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A MILP model and a heuristic algorithm for post-disaster connectivity problem with heterogeneous vehicles

JOURNAL OF HEURISTICS 2024年第5-6期30卷 359-396页

作者： Tukenmez, Ilknur Sarac, Tugba Kaya, Onur Bursa Tech Univ Ind Engn Dept Bursa Turkiye Eskisehir Osmangazi Univ Ind Engn Dept Eskisehir Turkiye Eskisehir Tech Univ Ind Engn Dept Eskisehir Turkiye

Throughout the response phase of the disaster, the speedy restoration of transportation by reconnecting the nodes where the connection is broken is absolutely critical for evacuating civilians, providing clear access to hospitals, and distributing aid. Following a disaster, some roads in a disaster area might be closed to transportation. In reality, some roads can be blocked due to debris, and some of roads can be blocked by collapsing. In this model, different types of road unblocking methods are included, and each road can only be opened to access by a vehicle suitable for that method. So, different types of vehicles may be needed to repair the roads depending on the type of damage. In addition, fast-built bridges built both on land and over water are also used if necessary following a disaster. In problems of this nature, it is essential to restore the roads to enable the complete connectivity of the network such that all nodes can be reached by one another. In addition, it is also critical for the speedy reach of critical nodes, such as hospitals, and emergency disaster centers. This study aims to reduce the maximum time for connection and minimize the total time in which to reach critical nodes. For this purpose, we developed a bi-objective mathematical model that considers the multiple vehicle types that can repair different types of damages. Since the problem is NP-hard, two heuristic methods were developed, and the numerical results were presented. It has been observed that the local search algorithm gives better results than the hybrid algorithm. Additionally, different scenario data was produced. Numbers of unconnected components from 3 to 10 are solved with heuristic algorithms for test data containing 80 and 250 nodes, and real-life data containing 223 nodes and 391 edges are solved with heuristic algorithms for the number of unconnected components 6, 9, 12, and 15.

关键词： Connectivity problem Arc routing local search algorithm Hybrid algorithm Disaster management

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A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2021年第2期29卷 499-513页

作者： Boumedine, Nabil Bouroube, Sadek Univ Sci & Technol Houari Boumediene Fac Math Dept Operat Res Algiers Algeria

The flawless functioning of the protein is essentially related to its three-dimensional structure. Therefore, predicting protein structure from its amino acid sequence is a fundamental problem that draws researchers' attention in many areas. The protein structure prediction problem (PSP) can be formulated as a combinatorial optimization problem based on simplified lattice models such as the hydrophobic-polar model. In this paper, we propose a new hybrid algorithm that combines three different known heuristic algorithms: the genetic algorithm, the tabu search strategy, and the local search algorithm to solve the PSP problem. Regarding the evaluation of the proposed approach, we present an experimental study, where we consider the quality of the product solution as the main assessment criterion. Furthermore, we compared the proposed algorithm with state-of-the-art algorithms using a selection of well-studied benchmark instances.

关键词： Protein structure prediction 2D triangular lattice HP model genetic algorithm local search algorithm tabu search strategy minimal energy conformation

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Cooperative search based on pheromone communication for Vehicle Routing Problems

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1998年第6期E81A卷 1089-1096页

作者： Kawamura, H Yamamoto, M Mitamura, T Suzuki, K Ohuchi, A Hokkaido Univ Grad Sch Engn Sapporo Hokkaido 0608628 Japan Asahikawa Med Coll Asahikawa Hokkaido 0788510 Japan

In this paper, we propose a new cooperative search algorithm based on pheromone communication for solving the Vehicle Routing Problems. In this algorithm, multi-agents can partition the problem cooperatively and search partial solutions independently using pheromone communication, which mimics the communication method of real ants. Through some computer experiments the cooperative search of multi-agents is confirmed.

关键词： multi-agent system Vehicle Routing Problem traveling salesman problem combinatorial optimization problem local search algorithm

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Polynomial transformations and data-independent neighborhood functions

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DISCRETE APPLIED MATHEMATICS 2004年第1-3期143卷 272-284页

作者： Armstrong, DE Jacobson, SH Alpatech Inc Arlington VA 22203 USA Univ Illinois Dept Mech & Ind Engn Urbana IL 61801 USA

A neighborhood function that is polynomial in size and independent of the problem data, except that it may depend on the maximum absolute value of a number in an instance, is termed semi-reasonable. A semi-data-independent order transformation (SDIOT) is introduced such that if problemA SDIOT to problem B and B has a semi-reasonable neighborhood function, where the number of local optima is polynomial, then problemA has a semi-reasonable neighborhood function such that the number of local optima is polynomial. A large class of optimization problems is shown to SDIOT to Maximum Clause Weighted Satisfiability. (C) 2004 Elsevier B.V. All rights reserved.

