Guaranteed Delivery Time Research Articles

Same-day delivery time-guarantee problem in online retail

This study introduces the same-day delivery time-guarantee (SDDTG) problem for supporting online retail. In the SDDTG, orders are placed online and are processed and delivered from a depot to customer locations in the same day. The problem seeks optimal delivery time guarantees to offer customers as they consider making purchases to increase purchase decision likelihood while accounting for delivery-related, supply-side costs that can affect profits. Time guarantees are decision variables rather than parameters (as is typical) and are designed around anticipated customer satisfaction levels and purchase likelihoods. Delivery deadlines are not merely given to customers once they make their purchases, but rather the attractiveness of the offered delivery guarantees affects whether they make their purchases, i.e., whether demand is realized. The problem is conceptualized as a multi-stage, stochastic, mixed-integer program in which uncertainties associated with customer reaction to delivery time guarantee offers and their arrival over time are captured. Given a shrinking horizon over a fixed planning horizon, the multi-stage program is approximated by a series of two-stage programs. A parallelized progressive hedging solution methodology is proposed and insights from its application on a case study. The problem recognizes tradeoffs between meeting promised delivery times and capturing the market.

IoT-enabled delivery time guarantee in logistics outsourcing and efficiency improvement

Industrial Internet of Things (IoT) technologies have been widely implemented in today’s logistics operations to reduce the handling time and improve service efficiency. This enables a firm to promise a delivery time (PDT) when selling products, which improves customer satisfaction and enhances the firm’s brand image. However, the costly PDT logistics service should be seriously evaluated when the market competition is intensified to aggravate the firm’s cost concern. In a competitive decentralized framework, this paper investigates the value of IoT-enabled PDT quotation in a brand’s logistics outsourcing decisions. We develop three-stage optimization models and show that the brand’s production quantity and equilibrium profit exhibit non-monotonic relationships to the competition intensity degree. Given a moderate competition intensity degree, the brand developing IoT and quoting PDT suffers from imbalanced payoffs between PDT cost-saving and sales volume expansion. This distorts the PDT cost and hence, investing in the IoT-enabled logistics system might not be beneficial. This paper contributes to the existing literature by examining the impact of heterogeneous logistics service incorporating the PDT quotation on traditional quantity competition within a dual-channel supply chain.

On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information

In this paper, we study the potential of using the metric of Age of Information (AoI) for enhancing delay-tolerant routing protocols. The latter have been proposed for alleviating the impact of long roundtrip time in networks operating in harsh environments, e.g., in distributed applications deployed in a desert/sparsely populated area without infrastructure, a space network, etc. Delay-tolerant routing protocols can prevent excessive packet timer expiration, but do not provide any packet delivery time guarantee. Thus, they are unsuitable for time-sensitive applications that are more intensely desired nowadays in the next generation networking applications. By incorporating AoI into the operation of delay-tolerant routing protocols, we aim at devising routing protocols that can cope with both long propagation times and challenges related to time-sensitivity in packet delivery. More specifically, in this work, we modify the operation of a well-known delay-tolerant routing protocol, namely FRESH, to make AoI-based packet forwarding decisions, aiming at achieving specific delay guarantees regarding the end-to-end delivery time. We investigate the advantages and disadvantages of such an approach compared to the traditional FRESH protocol. This work serves as a cornerstone for successfully demonstrating the potential of exploiting AoI in delay-tolerant routing and its applications.

