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Anisotropic Q-learning and waiting estimation based real-time routing for automated guided vehicles at container terminals J. Heuristics (IF 1.577) Pub Date : 2021-01-13 Pengfei Zhou, Li Lin, Kap Hwan Kim
Finding short and convenient routes for vehicles is an important issue on efficient operations of Automated Guided Vehicle (AGV) systems at container terminals. This paper proposes an anisotropic Q-learning method for AGVs to find the shortest-time routes in the guide-path network of cross-lane type according to real-time vehicle states, which includes current and destination positions, heading direction
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A max-conflicts based heuristic search for the stable marriage problem with ties and incomplete lists J. Heuristics (IF 1.577) Pub Date : 2021-01-06 Hoang Huu Viet, Nguyen Thi Uyen, SeungGwan Lee, TaeChoong Chung, Le Hong Trang
In this paper, we propose a heuristic search algorithm based on maximum conflicts to find a weakly stable matching of maximum size for the stable marriage problem with ties and incomplete lists. The key idea of our approach is to define a heuristic function based on the information extracted from undominated blocking pairs from the men’s point of view. By choosing a man corresponding to the maximum
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CMSA algorithm for solving the prioritized pairwise test data generation problem in software product lines J. Heuristics (IF 1.577) Pub Date : 2020-11-10 Javier Ferrer, Francisco Chicano, José Antonio Ortega-Toro
In Software Product Lines, it may be difficult or even impossible to test all the products of the family because of the large number of valid feature combinations that may exist (Ferrer et al. in: Squillero, Sim (eds) EvoApps 2017, LNCS 10200, Springer, The Netherlands, pp 3–19, 2017). Thus, we want to find a minimal subset of the product family that allows us to test all these possible combinations
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Iterated local search for single machine total weighted tardiness batch scheduling J. Heuristics (IF 1.577) Pub Date : 2020-11-09 Eduardo Queiroga, Rian G. S. Pinheiro, Quentin Christ, Anand Subramanian, Artur A. Pessoa
This paper presents an iterated local search (ILS) algorithm for the single machine total weighted tardiness batch scheduling problem. To our knowledge, this is one of the first attempts to apply ILS to solve a batching scheduling problem. The proposed algorithm contains a local search procedure that explores five neighborhood structures, and we show how to efficiently implement them. Moreover, we
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The just-in-time job-shop scheduling problem with distinct due-dates for operations J. Heuristics (IF 1.577) Pub Date : 2020-11-03 Mohammad Mahdi Ahmadian, Amir Salehipour
In the just-in-time job-shop scheduling (JIT–JSS) problem every operation has a distinct due-date, and earliness and tardiness penalties. Any deviation from the due-date incurs penalties. The objective of JIT–JSS is to obtain a schedule, i.e., the completion time for performing the operations, with the smallest total (weighted) earliness and tardiness penalties. This paper presents a matheuristic algorithm
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A reduced cost-based restriction and refinement matheuristic for stochastic network design problem J. Heuristics (IF 1.577) Pub Date : 2020-10-24 Fatemeh Sarayloo, Teodor Gabriel Crainic, Walter Rei
We propose a solution approach for stochastic network design problems with uncertain demands. We investigate how to efficiently use reduced cost information as a means of guiding variable fixing to define a restriction that reduces the complexity of solving the stochastic model without sacrificing the quality of the solution obtained. We then propose a matheuristic approach that iteratively defines
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Local search for the maximum k -plex problem J. Heuristics (IF 1.577) Pub Date : 2020-10-08 Wayne Pullan
The maximum k-plex problem is an important, computationally complex graph based problem. In this study an effective k-plex local search (KLS) is presented for solving this problem on a wide range of graph types. KLS uses data structures suitable for the graph being analysed and has mechanisms for preventing search cycling and promoting search diversity. State of the art results were obtained on 121
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A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem J. Heuristics (IF 1.577) Pub Date : 2020-09-20 Jonatas B. C. Chagas, Julian Blank, Markus Wagner, Marcone J. F. Souza, Kalyanmoy Deb
In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the overall traveling time and to maximize the profit of the
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Reactive VNS algorithm for the maximum k-subset intersection problem J. Heuristics (IF 1.577) Pub Date : 2020-09-04 Fabio C. S. Dias, Wladimir Araújo Tavares, José Robertty de Freitas Costa
