Article
Economics
Juyoung Wang, Mucahit Cevik, Saman Hassanzadeh Amin, Amir Ali Parsaee
Summary: The study focuses on a reverse logistics network for household hazardous wastes, utilizing multiobjective mixed-integer deterministic and stochastic mathematical models to optimize transportation costs, reduce risks, maximize convenience, and enhance participation. By proposing an optimization framework and using a testbed, the analysis aims to address waste management challenges.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2021)
Article
Management
Alejandro Gutierrez-Alcoba, Roberto Rossi, Belen Martin-Barragan, Tim Embley
Summary: In this work, we introduce the Stochastic Inventory Routing Problem on Electric Roads (S-IRP-ER), which addresses the non-stationary stochastic demand of retailers using a hybrid vehicle that navigates a road network with charging opportunities. We model the problem using isochrone graphs to track the vehicle's battery level continuously. A mathematical programming heuristics is formulated and proven effective. The model is applied to a realistic instance, showcasing different strategies based on fuel and electricity costs.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Janis Brammer, Bernhard Lutz, Dirk Neumann
Summary: This study introduces a reinforcement learning approach to minimize work overload situations in the mixed model sequencing problem. By generating sequences in a constructive way and using metaheuristics, the trained policy can quickly create an initial sequence to improve solution quality. Numerical evaluation on benchmark datasets shows superior performance to established methods when demand plan distribution aligns with learning process expectations.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Ahmad M. Alshamrani
Summary: This research focuses on developing a mathematical methodology for joint transmission network and wind power investment problem under a centralized approach. The objective function is defined as the ratio of total cost to total wind power generation, allowing the operator to minimize overall cost while maximizing wind power output.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Management
Alaleh Maskooki, Markku Kallio
Summary: This article discusses a variant of the moving target traveling salesman problem where the number and locations of targets vary with time and random trajectories. The aim is to maximize the number of visits to different targets and minimize the total travel distance. A two-stage stochastic programming model is developed using a linear value function for finding supported Pareto-efficient solutions. An iterative randomized dynamic programming (RDP) algorithm is proposed, which converges to a global optimum with probability one. The RDP algorithm involves backward and forward recursion stages, as well as options for improving any given schedule.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Mengyuan Xiang, Roberto Rossi, Belen Martin-Barragan, Armagan Tarim
Summary: This paper extends the single-item single-stocking location nonstationary stochastic inventory problem by relaxing the assumption of independent demand. It presents a mathematical programming-based solution method utilizing an existing piecewise linear approximation strategy. The method can handle various demand assumptions and demonstrates near-optimal plans compared to exact solutions obtained via stochastic dynamic programming.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Reza Rahmati, Hossein Neghabi, Mahdi Bashiri, Majid Salari
Summary: This article proposes a two-stage stochastic profit-maximizing hub location problem with uncertain demand and considers various carbon regulations. The proposed models use an enhanced sample average approximation method which incorporates k-means clustering and self-organizing map clustering algorithms to obtain suitable scenarios. The L-shaped algorithm is employed for efficient solving. The computational analysis using Australian Post data demonstrates that all carbon regulations can reduce carbon emissions, with carbon cap-and-trade policy achieving better economic results for transportation. The results also show that the self-organizing map clustering algorithm within the enhanced sample average approximation method is superior to k-means clustering and classical sample average approximation algorithms.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Mathematics, Applied
Bojan Nikolic, Marko Djukanovic, Dragan Matic
Summary: This paper proposes a new mixed-integer programming model to solve the multidimensional multi-way number partitioning problem. The obtained results indicate that our model significantly outperforms the model from literature for larger values of k.
COMPUTATIONAL & APPLIED MATHEMATICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Huseyin Tunc
Summary: The study addresses the capacitated stochastic lot-sizing problem under alpha service level constraints. A static uncertainty strategy and dynamic cut generation approach are proposed to solve the problem, and their superior computational performance is demonstrated through extensive numerical study.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Engineering, Civil
Nadire Ucler, Hale Gonce Kocken
Summary: Rapid population growth, industrialization, and lifestyle modernization have led to an increased demand for water. However, water supplies are decreasing due to declining precipitation, excessive use, and resource deterioration. This study proposes a multi-objective mixed-integer programming approach to create a feasible strategic plan for selecting alternative water supply projects. The proposed model considers various criteria and integrates the analytical hierarchical process technique. Simulation results demonstrate the applicability of the proposed model compared to a classic optimization model.