关键词： computational complexity local search algorithm NP-hard discrete optimization problem

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APPROXIMATION algorithm FOR SPHERICAL k-MEANS PROBLEM WITH PENALTY

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2022年第4期18卷 2277-2287页

作者： Wu, Chenchen Lv, Wei Wang, Yujie Xu, Dachuan Nankai Univ Sch Business Tianjin 300071 Peoples R China Tianjin Univ Technol Coll Sci Tianjin 300384 Peoples R China Tianjin Renai Coll Comp Sci & Technol Dept Tianjin 306136 Peoples R China Beijing Univ Technol Dept Operat Res & Informat Engn Beijing 100124 Peoples R China

The k-means problem is a classical combinatorial optimization problem which has lots of applications in many fields such as machine learn-ing, data mining, etc. We consider a variant of k-means problem in the spherical space, that is, spherical k-means problem with penalties. In the problem, it is allowable that some nodes in the spherical space can not be clustered by paying some penalty costs. Based on local search scheme, we propose a (4(11 + 4 root 7) + epsilon)-approximation algorithm using singe-swap oper-ation, where E is a positive constant.

关键词： k-means local search algorithm approximation algorithm

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Combating COVID-19 by placing facilities maintaining social distancing

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期238卷

作者： Banerjee, Suman Pal, Bithika Mahapatra, Maheswar Singha Indian Inst Technol Jammu Dept Comp Sci & Engn Nagrota 181221 India Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur 721302 W Bengal India Indian Inst Management Mumbai Mumbai 400087 Maharashtra India

In this paper, we introduce and study the problem of facility location along with the notion of 'social distancing'. The input to the problem is the road network of a city where the nodes are the residential zones, edges are the road segments connecting the zones along with their respective distance. We also have the information about the population at each zone, different types of facilities to be opened and in which number, and their respective demands in each zone. The goal of the problem is to locate the facilities such that the people can be served and at the same time the total social distancing is maximized. We formally call this problem as the SOCIAL DISTANCING-BASED FACILITy LOCATION PROBLEM. We mathematically quantify social distancing for a given allocation of facilities and proposed an optimization model. As the problem is NP-Hard, we propose three solution methodologies. The first one is a simulation-based approach, the second one is a greedy heuristic, and the third one is a local search heuristic. To validate the proposed solution methodologies, we collect the data from the Food Corporation of India for the city of Kolkata, which happens to be the largest city of eastern India. With this dataset, we perform an extensive set of experiments. From the results, we observe that the proposed local search heuristic allocates facilities that lead to minimum average queue length and greedy heuristic allocates facilities that lead to the maximum social distancing.

关键词： Facility location Social distancing Integer programming local search algorithm COVID-19

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A integral filter algorithm for unconstrained global optimization

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APPLIED MATHEMATICS AND COMPUTATION 2007年第2期184卷 173-180页

作者： Yang, Yongjian Du, Xuewu Li, Mingming Shanghai Univ Dept Math Shanghai 200444 Peoples R China Henan Polytech Univ Dept Appl Math Jiaozuo 454000 Peoples R China Shanghai Univ Engn Sci Dept Math Shanghai 200336 Peoples R China

In this paper, making use of an integral inequality, a necessary and sufficient condition is given for a point to be a global minimizer. Based on the integral inequality, a novel integral-form algorithm is proposed for unconstrained global optimization. It is different from the other deterministic global search algorithm. Under mild conditions it is proved that, in theory, a global minimizer of the objective function can be certainly found by the presented algorithm. In order to indicate the efficiency and reliability of the method, four numerical examples are reported. (C) 2006 Elsevier Inc. All rights reserved.

