An Exact Algorithm to Solve Vehicle Routing Problem with Drones for Delivery and Surveillance Tasks After Disasters

Problem definition: The main goal of many nonprofit or nongovernmental organizations is to increase the number of customers who receive service (i.e., service coverage) and social welfare. However, the limited number of employees, volunteers, and service locations results in long service wait. In addition, getting customers living in remote areas to receive services by traveling long distances is difficult. We propose an innovative distance-based service priority policy that would reduce the service waiting time for customers who must travel farther for the service by giving them higher service priority, thereby providing them with a new incentive to seek service. Methodology/results: Using a game-theoretic queueing model, we show that the proposed policy can significantly attract more customers to a service. The increase can be up to 50% compared with the ordinary first-come-first-served service discipline. The policy can also achieve higher social welfare, however, that may come at the cost of reduced customer welfare. We therefore propose a possible remedy for a social planner to coordinate welfare under such circumstance. It ensures all stakeholders, including the service provider, customers, and society, can benefit from the policy at the same time. Finally, we compare our distance-based service priority policy with two existing strategies from the literature—namely, the price discrimination strategy and the probabilistic priority strategy. Managerial implications: Our proposed policy can play a pivotal role in a nonprofit service provider’s mission to increase service coverage and social welfare, especially when customers’ travel costs to obtain service are significant. Furthermore, our policy may create fewer implementation and fairness concerns compared with related strategies. Funding: Z. Wang acknowledges financial support from the National Natural Science Foundation of China [Grants 72001118 and 72132007], and L. Fang acknowledges financial support from the National Natural Science Foundation of China [Grant 72271129]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1157 .

This paper investigates credit guarantees in a supply chain setting involving a supplier, a capital-constrained buyer, and a bank. We find that under complete information, credit guarantees increase order quantities but paradoxically raise wholesale prices, overall rendering the credit guarantee not useful for improving the operational efficiency. However, under information asymmetry, either a partial or full guarantee emerges in equilibrium, with the wholesale price maintained at its optimal full-information level. This underscores the pivotal role of credit guarantees in effective signaling and delineates the distinct functions of the wholesale price and the guarantee—the former on operational efficiency and the latter on signaling efficacy. Further comparative analysis of the supplier and buyer performances under the two contract types reveals that credit guarantees do not always benefit both parties. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: H. Liu was supported by the National Natural Science Foundation of China [Grants 72272149 and 72394374]. H. Song was supported by the National Natural Science Foundation of China [Grants 72501259 and W2411062]. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2024.07052 .

Assigning and sequencing storage locations under a two level storage policy: Optimization model and matheuristic approaches

Traffic safety poses a persistent challenge for society and public policy. Conventional law enforcement by human police is often cost-ineffective because of information asymmetry and negative externalities of unsafe driving behaviors. Automated enforcement, in the form of traffic cameras on the road, has gained prominence in recent decades, yet its effectiveness and underlying mechanisms remain debated. This study examines the impact of traffic cameras on road safety using longitudinal data from a metropolitan city in China. We distinguish between advanced cameras, which use machine learning to detect various traffic violations and constantly record video, and conventional cameras, which rely on triggered image capture for a limited number of violations. Using an event study design with staggered camera installations at road intersections, we observe a significant and sustained reduction in accidents near advanced cameras, compared with locations with no cameras or only conventional cameras. Further analysis identifies three key mechanisms driving the effects of advanced cameras: (i) automated detection effect—superior technical capabilities to automate violation detection; (ii) real-time recording effect—continuous monitoring and recording capability to augment accident cause identification; and (iii) driver learning effect—technology-enabled deterrence that increases driver awareness of these cameras and encourages behavioral adjustments to mitigate accident risks. This study contributes to information systems, transportation economics, and criminology, offering policy insights into the effective design and deployment of automated enforcement to improve traffic safety. This paper was accepted by D. J. Wu, information systems. Funding: Z. Cheng acknowledges the support of the Staff Research Fund at the London School of Economics and Political Science. Z. Dong acknowledges financial support from the National Natural Science Foundation of China (NSFC) Young Scientists Fund [Grant 72202247] and the Guangdong Basic and Applied Basic Research Foundation [Grant 2024A1515012806]. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.00575 .

