Showing papers in "Naval Research Logistics in 2020"
Uber 1
TL;DR: This work provides a review of matching and DP techniques in ride‐hailing, and shows that they are critical for providing an experience with low waiting time for both riders and drivers, and links the two levers together by studying a pool‐matching mechanism that varies rider waiting and walking before dispatch.
Abstract: Ride‐hailing platforms such as Uber, Lyft, and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing (DP) algorithms—the two key levers in ride‐hailing—have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride‐hailing platforms. We provide a review of matching and DP techniques in ride‐hailing, and show that they are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool‐matching mechanism called dynamic waiting (DW) that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing DP and DW, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.
153 citations
TL;DR: An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system.
Abstract: We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse condition. The model is applied to study the allocation of ventilator inventory in the COVID-19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non-COVID-19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non-COVID-19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top-most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst-case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk-aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp-up consideration can be based on a cost-benefit analysis.
124 citations
TL;DR: A novel reliability evaluation algorithm is proposed for the considered two‐dimensional networked system by extending the universal generating function technique and the proposed models and algorithms are illustrated by a matrix heating system in a thermoforming machine.
31 citations
TL;DR: This work first explores how stochastic production capacity affects supply chain decisions and QR implementation, then incorporates the manufacturer's ability to expand capacity into the model and extends the model to the two‐stage two‐ordering case and derives the optimal ordering policy by dynamic programming.
Abstract: The quick response (QR) system that can cope with demand volatility by shortening lead time has been well studied in the literature. Much of the existing literature assumes implicitly or explicitly that the manufacturers under QR can always meet the demand because the production capacity is always sufficient. However, when the order comes with a short lead time under QR, availability of the manufacturer's production capacity is not guaranteed. This motivates us to explore QR in supply chains with stochastic production capacity. Specifically, we study QR in a two‐echelon supply chain with Bayesian demand information updating. We consider the situation where the manufacturer's production capacity under QR is uncertain. We first explore how stochastic production capacity affects supply chain decisions and QR implementation. We then incorporate the manufacturer's ability to expand capacity into the model. We explore how the manufacturer determines the optimal capacity expansion decision, and the value of such an ability to the supply chain and its agents. Finally, we extend the model to the two‐stage two‐ordering case and derive the optimal ordering policy by dynamic programming. We compare the single‐ordering and two‐ordering cases to generate additional managerial insights about how ordering flexibility affects QR when production capacity is stochastic. We also explore the transparent supply chain and find that our main results still hold.
22 citations
17 citations
TL;DR: In this article, the authors considered a two-agent scheduling problem with linear resource-dependent processing times, in which each agent has a set of jobs that compete with that of the other agent for the use of a common processing machine, and each agent aims to minimize the weighted number of its tardy jobs.
Abstract: This paper considers a two‐agent scheduling problem with linear resource‐dependent processing times, in which each agent has a set of jobs that compete with that of the other agent for the use of a common processing machine, and each agent aims to minimize the weighted number of its tardy jobs. To meet the due date requirements of the jobs of the two agents, additional amounts of a common resource, which may be in discrete or continuous quantities, can be allocated to the processing of the jobs to compress their processing durations. The actual processing time of a job is a linear function of the amount of the resource allocated to it. The objective is to determine the optimal job sequence and resource allocation strategy so as to minimize the weighted number of tardy jobs of one agent, while keeping the weighted number of tardy jobs of the other agent, and the total resource consumption cost within their respective predetermined limits. It is shown that the problem is NP‐hard in the ordinary sense, and there does not exist a polynomial‐time approximation algorithm with performance ratio unless P=NP; however it admits a relaxed fully polynomial time approximation scheme. A proximal bundle algorithm based on Lagrangian relaxation is also presented to solve the problem approximately. To speed up convergence and produce sharp bounds, enhancement strategies including the design of a Tabu search algorithm and integration of a Lagrangian recovery heuristic into the algorithm are devised. Extensive numerical studies are conducted to assess the effectiveness and efficiency of the proposed algorithms.
14 citations
TL;DR: An easy‐to‐perform approximate algorithm for minimizing the makespan in a flexible two‐center job shop with one‐unit‐time operations in the first center and k‐ unit‐time Operations in the second center is proposed and has the absolute worst‐case error bound of k − 1, and thus for k = 1 it is optimal.
Abstract: Job shop scheduling with a bank of machines in parallel is important from both theoretical and practical points of view. Herein we focus on the scheduling problem of minimizing the makespan in a flexible two‐center job shop. The first center consists of one machine and the second has k parallel machines. An easy‐to‐perform approximate algorithm for minimizing the makespan with one‐unit‐time operations in the first center and k‐unit‐time operations in the second center is proposed. The algorithm has the absolute worst‐case error bound of k − 1, and thus for k = 1 it is optimal. Importantly, it runs in linear time and its error bound is independent of the number of jobs to be processed. Moreover, the algorithm can be modified to give an optimal schedule for k = 2.