Cross-docking cargo delivery routing for guaranteed minimum period

The article is devoted to the problem of effective use of cross-docking as a technology of cargo delivery with increased time requirements, which allows to resolve the contradictions of guaranteed delivery time ensuring and the efficiency of the existing fleet of trucks. The process of delivery organization is considered as the ordering on the transport network of many discrete freight flows in the form of their phases. If qualitative and / or quantitative changes do not occur from phase to phase with the flow, then the tact of such flow is constant. However, cross-docking flows change the size of the band of moving goods. Cargo can be moved as intended by any group size, which, however, is limited by the maximum and minimum values. A two-stage algorithm for solving the problem has been developed. The transport network is represented as a graph. The content of the route search problem is optimization, as it consists of multiple selections from the initial graph of arcs in the presence of restrictions on input and output flows. One needs to replace every each edge of the graph with an arc of the forward or reverse direction, or remove this edge. The criterion for the optimal solution of the problem, which is applied, is the minimum guaranteed duration of delivery of goods throughout the set of specified freight flows. At the first stage of the algorithm, the search for the shortest paths in the graph is performed, along which every given cargo flow can pass. The first stage of optimization is a linear problem of integer programming, the dimension of which is not too large. The initial data of the second stage is freight flows matrix, which is obtained as a result of optimization in the first stage. The content of the second stage of the algorithm is the solution of the equation of the balance of discrete goods flows. The balance equation means that all flows entering each peak including the sources of cargo flows of this peak have an average intensity equal to the intensity of the outgoing cargo flows from each source peak, including runoff. Due to the studied dependencies between the individual phases of the delivery process on the example of a cargo carrier on the transport network of Ukraine, the formulated restrictions and boundary conditions, the possibility of guaranteed accurate solution of a complex problem is obtained. At the same time, the shortest routes were found, reloading points were identified as well as time parameters of operation and the degree of loading of cars. According to the results of the research, a threefold increase in the productivity of the fleet of road trains with a reduction in the guaranteed delivery time by 30%.

Real-Time Delivery Time Forecasting and Promising in Online Retailing: When Will Your Package Arrive?

Problem definition: Providing fast and reliable delivery services is key to running a successful online retail business. To achieve a better delivery time guarantee policy, we study how to estimate and promise delivery time for new customer orders in real time. Academic/practical relevance: Delivery time promising is critical to managing customer expectations and improving customer satisfaction. Simply overpromising or underpromising is undesirable because of the negative impacts on short-/long-term sales. To the best of our knowledge, we are the first to develop a data-driven framework to predict the distribution of order delivery time and set promised delivery time to customers in a cost-effective way. Methodology: We apply and extend tree-based models to generate distributional forecasts by exploiting the complicated relationship between delivery time and relevant operational predictors. To account for the cost-sensitive decision-making problem structure, we develop a new split rule for quantile regression forests that incorporates an asymmetric loss function in split point selection. We further propose a cost-sensitive decision rule to decide the promised delivery day from the predicted distribution. Results: Our decision rule is proven to be optimal given certain cost structures. Tested on a real-world data set shared from JD.com, our proposed machine learning–based models deliver superior forecasting performance. In addition, we demonstrate that our framework has the potential to provide better promised delivery time in terms of sales, cost, and accuracy as compared with the conventional promised time set by JD.com. Specifically, our simulation results indicate that the proposed delivery time promise policy can improve the sales volume by 6.1% over the current policy. Managerial implications: Through a more accurate estimation of the delivery time distribution, online retailers can strategically set the promised time to maximize customer satisfaction and boost sales. Our data-driven framework reveals the importance of modeling fulfillment operations in delivery time forecasting and integrating the decision-making problem structure with the forecasting model.

A City-Wide Crowdsourcing Delivery System with Reinforcement Learning

The revolution of online shopping in recent years demands corresponding evolution in delivery services in urban areas. To cater to this trend, delivery by the crowd has become an alternative to the traditional delivery services thanks to the advances in ubiquitous computing. Notably, some studies use public transportation for crowdsourcing delivery, given its low-cost delivery network with millions of passengers as potential couriers. However, multiple practical impact factors are not considered in existing public-transport-based crowdsourcing delivery studies due to a lack of data and limited ubiquitous computing infrastructures in the past. In this work, we design a crowdsourcing delivery system based on public transport, considering the practical factors of time constraints, multi-hop delivery, and profits. To incorporate the impact factors, we build a reinforcement learning model to learn the optimal order dispatching strategies from massive passenger data and package data. The order dispatching problem is formulated as a sequential decision making problem for the packages routing, i.e., select the next station for the package. A delivery time estimation module is designed to accelerate the training process and provide statistical delivery time guarantee. Three months of real-world public transportation data and one month of package delivery data from an on-demand delivery platform in Shenzhen are used in the evaluation. Compared with existing crowdsourcing delivery algorithms and widely used baselines, we achieve a 40% increase in profit rates and a 29% increase in delivery rates. Comparison with other reinforcement learning algorithms shows that we can improve the profit rate and the delivery rate by 9% and 8% by using time estimation in action filtering. We share the data used in the project to the community for other researchers to validate our results and conduct further research.1 [1].

