Delivery Deadline Research Articles

Modeling Freight Consolidation in a Make-to-Order Supply Chain: A Simulation Approach

Shipment consolidation is one of main initiatives to reduce CO2 emissions and transportation cost. It reduces the number of shipments per customer and reduces transportation costs by using larger shipments. This paper investigates the temporal consolidation process in a central consolidation center in a make-to-order supply chain. This research was motivated by a case study of a design furniture company that has many suppliers and customers in large parts of Europe. Simulation was used to check the effect of a new and a special time-based temporal consolidation on the response time in outbound logistics. A soft delivery deadline that is less than the average lead time was used because of the long lead time. Arena Software was used to model the supply chain in order to find the best circumstances to use consolidation. Results showed that temporal consolidation could be more effective when order preparation time is with larger variability. The useful waiting is more when there is at least one order every four days. A formula that approximates the percent of reduced shipments was found. Furthermore, many shipments can be reduced without severely affecting the average response time. The value of the study is that it investigates consolidation problems in a high-mix low-volume environment that was overlooked by previous research.

A tailored Benders decomposition approach for last-mile delivery with autonomous robots

This work addresses an operational problem of a logistics service provider that consists of finding an optimal route for a vehicle carrying customer parcels from a central depot to selected facilities, from where autonomous devices like robots are launched to perform last-mile deliveries. The objective is to minimize a tardiness indicator based on the customer delivery deadlines. This article provides a better understanding of how three major tardiness indicators can be used to improve the quality of service by minimizing the maximum tardiness, the total tardiness, or the number of late deliveries. We study the problem complexity, devise a unifying Mixed Integer Programming formulation and propose an efficient branch-and-Benders-cut scheme to deal with instances of realistic size. Numerical results show that this novel Benders approach with a tailored combinatorial algorithm for generating Benders cuts largely outperforms all other alternatives. In our managerial study, we vary the number of available facilities, the coverage radius of autonomous robots and their speed, to assess their impact on the quality of service and environmental costs.

An adaptative bacterial foraging optimization algorithm for solving the MRCPSP with discounted cash flows

In this paper, a metaheuristic solution algorithm for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with discounted cash flows (MRCPSPDC) is proposed. This problem consists of determining a schedule such that the project is completed, maximizing the project’s net present value (NPV) while complying with the delivery deadline. The adaptative bacterial foraging optimization (ABFO) algorithm is a variation of the original bacterial foraging optimization (BFO), which is a nature-inspired metaheuristic optimization algorithm. We implement a version of the chemotactic operator based on a double justification of the activities given the cash flow. This metaheuristic has been tested in the PSPLIB and MMLIB benchmark datasets available in the literature with promising results. Our ABFO algorithm shows excellent performance in all tested instances and provides suitable solutions for the MRCPSP maximizing the NPV.

Deep Q-learning for same-day delivery with vehicles and drones

In this paper, we consider same-day delivery with vehicles and drones. Customers make delivery requests over the course of the day, and the dispatcher dynamically dispatches vehicles and drones to deliver the goods to customers before their delivery deadline. Vehicles can deliver multiple packages in one route but travel relatively slowly due to the urban traffic. Drones travel faster, but they have limited capacity and require charging or battery swaps. To exploit the different strengths of the fleets, we propose a deep Q-learning approach. Our method learns the value of assigning a new customer to either drones or vehicles as well as the option to not offer service at all. In a systematic computational analysis, we show the superiority of our policy compared to benchmark policies and the effectiveness of our deep Q-learning approach. We also show that the combination of state and action features is very valuable and that our policy can maintain effectiveness when demand data and the fleet size change moderately.

QoSA-ICN: An information-centric approach to QoS in vehicular environments

Heterogeneous content-based traffic distribution motivates Information-Centric Networking (ICN), where content delivery is of primary interest as a prominent solution. However, current work does not address Quality of Service (QoS) provisioning for prioritized traffic, which is required for different applications and content types. This paper extends ICN with data delivery deadline awareness and shapes the forwarding decisions to ensure prioritized packet treatment. The proposed QoS Aware-ICN (QoSA-ICN) classifies requests' priority with their QoS requirements by codifying a QoSInfo object in interest/data packets. QoSA-ICN also extends the existing NDN transmission mode to a converged best route with multi-hop multi-route forwarding, to avoid the overhead of pre-building routes in rapidly changing and dynamic environments. Furthermore, this work expands the network traffic control mechanism to optimally manage the potential impact of QoS on network congestion control. Extensive simulations results show a 3.5x and 1.2x improvement in data delivery time; and 13.5x and 4.6x reduction in packet loss when compared to DRSC-based native IP and NDN approaches, in networks of different sizes. Through simulations, we also found that QoSA-ICN reduces overall content retrieval time and packet loss while achieving a high ratio of successfully-received priority packets as compared to the existing techniques T-Move, MAP-Me and pNDN.

