查看更多>>摘要：The 2023 INFORMS Transportation Science and Logistics (TSL) Society Conference took place from July 23 to July 26, 2023, at Loyola University Chicago (LUC) in Chicago, Illinois, USA. The TSL Conference is a triennial event that is meant to complement other conferences serving the TSL community by taking place in a city in North America that is relatively accessible. Although not explicitly designed to be a conference for PhD students, one of the purposes of the conference is to provide an opportunity for students to discuss their work and network with other like-minded researchers.
Sifa QelikLayla MartinAlbert H. SchrotenboerTom Van Woensel...
210-228页
查看更多>>摘要：Next-day delivery logistics services are redefining the industry by increasingly focusing on customer service. Each logistics service provider's challenge is jointly optimizing time window assignment and vehicle routing for such next-day delivery services. To do so in a cost-efficient and customer-centric fashion, real-life uncertainty, such as stochastic travel times, needs to be incorporated into the optimization process. This paper focuses on the canonical optimization problem within this context: the time window assignment traveling salesperson problem with stochastic travel times (TWATSP-ST). It belongs to two-stage, stochastic, mixed-integer programming problems with continuous recourse. We introduce two-step Benders decomposition with scenario clustering (TBDS) as an exact solution methodology for solving such stochastic programs. The method utilizes a new two-step decomposition along the binary and continuous first stage decisions and introduces a new scenario-retention strategy that combines and generalizes state-of-the-art Benders approaches and scenario-clustering techniques. Extensive experiments show that TBDS is superior to state-of-the-art approaches in the literature. It solves TWATSP-ST instances with up to 25 customers to optimality. It provides better lower and upper bounds that lead to faster convergence than existing state-of-the-art methods. We use TBDS to analyze the structure of the optimal solutions. By increasing routing costs only slightly, customer service can be improved tremendously driven by smartly alternating between high- and low-variance travel arcs to reduce the impact of delay propagation throughout the executed vehicle route.
查看更多>>摘要：We consider an extension of the symmetric traveling salesman problem with generalized latency that explicitly models uncertainty. The stochastic traveling salesman problem with generalized latency (STSP-GL) aims to choose a subset of nodes of an undirected graph and determines a Hamiltonian tour among those nodes, minimizing an objective function that is a weighted combination of route design and passenger routing costs. These nodes are selected to ensure that a predefined percentage of uncertain passenger demand is served with a given probability. We formulate the STSP-GL as a stochastic program and propose a branch-and-price algorithm for solving its deterministic equivalent. We also develop a local search approach with which we improve the performance of the branch-and-price approach. We assess the efficiency of the proposed methods on a set of instances from the literature. We demonstrate that the proposed methods outperform a known benchmark, improving upper bounds by up to 85% and lower bounds by up to 55%. Finally, we show that solutions of the stochastic model are both more cost-effective and robust than those of the deterministic model.
查看更多>>摘要：Home delivery failures, traffic congestion, and relatively large handling times have a negative impact on the profitability of last-mile logistics. A potential solution is the delivery to parcel lockers or parcel shops, denoted by out-of-home (OOH) delivery. In the academic literature, models for OOH delivery are so far limited to static settings, contrasting with the sequential nature of the problem. We model the sequential decision-making problem of which OOH location to offer against what incentive for each incoming customer, taking into account future customer arrivals and choices. We propose dynamic selection and pricing of OOH (DSPO), an algorithmic pipeline that uses a novel spatial-temporal state encoding as input to a convolutional neural network. We demonstrate the performance of our method by benchmarking it against two state-of-the-art approaches. Our extensive numerical study, guided by real-world data, reveals that DSPO can save 19.9 percentage points (%pt) in costs compared with a situation without OOH locations, 7%pt compared with a static selection and pricing policy, and 3.8%pt compared with a state-of-the-art demand management benchmark. We provide comprehensive insights into the complex interplay between OOH delivery dynamics and customer behavior influenced by pricing strategies. The implications of our findings suggest that practitioners adopt dynamic selection and pricing policies.