This paper proposes a reactive VNS metaheuristic for the maximum intersection of k-subsets problem (kMIS). The kMIS is defined as: Given a set of elements, a subset family of the first set and an integer k. The problem consists of finding k subset so that the intersection is maximum. Our VNS metaheuristic incorporates strategies used in GRASP metaheuristics, such as the GRASP construction phase and
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Matheuristics to optimize refueling and maintenance planning of nuclear power plants J. Heuristics (IF 1.577) Pub Date : 2020-09-02 Nicolas Dupin, El-Ghazali Talbi
Planning the maintenance of nuclear power plants is a complex optimization problem, involving a joint optimization of maintenance dates, fuel constraints and power production decisions. This paper investigates Mixed Integer Linear Programming (MILP) matheuristics for this problem, to tackle large size instances used in operations with a time scope of 5 years, and few restrictions with time window constraints
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Vehicle routing for the urgent delivery of face shields during the COVID-19 pandemic J. Heuristics (IF 1.577) Pub Date : 2020-08-18 Joaquín Pacheco; Manuel Laguna
The speed by which the COVID-19 pandemic spread throughout the world caught some national and local governments unprepared. Healthcare systems found themselves struggling to increase capacity and procure key supplies, such as personal protective equipment. Protective face shields became essential for healthcare professionals. However, most hospitals and healthcare facilities did not have them in adequate
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Correction to: A genetic algorithm for finding realistic sea routes considering the weather J. Heuristics (IF 1.577) Pub Date : 2020-08-15 Stefan Kuhlemann, Kevin Tierney
The original version of this article has unfortunately contained an error in the Acknowledgement section.
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An adaptive large neighborhood search heuristic for the planar storage location assignment problem: application to stowage planning for Roll-on Roll-off ships J. Heuristics (IF 1.577) Pub Date : 2020-08-14 Jone R. Hansen, Kjetil Fagerholt, Magnus Stålhane, Jørgen G. Rakke
This paper considers a generalized version of the planar storage location problem arising in the stowage planning for Roll-on/Roll-off ships. A ship is set to sail along a predefined voyage where given cargoes are to be transported between different port pairs along the voyage. We aim at determining the optimal stowage plan for the vehicles stored on a deck of the ship so that the time spent moving
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Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances J. Heuristics (IF 1.577) Pub Date : 2020-08-05 Felipe Campelo, Elizabeth F. Wanner
This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by allowing researchers to design experiments based on the desired best, worst, mean or median-case statistical power to detect differences between algorithms larger
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A tabu search for the design of capacitated rooted survivable planar networks J. Heuristics (IF 1.577) Pub Date : 2020-07-27 Alain Hertz, Thomas Ridremont
Consider a rooted directed graph G with a subset of vertices called terminals, where each arc has a positive integer capacity and a non-negative cost. For a given positive integer k, we say that G is k-survivable if every of its subgraphs obtained by removing at most k arcs admits a feasible flow that routes one unit of flow from the root to every terminal. We aim at determining a k-survivable subgraph
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A genetic algorithm for finding realistic sea routes considering the weather J. Heuristics (IF 1.577) Pub Date : 2020-07-23 Stefan Kuhlemann, Kevin Tierney
The weather has a major impact on the profitability, safety, and environmental sustainability of the routes sailed by seagoing vessels. The prevailing weather strongly influences the course of routes, affecting not only the safety of the crew, but also the fuel consumption and therefore the emissions of the vessel. Effective decision support is required to plan the route and the speed of the vessel
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Evolutionary multi-level acyclic graph partitioning J. Heuristics (IF 1.577) Pub Date : 2020-07-15 Orlando Moreira; Merten Popp; Christian Schulz
Directed graphs are widely used to model data flow and execution dependencies in streaming applications. This enables the utilization of graph partitioning algorithms for the problem of parallelizing execution on multiprocessor architectures under hardware resource constraints. However due to program memory restrictions in embedded multiprocessor systems, applications need to be divided into parts
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A multi-start local search algorithm for the Hamiltonian completion problem on undirected graphs J. Heuristics (IF 1.577) Pub Date : 2020-07-01 Jorik Jooken; Pieter Leyman; Patrick De Causmaecker
This paper proposes a local search algorithm for a specific combinatorial optimisation problem in graph theory: the Hamiltonian completion problem (HCP) on undirected graphs. In this problem, the objective is to add as few edges as possible to a given undirected graph in order to obtain a Hamiltonian graph. This problem has mainly been studied in the context of various specific kinds of undirected