The international participation in organization of production of antibiotics in the USSR: "The penicillin project" of the United Nations Relief and Rehabilitation Administration (1946-1947)

On the basis of declassified materials from the Russian State Archive of Economics and State Archive of the Russian Federation, for the first time, the history of the United Nations Relief and Rehabilitation Administration (UNRRA) project for the supply of penicillin factories in the post-war years to two republics of the USSR - Ukraine and Belarus is considered. It is shown that the project initially had political significance, since the demonstration of humane intentions served to strengthen the political authority of the United States. However, despite the declaration, the Anglo-American leadership of UNRRA approached the implementation of the project formally and hardly planned its completion. This was expressed in non-compliance with requirements for completeness of equipment, violation of delivery deadlines, constant attempts to reduce and contain them, in non-supplying adequate technical documentation and specialists for setting up production. The delay in the delivery time of equipment resulted in its rapid moral obsolescence. The modernization of partially supplied factories was impeded by embargo on the export of advanced industrial equipment from the United States having strategic importance. The incompleteness of the project contributed to further split in international relations.

Packet Loss Recovery in Broadcast for Real-Time Applications in Dense Wireless Networks

Packet loss recovery in wireless broadcast is challenging, particularly for real-time applications which have strict and short delivery deadline. To recover the maximum number of lost packets within a short time, existing packet recovery solutions often rely on instantly decodable network coding (IDNC). Some of these solutions can recover nearly the maximum number of lost packets possible at the cost of collecting feedback from all (or a large percentage of) users. This is impractical in dense networks. In addition, their runtime grows with the number of users, which is not desirable due to the urgent delivery deadline of real-time applications. In this work, we introduce random instantly decodable network coding (RIDNC), a random encoding approach to IDNC. We propose RA ndom IDN C E ncoder (RACE), a fast RIDNC encoder that can recover nearly as many lost packets as the optimal RIDNC encoder. We compare RACE with the CrowdWiFi encoder, a high performing packet loss recovery solution used in CrowdWiFi, a commercial system for broadcasting live video in dense networks. We show that RACE is up to two orders of magnitude faster than the CrowdWiFi encoder, and recovers more lost packets in practice, where there is not enough time to collect feedback from many users.

Impact of External Conditions on Selecting Special Transport Vehicle for Perishable Cargo Transportation

The list of special transport vehicles used for transporting the perishable goods by rail in Russia is presented in the study. The maximum allowable cargo delivery time was estimated depending on the type of used vehicle. The impact of external factors on the heat transfer coefficient of a vehicle body and the temperature head during transportation of perishable goods by rail are presented. It is determined that the heat transfer coefficient increases by 3 ÷ 10% at a train speed of 100 km/h, which reduces the maximum permissible delivery time of refrigerated cargo to the consumer. The effect of insolation on the car surface increases the heat flux inside the car body when transporting refrigerated goods and reduces the heat flux when transporting heated goods in vehicles, which also affects the deadlines for cargo delivery. The method of selecting a special vehicle considering the known maximum allowable time of perishable goods transportation is exposed in the study. The conclusions indicate what changes should be implemented in the existing methodology for calculating the deadline for perishable goods transportation, which will reduce the risk of their delivery in inadequate condition.

La metodología socioformativa en el currículo universitario: estrategias para la planificación docente

The present work proposes to evaluate the Socio formation as a strategy of university curricular planning of a formative project. The research methodology used is of a qualitative approach with an action-research scope, built from practice, in such a way that students have a training according to their future work context. The sample is made up of 69 students from the Indoamérica Technological University, Basic Education career of the formative project of the formative project of Aesthetic Appreciation, Body Expression and Movement, distance modality. The results were favorable, indicating that the activities carried out provided knowledge, were pertinent and sufficient for professional application, however, the students made suggestions for improvement such as extending the delivery deadlines of activities, greater use of technology plus synchronous classes, improving the products of each activity so that they are more attached to the educational reality among others. It is concluded that each of the steps carried out in the planning, organize the teaching work providing sequentiality in the teaching-learning processes but that aspects such as transdisciplinarity and communication with the student in the distance modality still have to be strengthened. Keywords: contents, strategy, planning, training project.