查看更多>>摘要：We study a system in which a common delivery fleet provides service to both same-day delivery (SDD) and next-day delivery (NDD) orders placed by e-retail customers who are sensitive to delivery prices. We develop a model of the system and optimize with respect to two separate objectives. First, empirical research suggests that fulfilling e-retail orders ahead of promised delivery days increases a firm's long-run market share. Motivated by this phenomenon, we optimize for customer satisfaction by maximizing the quantity of NDD orders fulfilled one day early given fixed prices. Next, we optimize for total profit; we optimize for a single SDD price, and we then set SDD prices in a two-level scheme with discounts for early-ordering customers. Our analysis relies on continuous approximation techniques to capture the interplay between NDD and SDD orders and particularly the effect one day's operations have on the next, a novel modeling component not present in SDD-only models; a key technical result is establishing the model's convergence to a steady state using dynamical systems theory. We derive structural insights and efficient algorithms for both objectives. In particular, we show that, under certain conditions, the total profit is a piecewise-convex function with polynomially many breakpoints that can be efficiently enumerated. In a case study set in metropolitan Denver, Colorado, approximately 10% of NDD orders can be fulfilled one day early at optimality, and profit is increased by 1% to 3% in a two-level pricing scheme versus a one-level scheme. We conduct operational simulations for validation of solutions and analysis of initial conditions.
Pedro Zattoni ScroccaroPiet van BeekPeyman Mohajerin EsfahaniBilge Atasoy...
301-321页
查看更多>>摘要：We propose a method for learning decision makers' behavior in routing problems using inverse optimization (IO). The IO framework falls into the supervised learning category and builds on the premise that the target behavior is an optimizer of an unknown cost function. This cost function is to be learned through historical data, and in the context of routing problems, can be interpreted as the routing preferences of the decision makers. In this view, the main contributions of this study are to propose an IO methodology with a hypothesis function, loss function, and stochastic first-order algorithm tailored to routing problems. We further test our IO approach in the Amazon Last Mile Routing Research Challenge, where the goal is to learn models that replicate the routing preferences of human drivers, using thousands of real-world routing examples. Our final IO-learned routing model achieves a score that ranks second compared with the 48 models that qualified for the final round of the challenge. Our examples and results showcase the flexibility and real-world potential of the proposed IO methodology to learn from decision-makers' decisions in routing problems.
查看更多>>摘要：When vehicle routing decisions are intertwined with higher-level decisions, the resulting optimization problems pose significant challenges for computation. Examples are the multi-depot vehicle routing problem (MDVRP), where customers are assigned to depots before delivery, and the capacitated location routing problem (CLRP), where the locations of depots should be determined first. A simple and straightforward approach for such hierarchical problems would be to separate the higher-level decisions from the complicated vehicle routing decisions. For each higher-level decision candidate, we may evaluate the underlying vehicle routing problems to assess the candidate. As this approach requires solving vehicle routing problems multiple times, it has been regarded as impractical in most cases. We propose a novel deep learning-based approach called the genetic algorithm with neural cost predictor to tackle the challenge and simplify algorithm developments. For each higher-level decision candidate, we predict the objective function values of the underlying vehicle routing problems using a pretrained graph neural network without actually solving the routing problems. In particular, our proposed neural network learns the objective values of the HGS-CVRP open-source package that solves capacitated vehicle routing problems. Our numerical experiments show that this simplified approach is effective and efficient in generating high-quality solutions for both MDVRP and CLRP and that it has the potential to expedite algorithm developments for complicated hierarchical problems. We provide computational results evaluated in the standard benchmark instances used in the literature.