Problem definition: We study open-shop service networks where customers go through multiple services. We were motivated by a partnering health screening clinic, where customers are routed by a dispatcher and operational performance is measured at two levels: micro-level, via waits for individual services, and macro-level, via overall wait. Both measures reflect customer experience and could support its management. Our analysis revealed that waits were long and increased along the service process. Such long waits give rise to negative waiting experience and the increasing shape is detrimental as it is known to create perceived waits that are even longer. Our goal is hence to analyze strategies that shape and improve customers’ perceived experience. Methodology/results: Analytically, we use a stylized two-station open-shop network to show that prioritizing advanced customers, jointly with pooling (virtual) queues, can improve both macro- and micro-level performance. We validate these findings with a simulation model, calibrated with our clinic’s data. Practically, we find that an automated routing system (ARS), recently implemented in the clinic, had a negligible impact on overall wait—It simply redistributed waiting among wait-for-routing and wait-for-service. However, ARS renders applicable sophisticated priority and routing policies (that were infeasible under the manual routing practice), specifically the ones arising from the present research. Managerial implications: Our study amplifies performance benefits of accounting for individual customers’ system status in addition to station-level load information. We offer insights into the implementation of new technologies: Firms better plan for fundamental changes in their operation rather than harness new technology to their existing operation, which may be suboptimal due to past technical limitations. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: M. Chen acknowledges the support from the National Natural Science Foundation of China [Grant 72301280]. O. Baron acknowledges the support from the Natural Science and Engineering Research Council of Canada. J. Wang acknowledges the support from the City University of Hong Kong [Grant 11505421]. A. Mandelbaum is partially supported by the Israel Science Foundation [Grant 491/22]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0590 .

Health sensing for chronic disease management creates immense benefits for social welfare. Existing health sensing studies primarily focus on the prediction of physical chronic diseases. Depression, a widespread complication of chronic diseases is, however, understudied. We draw on the medical literature to support depression detection using motion sensor data. To connect humans in this decision making, safeguard trust, and ensure algorithm transparency, we develop an interpretable deep learning model: temporal prototype network (TempPNet). TempPNet is built on the emergent prototype learning models. To accommodate the temporal characteristic of sensor data and the progressive property of depression, TempPNet differs from existing prototype learning models in its capability of capturing temporal progressions of prototypes. Extensive empirical analyses using real-world motion sensor data show that TempPNet outperforms state-of-the-art benchmarks in depression detection. Moreover, TempPNet interprets its decision by visualizing the temporal progression of depression and its corresponding symptoms detected from sensor data. We further employ a user study and a medical expert panel to demonstrate its superiority over the benchmarks in interpretability. This study offers an algorithmic solution for impactful social good—collaborative care of chronic diseases and depression in health sensing. Methodologically, it contributes to extant literature with a novel interpretable deep learning model for depression detection from sensor data. Patients, doctors, and caregivers can deploy our model on mobile devices to monitor patients’ depression risks in real time. Our model’s interpretability also allows human experts to participate in the decision making by reviewing the interpretation and making informed interventions. This paper was accepted by D. J. Wu, information systems. Funding: J. Xie and X. Fang are supported by the University of Delaware Research Foundation Strategic Initiatives Grant and Alfred Lerner College of Business and Economics Research Grant, X. Zhao acknowledges financial support from the National Natural Science Foundation of China [Grant 72401172] and the Fundamental Research Funds for the Central Universities [Grant 2023110139, 2023110318]. J. Xie and X. Fang did not receive any form of support from, nor do they have any affiliation with, X. Zhao’s funding sources. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.04183 .

