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A two-stage stochastic programming model for collaborative asset protection routing problem enhanced with machine learning: a learning-based matheuristic algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第1期61卷 81-113页

作者： Nikzad, Erfaneh Bashiri, Mahdi Shahed Univ Fac Engn Ind Engn Dept Tehran Iran Coventry Univ Ctr Business Soc Coventry W Midlands England

In this paper, a two-stage stochastic mathematical model is developed for an asset protection routing problem under a wildfire. The main aim of this study is to reduce the negative impact of a wildfire. Some parameters, such as travel and service times, obtaining profit by protecting an asset, and upper bounds of time windows, are considered as stochastic parameters. Generating proper scenarios for uncertain parameters has a large impact on the accuracy of the obtained solutions. Therefore, artificial neural networks are employed to extract possible scenarios according to previous actual wildfire events. The problem cannot be solved by exact solvers for large instances, so two matheuristic algorithms are proposed in this study to solve the problem in a reasonable time. In the first algorithm, a set of feasible routes is generated based on a heuristic approach, then a route-based mathematical model is used to obtain the final solution. Also, another matheuristic algorithm based on adaptive large neighbourhood search (ALNS) is proposed. In this algorithm, routing decisions are determined using the ALNS algorithm while other decisions are achieved by solving an intermediate mathematical model. The numerical analysis confirms the efficiency of both proposed algorithms;however, the first algorithm performs more efficiently.

关键词： Asset protection location routing two-stage stochastic programming neural network matheuristic algorithm

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Extensive Model and matheuristic algorithm for the Train Platforming Problem with Two-Train-Capacity Tracks: A Case Study of Prague Central Station

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TRANSPORTATION RESEARCH RECORD 2024年第4期2678卷 131-154页

作者： Ozer, Emine Akyol Bulicek, Josef Sarac, Tugba Eskisehir Tech Univ Ind Engn Dept Eskisehir Turkiye Univ Pardubice Dept Transport Technol & Control Pardubice Czech Republic Eskisehir Osmangazi Univ Ind Engn Dept Eskisehir Turkiye

This paper provides a deeper insight into the train platforming problem (TPP). Many studies have focused on different versions of train scheduling and routing problems, and most of them assume that the platform track's capacity is one train. However, especially in busy and complex railway stations, most platform tracks are divided into two parts, allowing two trains to simultaneously share the same platform track for passenger boarding/alighting. This results in more efficient train assignment to the platform tracks. In addition, consideration of the track capacity makes the problem more difficult because directions of trains are problematic. Motivated by this challenge, we consider the TPP with two-train-capacity tracks. We first describe the problem in detail and then propose a mixed-integer programming model. The objective of the considered problem is to minimize the total weighted train delays, which are defined as the difference between the departure times calculated by the mathematical model (M1) and the scheduled departure times of the trains in the timetable. Because of the NP-hard nature of the problem, the proposed M1 may not find feasible solutions for large-size problems. Thus, a matheuristic algorithm (MA) is developed to solve large-size problems. We used randomly generated test problems to demonstrate the performance of the proposed M1 and MA. Experimental results showed that MA outperforms M1 in both solution quality and solution time. Additionally, a case study was conducted at the central station of Prague, Czechia.

关键词： matheuristic algorithm passenger rail transportation mixed integer linear programming platform track assignment railway optimization models transportation planning analysis and application train platforming problem

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A matheuristic algorithm for stochastic home health care planning

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期288卷 753-774页

作者： Nikzad, Erfaneh Bashiri, Mahdi Abbasi, Babak Shahed Univ Fac Engn Ind Engn Dept Tehran Iran Coventry Univ Fac Business & Law Sch Strategy & Leadership Coventry W Midlands England RMIT Univ Dept Informat Syst & Business Analyt Melbourne Vic 3000 Australia

Efficient human resource planning is the cornerstone of designing an effective home health care system. Human resource planning in home health care system consists of decisions on districting/zoning, staff dimensioning, resource assignment, scheduling, and routing. In this study, a two-stage stochastic mixed integer model is proposed that considers these decisions simultaneously. In the planning phase of a home health care system, the main uncertain parameters are travel and service times. Hence, the proposed model takes into account the uncertainty in travel and service times. Districting and staff dimensioning are defined as the first stage decisions, and assignment, scheduling, and routing are considered as the second stage decisions. A novel algorithm is developed for solving the proposed model. The algorithm consists of four phases and relies on a matheuristic-based method that calls on various mixed integer models. In addition, an algorithm based on the progressive hedging and Frank and Wolf algorithms is developed to reduce the computational time of the second phase of the proposed matheuristic algorithm. The efficiency and accuracy of the proposed algorithm are tested through several numerical experiments. The results prove the ability of the algorithm to solve large instances. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Home health care matheuristic algorithm Districting Staff dimensioning Progressive hedging algorithm Fix and optimize method

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A matheuristic algorithm for the pollution and energy minimization traveling salesman problems