WATER RESOURCES MANAGEMENT
(2023)
Article
Management
Mirko Dahlbeck
Summary: The article introduces a new facility layout problem, TRFLP, aiming to minimize center-to-center distances with non-overlapping department assignments. A mixed-integer linear programming approach is used, successfully solving instances with up to 18 departments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Engineering, Civil
Canqi Yao, Shibo Chen, Zaiyue Yang
Summary: This paper proposes an efficient two-stage algorithm that decomposes the original MIP problem into two LP problems, achieving near-optimal solution in polynomial time by exploiting the exactness of LP relaxation and eliminating the coupled term. Additionally, a variant algorithm based on the two-stage one is introduced to further improve the quality of the solution. Extensive simulations validate the effectiveness of the proposed algorithm compared to state-of-the-art methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Operations Research & Management Science
Saravanan Venkatachalam, Lewis Ntaimo
Summary: This paper develops the theory of integer set reduction for solving two-stage stochastic mixed-integer programs with general integer variables in the second-stage. The goal is to generate a valid inequality by using the smallest possible subset of the subproblem feasible integer set, similar to Fenchel decomposition cuts, in order to reduce computation time. An algorithm is devised to obtain such a subset based on the solution of the subproblem linear programming relaxation and incorporated into a decomposition method for SMIP. A computational study based on randomly generated knapsack test instances demonstrates the effectiveness of the new integer set reduction methodology in speeding up cut generation and obtaining better bounds compared to using a direct solver in solving SMIPs with pure integer recourse.
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Mohsen Mohammadi, Monica Gentili
Summary: This paper examines the outcome range problem in linear programming, proposing two approximation methods to solve it and demonstrating the relevance through a real case study.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Vicky Mak-Hau, Brendan Hill, David Kirszenblat, Bill Moran, Vivian Nguyen, Ana Novak
Summary: This paper addresses a unique combinatorial optimization problem derived from helicopter aircrew training for the Royal Australian Navy. The main objective is to find optimal course scheduling solutions and minimize the total time required to complete the syllabus.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Operations Research & Management Science
Dongdong He, Yang Liu, Qiuyan Zhong, David Z. W. Wang
Summary: This paper examines the impact of staggering policy on traffic congestion and social welfare in the morning commute problem with both household commuters and individual commuters. The results show that optimizing the schedule gap between work and school start times can significantly improve social welfare. A Pareto frontier is derived to provide policymakers with an optimal staggering policy for system performance. Furthermore, the capacity expansion paradox is re-examined, and it is found that expanding the capacity at the downstream bottleneck can reduce the total system cost.
TRANSPORTATION SCIENCE
(2022)
Article
Computer Science, Information Systems
Liang Du, Ruobin Gao, Ponnuthurai Nagaratnam Suganthan, David Z. W. Wang
Summary: In this paper, a Bayesian optimization-based dynamic ensemble (BODE) method is proposed for time series forecasting. The BODE method combines ten different model candidates and uses a model-based Bayesian optimization algorithm for combination hyperparameter tuning. The method demonstrates robust performance and better generalization capability.
INFORMATION SCIENCES
(2022)
Article
Transportation Science & Technology
Zhihong Guo, David Z. W. Wang, Danwei Wang
Summary: With the emergence of automated vehicles, the future traffic system is expected to consist of a mix of self-driving autonomous vehicles (AVs) and human-driven conventional vehicles. Therefore, it is crucial to propose new traffic management measures to handle this future traffic system. This study aims to utilize the controllable property of AVs' routing choices to develop a daily routing allocation scheme for a certain number of autonomous vehicles, in order to achieve the desired traffic state in the mixed traffic system.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Engineering, Electrical & Electronic
Wenbin Zhang, Zihao Tian, Lixin Tian, David Z. W. Wang, Yi Yao
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Economics
Chang Zhou, Qiong Tian, David Z. W. Wang
Summary: This study proposes a novel control technique to mitigate bus bunching by providing passengers with real-time wait time information and degrees of in-vehicle congestion. The numerical results show that providing in-vehicle congestion information is as effective as the schedule-based and headway-based control methods in achieving mitigation of bus bunching.
