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Multi-objective optimization of two-echelon vehicle routing problem: Vaccines distribution as a case study

COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Al Theeb, Nader Abu-Aleqa, Mohammed Diabat, Ali Jordan Univ Sci & Technol Dept Ind Engn Irbid 22110 Jordan New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 Saadiyat Isl U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA

During pandemics, the efficiency of the vaccine supply chain may be compromised, especially the last-mile distribution, due to poor infrastructure that is unable to support the urgent need for vaccination. In developing countries, this becomes even more challenging due to limited vehicles, road conditions, and inadequate cold storage. Since it is impractical to construct permanent warehouses when pandemics occur, vaccine distribution would be extravagant both environmentally and financially. In this study, a new multi-objective MILP model combining two-echelon vehicle routing problem (2E-VRP) and vaccine supply chain (VSC) is presented to minimize the number of unsatisfied doses undelivered to customers. A heuristic solution based on the greedy random search is proposed to solve the model, as it is classified as NP-hard model. The model is solved using the commercial solver CPLEX for different datasets. Then the heuristic is used to solve the same datasets, and the results are compared based on the solution's quality and computation efforts. Moreover, Pareto fronts were constructed to demonstrate the trade-offs between the conflicting objective functions. Finally, a real case study is solved using the proposed model to demonstrate its effectiveness compared to the original VRP, and the results showed an improvement of average 11.97% in the number of doses delivered.

关键词： multiple objective programming Two-echelon vehicle routing problem Vaccine distribution

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An effective subgradient algorithm via Mifflin's line search for nonsmooth nonconvex multiobjective optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第2期319卷 505-516页

作者： Maleknia, Morteza Soleimani-damaneh, Majid Isfahan Univ Technol Dept Math Sci Esfahan *** Iran Univ Tehran Coll Sci Sch Math Stat & Comp Sci Tehran Iran

We propose a descent subgradient algorithm for unconstrained nonsmooth nonconvex multiobjective optimization problems. To find a descent direction, we present an iterative process that efficiently approximates the epsilon-subdifferential of each objective function. To this end, we develop a new variant of Mifflin's line search in which the subgradients are arbitrary and its finite convergence is proved under a semismooth assumption. To reduce the number of subgradient evaluations, we employ a backtracking line search that identifies the objectives requiring an improvement in the current approximation of the epsilon-subdifferential. Meanwhile, for the remaining objectives, new subgradients are not computed. Unlike bundle-type methods, the proposed approach can handle nonconvexity without the need for algorithmic adjustments. Moreover, the quadratic subproblems have a simple structure, and hence the method is easy to implement. We analyze the global convergence of the proposed method and prove that any accumulation point of the generated sequence satisfies a necessary Pareto optimality condition. Furthermore, our convergence analysis addresses a theoretical challenge in a recently developed subgradient method. Through numerical experiments, we observe the practical capability of the proposed method and evaluate its efficiency when applied to a diverse range of nonsmooth test problems.

关键词： multiple objective programming Nonlinear optimization Nonsmooth optimization Nonconvex programming Subgradient

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Intensifying the fumigation policy to minimize the risk of mosquito-borne diseases implementing a stochastic optimization approach

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CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION 2024年 197卷

作者： Alfaro-Bernardino, Angel Diaz-Trujillo, Luis A. Cerda-Flores, Sandra C. Ponce-Ortega, Jose M. Napoles-Rivera, Fabricio Univ Michoacana Chem Engn Dept Ave Francisco J MugSNBldg V1 Morelia 58060 Michoacan Mexico Univ Intercultural Indigena Michoacan Acad Program Masters Engn Sustainable Energy Finca Tzipekua S-N Carretera Patzcuaro Huecorio Km Patzcuaro 61614 Michoacan Mexico

This paper presents a stochastic mathematical model for the planning, dosage, and strategic scheduling of fumigation policies to reduce mosquito-borne diseases in a geographic location. multiple scenarios were generated to account for uncertainty in the existing mosquito population based on a random normal distribution. In addition, the model includes occupational health, considering the permissible limits of each insecticide applied and their residual effect. This stochastic mathematical model applies a Pareto solution method, using a Conditional Value at Risk approach to select solutions that trade off the different objective functions. Results show that the optimal solutions found by the model provide a compromise between the expected infected people and the total cost of applying the insecticides. This methodology improves the current practice by an intensified approach that considers optimal scheduling alternatives that minimize cost while considering the permissible concentration limits to guarantee the health and comfort of the population to minimize the possible incidences of infections.