关键词： global optimization integral branch and bound local search algorithm

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Optimizing copious activity type classes based on classification accuracy and entropy retention

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 110卷 338-349页

作者： Ectors, Wim Reumers, Sofie Lee, Won Do Kochan, Bruno Janssens, Davy Bellemans, Tom Wets, Geert UHasseit Hasselt Univ Transportat Res Inst IMOB B-3590 Diepenbeek Belgium Manchester Metropolitan Univ Crime & Well Being Big Data Ctr Manchester M15 6BH Lancs England

Despite the advantages, big transport data are characterized by a considerable disadvantage as well. Personal and activity-travel information are often lacking, making it necessary to deduce this information with data mining techniques. However, some studies predict many unique activity type classes (ATCs), while others merge multiple activity types into larger ATCs. This action enhances the activity inference estimation, but destroys important activity information. Previous studies do not provide a strong justification for this practice. An objectively optimized set of ATCs, balancing model prediction accuracy and preserving activity information from the original data, becomes essential. Previous research developed a classification methodology in which the optimal set of ATCs was identified by analyzing all possible ATC combinations. However, this approach is practically impossible in a finite amount of time for e.g. the US National Household Travel Survey (NHTS) 2009 data set, which comprises 36 ATCs (home activity excluded), since there would be 3.82 . 10(30) unique combinations (an exponential increase). The aim of this paper is to optimize which original ATCs should be grouped into a new class, and this for data sets for which it is impossible or impractical to simply calculate all ATC combinations. The proposed method defines an optimization parameter U (based on classification accuracy and information retention) which is maximized in an iterative local search algorithm. The optimal set of ATCs for the NHTS 2009 data set was determined. A comparison finds that this optimum is considerably better than many expert opinion activity type classification systems. Convergence was confirmed and large performance gains were found. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Activity type classification (Big) Transport data annotation Optimal set of activity types local search algorithm Classification accuracy Entropy indices

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Inscribed ball and enclosing box methods for the convex maximization problem

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OPTIMIZATION LETTERS 2016年第2期10卷 417-432页

作者： Guerard, Guillaume Tseveendorj, Ider Univ Versailles Lab PRiSM 45 Ave Etats Unis F-78035 Versailles France

Many important classes of decision models give rise to the problem of finding a global maximum of a convex function over a convex set. This problem is known also as concave minimization, concave programming or convex maximization. Such problems can have many local maxima, therefore finding the global maximum is a computationally difficult problem, since standard nonlinear programming procedures fail. In this article, we provide a very simple and practical approach to find the global solution of quadratic convex maximization problems over a polytope. A convex function achieves its global maximum at extreme points of the feasible domain. Since an inscribed ball does not contain any extreme points of the domain, we use the largest inscribed ball for an inner approximation while a minimal enclosing box is exploited for an outer approximation of the domain. The approach is based on the use of these approximations along with the standard local search algorithm and cutting plane techniques.

关键词： local search algorithm Non-convex optimization Convex maximization Inscribed ball

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Integrated multi-track station layout design and train scheduling models on railway corridors

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TRANSPORTATION REsearch PART C-EMERGING TECHNOLOGIES 2016年 69卷 91-119页

作者： Qi, Jianguo Yang, Lixing Gao, Yuan Li, Shukai Gao, Ziyou Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

The dwelling capacity of the station (mainly determined by its multi-track layout) is a practically significant factor to influence the quality of the train schedules, especially in a busy railway corridor with heterogeneous trains and complex operations. To improve the passing capacity and transportation efficiency, this paper focuses on a network design problem over a railway corridor, in which some critical stations are considered to enlarge the number of siding tracks or platforms within the budget constraints. To evaluate the quality of design strategies, the construction cost and total travel time in the corresponding optimal train schedule are adopted as evaluation indexes. Based on two specific modeling methodologies, two types of optimization models are particularly formulated with different considerations. One is a single-level linear mixed-integer programming (S-LMIP) model based on the space-time network representation method;the other is a bi-level programming model associated with the platform choice-based method, where the upper level of the proposed model aims to design new siding tracks/platforms in the candidate stations, and the lower level is a train scheduling model with assigning the tracks for each train at each station. The commercial software GAMS with CPLEX solver and local searching based heuristic with integrated CPLEX solver are respectively employed to solve the near-optimal solutions for these two types of models. Finally, two sets of examples, in which a sample railway corridor and the Wuhan-Guangzhou high-speed railway corridor are adopted as the experimental environments, are implemented to illustrate the performance and effectiveness of the proposed approaches. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Train scheduling Multi-track station layout design Linear mixed-integer programming model Bi-level optimization model local search algorithm

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