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Distance-guided local search J. Heuristics (IF 1.577) Pub Date : 2020-05-26 Daniel Porumbel; Jin-Kao Hao
We present several techniques that use distances between candidate solutions to achieve intensification in Local Search (LS) algorithms. An important drawback of classical LS is that after visiting a very high-quality solution the search process can “forget about it” and continue towards very different areas. We propose a method that works on top of a given LS to equip it with a form of memory so as
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IoT networks 3D deployment using hybrid many-objective optimization algorithms J. Heuristics (IF 1.577) Pub Date : 2020-05-18 Sami Mnasri; Nejah Nasri; Malek Alrashidi; Adrien van den Bossche; Thierry Val
When resolving many-objective problems, multi-objective optimization algorithms encounter several difficulties degrading their performances. These difficulties may concern the exponential execution time, the effectiveness of the mutation and recombination operators or finding the tradeoff between diversity and convergence. In this paper, the issue of 3D redeploying in indoor the connected objects (or
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An automatic constructive matheuristic for the shift minimization personnel task scheduling problem J. Heuristics (IF 1.577) Pub Date : 2020-02-13 Reshma Chirayil Chandrasekharan, Pieter Smet, Tony Wauters
The shift minimization personnel task scheduling problem is an NP-complete optimization problem that concerns the assignment of tasks to multi-skilled employees with a view to minimize the total number of assigned employees. Recent literature indicates that hybrid methods which combine exact and heuristic techniques such as matheuristics are efficient as regards to generating high quality solutions
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Heuristic/meta-heuristic methods for restricted bin packing problem J. Heuristics (IF 1.577) Pub Date : 2020-03-30 Yu Fu; Amarnath Banerjee
This paper addresses a special bin packing problem in which each item can only be assigned to a subset of the bins. We name this problem as the restricted bin packing problem (RBPP). This paper is designed to explore the relationships of RBPP with classic NP-complete problems, and to resolve the restrictions of assignment through heuristic and meta-heuristic algorithms. A new heuristic algorithm named
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Meta-heuristics for the one-dimensional cutting stock problem with usable leftover J. Heuristics (IF 1.577) Pub Date : 2020-03-03 Santiago V. Ravelo; Cláudio N. Meneses; Maristela O. Santos
This work considers the one-dimensional cutting stock problem in which the non-used material in the cutting patterns may be used in the future, if large enough. We show that a multiobjective criteria to classify the solutions could be more accurate than previous classifications attempts, also we give a heuristic algorithm and two meta-heuristic approaches to the problem and we use them to solve practical
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Pareto-based evolutionary multiobjective approaches and the generalized Nash equilibrium problem J. Heuristics (IF 1.577) Pub Date : 2020-02-14 Rodica Ioana Lung; Noémi Gaskó; Mihai Alexandru Suciu
Pareto-based evolutionary multiobjective approaches are methods that use the Pareto dominance concept to guide the search of evolutionary algorithms towards the Pareto frontier of a problem. To address the challenge of providing an entire set of optimal solutions they use specially designed mechanisms for preserving search diversity and maintaining the non-dominated solutions set. The limitation of
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Heuristic solutions to robust variants of the minimum-cost integer flow problem J. Heuristics (IF 1.577) Pub Date : 2020-02-14 Marko Špoljarec; Robert Manger
This paper deals with robust optimization applied to network flows. We consider two robust variants of the minimum-cost integer flow problem. Thereby, uncertainty in problem formulation is limited to arc costs and expressed by a finite set of explicitly given scenarios. It turns out that both problem variants are NP-hard. To solve the considered variants, we propose several heuristics based on local
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New heuristic algorithms for the Dubins traveling salesman problem J. Heuristics (IF 1.577) Pub Date : 2020-02-13 Luitpold Babel
The problem of finding a shortest curvature-constrained closed path through a set of targets in the plane is known as Dubins traveling salesman problem (DTSP). Applications of the DTSP include motion planning for kinematically constrained mobile robots and unmanned fixed-wing aerial vehicles. The difficulty of the DTSP is to simultaneously find an order of the targets and suitable headings (orientation
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MIP neighborhood search heuristics for a service network design problem with design-balanced requirements J. Heuristics (IF 1.577) Pub Date : 2020-02-04 Naoto Katayama
