Article
Management
Carl Perreault-Lafleur, Margarida Carvalho, Guy Desaulniers
Summary: Nowadays, companies face the important and challenging task of achieving high employee satisfaction in efficient schedules. We address a new variant of the personnel scheduling problem which considers employee satisfaction through endogenous uncertainty related to their preferred and received schedules. This problem is studied in the context of reserve staff scheduling, an operational problem in the transit industry that has not been previously examined. By formulating the problem as a two-stage stochastic integer program with mixed-integer recourse, we take into account the challenges posed by the uncertainty sources of regular employee and reserve employee absences. The model focuses on determining the reserve employees' days off and scheduling their duties after the regular employee absences are revealed, incorporating the reserve employees' preferences to examine the impact of employee satisfaction on their absence rates.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2023)
Article
Construction & Building Technology
Amirhossein Fani, Amir Golroo, S. Ali Mirhassani, Amir H. Gandomi
Summary: The study aims to develop an optimization framework for network-level pavement maintenance and rehabilitation planning considering the uncertain nature of pavement deterioration and the budget with a multistage stochastic mixed-integer programming model. The proposed model can find the optimal plan feasible for all possible scenarios of uncertainty and optimize the expectation of the objective function.
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
(2022)
Article
Energy & Fuels
Dongjin Cho, Jorge Valenzuela
Summary: Renewable energy sources, particularly solar power, offer a promising solution for environmentally friendly and sustainable electricity generation in remote areas with limited access to other power sources. However, the high initial investment costs and uncertainties related to solar radiation and climate changes can pose economic challenges. By developing a scenario-based optimization model, it is possible to determine the capacity of off-grid PV-battery systems to efficiently meet energy demands and minimize costs.
Article
Thermodynamics
Sepideh Saravani Ghayour, Taghi Barforoushi
Summary: This study proposes a framework for optimal scheduling of electrical appliances and energy sources in a smart home, utilizing renewable energy and combined heat and power technology. Uncertainties are modeled using scenario analysis, with the objective of minimizing the expected cost. Simulation results show significant cost reduction through the cogeneration of electricity and heat in the smart home.
Article
Environmental Studies
Matheus Furtado e Faria, Roussos Dimitrakopoulos, Claudio Lucio Lopes Pinto
Summary: In the commonly used underground mine planning framework, the sequential optimization approach cannot capture the synergies between planning steps, resulting in suboptimal solutions. This study proposes a two-stage stochastic integer program (SIP) for integrated optimization of stope design and mine production scheduling, considering grade uncertainty and variability. The proposed approach provides a physically different design and production schedule with higher net present value (NPV) and shorter mine life compared to the stepwise framework.
Article
Green & Sustainable Science & Technology
Devendra Joshi, Hamed Gholami, Hitesh Mohapatra, Anis Ali, Dalia Streimikiene, Susanta Kumar Satpathy, Arvind Yadav
Summary: The purpose of this study is to develop a computationally efficient algorithm for solving open-pit production scheduling problems with uncertain geological parameters. A case study of an Indian iron ore mine is conducted to demonstrate the effectiveness of the proposed method. The results show that both methods produce similar results in terms of materials, ore, metal, and risk profiles, but Model 2 has a slightly better performance in terms of discounted cash flow and computational time compared to Model 1.
Article
Transportation Science & Technology
Mingyu Li, Chi Xie, Xinghua Li, Ampol Karoonsoontawong, Ying-En Ge
Summary: With the opening of the Northern Sea Route, commercial shipping developments have become a reality, and new liner services along this route have been proposed. However, uncertainties in weather and ocean conditions in this region may result in delays, leading to significant financial losses. These uncertainties inevitably affect cargo shippers' willingness to choose this route, despite its shorter shipping time compared to traditional Asia-Europe lines.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Operations Research & Management Science
Hongbo Li, Hanyu Zhu, Linwen Zheng, Fang Xie
Summary: The main resources in software projects are human resources equipped with various skills, which makes software development a typical intelligence-intensive process. Therefore, effective human resource scheduling is indispensable for the success of software projects. We investigate the software project scheduling problem with uncertain activity durations (SPSP-UAD) and aim at obtaining effective scheduling policies for the problem.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Engineering, Chemical
Congqin Ge, Lifeng Zhang, Wenhui Yang, Zhihong Yuan
Summary: Traditional supply chains are becoming more volatile, leading to an increasing adoption of mobile modularization. This allows for flexible capacity adjustments and facility relocations to tackle market volatility. A mixed-integer linear programming model is proposed for closed-loop supply chain network planning with modular distribution and collection facilities. The model is further extended to incorporate uncertain customer demands and recovery rates, and is efficiently solved using a tailored stochastic dynamic dual integer programming approach. Computational experiments demonstrate the effectiveness of the proposed algorithm and the benefits of mobile modules in high temporal and spatial variability of customer demand.
Article
Mathematics
Adrian Gonzalez-Maestro, Elena Brozos-Vazquez, Balbina Casas-Mendez, Rafael Lopez-Lopez, Rosa Lopez-Rodriguez, Francisco Reyes-Santias
Summary: In this paper, the authors propose deterministic and stochastic mathematical programming models to distribute treatment schedules for patients in an oncology day hospital. The models consider constraints and uncertainties in the circuit, and also allow for the reorganization of medical appointments. The work is motivated by a real hospital and compared with data from the case.