13 citations
TL;DR: This paper surveys state‐of‐the‐art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management—and points out some interesting future research possibilities.
Abstract: We review the operations research/management science literature on data‐driven methods in retail operations. This line of work has grown rapidly in recent years, thanks to the availability of high‐quality data, improvements in computing hardware, and parallel developments in machine learning methodologies. We survey state‐of‐the‐art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management. We then conclude the paper by pointing out some interesting future research possibilities for our community.
13 citations
13 citations
TL;DR: A two‐stage exact algorithm is proposed to obtain an optimal solution to the CCOP, which is a generalization of the classical orienteering problem and can effectively solve instances with up to 60 sampling sites.
Abstract: This study investigates a clustered coverage orienteering problem (CCOP), which is a generalization of the classical orienteering problem. The problem is widely motivated by the emerging unmanned techniques (eg, unmanned surface vehicles and drones) applied to environmental monitoring. Specifically, the unmanned surface vehicles (USVs) are used to monitor reservoir water quality by collecting samples. In the CCOP, the water sampling sites (ie, the nodes) are grouped into clusters, and a minimum number of nodes must be visited in each cluster. With each node representing a certain coverage area of the water, the objective of the CCOP is to monitor as much as possible the total coverage area in one tour of the USV, considering that overlapping areas provide no additional information. An integer programming model is first formulated through a linearization procedure that captures the overlapping feature. A two‐stage exact algorithm is proposed to obtain an optimal solution to the problem. The efficiency and effectiveness of the two‐stage exact algorithm are demonstrated through experiments on randomly generated instances. The algorithm can effectively solve instances with up to 60 sampling sites.
13 citations
TL;DR: A risk‐adjusted fulfillment model is developed to address the differences between two fulfillment choices—fulfillment by Amazon (FBA) and fulfillment by seller (FBS), and the goal is to maximize the E‐retailer's total rewards using predictive analytics.
Abstract: With dual‐channel choices, E‐retailers fulfill their demands by either the inventory stored in third‐party distribution centers, or by in‐house inventory. In this article, using data from a wedding gown E‐retailer in China, we analyze the differences between two fulfillment choices—fulfillment by Amazon (FBA) and fulfillment by seller (FBS). In particular, we want to understand the impact of FBA that will bring to sales and profit, compared to FBS, and how the impact is related to product features such as sizes and colors. We develop a risk‐adjusted fulfillment model to address this problem, where the E‐retailer's risk attitude to FBA is incorporated. We denote the profit gaps between FBA and FBS as the rewards for this E‐retailer fulfilling products using FBA, our goal is to maximize the E‐retailer's total rewards using predictive analytics. We adopt the generalized linear model to predict the expected rewards, while controlling for the variability of the reward distribution. We apply our model on a set of real data, and develop an explicit decision rule that can be easily implemented in practice. The numerical experiments show that our interpretable decision rule can improve the E‐retailer's total rewards by more than 35%.
TL;DR: In this article, the authors investigate an inventory transshipment game with two newsvendor-type retailers under limited total supply and check whether the retailers are better off than the case without trans-shipment, and they derive the ordering strategies for the retailers and show that unlike the unlimited supply case, a pure Nash equilibrium only exists under certain conditions.
Abstract: Inventory transshipment is generally shown to be beneficial to retailers by matching their excess demand with surplus inventory. We investigate an inventory transshipment game with two newsvendor‐type retailers under limited total supply and check whether the retailers are better off than the case without transshipment. We derive the ordering strategies for the retailers and show that unlike the unlimited supply case, a pure Nash equilibrium only exists under certain conditions. Furthermore, contrary to the conventional wisdom, we show that inventory transshipment may not always benefit both retailers. Although one of the retailers is guaranteed to be better off, the other could be worse off. The decision criteria are then provided for the retailers to determine if they will benefit from the exercise of inventory transshipment. Numerical study indicates that the carefully chosen transshipment prices play an important role in keeping inventory transshipment beneficial to both retailers. Subsequently, a coordinating mechanism is designed for the retailers to negotiate transshipment prices that maximize the total profit of the two retailers while keeping each of them in a beneficial position.
TL;DR: In this paper, the authors examined how sales are affected by three information sources for logistics services: online word of mouth (WOM) about logistics, self-reported logistics services, and expected delivery time.