Problem definition: E-commerce giants scale up their order-picking operations by adopting robotic fulfillment centers (RFCs). In RFCs, automated guided vehicles transport movable shelf racks to pickers’ workstations, instead of having human pickers travel to pick items. Unfortunately, this apparent relief for pickers turns out to be a curse: Pickers become the bottleneck in the order-picking process. They undertake high-intensity, stationary, and repetitive tasks, which often cause both physical and mental health problems. To ease this tension, we collaborate with a major e-commerce firm to study how RFCs can improve picking efficiency by accounting for heterogeneous picker performance. Methodology/results: We propose a novel distributionally robust human-centric picking performance prediction (DHPP) model to forecast two critical metrics of picker performance: picking time and performance inconsistency. The DHPP model addresses distributional uncertainty by incorporating probabilistic constraints without requiring knowledge of the true underlying distribution. It leverages the empirical mean and covariance of random features that characterize picker behavior to hedge against worst-case prediction errors. We reformulate the DHPP model into a tractable second-order cone program. Using the predicted metrics, we then design a mixed 0–1 program to optimize the picker-order assignments. Managerial implications: Our computational study demonstrates that the DHPP model significantly outperforms state-of-the-art forecasting models in prediction accuracy. Our simulation, calibrated with real data from JD.com, shows that our strategy reduces the number of unfulfilled items by 14.2% and improves average pickers’ picking productivity by 7.5%. These improvements suggest significant welfare gains for pickers, increasing their income while helping alleviate stress and health issues. Funding: This work was supported by the National Natural Science Foundation of China [J. Luo was supported by Grant 72261008, Z. Wu was supported by Grants 72172027 and 72293563, Z. Hao was supported by Grants 72001036 and 72232001, and W. Qi was supported by Grants 72242106, 72521001, and 72188101]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0644 .

Formulations and branch-and-cut algorithms for multi-product multi-vehicle production routing problems with startup cost

Healthy lifestyle in late-life, longevity genes, and life expectancy among older adults: a 20-year, population-based, prospective cohort study

This paper presents multiple readings to solve a vehicle routing problem with pickup and delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real-life ones are more difficult to address due to their richness and complexity. To handle multiple points of view in modeling our problem, we developed three different Mixed Integer Linear Programming (MILP) models, where each model covers particular constraints. The suggested models are designed for a mega poultry company in Tunisia, called CHAHIA. Our mission was to develop a prototype for CHAHIA that helps decision-makers find the best path for simultaneously delivering the company’s products and collecting the empty boxes. Based on data provided by CHAHIA, we conducted computational experiments, which have shown interesting and promising results.

Mixed Integer Linear Programming Model for Vehicle Routing Problem for Hazardous Materials Transportation

Matheuristic is an optimisation methodology that integrates mathematical approaches and heuristics to address intractable combinatorial optimisation problems, where a common framework is to insert mixed integer linear programming (MILP) models as local search functions for evolutionary algorithms. However, since a mathematical programming formulation only tries to find the solution with the best objective value, matheuristics are rarely adopted to multi‐objective scenarios asking for a set of Pareto optimal solutions, for example, vehicle routing problems and production scheduling problems. In this situation, the ɛ ‐constraint, which transforms multi‐objective problems into single‐objective formulations by considering selected objectives as constraints, seems to be a promising approach. First, an augmented ɛ ‐constraint‐based matheuristic methodology ( ɛ ‐MH) is proposed to apply the idea of ɛ ‐constraint to embedded MILP models, so that Pareto fronts obtained by meta‐heuristics can be further improved by solving a set of MILP models. Afterwards, four speed‐up strategies are developed to alleviate the computational burden resulting from repeatedly solving mathematical formulations, which also imply preferable scenarios for taking advantages of the ɛ ‐MH. Finally, several real‐world bi‐objective scheduling problems are discussed to present potential applications for the proposed methodology.

Traditional symmetric operation strategies, which maintain equal service frequencies in both directions, often fail to accommodate the inherent passenger demand asymmetry in urban metro systems. To bridge this gap, this study develops a mixed integer linear programming model that enhances the practicality of asymmetric operation strategies by incorporating critical real-world constraints, particularly the limited train storage capacity at terminal stations unconnected to depots or yards. The proposed model integrates frequency setting, train timetabling, rolling stock circulation, and passenger flow assignment, with the dual objective of optimizing cost-efficiency and service quality. A hybrid algorithm combining an enhanced adaptive large-scale neighborhood search with the GUROBI solver is designed to search for high-quality solutions. A case study on Shanghai Metro Line 17 demonstrates the model’s effectiveness, showing a 35% reduction in total train running time and a 12.55% improvement in suitable-section rates compared to conventional approaches. These results underscore the model’s capability to better align transportation resources with tidal passenger flow patterns, providing metro operators with a comprehensive framework for train service optimization, including coordinated train movements between depots, rolling stock yards, and operational lines.

An enhanced estimation of distribution algorithm with problem-specific knowledge for distributed no-wait flowshop group scheduling problems

Signal timing at an isolated intersection under mixed traffic environment with self‐organizing connected and automated vehicles