ПЕРСПЕКТИВЫ РАЗВИТИЯ РЫНКА ИНТЕРНЕТ-ТОРГОВЛИ

The e-trading market is a relatively new phenomenon for the Russian economy. Due to the development of Internet technologies in the country and the growth in the number of Internet users, strong momentum has been formed for the active development of this sector of the economy. Despite the economic crisis in the country, this economic sector continues to develop actively, which allows concluding that it is potentially attractive. Aggravation of competition requires a business to search for new possibilities for attracting and retaining consumers. The authors consider the prospects of e-trading development, the concept of e-trading, and its difference from e-commerce. The paper presents the classification of e-trading business models in their relation to a management entity. The study identified the positive and negative factors of e-commerce development. The paper considers the prospective directions of e-trading market development in the interrelation with logistics. The study showed the dependence of e-trading development on the logistic processes, macroeconomic factors, and economic infrastructure as a whole. The interdisciplinarity of electronic trade, its development at the confluence of several sciences allows assuming the dependence of the processes of the good creation and realization on the possibilities of marketing, logistics, and the formats of goods delivery to an end consumer. Logistics allows overcoming many constraints, such as doubts about the reliability of a supplier, the risks of non-fulfillment of delivery deadlines, loss of a parcel, and the doubts about the compliance of the selected product with expectations (the inability to examine an ordered product visually). At the same time, the sphere of logistics and e-trading have a mutual positive impact on each other, since the active development of logistics is primarily caused by the e-trading development: the creation of new forms of goods delivery and consolidation of legal entities to ensure the delivery goods are influenced by the development of e-trading.

Route Duration Prediction in a Stochastic and Dynamic Vehicle Routing Problem with Short Delivery Deadlines

Abstract We are facing a real-world vehicle routing problem where orders arrive dynamically over the day at an online store and have to be delivered within short time. Stochastic information in form of the expected number and weight of orders and the traffic congestion level is available upfront. The goal is to predict the average time needed to deliver an order for a given time and day. This information is desirable for both routing decisions in the short horizon and planning vehicle drivers’ shifts with just the right capacity prior to the actual day. We compare a white box linear regression model and a neural network based black box model on historic route data collected over three months. We employ a hourly data aggregation approach with sampling statistics to estimate the ground truth and features. The weighted mean square error is used as loss function to favor samples with less uncertainty. A mean validation R2 score over 10 x 5-fold cross-validations of 0.53 indicates a substantial amount of unexplained variance. Both predictors are slightly optimistic and produce median standardized absolute residuals of about one.

A Tabu Search Method for the Multi-objective Winner Determination Problem of Combinatorial Auctions

We are interested by the problem of combinatorial auctions in which multiple items are sold and bidders submit bids on packages. First, we present a multi-objective formulation for a combinatorial auctions problem extending the existing single-objective models. Indeed, the bids may concern several specifications of the item, involving not only its price, but also its quality, delivery conditions, delivery deadlines, the risk of not being paid after a bid has been accepted and so on. The seller expresses his preferences upon the suggested items and the buyers are in competition with all the specified attributes done by the seller. Second, we develop and implement a metaheuristic algorithm based on a tabu search method.

An Exact Method for the Multi-objective Winner Determination Problem of Combinatorial Auctions

We are interested by the problem of combinatorial auctions in which multiple items are sold and bidders submit bids on packages. First, we present a multi-objective formulation for a combinatorial auctions problem extending the existing single-objective models. Indeed, the bids may concern several specifications of the item, involving not only its price, but also its quality, delivery conditions, delivery deadlines, the risk of not being paid after a bid has been accepted and so on. The seller expresses his preferences upon the suggested items and the buyers are in competition with all the specified attributes done by the seller. Second, we develop and implement an exact algorithm based on a multi-objective branch-and-bound method.