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2023年第2期30卷 655-687页

作者： Cacchiani, Valentina Contreras-Bolton, Carlos Escobar-Falcon, Luis Miguel Toth, Paolo Univ Bologna DEI Viale Risorgimento 2 I-40136 Bologna Italy Univ Concepcion Dept Ingn Ind Edmundo Larenas 219 Concepcion 4070409 Chile Univ Libre Program Syst Engn Belmonte Ave Amer Pereira 660001 Colombia

The pollution traveling salesman problem (PTSP) and the energy minimization traveling salesman problem (EMTSP) generalize the well-known asymmetric traveling salesman problem by including environmental issues and the goal of reducing carbon emissions. Both problems call for determining a Hamiltonian tour that, in the PTSP, minimizes a function of fuel consumption and driver cost (where the fuel consumption depends on the distance traveled, the vehicle speed, and the vehicle load), while, in the EMTSP, minimizes a function depending on the vehicle load and the traveled distances. For both PTSP and EMTSP, we propose a matheuristic algorithm that uses the solution of the linear programming relaxation of a mixed integer linear programming model for the considered problem to determine good initial feasible solutions, applies a multioperator genetic algorithm to improve these solutions, and refines the best solution found through an iterated local search procedure. In order to evaluate the performance of the proposed matheuristics, we compare them with exact and heuristic algorithms from the literature on benchmark instances of both problems.

关键词： pollution traveling salesman problem energy minimization traveling salesman problem matheuristic algorithm linear programming relaxation

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A matheuristic algorithm for multi-objective unrelated parallel machine scheduling problem

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2023年第3期38卷 1953-1966页

作者： Sarac, Tugba Ozcelik, Feristah Eskisehir Osmangazi Univ Fac Engn & Architecture Dept Ind Engn TR-26480 Eskisehir Turkiye

Machine scheduling problems are one of the basic manufacturing problems. Thus, there are a lot of study in the literature. In most of these studies, the problem is considered as single objective. Although the single objective approach makes it easier to solve problems theoretically, it is often not possible to provide realistic solutions because almost all real-life problems have multi-objective properties. It is aimed to close this gap in the literature by using the multi-objective programming method that emerged as a powerful solution approach. The objectives are minimizing the makespan and minimizing the total tardiness. A matheuristic algorithm is developed for solving the considered problem. The performance of the algorithm is compared with the results of the augmented e-constraint method. With the proposed matheuristic algorithm, both a solution time advantage is obtained and dominant solutions that could not be obtained with the augmented e-constraint method are reached.

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Drop-and-pull container drayage with route balancing and its matheuristic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期255卷

作者： Wang, Decheng Zhang, Ruiyou Dong, Minggang Xie, Xiaolan Northeastern Univ Coll Informat Sci & Engn 11 Lane 3Wenhua Rd Shenyang 110819 Liaoning Peoples R China Guilin Univ Technol Coll Informat Sci & Engn Guangxi Key Lab Embedded Technol & Intelligent Sys 319 Yanshan St Guilin 541006 Peoples R China

Route balancing addresses fair workloads allocation among stakeholders to balance utilization of transportation resources and to guarantee equity among employees. Drop-and-pull transportation has been verified to be beneficial in container drayage and has gradually received attentions from the scientific literature. A container drayage problem that simultaneously considers the drop-and-pull mode and the issue of route balancing is investigated. In the problem, apart from the economic objectives, balanced distribution of workloads from social perspective is especially pursued. A mixed-integer programming model is formulated and a matheuristic algorithm is developed based on embedding the model in a heuristic framework to solve the problem. A division approach is designed to transform the original problem into sub-problems to ease the solving pressure. A fix-and- optimize method is adopted to further reduce the computational burdens from substantial decision variables. The proposed algorithm is tested on solving different application scenarios as single- and multi-trailer drop-and-pull problems. Experimental results indicate that the algorithm can provide much better solutions in shorter computational time when compared to standard approaches. Some managerial insights and sensitivity analyses concerning the route balancing are also presented.

关键词： Container drayage Route balancing Fix-and-optimize method matheuristic algorithm Drop-and-pull transportation

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An ALNS-based matheuristic algorithm for a multi-product many-to-many maritime inventory routing problem

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COMPUTATIONAL MANAGEMENT SCIENCE 2023年第1期20卷 44-44页

作者： Heidari, Nooshin Hemmati, Ahmad Univ Bergen Dept Informat Bergen Norway

In this paper, we propose an adaptive large neighborhood search-based matheuristic algorithm to solve a multi-product many-to-many maritime inventory routing problem. The problem addresses a short sea inventory routing problem that aims to find the best route and distribution plan for multiple products with a heterogeneous fleet of vessels through a network including several producers and customers. Each port can be visited a given number of times during the planning horizon, and the stock level for each product should lie within the predefined bound limits. The problem was introduced by Hemmati et al. (Eur J Oper Res 252:775-788, 2016). They developed a mixed integer programming formulation and proposed a matheuristic algorithm to solve the problem. Although their proposed algorithm worked well in terms of running time, it suffers from disregarding a part of the solution space. In this study, we propose a new matheuristic algorithm to find better solutions by exploring the entire solution space for the same problem. In our solution methodology, we split the variables into routing and non-routing variables. Then in an iterative process, we determine the values of the routing variables with an adaptive large neighborhood search algorithm, and we pass them as input to a penalized model which is a relaxed and modified version of the mathematical model introduced in Hemmati et al. (2016). The information from solving the penalized model, including the values of the non-routing variables, is then passed to the adaptive large neighborhood search algorithm for the next iteration. Several problem-dependent operators are defined. The operators use the information they get from the penalized model and focus on decreasing the penalty values. Computational results show up to 26% improvement in the quality of the solutions for the group of instances with a large feasible solution space. We get the optimal value for the remaining instances matched with the reported results.