Service network design problems are used to address a variety of services in transportation and logistics planning. In the present paper, we consider the service network design problem with design-balanced requirements. This problem is particularly relevant to operations for consolidation transportation systems and determines the transportation network configuration and the characteristics of the corresponding
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On combining variable ordering heuristics for constraint satisfaction problems J. Heuristics (IF 1.577) Pub Date : 2020-01-21 Hongbo Li; Guozhong Feng; Minghao Yin
Variable ordering heuristics play a central role in solving constraint satisfaction problems. Combining two variable ordering heuristics may generate a more efficient heuristic, such as dom/deg. In this paper, we propose a novel method for combining two variable ordering heuristics, namely Pearson-Correlation-Coefficient-based Combination (PCCC). While the existing combination strategies always combine
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Enhancement for human resource management in the ULD build-up process of air-cargo terminal: a strategic linkage approach J. Heuristics (IF 1.577) Pub Date : 2020-01-21 Hyun Jun Yang; Suk Jae Jeong; Sung Wook Yoon
This study proposes a strategic linkage method with mathematical model, simulation model and heuristic approaches to an adaptive workforce scheduling problem reflecting the work site situation of the air cargo terminal. For the application of the proposed method, we first generate an initial workforce schedule using the optimization model for minimizing labor costs. The simulation model is then used
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Logistics optimization for a coal supply chain J. Heuristics (IF 1.577) Pub Date : 2020-01-14 Gleb Belov; Natashia L. Boland; Martin W. P. Savelsbergh; Peter J. Stuckey
The Hunter Valley coal export supply chain in New South Wales, Australia, is of great importance to the Australian economy. Effectively managing its logistics, however, is challenging, because it is a complex system, covering a large geographic area and comprising a rail network, three coal terminals, and a port, and has many stakeholders, e.g., mining companies, port authorities, coal terminal operators
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Basic variable neighborhood search for the minimum sitting arrangement problem J. Heuristics (IF 1.577) Pub Date : 2020-01-13 Eduardo G. Pardo; Antonio García-Sánchez; Marc Sevaux; Abraham Duarte
The minimum sitting arrangement (MinSA) problem is a linear layout problem consisting in minimizing the number of errors produced when a signed graph is embedded into a line. This problem has been previously tackled by theoretical and heuristic approaches in the literature. In this paper we present a basic variable neighborhood search (BVNS) algorithm for solving the problem. First, we introduce a
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A hybrid genetic algorithm for the traveling salesman problem with drone J. Heuristics (IF 1.577) Pub Date : 2019-11-13 Quang Minh Ha; Yves Deville; Quang Dung Pham; Minh Hoàng Hà
This paper addresses the traveling salesman problem with drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years reflecting the recent trends
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Heuristics for a vehicle routing problem with information collection in wireless networks J. Heuristics (IF 1.577) Pub Date : 2019-10-24 Luis Flores-Luyo; Agostinho Agra; Rosa Figueiredo; Eladio Ocaña
We consider a wireless network where a given set of stations is continuously generating information. A single vehicle, located at a base station, is available to collect the information via wireless transfer. The wireless transfer vehicle routing problem (WTVRP) is to decide which stations should be visited in the vehicle route, how long shall the vehicle stay in each station, and how much information
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Bilevel optimization based on iterative approximation of multiple mappings J. Heuristics (IF 1.577) Pub Date : 2019-09-24 Ankur Sinha; Zhichao Lu; Kalyanmoy Deb; Pekka Malo
A large number of application problems involve two levels of optimization, where one optimization task is nested inside the other. These problems are known as bilevel optimization problems and have been studied by both classical optimization community and evolutionary optimization community. Most of the solution procedures proposed until now are either computationally very expensive or applicable to
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A comparative study of multi-objective machine reassignment algorithms for data centres J. Heuristics (IF 1.577) Pub Date : 2019-09-20 Takfarinas Saber; Xavier Gandibleux; Michael O’Neill; Liam Murphy; Anthony Ventresque
At a high level, data centres are large IT facilities hosting physical machines (servers) that often run a large number of virtual machines (VMs)—but at a lower level, data centres are an intricate collection of interconnected and virtualised computers, connected services, complex service-level agreements. While data centre managers know that reassigning VMs to the servers that would best serve them