Logistics Performance, Ratings, and its impact on Customer Purchasing Behavior and Sales in E-commerce Platforms

Problem Definition: We examine the impact of logistics performance metrics such as delivery time, and customer’s requested delivery speed on logistics service ratings and third-party sellers’ sales on an e-commerce platform. Academic/Practical Relevance: While e-commerce retailers like Amazon have recently invested heavily in their logistics networks to provide faster delivery to customers, there is scant academic literature that tests and quantifies the premise that convenient and fast delivery will drive sales. In this paper, we provide empirical evidence on whether this relationship holds in practice by analyzing a mechanism that connects delivery performance to sales through logistics ratings. Prior academic work on online ratings in ecommerce platforms has mostly analyzed customers’ response to product functional performance and biases that exist within. Our study contributes to this stream of literature by examining customer experience from a service quality perspective by analyzing logistics service performance, logistics ratings, and its impact on customer purchase probability and sales. Methodology: Using an extensive data set of more than 15 million customer orders on the Tmall platform and Cainiao network (logistics arm of Alibaba), we employ the Heckman ordered regression model to explain the variation in customers’ rating of logistics performance, as well as the likelihood of customers posting a logistics rating. Next, we develop a generic customer choice model that links the customer’s likelihood of making a purchase to the logistics ratings provided by prior customers. We implement a two-step estimation of the choice model to quantify the impact of logistics ratings on customer purchase probability and third-party seller sales. Results: We surprisingly find that even customers with no promise on delivery speed are likely to post lower logistics ratings for delivery times longer than two days. Although these customers are not promised an explicit delivery deadline, they seem to have a mental threshold of two days and expect deliveries to be made within that time. Similarly, we find that priority customers (those with two-day and one-day promise speed) provide lower logistics ratings for delivery times longer than their anticipated delivery date. We estimate that reducing the delivery time of all three-day delivered orders on this platform (which makeup approximately 35% of the total orders) to two days would improve the average daily third-party seller sales by 13.3% on this platform. The impact of delivery time performance on sales is more significant for sellers with a higher percentage of three-day delivered orders and a higher spend per order. Managerial Implications: Our study emphasizes that delivery performance and logistics ratings, which measure service quality, are essential drivers of the customer purchase decision on e-commerce platforms. Further, by quantifying the impact of delivery time performance on sales, our study also provides a framework for online retailers to assess if the increase in sales due to improved logistics performance can offset the increase in additional infrastructure costs required for faster deliveries. Our study’s insights are relevant to third-party sellers and e-commerce platform managers who aim to improve long-term online customer traffic and sales.

Shipping Consolidation Across Two Warehouses with Delivery Deadline and Expedited Options for E-commerce and Omni-channel Retailers

Problem definition: Shipment consolidation (i.e., shipping multiple orders together instead of shipping them separately) is commonly used to decrease total shipping costs. However, when the delivery of some orders is delayed, so they can be consolidated with future orders, a more expensive expedited shipment may be needed to meet shorter deadlines. In this paper, we study the optimal consolidation policy focusing on the trade-off between economies of scale due to combining orders and expedited shipping costs, in the setting of two warehouses. Academic/practical relevance: Our work is motivated by the application of fulfillment consolidation in e-commerce and omni-channel retail, especially with the rise of so-called on-demand logistics services. Sellers have the flexibility to take advantage of consolidation by deciding when to ship the orders and from which warehouse to fulfill the orders, as long as the orders’ deadlines are met. Methodology: We use Dynamic Programming to study the optimal policy and its structure. We also conduct extensive simulation tests to evaluate the performance of heuristics that are based on structures of the optimal policies. Results: The optimal policies and their structures are characterized. Using the insights of these structural properties, we propose two easily implementable heuristics that perform within 1%–2% of the optimal solution and outperform other benchmark consolidation methods in numerical tests. Managerial implications: Consolidation is shown to significantly reduce the outbound shipping costs. Retailers can take advantage of it to effectively improve the standard policies by simply applying the threshold-form heuristics we propose.

Round Trip Routing for Energy-Efficient Drone Delivery Based on a Public Transportation Network

Focusing on using drones to deliver packages, this article considers a new delivery mode exploiting public transportation vehicles. Due to the battery capacity constraint, which limits the delivery area of drones, the public transportation vehicles are used to transport a drone to someplace unreachable by flying only. The problem under investigation is the round trip routing problem that finds the shortest path in terms of time in a time-dependent network, subject to the delivery deadline constraint and the energy budget constraint. The time-dependent network composes of the transit network and some extra drone flights. An exact solution algorithm is developed to find the optimal round trip path, and then, it is extended to accommodate the randomness of the considered network. The effectiveness is demonstrated via computer simulations.