Article
Environmental Studies
Roy Tan, Harilaos N. Psaraftis, David Z. W. Wang
Summary: This paper revisits speed optimization and speed reduction models for liner shipping in a multi/flexible fuel context, analyzing the influence of a maximum average speed limit on optimal speeds, carbon intensity and emissions for dual fuel Neopanamax container vessels utilizing liquefied natural gas.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2022)
Article
Economics
Qingyun Tian, David Z. W. Wang, Yun Hui Lin
Summary: This paper proposes a multi-stage mathematical modeling framework to optimize the deployment strategy of autonomous buses in public transit service operations. Passenger acceptance attitudes and the diffusion model are considered to forecast the passengers' adoption rate. The optimal deployment strategy that minimizes the total travel cost is determined through solving a mixed-integer nonlinear program.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Liang Du, Ruobin Gao, Ponnuthurai Nagaratnam Suganthan, David Z. W. Wang
Summary: Traffic forecasting is crucial for smart city development, and this study proposes a novel graph ensemble deep random vector functional link network (GEdRVFL) to achieve node-wise traffic forecasting, which outperforms state-of-the-art models in most cases.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Civil
Zhen Di, David Z. W. Wang, Lixing Yang
Summary: The high-speed rail plays a pivotal role in intercity commuting, but there are issues with ticket pricing and seat allocation. This study proposes a new ticketing/exchanging scheme to address demand fluctuation and validates its effectiveness through numerical experiments.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Jie Zhang, Meng Meng, David Z. W. Wang, Li Zhou, Linghui Han
Summary: This paper investigates the problem of bike allocation in a competitive bike sharing market. A continuum approximation (CA) approach is used to handle computational challenges by assuming that allocation points and user demand are continuously distributed in a two-dimensional region. Bike sharing companies bear allocation and bike depreciation costs while earning revenue from fare collection. User's choice of bike service depends on walking distance and bike quality preference. The demand elasticity is considered in relation to the density of allocation points. A leader-follower Stackelberg competition model is developed to derive the optimal allocation strategy for the market leader. Numerical studies are conducted for both hypothetical and real cases to examine the impact of parameters on model performance and demonstrate the application of the proposed model in decision making.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Transportation Science & Technology
Qingyun Tian, Yun Hui Lin, David Z. W. Wang
Summary: This paper focuses on the operation design of a future public transit service adopting modular vehicles. The unique feature of modular vehicles allows for assembling and disassembling operations along each trip to dynamically adjust the vehicle formation at stations. A mathematical model is proposed to determine the optimal scheduling and modular vehicle formation, considering time-dependent travel demand and module availability. The model is solved using exact reformulation techniques and a two-step heuristic approach, showing the validity and efficiency of the formulation and solution methods. It is found that modular transit services have remarkable advantages in reducing both operator's and passengers' costs.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Engineering, Electrical & Electronic
Wenbin Zhang, Zihao Tian, Lixin Tian, David Z. W. Wang
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2023)
Article
Transportation
Yuanyuan Wu, David Z. W. Wang, Feng Zhu
Summary: This study proposes a deep reinforcement learning approach to address the issue of optimizing traffic efficiency at congested major-minor intersections, which can negatively impact vehicle fairness. The proposed method optimizes both efficiency and fairness by measuring traffic fairness using the difference between the crossing order and the approaching order of vehicles, and measuring traffic efficiency using average travel time. The effectiveness of the method is evaluated in a simulated real-world intersection and compared with benchmark policies, and it shows outstanding performance in balancing traffic fairness and efficiency.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Liang Du, Ruobin Gao, Ponnuthurai Nagaratnam Suganthan, David Z. W. Wang
Summary: This paper proposes a novel online performance-based ensemble deep random vector functional link neural network model for time series forecasting tasks. The model supports non-iterative online learning and dynamic ensemble method, and outperforms existing statistical, machine learning-based, and deep learning-based models in extensive experiments.
2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2022)
Article
Green & Sustainable Science & Technology
J. Zhang, M. Meng, David Z. W. Wang, B. Du
Summary: This study develops a methodology to determine the optimal allocation position to deploy bikes in a competitive dockless bike sharing market. Two different scenarios are considered, one with a potential competitor and one without. The study proposes two different heuristics to handle these scenarios based on different design objectives.
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
(2022)
Article
Economics
Songyot Kitthamkesorn, Anthony Chen, Seungkyu Ryu, Sathaporn Opasanon
Summary: The study introduces a new mathematical model to determine the optimal location of park-and-ride facilities, addressing the limitations of traditional models and considering factors such as route similarity and user heterogeneity.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2024)