查看更多>>摘要：This paper proposes a nonmonetary traffic demand management scheme, named CARMA, as a fair solution to the morning commute congestion. We consider heterogeneous commuters traveling through a single bottleneck that differ in both the desired arrival time and value of time (VOT). We consider a generalized notion of VOT by allowing it to vary dynamically on each day (e.g., according to trip purpose and urgency) rather than being a static characteristic of each individual. In our CARMA scheme, the bottleneck is divided into a fast lane that is kept in free flow and a slow lane that is subject to congestion. We introduce a nontradable mobility credit, named karma, that is used by commuters to bid for access to the fast lane. Commuters who get outbid or do not participate in the CARMA scheme instead use the slow lane. At the end of each day, karma collected from the bidders is redistributed, and the process repeats day by day. We model the collective commuter behaviors under CARMA as a dynamic population game (DPG), in which a stationary Nash equilibrium (SNE) is guaranteed to exist. Unlike existing monetary schemes, CARMA is demonstrated, both analytically and numerically, to achieve (a) an equitable traffic assignment with respect to heterogeneous income classes and (b) a strong Pareto improvement in the long-term average travel disutility with respect to no policy intervention. With extensive numerical analysis, we show that CARMA is able to retain the same congestion reduction as an optimal monetary tolling scheme under uniform karma redistribution and even outperform tolling under a well-designed redistribution scheme. We also highlight the privacy-preserving feature of CARMA, that is, its ability to tailor to the private preferences of commuters without centrally collecting the information.
R. M. van SteenbergenW. J. A. van HeeswijkM. R. K. Mes
360-390页
查看更多>>摘要：Humanitarian logistics operations face increasing difficulties due to rising demands for aid in disaster areas. This paper investigates the dynamic allocation of scarce relief supplies across multiple affected districts over time. It introduces a novel stochastic dynamic postdisaster inventory allocation problem (SDPDIAP) with trucks and unmanned aerial vehicles (UAVs) delivering relief goods under uncertain supply and demand. The relevance of this humanitarian logistics problem lies in the importance of considering the intertemporal social impact of deliveries. We achieve this by considering social costs (transportation and deprivation costs) when allocating scarce supplies. Furthermore, we consider the inherent uncertainties of disaster areas and the potential use of cargo UAVs to enhance operational efficiency. This study proposes two anticipatory solution methods based on approximate dynamic programming, specifically decomposed linear value function approximation (DL-VFA) and neural network value function approximation (NN-VFA) to effectively manage uncertainties in the dynamic allocation process. We compare DL-VFA and NN-VFA with various state-of-the-art methods (e.g., exact reoptimization and proximal policy optimization) and results show a 6%-8% improvement compared with the best benchmarks. NN-VFA provides the best performance and captures nonlinea-rities in the problem, whereas DL-VFA shows excellent scalability against a minor performance loss. From a practical standpoint, the experiments reveal that consideration of social costs results in improved allocation of scarce supplies both across affected districts and over time. Finally, results show that deploying UAVs can play a crucial role in the allocation of relief goods, especially in the first stages after a disaster. The use of UAVs reduces transportation and deprivation costs together by 16%-20% and reduces maximum deprivation times by 19%-40% while maintaining similar levels of demand coverage, showcasing efficient and effective operations.
查看更多>>摘要：The liner shipping industry is undergoing an extensive decarbonization process to reduce its 275 million tons of CO_2 emissions as of 2018. In this process, the long-term solution is the introduction of new alternative maritime fuels. The introduction of alternative fuels presents a great set of unknowns. Among these are the strategic concerns regarding sourcing of alternative fuels and, operationally, how the new fuels might affect the network of shipping routes. We propose a problem formulation that integrates fuel supply /demand into the liner shipping network design problem. Here, we present a model to determine the production sites and distribution of new alternative fuels—we consider methanol and ammonia. For the network design problem, we apply an adaptive large neighborhood search combined with a delayed column generation process. In addition, we wish to test the effect of designing a robust network under uncertain demand conditions because of the problem's strategic nature and importance. Therefore, our proposed solution method will have a deterministic and stochastic setup when we apply it to the second-largest multihub instance, WorldSmall, known from LINER-LIB. In the deterministic setting, our proposed solution method finds a new best solution to three instances from LINER-LIB. For the main considered WorldSmall instance, we even noticed a new best solution in all our tested fuel settings. In addition, we note a profit drop of 7.2% between a bunker-powered and pure alternative fuel-powered network. The selected alternative fuel production sites favor a proximity to European ports and have a heavy reliance on wind turbines. The stochastic results clearly showed that the found networks were much more resilient to the demand changes. Neglecting the perspective of uncertain demand leads to highly fluctuating profits.
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