Dynamic Pricing and Routing for Same-Day Delivery

An increasing number of e-commerce retailers offers same-day delivery. To deliver the ordered goods, providers dynamically dispatch a fleet of vehicles transporting the goods from the warehouse to the customers. In many cases, retailers offer different delivery deadline options, from four-hour delivery up to next-hour delivery. Due to the deadlines, vehicles often only deliver a few orders per trip. The overall number of served orders within the delivery horizon is small and the revenue low. As a result, many companies currently struggle to conduct same-day delivery cost-efficiently. In this paper, we show how dynamic pricing is able to substantially increase both revenue and the number of customers we are able to serve the same day. To this end, we present an anticipatory pricing and routing policy (APRP) method that incentivizes customers to select delivery deadline options efficiently for the fleet to fulfill. This maintains the fleet’s flexibility to serve more future orders. We model the respective pricing and routing problem as a Markov decision process (MDP). To apply APRP, the state-dependent opportunity costs per customer and option are required. To this end, we use a guided offline value function approximation (VFA) based on state space aggregation. The VFA approximates the opportunity cost for every state and delivery option with respect to the fleet’s flexibility. As an offline method, APRP is able to determine suitable prices instantly when a customer orders. In an extensive computational study, we compare APRP with a policy based on fixed prices and with conventional temporal and geographical pricing policies. APRP outperforms the benchmark policies significantly, leading to both a higher revenue and more customers served the same day.

E-commerce shipping through a third-party supply chain

We consider an e-commerce retailer who must ship orders from a warehouse to a set of customers with delivery deadlines. As is often the case, the retailer uses a third-party service provider to ensure its distribution. The retailer can enter the supply chain of the service provider at various levels. Entering it at a higher level entails lower sorting costs for the retailer, but higher delivery costs, and longer delivery times. The customer orders arrive at various moments over a rolling planning horizon. This means that the retailer must also make consolidation decisions. We model and solve the static and dynamic cases of this problem. The static case is modeled as an integer linear program and solved by CPLEX. We develop and compare four shipping policies for the dynamic case. Extensive computational results based on real location data from California and Texas are reported.

Членството на България в ЕС – предпоставка или предизвикателство за повишена конкурентоспособност на международния товарен автотранспорт

Bulgaria’s EU Membership – a Prerequisite or a Challenge to Increasing the Competitiveness of International Cargo Motor Transport The article studies Bulgaria’s cargo motor transport as the most dynamically developing transport in the country, following the economy changes in the 90s. Land automobile transport is the one that meets the requirements of Bulgarian economy the best after adopting the principles of market economy. The development of Bulgaria’s logistic potential also relies on motor transport. A short survey of the special characteristics of motor transport, which are a prerequisite for its preemptive development compared to other types of transport, is done. The motor transport services market is studied as a market structure according to the degree of competitiveness. The basic pricing factors are described and the influence of Bulgaria’s EU membership on them is analyzed. The effect of the EU membership is also traced in the basic parameters of motor transport – cargo flows, prices, delivery deadlines, company efficiency. The effect of these changes on the competitiveness of Bulgarian trade companies is also observed.

Characterizing IoT Networks With Asynchronous Time-Sensitive Periodic Traffic

This letter develops a novel spatiotemporal model for large-scale IoT networks with asynchronous periodic traffic and hard-packet deadlines. A static marked Poisson bipolar point process is utilized to model the spatial locations of the IoT devices, where the marks mimic the relative time-offsets of traffic duty cycles at different devices. At each device, an absorbing Markov chain is utilized to capture the temporal evolution of packets from generation until either successful delivery or deadline expiry. The temporal evolution of packets is defined in terms of the Aloha transmission/backoff states. From the network perspective, the meta distribution of the transmission success probability is used to characterize the mutual interference among of the coexisting devices. To this end, the network performance is characterized in terms of the probabilities of meeting/missing the delivery deadlines and transmission latency. The results unveil counter-intuitive superior performance of strict packet deadlines in terms of transmission success and latency.