Time- and price-based product differentiation in hybrid distribution with stockout-based substitution

A delivery mix that includes delivery from stock and drop-shipping is of interest to many internet retailers. We consider a retailer serving a time- and price-sensitive market with two substitutable products that differ in the guaranteed delivery time and price, an express product (delivered from the stock) and a regular product (drop-shipped). In case of stockout, customers may switch from the express product to the regular product. We study how to differentiate the products in terms of delivery times and prices and how to determine the stock level to maximize the retailer's expected profit while satisfying service constraints. We solve different variants of the problem and derive insights into the optimal retailer's strategy. In addition, we study the impact of stockout-based substitution. This paper is the first to investigate time- and price-based differentiation along with inventory decisions for a retailer who relies on a hybrid distribution to satisfy a time- and price-sensitive demand subject to stockout-based substitution. When prices and stock are fixed, in addition to minimum and maximum time differentiations, a medium differentiation strategy may be optimal but depends on the stock level. When only prices are fixed, there exists a price differentiation limit below which a minimum time differentiation is optimal, and above which only the express product should be offered. For the general model, numerical experiments show that a higher stockout-based substitution leads to greater time differentiation (which is consistent with the results of previous models) and more stock. However, this would not impact the price differentiation.

Profits Optimization Reasearch of Dual-Channel Supply Chain Oriented to Manufacturers Online Customization and Traditional Retail

Under the background of "Internet +", increasing manufacturers sell standard products through retail channels and sell customized products in electronic channel. The competition about dual-channel supply, relationship between production cost and delivery time and the influences of online market acceptance, product customization attribute value, price and delivery time on online customization and traditional retail markets are considered, based on the decision variables of the price of custom and standard products and the guaranteed delivery time, the Stackelberg models are constructed respectively for the manufacturer-led and retail-led supply chain under decentralized system and the overall profits maximization of the supply chain is modeled under centralized system, the existence and uniqueness of the optimal solutions is proved by the optimization theory. The optimal decisions, market demands and enterprises profits under different systems are compared through numerical examples, the relation between the sensitivity coefficients and the optimal enterprises profits are obtained through the parameters sensitivity analysis. The idea of model construction and relevant conclusions are of reference and significance for manufacturing enterprise management practices.

Crowdsourcing Last-Mile Deliveries

Problem definition: Because of the emergence and development of e-commerce, customers demand faster and cheaper delivery services. However, many retailers find it challenging to efficiently provide fast and on-time delivery services to their customers. Academic/practical relevance: Amazon and Walmart are among the retailers that are relying on independent crowd drivers to cope with on-demand delivery expectations. Methodology: We propose a novel robust crowdsourcing optimization model to study labor planning and pricing for crowdsourced last-mile delivery systems that are utilized for satisfying on-demand orders with guaranteed delivery time windows. We develop our model by combining crowdsourcing, robust queueing, and robust routing theories. We show the value of the robust optimization approach by analytically studying how to provide fast and guaranteed delivery services utilizing independent crowd drivers under uncertainties in customer demands, crowd availability, service times, and traffic patterns; we also allow for trend and seasonality in these uncertainties. Results: For a given delivery time window and an on-time delivery guarantee level, our model allows us to analytically derive the optimal delivery assignments to available independent crowd drivers and their optimal hourly wage. Our results show that crowdsourcing can help firms decrease their delivery costs significantly while keeping the promise of on-time delivery to their customers. Managerial implications: We provide extensive managerial insights and guidelines for how such a system should be implemented in practice.