关键词： Intensifying fumigation policy multiple objective programming Stochastic mathematical model Mosquito population Fumigation strategy

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Cluster ensemble selection and consensus clustering: A multi-objective optimization approach

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期314卷 1065-1077页

作者： Aktas, Dilay Lokman, Banu Inkaya, Tulin Dejaegere, Gilles Ctr Ind Management KU Leuven Celestijnenlaan 300 B-3001 Leuven Belgium Univ Portsmouth Ctr Operat Res & Logist Sch Org Syst & People Portsmouth PO1 3DE England Bursa Uludag Univ Dept Ind Engn TR-16240 Nilufer Bursa Turkiye Univ Libre Bruxelles Serv Math Gest Blvd Triomphe CP 210-01 B-1050 Brussels Belgium

Cluster ensembles have emerged as a powerful tool to obtain clusters of data points by combining a library of clustering solutions into a consensus solution. In this paper, we address the cluster ensemble selection problem and design a multi -objective optimization -based solution framework to produce consensus solutions. Given a library of clustering solutions, we first design a preprocessing procedure that measures the agreement of each clustering solution with the other solutions and eliminates the ones that may mislead the process. We then develop a multi -objective optimization algorithm that selects representative clustering solutions from the preprocessed library with respect to size, coverage, and diversity criteria and combines them into a single consensus solution, for which the true number of clusters is assumed to be unknown. We conduct experiments on different benchmark data sets. The results show that our approach yields more accurate consensus solutions compared to full -ensemble and the existing approaches for most data sets. We also present an application on the customer segmentation problem, where our approach is used to segment customers and to find a consensus solution for each

关键词： multiple objective programming Cluster ensembles Ensemble selection Consensus clustering

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Designing empirical experiments to compare interactive multiobjective optimization methods

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2023年第11期74卷 2327-2338页

作者： Afsar, Bekir Silvennoinen, Johanna Misitano, Giovanni Ruiz, Francisco Ruiz, Ana B. Miettinen, Kaisa Univ Jyvaskyla Fac Informat Technol FI-40014 Jyvaskyla Finland Univ Malaga Dept Appl Econ Math Malaga Spain

Interactive multiobjective optimization methods operate iteratively so that a decision maker directs the solution process by providing preference information, and only solutions of interest are generated. These methods limit the amount of information considered in each iteration and support the decision maker in learning about the trade-offs. Many interactive methods have been developed, and they differ in technical aspects and the type of preference information used. Finding the most appropriate method for a problem to be solved is challenging, and supporting the selection is crucial. Published research lacks information on the conducted experiments' specifics (e.g. questions asked), making it impossible to replicate them. We discuss the challenges of conducting experiments and offer realistic means to compare interactive methods. We propose a novel questionnaire and experimental design and, as proof of concept, apply them in comparing two methods. We also develop user interfaces for these methods and introduce a sustainability problem with multiple objectives. The proposed experimental setup is reusable, enabling further experiments.

关键词： multiple objective programming interactive methods performance comparison experimental study human decision makers

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A Barzilai-Borwein descent method for multiobjective optimization problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期311卷 196-209页

作者： Chen, Jian Tang, Liping Yang, Xinmin Shanghai Univ Dept Math Shanghai 200444 Peoples R China Natl Ctr Appl Math Chongqing Chongqing 401331 Peoples R China Chongqing Normal Univ Sch Math Sci Chongqing 401331 Peoples R China

The steepest descent method proposed by Fliege and Svaiter has motivated the research on descent meth-ods for multiobjective optimization, which has received increasing attention in recent years. However, empirical results show that the Armijo line search often results in a very small stepsize along the steepest descent direction, which decelerates the convergence seriously. This paper points out the issue is mainly due to imbalances among objective functions. To address this issue, we propose a Barzilai-Borwein de-scent method for multiobjective optimization (BBDMO), which dynamically tunes gradient magnitudes using Barzilai-Borwein's rule in direction-finding subproblem. We emphasize that the BBDMO produces a sequence of new descent directions compared to Barzilai-Borwein's method proposed by Morovati et al. With monotone and nonmonotone line search techniques, we prove that accumulation points generated by BBDMO are Pareto critical points, respectively. Furthermore, theoretical results indicate that the Armijo line search can achieve a better stepsize in BBDMO. Finally, comparative results of numerical experiments are reported to illustrate the efficiency of BBDMO and verify the theoretical results. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Imbalanced objective functions Barzilai-Borwein's rule Pareto critical Convergence

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Real-time truck dispatching in open-pit mines

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2023年第7期37卷 504-523页

作者： Wang, Xiaowei Dai, Qi Bian, Yougang Xie, Guotao Xu, Biao Yang, Zeyu Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Hunan Univ Wuxi Intelligent Control Res Inst Wuxi 214115 Peoples R China

Real-time truck dispatching is an important function of the open-pit mine transportation system. However, most of the existing methods are not comprehensive enough to consider the optimisation of both full truck hauling and empty truck travelling stages. Further, most of the real-time dispatching criteria are fixed, which cannot meet the variety of production requirements. To address the above problems, we consider both full and empty truck dispatching, and different from previous studies, we develop a real-time dispatching model with two parts for heterogeneous fleets in open-pit mines: a full truck dispatching model for truck-finished loading at the loading place, and an empty truck dispatching model for truck-finished dumping at the dumping place. Specifically, the proposed model has three goals for minimisation: (a) the waiting time of the trucks, (b) the deviation from the planned path flow rate, and (c) the transportation cost. Furthermore, the weights of the three sub-objectives can be changed to meet the production requirements for different real scenarios. Simulation results show that the proposed model can achieve a higher production by at least 14% and decrease the cost by at least 6% and has better adaptability to different production requirements.

