in distribution channels of a manufacturing company to its retail customers: one from single-DC to dual-DC distribution and the other from single-DC to factory-direct distribution. To account for the trade-offs between order fulfillment and the costs associated with each distribution channel, I develop three equations for fill rate, lead time, and production and distribution costs in a difference-in-difference framework and then estimate the equations using proprietary data of retail orders and delivery records. Results: The results quantify the contributions of distribution channels to order fulfillment. Compared with the single-DC distribution channel, the dual-DC distribution channel raises the fill rate by 0.4% and reduces the lead time by 9.7% without incurring additional costs, whereas the factory-direct distribution channel increases the fill rate by 0.5% and provides a 5.2% cost savings but extends the lead time by 12.5%. I further analyze these contributions to order fulfillment across demand variability and order quantity. Managerial implications: The findings provide manufacturing companies with valuable knowledge of their distribution channel choices and means to find a cost-effective distribution channel to improve order fulfillment for various customers and products.

Design an Effective Blood Distribution Network with Minimal Impacts on the Environment and Blood Supply Assurance

As the world's population grows, resulting in the aggravating trend of aging population, it brings with it an increase in the demand for blood. Nowadays, in most cities, the blood distribution network is based on a single distribution centre pattern, with the blood centre acting as distribution centre for one-to-one distribution. However, despite its convenience, this pattern has a high frequency of delivery, increased risk of blood shortage, and generates high carbon emissions. This paper aims to understand the real-life problems of the current blood distribution network and to design a more rational blood distribution network by taking the characteristics of the blood supply chain into account. Two blood distribution network patterns are considered, the current single distribution centre pattern and the proposed multiple distribution centre pattern. In order to minimise environmental impacts, we introduce open vehicle routing problem for blood delivery routes planning, using mixed integer programming for modelling, to compare the carbon emissions between the two blood distribution network patterns. Numerical experimental results demonstrate that applying the proposed BDN can reduce carbon emissions by an average of 25.84% and up to 29.59%, and the delivery time in emergency situations is significantly reduced by an average of 33.15%. Such studies are essential for both reducing carbon emissions and safeguarding patients' lives.

A New Hybrid Butterfly Optimization Algorithm for Green Vehicle Routing Problem

In the industrial sector, transportation plays an essential role in distribution. This activity impacts climate change and global warming. One of the critical problems in distribution is the green vehicle routing problem (G-VRP). This study focuses on G-VRP for a single distribution center. The objective function is to minimize the distribution costs by considering fuel costs, carbon costs, and vehicle use costs. This research aims to develop the hybrid butterfly optimization algorithm (HBOA) to minimize the distribution costs on G-VRP. It was inspired by the butterfly optimization algorithm (BOA), which was by combining the tabu search (TS) algorithm and local search swap and flip strategies. BOA is a new metaheuristic algorithm that has been successfully applied in various engineering fields. Experiments were carried out to test the parameters of the proposed algorithm and vary the speed of vehicles. The proposed algorithm was also compared with several procedures of prior study. The experimental results proved that the HBOA could minimize the total distribution cost compared to other algorithms. Moreover, the computation time is also included in the analysis.

The Sustainable Parcel Delivery (SPD) Problem: Economic and Environmental Considerations for 3PLs

Sustainability in parcel delivery is a growing area of interest, especially for third-party logistics providers (3PLs). The recent increase of urban population is directly related to the increase request of goods in urban areas, and consequently to the growth of the urban freight transport and CO2 emissions. For these reasons, national and local institutions carried out regulations and incentives to reduce urban pollution and promote zero-emission vehicles. In particular, daily tickets to access to city centers is a common regulation applied to reduce freight transport. This paper presents a new SPD model that compares Eclectic Vehicles (EVs) and Fossil Fuel Vehicles (FFVs) considering economic savings and CO2 emissions, for parcel delivery from a single distribution center to a set of delivery point located inside and/or outside an urban area. Limitations as the daily ticket, the fuel cost, the battery duration are considered to provide 3PLs an innovative model to evaluate both the economic convenience and the environmental impact of its vehicles fleet. Through an explanatory study, economic considerations are carried out, related to the length of the route, the daily ticket cost, and the fuel cost to evaluate and to assess the different transportation options. It demonstrates that EVs are more convenient in terms of economic savings when the route (urban distances) and the daily ticket cost increase.

Integrated Inventory-Transportation Problem in Vendor-Managed Inventory System

The paper presents a two-echelon inventory-transportation problem in Vendor Managed Inventory (VMI) system. We consider a distribution system composed with single supplier, single distribution center and multiple retailers. Single kind of products are required to deliver from the manufacturer through distribution center to the retailers within soft time window. The objective of the problem is to minimize total logistics cost in the distribution network, including inventory cost, distribution cost, and time penalty cost. The upper echelon model focuses on minimizing inventory cost while the lower echelon model on vehicle routing problem. A mixed algorithm is designed to solve the problem with simulated annealing and ant colony with local search. The solution of upper and lower echelon model are substituted into each other based on the mixed algorithm step by step to get the optimization solutions. Computational experiments are executed to compare the performance of independent and integrated inventory-transportation optimization from the dimension of to verify the effectiveness of the model and the algorithms.

Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm.

Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg–Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.