Cloud-Kitchens in High-Density Cities: Economies of Scale Through Co-Location

Delivery platforms have recently ventured into (also called ghost or virtual kitchens), which are delivery-only facilities, built typically in locations with high population density, that provide commercial space to multiple restaurant kitchens for preparing their menu items. A cloud kitchen's central location allows for better access to customers, enabling the delivery platform to provide a lower delivery-time guarantee to customers -- this increases profitability for both the delivery platform and the restaurants. A potential second advantage accrues from the ability of the delivery platform to consolidate its driver capacity if multiple restaurants co-locate at the cloud kitchen -- this increases the delivery platform's profitability as it can use the same set of drivers to deliver orders for multiple restaurants. Cloud kitchens, however, come with high rental costs that are directly passed down to the restaurants. We examine the conditions under which a cloud kitchen benefits the three primary stakeholders, namely, the delivery platform, the restaurants, and the customers. Our game-theoretic analysis is based on a stylized model consisting of two restaurants and a delivery platform. The two restaurants simultaneously decide whether to stay at their initial (extreme) locations or relocate to a cloud kitchen at a central location. Given the location decisions of the restaurants, the delivery platform decides the delivery-time guarantee for customers and the proportion of the market to serve. Our findings show that both the population density of the city and the economies of scale provided by the cloud kitchen play a significant role in driving the restaurants' location decisions. In line with current industry trends, we show that as the population density increases beyond a threshold, co-locating at the cloud kitchen is first a Pareto-dominant equilibrium for the restaurants and then the unique equilibrium. This equilibrium is also preferred by the delivery platform and the customers, leading to a win-win-win for all the stakeholders. If the population density is below the threshold, then we identify an interesting lever through which the delivery platform can incentivize the restaurants to move to the cloud kitchen.

Real-Time Delivery Time Forecasting and Promising in Online Retailing: When Will Your Package Arrive?

Providing fast and reliable delivery services is key to running a successful online retail business. To achieve a better delivery time guarantee policy, we study how to estimate and promise delivery time for new customer orders in real time. Delivery time promising is critical to managing customer expectations and improving customer satisfaction. Simply over-promising or under-promising is undesirable due to their negative impacts on short-term/long-term sales. We are the first to develop a data-driven framework to predict the distribution of order delivery time and set promised delivery time to customers in a cost-effective way. We adapt regression tree and quantile regression forests to generate distributional forecasts by exploiting the complicated relationship between delivery time and relevant predictors, which include the queue-length predictors to model the distribution center operations. We further propose a cost-sensitive classification decision rule to decide the promised delivery day from the predicted distribution. Tested on a real-world data set shared from JD.com, our proposed machine learning based models deliver superior forecasting performance. In addition, we demonstrate that our framework has the potential to provide better promised delivery time in terms of both cost and accuracy, as compared to the conventional promised time set by JD.com. Through a more accurate estimation of the delivery time distribution, online retailers can strategically set the promised time to maximize customer satisfaction and boost sales. Our data-driven framework reveals the importance of modeling fulfillment operations in delivery time forecasting, which sheds light on further improvement directions.

Managing product variety with mixed make-to-stock/make-to-order production strategy and guaranteed delivery time under stochastic demand

Firms often use a facility to produce both Make-to-Stock (MTS) and Make-to-Order (MTO) items. This study provides insights into conditions favoring the adoption of such a mixed MTS-MTO production strategy. Past studies ignore the influence of a one-to-many relation between semi-finished goods and products, and a guaranteed lead-time for MTO products, with uncertain product demand. The problem is modeled as a two-stage stochastic 0–1 integer, non-linear programming problem. In Stage 1, before demand realization, operations determine the product-wise mode of production and inventory level of semi-finished goods/MTS products, and marketing determine the guaranteed lead-time for MTO products. In Stage 2, product demand is realized, and it decreases linearly with lead-time and has a stochastic error component. The demand for MTS products is fulfilled from available inventory, and semi-finished goods are allocated to meet the demand of MTO products and build a stock of MTS products. The objective is to maximize the expected profit per-unit time. Sensitivity analysis of key problem parameters demonstrates that: (1) the integrated-firm can provide smaller lead-times under demand variability, (2) high holding cost of semi-finished goods, high lead-time sensitivity of demand, and high demand variability & low production rate promote mixed strategy over MTO strategy, and (3) high demand variability promotes substitution among semi-finished goods. A Hybrid Greedy and Non-linear Branch & Bound method (featuring problem-specific cuts and search & branching strategy) is proposed to solve real-life problems. This method provides a better solution and a smaller relative gap than commercial solver Simple Branch and Bound.