关键词： Truck dispatching fleet management system open-pit mining multiple objective programming

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Biobjective route planning of an unmanned air vehicle in continuous space

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2023年第1期168卷 151-169页

作者： Ozturk, Diclehan Tezcaner Koksalan, Murat Hacettepe Univ Dept Ind Engn TR-06800 Ankara Turkiye Univ Michigan Ross Sch Business Ann Arbor MI 48104 USA Middle East Tech Univ Dept Ind Engn TR-06800 Ankara Turkiye

We consider the route planning problem of an unmanned air vehicle (UAV) in a continuous space that is monitored by radars. The UAV visits multiple targets and returns to the base. The routes are constructed considering the total distance traveled and the total radar detection threat ob-jectives. The UAV is capable of moving to any point in the terrain. This leads to infinitely many efficient trajectories between target pairs and infinitely many efficient routes to visit all targets. We use a two stage approach in solving the complex problem of finding all efficient routes. In the first stage, we structure the nondominated frontiers of the efficient trajectories between all target pairs. For this, we first identify properties shared by efficient trajectories between target pairs that are protected by a radar. This helps to structure the nondominated frontier between any target pair by identifying at most four specific efficient trajectories. We develop a search-based algo-rithm that finds these efficient trajectories effectively. For the second stage, we develop a mixed integer nonlinear program that exploits the structured nondominated frontiers between target pairs to construct the efficient routes. We compare the nondominated front we generate in the continuous space with its counterpart in a terrain discretized with three different grid fidelities. The continuous space representation outperforms all discrete representations in terms of solution quality and computational times.

关键词： multiple objective programming UAV routing Continuous space

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Bi-objective reliability based optimization: an application to investment analysis

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ANNALS OF OPERATIONS RESEARCH 2024年第1期333卷 47-78页

作者： Sengupta, Raghu Nandan Gupta, Aditya Mukherjee, Subhankar Weiss, Gregor McKinsey & Co Inc Chennai Tamil Nadu India Indian Inst Technol Kanpur Dept Management Studies DoMS Kanpur Uttar Pradesh India Univ Leipzig Fac Econ & Management Leipzig Germany

Portfolio optimization problems are easy to address if single linear objective functions are considered, with the assumption of normality of asset returns distributions, subject to different risks, returns, and investment constraints. Higher complexities arise if combinations of multi-objective formulations, non-linear assets, non-normal asset return distributions, and uncertainty in parameter estimates are studied. In this paper, we solve two interesting variants of multi-objective investment analysis problems considering both non-normal asset return distributions and uncertainty in parameter estimates. Data used for the optimization models are pre-processed using ARCH/GARCH combined with extreme value asset returns distribution (EVD). The efficacy of our proposed multi-objective reliability-based portfolio optimization (MORBPO) problems is validated using Indian financial market data (Details of plan of codes, pseudo-codes and other set of detailed runs results (not discussed in this paper) are given in the open access link, https://***/RNSengupta/Bi-objective_RBDO_Paper). We present the optimal values of investment weights, portfolio returns, portfolio risks (variance, CVaR, EVaR), reliability indices (beta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}) as well as Pareto optimal frontiers and analyze the outputs in the context of their practical implications. The run results highlight the fact that investors' uncertainty levels (i.e., beta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}) play a crucial role in deciding the investment outcomes and thus faci

关键词： Investment analysis multiple objective programming Portfolio optimization Risk analysis

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Integrated strategic energy mix and energy generation planning with multiple sustainability criteria and hierarchical stakeholders

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期308卷 864-883页

作者： Irawan, Chandra Ade Jones, Dylan Hofman, Peter S. Zhang, Lina Univ Nottingham Ningbo Nottingham Univ Business Sch China Ningbo Peoples R China Nottingham Ningbo China Beacons Excellence Res & I Ningbo Peoples R China Univ Portsmouth Ctr Operat Res & Logist Sch Math & Phys Portsmouth England James Cook Univ Singapore Singapore Univ Exeter Univ Exeter Business Sch Ctr Simulat Analyt & Modelling Exeter England

This paper proposes a combination of two optimization models for simultaneously determining strategic energy planning at both national and regional levels. The first model deals with a single-period energy mix where the electricity production configuration at a future date (e.g., 2050), based on the available generation sources, is optimally obtained. An optimization model, based on a non-linear goal programming method, is designed to ensure a mixed balance between national and regional goals. The desired energy mix configuration, which is the solution obtained by solving the first model, is then fed into the second model as the main data input. In the second model, a multiple-period generation expansion plan is designed which optimizes the energy transition over the time horizon from the present until the future planning date (2050). The model considers uncertain parameters, including the regional energy demand, fuel cost, and national peak load. A two-stage stochastic programming model is developed where the sample average approximation approach is used as a method of solution. The practical use of the proposed models has been assessed through application to the electricity generation system in China.(c) 2022 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Energy planning Goal programming Two -stage stochastic programming

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