Reduced Variable Neighborhood Search Research Articles

Evaluation of combinatorial algorithms for optimizing highly nonlinear structural problems

Optimizing highly nonlinear structural problems can be very challenging due to the large number of parameters. Classical compliance minimization does not work for such problems. Common optimization algorithms also do not find good solutions. This work evaluates both commonly used optimization algorithms and algorithms not yet used in topology optimization. The algorithms are evaluated using a simple nonlinear problem: minimizing the end displacement of a cantilever beam fixed on one side and loaded by gravity. The global optimum for a coarse mesh grid is computed by simulating nearly 60 million possible topology designs using a Brute-Force search. We use this benchmark to evaluate the computational cost and objective values of known and newly developed optimization methods. The known methods are binary-coded Genetic Algorithm, Simulated Annealing, and Free Shape Optimization. The Reduced Variable Neighborhood Search (RVNS) has not yet been applied to topology optimization. We provide two implementations of RVNS: Breadth-First Search with a limited search depth (BFSL) and with an optional restriction for the size of the simultaneously modified area (TBFSL). According to the benchmark, TBFSL is the most efficient approach. For the optimization on a finer mesh grid, TBFSL is combined with a multi-grid approach to further increase efficiency.

Reaching for upper bound ROUGE score of extractive summarization methods.

The extractive text summarization (ETS) method for finding the salient information from a text automatically uses the exact sentences from the source text. In this article, we answer the question of what quality of a summary we can achieve with ETS methods? To maximize the ROUGE-1 score, we used five approaches: (1) adapted reduced variable neighborhood search (RVNS), (2) Greedy algorithm, (3) VNS initialized by Greedy algorithm results, (4) genetic algorithm, and (5) genetic algorithm initialized by the Greedy algorithm results. Furthermore, we ran experiments on articles from the arXive dataset. As a result, we found 0.59 and 0.25 scores for ROUGE-1 and ROUGE-2, respectively achievable by the approach, where the genetic algorithm initialized by the Greedy algorithm results, which happens to yield the best results out of the tested approaches. Moreover, those scores appear to be higher than scores obtained by the current state-of-the-art text summarization models: the best score in the literature for ROUGE-1 on the same data set is 0.46. Therefore, we have room for the development of ETS methods, which are now undeservedly forgotten.

An improved variable neighborhood search for parallel drone scheduling traveling salesman problem

Parallel drone scheduling traveling salesman problem (PDSTSP) is a typical optimization problem of the truck-drone hybrid delivery system and hardly solved by using meta-heuristics. In this study, an improved variable neighborhood search (IVNS) is presented to minimize the completion time of all vehicles for serving all delivery tasks. Three methods based on the longest processing time (LPT) rule and two reduced variable neighborhood search (RVNS) algorithms are used to produce an initial solution. Then IVNS based on a shaking method and an adaptive RVNS is used to improve the initial solution. 20 neighborhood structures are proposed, 14 of which are used to assign customers between the truck and drones. A number of experiments are conducted on 90 instances. The results reveal that IVNS is very competitive for PDSTSP and obtains state-of-the-art solutions of 12 instances.

A simulation-based approach to design an automated high-mix low-volume manufacturing system

In this paper, we address the profit optimization problem of an automated high-mix low-volume manufacturing system, which originates from a real-world problem at our industry partner. The manufacturing system includes buffer units from which jobs are automatically transported to workstations, i.e., using automated material handling devices. We consider three different automation concepts for the system: (1) a configuration with parallel buffers and a dedicated robot to work them, (2) a configuration that employs shared buffers that are tended to by automated guided vehicles (AGVs), and (3) a proposed hybrid configuration that takes elements of both aforementioned configurations. We propose a simulation-based approach, which uses simulated-annealing (SA), enriched with the reduced variable neighborhood search (RVNS), to determine the best system configuration for a high-mix, low-volume manufacturer. Decisions concern the choice of automation equipment and the capacity of both parallel and shared buffers. We illustrate the efficacy of the proposed hybrid concept and the proposed SA-RVNS approach with an industry case study using real-world data from our industry partner. Our analysis shows that the proposed concept increases the profit by around 10–30% compared to the others, and the AGV travel time plays an important factor in the proposed concept to yield its true potential.