Crowdsourcing Last-Mile Deliveries

Problem definition: Due to the emergence and development of e-commerce, customers demand faster and cheaper delivery services. However, many retailers find it challenging to efficiently provide fast and on-time delivery services to their customers. Academic/practical relevance: Amazon and Walmart are among the retailers that are relying on independent crowd drivers to cope with on-demand delivery expectations. Methodology: We propose a novel robust crowdsourcing optimization model to study labor planning and pricing for crowdsourced last-mile delivery systems that are utilized for satisfying on-demand orders with guaranteed delivery time windows. We develop our model by combining crowdsourcing, robust queueing, and robust routing theories. We show the value of the robust optimization approach by analytically studying how to provide fast and guaranteed delivery services utilizing independent crowd drivers under uncertainties in customer demands, crowd availability, service times, and traffic patterns; we also allow for trend and seasonality in these uncertainties. Results: For a given delivery time window and an on-time delivery guarantee level, our model allows us to analytically derive the optimal delivery assignments to available independent crowd drivers and their optimal hourly wage. Our results show that crowdsourcing can help firms decrease their delivery costs significantly, while keeping the promise of on-time delivery to their customers. Managerial implications: We provide extensive managerial insights and guidelines for how such a system should be implemented in practice.

Modelling Shipment Consolidation and Pricing Decisions for a Manufacturer-Distributor

Consider a supply chain with one vendor and multiple buyers. That vendor not only manufactures the goods demanded, but also transports them by its own fleet of trucks to buyers that are densely dispersed in a distribution zone. The buyers are sensitive to both price and delivery-time guarantee. We model and maximise the vendor’s expected profit rate when the prices charged depend on the arrival times of orders. The optimal profit rate is found to be concave in the vehicle capacity. We illustrate our theoretical findings with numerical examples and sensitivity analyses to furnish managerial insights.

Capacity and lead-time management when demand for service is seasonal and lead-time sensitive

In today’s competitive business environment, quick service with minimal waiting time is an important factor for customers when choosing a service. Many service organizations guarantee a uniform lead-time to all customers in order to gain competitive advantages in the market. In selecting a lead-time to quote, the firm has to take into consideration not only how customers will react to the delivery time guarantee, but also whether it has adequate capacity to fulfill the commitment. A short lead-time can bring both benefits and costs. It can increase customer demand, but might require a higher capacity level. We present a mathematical model and a solution method for determining the optimal quoted lead-time and capacity level for a profit-maximizing firm with time-varying and lead-time sensitive demand. The firm incurs convex capacity costs and pays lateness penalties whenever the actual lead-time exceeds the quoted lead-time. A few studies have been conducted on the relationship between uniform lead-time, capacity, demand, and overall profitability. However, none of them takes the time variation of demand into account. Our work differs from previous research in that we explicitly model such a demand pattern.

The Study of MTO Online Manufacturers' Guaranteed Delivery Time Model with Order Batching

To MTO on-line manufacturers, one of the most popular time-based competitive strategies is to widely advertise a uniform delivery time guarantee to all the customers. While providing time guarantee can be an effective marketing approach, it is critical for firms to reduce lead time to keep the promise. Decreasing lot size in batching is one of the most important levers to compress lead time in operation. This research expands existing blanket delivery-time guarantee models by integrating operation approach and marketing approach. The online manufacturers guaranteed delivery time model with order batching is established. Some analytic results are provided, and numerical examples are conducted to provide further insight into the problem. The effects of batch processing setup cost, unit inventory holding cost and unit compression cost of transportation time are analyzed. The results indicate that when batch processing setup cost decrease, unit inventory holding cost or unit compression cost of transportation time increase, the online manufacturer should decrease the lot size and shorten the guaranteed delivery time. The customers time and price sensitivities have adverse influences on the manufacturers delivery time decision.

Integrating operations and marketing decisions using delayed differentiation of products and guaranteed delivery time under stochastic demand