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Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem J. Heuristics (IF 1.577) Pub Date : 2019-09-09 Mohamed Amine Masmoudi; Manar Hosny; Emrah Demir; Erwin Pesch
The mixed fleet heterogeneous dial-a-ride problem (MF-HDARP) consists of designing vehicle routes for a set of users by using a mixed fleet including both heterogeneous conventional and alternative fuel vehicles. In addition, a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. We propose an efficient hybrid adaptive large neighborhood
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MIQP model and improvement heuristic for power loss minimization in distribution system with network reconfiguration J. Heuristics (IF 1.577) Pub Date : 2019-08-01 Karla B. Freitas; Márcio S. Arantes; Claudio F. M. Toledo; Alexandre C. B. Delbem
The problem of reconfiguration in electrical power distribution systems deals with changes in the network topology using maneuvers switches. This is an optimization problem where one of the goals is to minimize losses following constraints such as faults isolation, load feeders balancing and voltage profile improvement. The present paper solves such problem by introducing a mixed-integer quadratic
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A coordinated production and transportation scheduling problem with minimum sum of order delivery times J. Heuristics (IF 1.577) Pub Date : 2019-07-25 Ling Liu; Wenli Li; Kunpeng Li; Xuxia Zou
In this paper, a coordinated production scheduling and vehicle routing problem aiming at minimizing the sum of order delivery times is considered, where there are a single machine for production and limited number of homogenous capacitated vehicles for transportation. Given the complexity of the studied problem, a variable neighborhood search (VNS) algorithm is proposed to address this problem. To
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A heuristic for fair dynamic resource allocation in overloaded OFDMA systems J. Heuristics (IF 1.577) Pub Date : 2019-07-22 Adam N. Letchford; Qiang Ni; Zhaoyu Zhong
OFDMA is a popular coding scheme for mobile wireless communications. In OFDMA, one must allocate the available resources (bandwidth and power) dynamically, as user requests arrive and depart in a stochastic manner. Several exact and heuristic methods exist to do this, but they all perform poorly in the “over-loaded” case, in which the user demand is close to or exceeds the system capacity. To address
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Objective scaling ensemble approach for integer linear programming J. Heuristics (IF 1.577) Pub Date : 2019-06-29 Weili Zhang; Charles D. Nicholson
The objective scaling ensemble approach is a novel two-phase heuristic for integer linear programming problems shown to be effective on a wide variety of integer linear programming problems. The technique identifies and aggregates multiple partial solutions to modify the problem formulation and significantly reduce the search space. An empirical analysis on publicly available benchmark problems demonstrate
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Predicting effective control parameters for differential evolution using cluster analysis of objective function features J. Heuristics (IF 1.577) Pub Date : 2019-06-20 Sean P. Walton; M. Rowan Brown
A methodology is introduced which uses three simple objective function features to predict effective control parameters for differential evolution. This is achieved using cluster analysis techniques to classify objective functions using these features. Information on prior performance of various control parameters for each classification is then used to determine which control parameters to use in
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Creating dispatching rules by simple ensemble combination J. Heuristics (IF 1.577) Pub Date : 2019-05-29 Marko Ɖurasević; Domagoj Jakobović
Dispatching rules are often the method of choice for solving scheduling problems since they are fast, simple, and adaptive approaches. In recent years genetic programming has increasingly been used to automatically create dispatching rules for various scheduling problems. Since genetic programming is a stochastic approach, it needs to be executed several times to ascertain that good dispatching rules
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An application of generalised simulated annealing towards the simultaneous modelling and clustering of glaucoma J. Heuristics (IF 1.577) Pub Date : 2019-05-14 Mohd Zairul Mazwan Bin Jilani; Allan Tucker; Stephen Swift
Optimisation methods are widely used in complex data analysis, and as such, there is a need to develop techniques that can explore huge search spaces in an efficient and effective manner. Generalised simulated annealing is a continuous optimisation method which is an advanced version of the commonly used simulated annealing technique. The method is designed to search for the global optimum solution
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Dynamic heuristic acceleration of linearly approximated SARSA( $$\lambda $$ λ ): using ant colony optimization to learn heuristics dynamically J. Heuristics (IF 1.577) Pub Date : 2019-05-03 Stefano Bromuri