Economic model predictive inventory routing and control

The paper proposes an economic model predictive control (EMPC) strategy for the inventory routing problem under demand uncertainty. The strategy is illustrated using an application on industrial gas distribution systems, where the product is transported to customers in small tanks and the inventory levels at the customers’ sites are monitored and controlled by the supplier following a vendor managed inventory approach. The objective is to produce balanced decisions for the joint routing and the inventory control problem over the planning horizon with respect to the decision maker’s perspective against stock-out risk. The proposed EMPC strategy makes use of a mixed integer mathematical programming optimization model that describes the deterministic inventory routing problem with simultaneous pickups and deliveries over a specific planning horizon. A time series decomposition forecasting model is used for predicting future demand and an exact linearization of the quadratic term of the objective function guarantees optimality of the solutions. The proposed methodology is illustrated using two examples featuring a single distribution centre, and three customers with simple and complex demand profiles. It is shown that EMPC offers a useful tool for producing balanced decisions between transportation and inventory costs and tracking of the safety inventory levels.

Case Article—Growing Pains: A Case Study for Large-Scale Vehicle Routing

This is a case study focused on the quantitative modeling of transportation services for use in undergraduate and graduate level transportation and logistics or optimization courses. The problem considered in the case is a capacitated vehicle routing problem (CVRP) with additional side constraints corresponding to delivery windows and United States Department of Transportation drive and duty time regulations. The case is presented in the context of a fictional company, Northeastern Home Goods (NHG), but is based on a problem encountered by a real organization. NHG is considering outsourcing its transportation services to a firm having logistics as a core competency. Specifically, NHG wishes to evaluate the transportation costs that will result if a single distribution center is used to serve all of NHG’s current stores according to a preexisting store delivery schedule. The primary objective of the case is to provide students with hands-on experience developing and applying solution techniques for a large unstructured vehicle routing problem. Secondary objectives include requiring students to think logically through the process of creating good solutions, promoting continuous improvement, and encouraging students to consider the business implications of their recommendations.

Logistics Distribution Center Location Optimizatio Model An Example Study

This paper analyzes and investigates the situation of the today’s site of distribution centers .Then taking SF Express for an example ,In the light of minimize the total expense ,the location models for single distribution centers is established with the corresponding resolutions given .After that ,The location of the single distribution center of SF Express ,the starting point of which is Zhengzhou Railway Station ,can be determined with the help of distance-testing function of 360 map to conclude its coordinate and then determine its exact service spot .This paper will test the feasibility of above models by the chosen cases with the application of LINGO software.

The impact of the number of parallel warehouses on total inventory

In the strategic design of a distribution system, the right number of stock points for the various products is an important question. In the past decade, a strong trend in the consumer goods industry led to centralizing the inventory in a single echelon consisting of a few parallel warehouses or even a single distribution center for a Europe-wide distribution system. Centralizing inventory is justified by the reduction in total stock which mostly overcompensates the increasing transportation cost. The effect of centralization is usually described by the "Square Root Law", stating that the total stock increases with the square root of the number of stock points. However, in the usual case where the warehouses are replenished in full truck loads and where a given fill rate has to be satisfied, the Square Root Law is not valid. This paper explores that case. It establishes functional relationships between the demand to be served by a warehouse and the necessary safety and cycle stock for various demand settings and control policies, using an approximation of the normal loss function and its inverse. As a consequence, the impact of the number of parallel warehouses on the total stock can be derived. The results can be used as tools in network design models.

Multiple Attribute Authority based Access Control with Anonymous Authentication using User Revocation in Decentralized Cloud

Cloud computing is the delivery of computing services over the Internet. The cloud computing model allows access to information and computing resources from anywhere and at any-time. Here the information security and data privacy are the major concerns over the cloud storage system. Normally access control in cloud are centralized in nature. In centralized system only single key distribution center is available and use the same key for encryption and decryption and does not support anonymous authentication. So to avoid the leakage of user personal details Multi-authority attribute based decentralized access control schemes are used for data storage. In the proposed scheme, attribute authority issues all attribute keys to the user without knowing the user personal details using Cipher-text policy attribute based encryption. In addition to that user revocation mechanism is used to activate and deactivate the users using Diffie-Hellman Merkle key exchange algorithm. Revoked users are maintained in the user revocation list and make publicly available in the cloud. User revocation scheme will decide which user should be permitted to access data or removed from cloud. The data access privilege will be depending upon the behaviour of users in cloud server.

Vehicle scheduling under the warehouse-on-wheels policy

The warehouse-on-wheels (WOW) policy is a widespread concept to implement material supply in just-in-time production systems. Instead of storing part inventories directly on the shop-floor where space is notoriously scare, the transport vehicles that deliver the materials serve as mobile storages, either while waiting in the trailer yard or while docking at inbound delivery gates. We investigate a vehicle scheduling problem under the WOW policy between a single distribution center and a production facility. Multiple containers of parts each associated with an individual delivery date and a–potentially individual–capacity requirement are to be shipped to the factory. The docking time of transporting vehicles is determined by their respective freight from the delivery date of the most urgent container up to that of the least urgent one. Only then, a vehicle may return to the distribution center to transship another subset of containers. We formalize the resulting vehicle scheduling problem under different objectives, investigate the computational complexity of the problem and develop efficient solution procedures for important problem variants.

Small Quantities of Cargo Transportation Route Selection Base on Minimum Spanning Tree Algorithm

This paper mainly studies the single distribution center in the circumstances, transport path selection and vehicle allocation problem of small batch of goods. Using the theory of minimum spanning tree theory to the entire logistics network path selection research, while determining the nodes need to configure the transport vehicle for shunt transport, further establish the logistic distribution routing out several cars choice model, but also provides a direction for future research on this type of problem.