关键词： Maritime inventory routing problem Many-to-many distribution network Adaptive large neighborhood search matheuristic algorithm

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A matheuristic approach to the orienteering problem with service time dependent profits

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期273卷 488-503页

作者： Yu, Qinxiao Fang, Kan Zhu, Ning Ma, Shoufeng Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China

This paper addresses the orienteering problem with service time dependent profits (OPSTP), in which the profit collected at each vertex is characterized by a nonlinear function of service time, and the objective is to maximize the total profits by determining a subset of the vertices to be visited and assigning appropriate service time to each of them within a given time budget. To solve this problem, a mixed integer nonlinear programming model is formulated, and a two-phase matheuristic algorithm that consists of a tabu search method and a nonlinear programming is implemented. Extensive computational experiments are conducted on both randomly generated instances and instances that are adapted from the TSPLIB. The results show that our proposed matheuristic algorithm could be quite effective in finding good-quality solutions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Routing Orienteering problem Service time dependent profits matheuristic algorithm Nonlinear programming

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A bi-objective mathematical model for an unrelated parallel machine scheduling problem with job-splitting

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2022年第4期37卷 2293-2307页

作者： Sarac, Tugba Tutumlu, Busra Eskisehir Osmangazi Univ Fac Engn & Architecture Ind Enginering Dept TR-26480 Eskisehir Turkey Kutahya Dumlupinar Univ Fac Engn Ind Enginering Deparment TR-43100 Kutahya Turkey

Studies dealing with parallel machine scheduling problems generally assume that jobs are not split. However, splitting of jobs can offer opportunities such as delivering the jobs on time or evenly distributing the jobs to the machines. In this study, two different multi-objective mathematical models are proposed for the unrelated parallel machine scheduling problem without and with job-splitting. The first model is for the unrelated parallel machine scheduling problem without job-splitting and it was developed based on the model proposed by Sarac and Tutumlu [1]. In the second model, in which the jobs can split, besides the machines to which the jobs will be assigned and their order, it is also determined how the jobs will split and in which ratios the jobs will be processed on which machines. The objectives of both models are to minimize the completion time of the last job and the number of machines to be used. Multi-objective models were transformed into a single-objective structure with the epsilon constraint method. Randomly generated test problems were solved with the proposed models using the GAMS/Cplex solver, and the results were compared. Considering the problems for which the optimum solution is obtained, the first model found a solution in an average of 85% less time than the model in the literature [1], and the second model shortens the completion time of the last job in an average of 14%. In addition, a mathematical heuristic algorithm is proposed for solving large-size problems.

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A MIP-based approach to solve the prize-collecting local access network design problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第3期235卷 727-739页

作者： Ljubic, Ivana Putz, Peter Salazar-Gonzalez, Juan-Jose Univ Vienna Dept Stat & Operat Res A-1010 Vienna Austria Univ La Laguna DEIOC Tenerife Spain

This paper presents a new combinatorial optimization problem that can be used to model the deployment of broadband telecommunications systems in which optical fiber cables are installed between a central office and a number of end-customers. In this capacitated network design problem the installation of optical fiber cables with sufficient capacity is required to carry the traffic from the central office to the end-customers at minimum cost. In the situation motivating this research the network does not necessarily need to connect all customers (or at least not with the best available technology). Instead, some nodes are potential customers. The aim is to select the customers to be connected to the central server and to choose the cable capacities to establish these connections. The telecom company takes the strategic decision of fixing a percentage of customers that should be served, and aims for minimizing the total cost of the network providing this minimum service. For that reason the underlying problem is called the Prize-Collecting Local Access Network Design problem (PC-LAN). We propose a branch-and-cut approach for solving small instances. For large instances of practical importance, our approach turns into a mixed integer programming (MIP) based heuristic procedure which combines the cutting-plane algorithm with a multi-start heuristic algorithm. The multi-start heuristic algorithm starts with fractional values of the LP-solutions and creates feasible solutions that are later improved using a local improvement strategy. Computational experiments are conducted on small instances from the literature. In addition, we introduce a new benchmark set of real-world instances with up to 86,000 nodes, 116,000 edges and 1500 potential customers. Using our MIP-based approach we are able to solve most of the small instances to proven optimality. For more difficult instances, we are not only able to provide high-quality feasible solutions, but also to provide certificate

关键词： Network loading Local access Prize-collecting matheuristic algorithm

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