Abstract: Facing fierce competition from rivals, sellers in online marketplaces are eager to improve their sales by delivering items faster and more reliably. Because logistics quality can be known only after a transaction, sellers must identify effective ways to communicate logistics information to consumers. Drawing on the accessibility-diagnosticity framework, we theorize that the sales impacts of logistics information depend on its relative diagnostic value. Using data on 1,493 items with 505,785 consumer reviews from an online marketplace, we examine how sales are affected by three information sources for logistics services: online word of mouth (WOM) about logistics, self-reported logistics services, and expected delivery time. We use an instrumental variable method to address the endogeneity issue between sales and WOM. We find that, ceteris paribus, consumers give more weight to WOM about logistics and delivery time when they make purchase decisions but less weight to self-reported logistics service. The effects of logistics information on sales are asymmetric for large and small sellers.
TL;DR: This work studies the problem of optimally positioning active multistatic sonar sources for a point coverage application where all receivers and points of interest are fixed and stationary and formulates exact methods and approximation algorithms for this problem and compares these algorithms via computational experiments.
TL;DR: This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models by proposing an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations.
Abstract: Quantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.
TL;DR: Wang et al. as discussed by the authors investigated the joint and dynamic decisions on the selling price of new product and the trade-in price involved in the program and characterized the structural properties of the joint pricing decisions and compared them with the optimal pricing policy under regular selling.
Abstract: Trade‐in programs have been widely adopted to enhance repeat purchase from replacement customers. Considering that a market consists of replacement and new segments, we study the joint and dynamic decisions on the selling price of new product (hereafter, “selling price”) and the trade‐in price involved in the program. By adopting a vertical product differentiation choice model, we investigate two scenarios in this paper. In the base model, the manufacturer has sufficiently large production capacity to fulfill the customer demand. We characterize the structural properties of the joint pricing decisions and compare them with the optimal pricing policy under regular selling. We further propose a semi‐dynamic trade‐in program, under which the new product is sold at a fixed price and the trade‐in price can be adjusted dynamically. Numerical experiments are conducted to evaluate the performance of the dynamic and semi‐dynamic trade‐in programs. In an extended model, we consider the scenario in which the manufacturer stocks a batch of new products in the beginning of the selling horizon and the inventory cannot be replenished. Following a revenue management framework, we characterize the structural properties with respect to time period and inventory level of new products.
TL;DR: It is shown that compared to the sequential approaches, the integrated approach to the assortment optimization problem with position effects can improve revenue by 10.38% on average, which suggests that firms should take into consideration position effects when making assortment decisions.
TL;DR: It is shown that the PBSPCC is NP‐hard by a transformation of the Hamiltonian graph decision problem into the problem of finding a minimum cyclic covering of a patrol network, subject to the requirement that patrol covering solutions are cyclic of a bounded polynomial order.
Abstract: Our study is primarily concerned with analyzing the computational complexity of the patrol boat scheduling problem with complete coverage (PBSPCC). This combinatorial optimization problem has important implications for maritime border protection and surveillance operations. The objective of the PBSPCC is to find a minimum size patrol boat fleet to provide ongoing continuous coverage at a set of maritime patrol regions, ensuring that there is at least one vessel on station in each patrol region at any given time. This requirement is complicated by the necessity for patrol vessels to be replenished on a regular basis in order to carry out patrol operations indefinitely. We introduce the PBSPCC via an example, discuss its relationship to related but dissimilar problems in the literature and proffer a mathematical description of the problem. We then show that the PBSPCC is NP‐hard by a transformation of the Hamiltonian graph decision problem into the problem of finding a minimum cyclic covering of a patrol network. We conclude that the associated decision problem of whether a patrol network has a continuous cover is NP‐complete, subject to the requirement that patrol covering solutions are cyclic of a bounded polynomial order.
TL;DR: In this paper, a strategic government investment policy, share acquisition, was proposed to achieve societal objectives in a Cournot quantities-choice market, where the government intervenes to buy shares, and turning private firms into state-owned enterprises.
Abstract: A critical issue for many governments is boosting the adoption rates of products or technologies that enhance consumer surplus or total social welfare. Governments may, for example, pay subsidies to producers or to consumers to stimulate the manufacture or consumption of specific products, for example, energy-efficient appliances or more effective drugs. This research proposes a strategic government investment policy, share acquisition, and demonstrates its effectiveness in reaching societal objectives. We consider a Cournot quantities-choice market comprised of homogeneous firms where the government intervenes to buy shares, and turning private firms into state-owned enterprises (SOEs). We recognize that purchasing a single private firm is the optimal policy for the government to reach its societal objectives. Additionally, taking into consideration financial constraints, we find that the optimal stake increases with the budget. Compared with the optimal output-based subsidy policy, when the budget is low, the optimal government investment policy induces a higher consumer surplus. In addition, in differentiated Cournot competition, under which firms compete in selling substitutable products, we find that when the budget is sufficient, the optimal stake purchased first decreases and then increases according to the substitutability level among products.