In this research, we integrate the issues related to operations and marketing strategy of firms characterized by large product variety, short lead times, and demand variability in an assemble-to-order environment. The operations decisions are the inventory level of components and semi-finished goods, and configuration of semi-finished goods. The marketing decisions are the products price and a lead time guarantee which is uniform for all products. We develop an integrated mathematical model that captures trade-offs related to inventory of semi-finished goods, inventory of components, outsourcing costs, and customer demand based on guaranteed lead time and price.The mathematical model is a two-stage, stochastic, integer, and non-linear programming problem. In the first stage, prior to demand realization, the operation and marketing decisions are determined. In the second stage, inventory is allocated to meet the demand. The objective is to maximize the expected profit per-unit time. The computational results on the test problems provide managerial insights for firms faced with the conflicting needs of offering: (i) low prices, (ii) guaranteed and short lead time, and (iii) a large product variety by leveraging operations decisions.We also develop a solution procedure to solve large instances of the problem based on an accelerated version of the Generalized Benders’ Decomposition (GBD) method. The accelerating mechanism involves search intensification and diversification around solutions which improve the upper bound. The suggested GBD method gives a better solution and a tighter lower bound in a given time period than the conventional GBD implementation and the non-linear branch-and-bound method.

Market-based supplier selection with price, delivery time, and service level dependent demand

A market-based strategy is proposed for joint decisions on price, delivery time, service level, and supplier selection or investment. Product or service demand is modeled as a linear function of attributes including price, guaranteed delivery time, service level, or other quality-like performances and the profit is maximized as the objective. For different market segments or markets, market characteristics, namely customer sensitivity on attributes, differ. A supplier could be characterized by operation performance terms of cost, delivery time, service level, or quality. Results denote that operation characteristics of the supplier chosen should match market characteristics. Results show that, with stochastic delivery time, the service level is not always binding at the minimal value reserved by the manager or the market, which is assumed in the literature. There exists a limit on performance improvement due to cost increase, and there is a limit on cost reduction with decreased performance. Specifically, the less costly supplier must be chosen if the cost difference is more than one value, while the supplier with better performance must be chosen if the performance difference is more than another value. Those values are determined by current performance characteristics and market characteristics. The model is also applied in investment decision on cost reduction and delivery time reduction. There is a maximum value in spending to reduce delivery time and improve service level and quality. The model suggests that one firm could focus more on cost reduction or quality-like performance improvement based on market characteristics. The best market segment may exist for a firm with fixed operation performance. In one word, a firm′s operation characteristics and market characteristics must be matched for better profitability. Furthermore, concepts of lean manufacturing, emphasizing more on cost reduction, and flexible or agile manufacturing, emphasizing more on less delivery time or better service level, should be combined accordingly based on market characteristics.

Sourcing under cost information asymmetry when facing time-sensitive customers

Abstract We study a supply chain with a manufacturer and two different suppliers. One supplier is a big company abroad, who is prime and offers high quality modules with long lead time. The other supplier is a small local company, taken as the urgent supplier by the manufacturer, who can produce modules in inferior quality but with flexible delivery time. Excess customers beyond the orders from the prime supplier, who are sensitive to the delivery time, may be satisfied by modules from the urgent supplier. As the urgent supplier owns private cost information (either low or high cost type) and the prime one is the wholesale price setter, the manufacturer should carefully design his sourcing strategy. In this paper, we show that the manufacturer may deny the urgent supplier, allow only the low cost type or permit both cost types of the urgent supplier, which follows a threshold type policy. When both cost types participate, whether the manufacturer differentiates the urgent supplier is also threshold type controlled. With fully characterizing the contracting issue with the urgent supplier, we derive the optimal wholesale price of the prime supplier and related order quantity from him. We further analyze how the delivery time guarantee elasticity affects the performance of each party in the supply chain through either analytical or numerical studies.

Optimal quoting of delivery time by a third party logistics provider: The impact of shipment consolidation and temporal pricing schemes

Increased competition in business environments requires that firms provide not only quality but also timely service with minimal cost. Offering a delivery-time guarantee may increase the demand for a product or service, or allow the firm to charge a price premium. This paper investigates the effects of different pricing schemes for a Third Party Logistics (3PL) provider. The 3PL tenders a consolidated load to a carrier that line-hauls over a certain origin–destination lane. In a price- and time-sensitive logistics market, we derive the optimal quotations that should be made for price and delivery-time, with the objective of maximizing the profit rate of the 3PL provider. We propose four easy-to-use temporal pricing schemes, and derive the corresponding optimal length of shipment consolidation cycles and the prices. Depending on the logistics market parameters, we show that charging according to an order’s time of arrival is not necessarily the best pricing scheme. Various managerial insights and numerical examples with sensitivity analysis are provided.