Article
Computer Science, Artificial Intelligence
Mojahid Saeed Osman
Summary: This paper introduces an algorithmic method that hybridizes solution procedures with an optimization model to solve the problem of scheduling and allocating changeover tasks. Three priority rules are identified and investigated as objective functions to minimize total changeover time and maximize worker utilization, while satisfying task-sequence-dependency and worker-limit constraints. The proposed hybrid approach provides effective changeover time and worker utilization.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Green & Sustainable Science & Technology
Fengyuan Wang, Shuai Zhang, Chenyu Lv, Linlin Liu, Yu Zhuang, Lei Zhang, Jian Du
Summary: This study proposes a systematic method for designing a solar-assisted steam and power system to improve process sustainability and flexibility. A two-stage stochastic programming model is used to consider uncertainty factors such as solar radiation and real-time demand. The proposed model is validated through a case study and provides guiding opinions for the system design stage.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Management
Teobaldo Bulhoes, Rubens Correia, Anand Subramanian
Summary: Scheduling technical sessions for scientific events is a challenging task, with different conferences having unique characteristics requiring individual approaches. Maximizing clustering of papers with common topics in the same session is a beneficial strategy. Mathematical formulations prove NP-hard, with different model variations providing solutions for instances of varying sizes.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Yiping Huang, Libao Deng, Jianlei Wang, Weiwei Qiu, Jinfeng Liu
Summary: This study considers uncertainty factors and applies two-stage stochastic programming to model and solve a hybrid flow-shop scheduling problem. A two-stage scenario tree is used to describe the uncertain factors, and a mixed-integer linear programming model is formulated using stochastic programming theory. A novel variant algorithm (H-PDDE) is proposed, which effectively solves the problem by adopting a permutation scheduling decoding method and introducing three new improvement strategies. Computational experiments validate the proposed model and algorithm, showing that the H-PDDE outperforms existing algorithms and conventional PDDE variants in solving the problem more effectively. The stochastic programming model is increasingly superior to the deterministic model in an uncertain environment. The source code and data files of the H-PDDE algorithm can be found at https://github.com/huangyiping-ai/H-PDDE-algorithm.git.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
Kim-Manuel Klein
Summary: In this paper, we improve upon an algorithm to solve 2-stage stochastic IPs by introducing an explicit doubly exponential bound on the size of the augmenting steps. This improvement is based on a novel theorem regarding intersections of paths in a vector space. Additionally, we present a doubly exponential lower bound for the size of the augmenting steps, complementing our algorithmic result.
MATHEMATICAL PROGRAMMING
(2022)
Article
Robotics
Zhongqiang Ren, Sivakumar Rathinam, Howie Choset
Summary: This letter presents an approach to avoid the "curse of dimensionality" by leveraging prior multi-agent work and a framework called subdimensional expansion, resulting in a new algorithm called multi-objective M*(MOM*). MOM* efficiently computes the complete Pareto-optimal set for multiple agents, naturally trading off sub-optimal approximations of the Pareto-optimal set and computational efficiency.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Operations Research & Management Science
Adriana Piazza, Bernardo K. Pagnoncelli, Lewis Ntaimo
Summary: In mine planning problems, cutoff grade optimization is the process of determining a threshold at each time period to process materials above this value, considering the rest as waste. When multiple minerals occur in the orebody, a cutoff surface is considered. The optimal solution is a line in two dimensions and a hyperplane in n dimensions.
OPERATIONS RESEARCH LETTERS
(2022)
Article
Robotics
Zhongqiang Ren, Sivakumar Rathinam, Maxim Likhachev, Howie Choset
Summary: This article introduces a new multi-objective incremental search algorithm called MOPBD*, which leverages path-based expansion strategy to prune dominated solutions. A sub-optimal variant of MOPBD* is also introduced to improve search efficiency while approximating the Pareto-optimal front. Numerical evaluations show that our approach is more efficient than search from scratch and runs up to an order of magnitude faster than the existing incremental method for multi-objective path planning.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Engineering, Civil
Vamsi Vegamoor, Sivakumar Rathinam, Swaroop Darbha
Summary: This paper discusses the issue of string stability in autonomous vehicle platoons, reconsiders the definition of string stability to ensure traffic safety, and validates the feasibility through model development and time headway selection algorithms.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Krishna Reddy Gujjula, Jiangyue Gong, Brittany Segundo, Lewis Ntaimo
Summary: We developed a new stochastic programming methodology for determining optimal vaccination policies for a multi-community heterogeneous population. The method considers the uncertainty in COVID-19 related parameters and variation in age, household composition, and human interactions. The results show that each community should vaccinate a certain percentage of the population to control outbreaks.
Article
Chemistry, Analytical
Abhay Singh Bhadoriya, Vamsi Vegamoor, Sivakumar Rathinam
Summary: This paper explores the use of thermal cameras to fill the sensory gap in adverse visibility conditions for self-driving vehicles. By training a machine learning-based image detector on thermal image data and fusing it with radar information, the vehicle's perception capability is improved under various weather conditions.