TL;DR: In this paper, the authors consider strategic customer waiting behavior in the classical economic order quantity (EOQ) setting and provide concrete managerial recommendations that are against the conventional wisdom on "everyday low price" versus "high-low pricing" (Hi•Lo).
Abstract: We incorporate strategic customer waiting behavior in the classical economic order quantity (EOQ) setting. The seller determines not only the timing and quantities of the inventory replenishment, but also the selling prices over time. While similar ideas of market segmentation and intertemporal price discrimination can be carried over from the travel industries to other industries, inventory replenishment considerations common to retail outlets and supermarkets introduce additional features to the optimal pricing scheme. Specifically, our study provides concrete managerial recommendations that are against the conventional wisdom on “everyday low price” (EDLP) versus “high‐low pricing” (Hi‐Lo). We show that in the presence of inventory costs and strategic customers, Hi‐Lo instead of EDLP is optimal when customers have homogeneous valuations. This result suggests that because of strategic customer behavior, the seller obtains a new source of flexibility—the ability to induce customers to wait—which always leads to a strictly positive increase of the seller's profit. Moreover, the optimal inventory policy may feature a dry period with zero inventory, but this period does not necessarily result in a loss of sales as customers strategically wait for the upcoming promotion. Furthermore, we derive the solution approach for the optimal policy under heterogeneous customer valuation setting. Under the optimal policy, the replenishments and price promotions are synchronized, and the seller adopts high selling prices when the inventory level is low and plans a discontinuous price discount at the replenishment point when inventory is the highest.
TL;DR: In this article, the authors consider a general consumer choice model and develop the optimal strategy for callable products, which is similar to callable bonds where the issuer has the right, but not the obligation, to buy back the bonds at a certain price by a certain date.
Abstract: Capacity providers such as airlines often sell the same capacity to different market segments at different prices to improve their expected revenues. The absence of a secondary market, due to the nontransferability of airline tickets, gives rise to an opportunity for airlines to broker capacity between consumers with different willingness to pay. One way to broker capacity is by the introduction of callable products. The idea is similar to callable bonds where the issuer has the right, but not the obligation, to buy back the bonds at a certain price by a certain date. The idea of callable products was introduced before under the assumption that the fare‐class demands are all independent. The independent assumption becomes untenable when there is significant demand recovery (respectively, demand cannibalization) when lower fares are closed (respectively, opened). In this case, consumer choice behavior should be modeled explicitly to make meaningful decisions. In this paper, we consider a general consumer choice model and develop the optimal strategy for callable products. Our numerical study illustrates how callable products are win‐win‐win, for the capacity provider and for both high and low fare consumers. Our studies also identify conditions for callable products to result in significant improvements in expected revenues.
TL;DR: In this paper, the authors considered a polling system with arbitrary arrivals and revealed service time upon a job's arrival and provided conditions for the existence of constant competitive ratios, and competitive lower bounds for general scheduling policies in polling systems.
Abstract: Polling systems have been widely studied, however most of these studies focus on polling systems with renewal processes for arrivals and random variables for service times. There is a need driven by practical applications to study polling systems with arbitrary arrivals (not restricted to time‐varying or in batches) and revealed service time upon a job's arrival. To address that need, our work considers a polling system with generic setting and for the first time provides the worst‐case analysis for online scheduling policies in this system. We provide conditions for the existence of constant competitive ratios, and competitive lower bounds for general scheduling policies in polling systems. Our work also bridges the queueing and scheduling communities by proving the competitive ratios for several well‐studied policies in the queueing literature, such as cyclic policies with exhaustive, gated or l‐limited service disciplines for polling systems.
TL;DR: This work considers the salvo policy problem, in which there are k moments, called salvos, at which the authors can fire multiple missiles simultaneously at an incoming object, and presents an iterative approximation algorithm for the Quota version, and shows that a related problem is NP‐complete.
TL;DR: It is proved that the expected cumulative regret of the KWSA algorithm is bounded above by κ1T+κ2, which achieves the lower bounds known for parametric dynamic pricing problems and shows that the nonparametric problems are not necessarily more difficult to solve than the parametric ones.
Abstract: We consider the problem of nonparametric multi-product dynamic pricing with unknown demand and show that the problem may be formulated as an online model-free stochastic program, which can be solved by the classical Kiefer-Wolfowitz stochastic approximation (KWSA) algorithm. We prove that the expected cumulative regret of the KWSA algorithm is bounded above by κ1 √ T +κ2 where κ1, κ2 are positive constants and T is the number of periods for any T = 1, 2, . . .. Therefore, the regret of the KWSA algorithm grows in the order of √ T , which achieves the lower bounds known for parametric dynamic pricing problems and shows that the nonparametric problems are not necessarily more difficult to solve than the parametric ones. Numerical experiments further demonstrate the effectiveness and efficiency of our proposed KW pricing policy by comparing with some pricing policies in the literature.