Heuristically accelerated reinforcement learning (HARL) is a new family of algorithms that combines the advantages of reinforcement learning (RL) with the advantages of heuristic algorithms. To achieve this, the action selection strategy of the standard RL algorithm is modified to take into account a heuristic running in parallel with the RL process. This paper presents two approximated HARL algorithms
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An evolutionary hybrid search heuristic for monitor placement in communication networks J. Heuristics (IF 1.577) Pub Date : 2019-05-02 Robin Mueller-Bady; Martin Kappes; Inmaculada Medina-Bulo; Francisco Palomo-Lozano
In this paper, a heuristic method for the optimal placement of monitors in communication networks is proposed. In order to be able to make informed decisions, a first step towards securing a communication network is deploying an adequate sensor infrastructure. However, appropriate monitoring should take into account the priority of the communication links as well as the location of monitors. The goal
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Cascading failures in complex networks caused by overload attacks J. Heuristics (IF 1.577) Pub Date : 2019-05-02 Volker Turau; Christoph Weyer
Complex networks are known to be vulnerable to the failure of components in terms of structural robustness. An as yet less researched topic is dynamical robustness, which refers to the ability of a network to maintain its dynamical activity against local disturbances. This paper introduces a new type of attack—the overload attack—to disturb the network’s dynamical activity. The attack is based on the
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Three multi-start data-driven evolutionary heuristics for the vehicle routing problem with multiple time windows J. Heuristics (IF 1.577) Pub Date : 2019-04-08 Slim Belhaiza; Rym M’Hallah; Ghassen Ben Brahim; Gilbert Laporte
This paper considers the vehicle routing problem with multiple time windows. It introduces a general framework for three evolutionary heuristics that use three global multi-start strategies: ruin and recreate, genetic cross-over of best parents, and random restart. The proposed heuristics make use of information extracted from routes to guide customized data-driven local search operators. The paper
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Parallelism in divide-and-conquer non-dominated sorting: a theoretical study considering the PRAM-CREW model J. Heuristics (IF 1.577) Pub Date : 2019-02-04 Sumit Mishra; Carlos A. Coello Coello
Non-dominated sorting is a crucial component of Pareto-based multi- and many-objective evolutionary algorithms. As the number of objectives increases, the execution time of a multi-objective evolutionary algorithm increases, too. Since multi-objective evolutionary algorithms normally have a low data dependency, research-ers have increasingly adopted parallel programming techniques to reduce their execution
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A bi-objective study of the minimum latency problem J. Heuristics (IF 1.577) Pub Date : 2019-01-29 N. A. Arellano-Arriaga; J. Molina; S. E. Schaeffer; A. M. Álvarez-Socarrás; I. A. Martínez-Salazar
We study a bi-objective problem called the Minimum Latency-Distance Problem (mldp) that aims to minimise travel time and latency of a single-vehicle tour designed to serve a set of client requests. This tour is a Hamiltonian cycle for which we aim to simultaneously minimise the total travel time of the vehicle and the total waiting time (i.e., latency) of the clients along the tour. This problem is
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The selective traveling salesman problem with draft limits J. Heuristics (IF 1.577) Pub Date : 2019-01-29 Shahin Gelareh; Bernard Gendron; Saïd Hanafi; Rahimeh Neamatian Monemi; Raca Todosijević
This paper introduces the selective traveling salesman problem with draft limits, an extension of the traveling salesman problem with draft limits, wherein the goal is to design a maximum profit tour respecting draft limit constraints at the visited nodes. We propose a mixed integer linear programming (MILP) formulation for this problem. This MILP model is used to solve—to optimality—small size instances
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An analysis of heuristic subsequences for offline hyper-heuristic learning J. Heuristics (IF 1.577) Pub Date : 2019-01-04 W. B. Yates; E. C. Keedwell
A selection hyper-heuristic is used to minimise the objective functions of a well-known set of benchmark problems. The resulting sequences of low level heuristic selections and objective function values are used to generate a database of heuristic selections. The sequences in the database are broken down into subsequences and the mathematical concept of a logarithmic return is used to discriminate
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Clustering-driven evolutionary algorithms: an application of path relinking to the quadratic unconstrained binary optimization problem J. Heuristics (IF 1.577) Pub Date : 2019-01-01 Michele Samorani; Yang Wang; Yang Wang; Zhipeng Lv; Fred Glover