Article
Robotics
Zhongqiang Ren, Sivakumar Rathinam, Maxim Likhachev, Howie Choset
Summary: Path planning among dynamic obstacles is a fundamental problem in Robotics. In this work, we propose an algorithm called MO-SIPP that efficiently solves the problem of Multi-Objective Path Planning with Dynamic Obstacles by combining the ideas from SIPP and multi-objective A* algorithms.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Automation & Control Systems
Zhongqiang Ren, Sivakumar Rathinam, Howie Choset
Summary: Conventional multi-agent path planners focus on optimizing a single objective, such as path length, but many applications require multiple objectives to be simultaneously optimized. This article presents a novel approach called MO-CBS that leverages prior algorithms to address the curse of dimensionality and compute the Pareto-optimal set efficiently. Numerical results show that MO-CBS outperforms existing state-of-the-art planners.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Robotics
Sai Krishna Kanth Hari, Sivakumar Rathinam, Swaroop Darbha, Satyanarayana Gupta Manyam, Krishna Kalyanam, David Casbeer
Summary: Persistent monitoring missions require up-to-date knowledge of the changing environment. Unmanned aerial vehicles (UAVs) can collect data from tasks in the environment over long periods of time. Efficient monitoring requires minimizing the revisit time between targets. We propose an algorithm to quickly find near-optimal solutions for this problem. Numerical simulations show that the constructed solutions are, on average, within 0.01% of the optimum.
IEEE TRANSACTIONS ON ROBOTICS
(2023)
Article
Economics
Jiangyue Gong, Krishna Reddy Gujjula, Lewis Ntaimo
Summary: Despite efforts to contain COVID-19, the virus continues to spread and mutate, making it necessary to develop new data-driven models for optimal vaccination strategies. In response to this challenge, an integrated chance constraints stochastic programming approach is proposed, incorporating population demographics, uncertain transmission, and vaccine efficacy. This approach provides a quantitative method to bound the expected excess of the reproduction number above one based on the decision-maker's risk tolerance. Testing on real data in Texas shows promising results, suggesting the importance of prioritizing certain household sizes and age groups in vaccination strategies.
SOCIO-ECONOMIC PLANNING SCIENCES
(2023)
Article
Remote Sensing
Sai Krishna Kanth Hari, Sivakumar Rathinam, Swaroop Darbha, David Casbeer
Summary: This article discusses a route planning problem for two unmanned vehicles to complete tasks with minimum energy consumption while ensuring safe operation through communication. It formulates the problem as an Integer program and develops approximation and heuristic algorithms to efficiently compute high-quality solutions. The algorithms are shown to provide swift solutions for large-scale instances and the approximation ratio varies based on the weighted case of the problem.
Article
Robotics
Zhongqiang Ren, Sivakumar Rathinam, Howie Choset
Summary: The article discusses a generalized version of multiagent path finding (MAPF) called multiagent combinatorial path finding, which involves not only planning collision-free paths for multiple agents but also assigning targets and specifying the visiting order for each agent. To solve the problem, the article proposes a novel approach called conflict-based Steiner search (CBSS), which leverages conflict-based search (CBS) for MAPF. CBSS combines the collision resolution strategy in CBS and multiple traveling salesman algorithms to compute optimal or bounded suboptimal paths for agents while visiting all the targets.
IEEE TRANSACTIONS ON ROBOTICS
(2023)
Article
Chemistry, Analytical
Abhishek Nayak, Sivakumar Rathinam
Summary: This paper addresses the MinMax variant of the Dubins multiple traveling salesman problem (mTSP) in mission planning applications involving UAVs and ground robots. A mixed-integer linear program (MILP) is formulated to solve the routing problem. Heuristic-based search methods using tour construction algorithms and variable neighborhood search (VNS) metaheuristic are developed. Additionally, a graph neural network is explored to learn policies for the problem using reinforcement learning. The proposed algorithms are implemented and their performance is evaluated on modified TSPLIB instances, showing effectiveness for different instance sizes.
Article
Engineering, Civil
Zhongqiang Ren, Zachary B. Rubinstein, Stephen F. Smith, Sivakumar Rathinam, Howie Choset
Summary: The Resource Constrained Shortest Path Problem (RCSPP) aims to find a minimum-cost path between a start and a goal location while keeping the resource consumption within limits. Solving RCSPP is challenging due to the need to compare and maintain partial paths based on multiple criteria and the absence of a single path that optimizes all criteria simultaneously. This paper presents ERCA*, a fast algorithm based on A* that efficiently handles multiple resource constraints and outperforms existing algorithms in terms of runtime efficiency.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Mengke Liu, Kenny Chour, Sivakumar Rathinam, Swaroop Darbha
Summary: This paper focuses on lateral control of autonomous and connected vehicles, proposing a method to synthesize a lateral control algorithm. By tracking trajectory information through GPS waypoints, estimating the curvature radius, and developing a feedback control scheme to follow the leading vehicle.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2021)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)