A long-standing challenge in the metaheuristic literature is to devise a way to select parent solutions in evolutionary population-based algorithms to yield better offspring, and thus provide improved solutions to populate successive generations. We identify a way to achieve this goal that simultaneously improves the efficiency of the evolutionary process. Our strategy derives from a proposal associated
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Real-time heuristic algorithms for the static weapon target assignment problem J. Heuristics (IF 1.577) Pub Date : 2018-11-29 Alexander G. Kline; Darryl K. Ahner; Brian J. Lunday
The problem of targeting and engaging individual missiles (targets) with an arsenal of interceptors (weapons) is known as the weapon target assignment problem. Many optimal solution techniques are applied to solve problem variants having linear approximations of the objective function, and their final solutions rarely yield optimal solutions to the original problem. Herein, we propose a nonlinear branch
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Diversification methods for zero-one optimization J. Heuristics (IF 1.577) Pub Date : 2018-11-07 Fred Glover; Gary Kochenberger; Weihong Xie; Jianbin Luo
We introduce new diversification methods for zero-one optimization that significantly extend strategies previously introduced in the setting of metaheuristic search. Our methods incorporate easily implemented strategies for partitioning assignments of values to variables, accompanied by processes called augmentation and shifting which create greater flexibility and generality. We then show how the
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Virtual machine consolidation using constraint-based multi-objective optimization J. Heuristics (IF 1.577) Pub Date : 2018-11-02 Miguel Terra-Neves; Inês Lynce; Vasco Manquinho
With the blooming of cloud computing, the demand for data centers has been rising greatly in recent years. Their energy consumption and environmental impact has become much more significant due to the continuous growth of data center supply. It is possible to reduce the amount of energy consumed by a data center by shutting down unnecessary servers and maintaining only a subset running, such that it
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Swarm hyperheuristic framework J. Heuristics (IF 1.577) Pub Date : 2018-10-26 Surafel Luleseged Tilahun; Mohamed A. Tawhid
Swarm intelligence is one of the central focus areas in the study of metaheuristic algorithms. The effectiveness of these algorithms towards solving difficult problems has attracted researchers and practitioners. As a result, numerous type of this algorithm have been proposed. However, there is a heavy critics that some of these algorithms lack novelty. In fact, some of these algorithms are the same
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An effective multi-wave algorithm for solving the max-mean dispersion problem J. Heuristics (IF 1.577) Pub Date : 2018-10-25 Jiawei Song; Yang Wang; Haibo Wang; Qinghua Wu; Abraham P. Punnen
We propose an effective multi-wave algorithm organized in multiple search phases for the max-mean dispersion problem, which offers enhancement of neighborhood search algorithms by incorporating the notion of persistent attractiveness in memory based strategies. In each wave, a vertical phase and a horizontal phase are first alternated to reach a boundary solution. Then a concluding horizontal phase
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Sample size estimation for power and accuracy in the experimental comparison of algorithms J. Heuristics (IF 1.577) Pub Date : 2018-10-04 Felipe Campelo; Fernanda Takahashi
Experimental comparisons of performance represent an important aspect of research on optimization algorithms. In this work we present a methodology for defining the required sample sizes for designing experiments with desired statistical properties for the comparison of two methods on a given problem class. The proposed approach allows the experimenter to define desired levels of accuracy for estimates
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Patient classification considering the risk of restenosis after coronary stent placement J. Heuristics (IF 1.577) Pub Date : 2018-09-29 Halenur Şahin; Serhan Duran; Ertan Yakıcı; Mahmut Şahin
Aging and some lifestyle habits cause plaque accumulation in the blood vessels of the heart and this causes narrowing of the arteries. Stents are tiny wire mesh tubes which are used in balloon angioplasty to keep the vessels open. However, the stented vessel has a risk of re-narrowing due to the recovery response of the stented vessel segment and this is called in-stent-restenosis. The objective of
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A variable space search heuristic for the Capacitated Team Orienteering Problem J. Heuristics (IF 1.577) Pub Date : 2018-09-28 Asma Ben-Said; Racha El-Hajj; Aziz Moukrim
The Capacitated Team Orienteering Problem (CTOP) is a variant of the well-known Team Orienteering Problem where additional capacity limitation constraints are considered for each vehicle. Solving CTOP consists of organizing a set of routes that maximize the total profit collected from the served customers while taking into consideration the capacity and travel time limitation for each vehicle. In this
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