An adaptive artificial bee colony for unrelated parallel machine scheduling with additional resource and maintenance

Unrelated parallel machine scheduling problem with additional resource (UPMSPR) has been extensively considered; however, UPMSPR with preventive maintenance (PM) is seldom studied; on the other hand, artificial bee colony (ABC) has potential advantages on solving UPMSPR with PM because of the extensive applications of ABC to unrelated parallel machine scheduling. In this paper, an adaptive artificial bee colony (AABC) algorithm is proposed to solve UPMSPR with PM and makespan minimization. A new solution representation is presented Evolution quality of population is evaluated and an adaptive onlooker bee phase is implemented, in which multiple search operators including reduced variable neighborhood search (RVNS) are constructed, the number of the used onlooker bees and search operator are dynamically determined. Historical optimization data and a new scout phase are also used. A number of experiments are conducted on 300 instances from the literature. The computational results demonstrate that the new strategies are effective and AABC can provide better results than the algorithms from the literature.

A Reduced Variable Neighborhood Search Approach to the Heterogeneous Vector Bin Packing Problem

The two-dimensional heterogeneous vector bin packing problem (2DHet-VBPP) consists of packing the set of items into the set of various type bins, respecting their two resource limits. The problem is to minimize the total cost of all bins. The problem, known to be NP-hard, can be formulated as a pure integer linear program, but optimal solutions can be obtained by the CPLEX Optimizer engine only for small instances. This paper proposes a metaheuristic approach to the 2DHet-VBPP, based on Reduced variable neighborhood search (RVNS). All RVNS elements are adapted to the considered problem and many procedures are designed to improve efficiency of the method. As the Two-dimensional Homogeneous-VBPP (2DHom-VBPP) is more often treated, we considered also a special version of the RVNS algorithm to solve the 2DHom-VBPP. The results obtained and compared to both CPLEX results and results on benchmark instances from literature, justify the use of the RVNS algorithm to solve large instances of these optimization problems.

A reduced VNS based approach for the dynamic continuous berth allocation problem in bulk terminals with tidal constraints

This paper deals with the problem of allocating berth positions for vessels in export bulk port terminals considering tidal constraints and was first formulated by Ernst et al., (2017). This study investigates the dynamic and continuous berth allocation problem (BAP) with respect to tidal constraints (BAP_TC), and seeks to minimize the total service time of berthed vessels. Since the BAP problem is NP-hard the BAP_TC is also NP-hard. A reduced variable neighborhood search (RVNS) based approach is developed to solve the problem. For parameters tuning a machine learning algorithm is developed and used. Problem instances are benchmarked with CPLEX and the numerical experiments proved that the proposed algorithm is capable of generating high-quality solutions in rather short time. Both small and large-scale instances in the literature are tested to evaluate the metaheuristic effectiveness using other solution approaches from the literature. The computational experiment proves that the proposed algorithm provides state of the art results.

Adaptive and Fault-Tolerant Data Processing in Healthcare IoT Based on Fog Computing

In recent years, healthcare IoT have been helpful in mitigating pressures of hospital and medical resources caused by aging population to a large extent. As a safety-critical system, the rapid response from the health care system is extremely important. To fulfill the low latency requirement, fog computing is a competitive solution by deploying healthcare IoT devices on the edge of clouds. However, these fog devices generate huge amount of sensor data. Designing a specific framework for fog devices to ensure reliable data transmission and rapid data processing becomes a topic of utmost significance. In this paper, a Reduced Variable Neighborhood Search (RVNS)-based sEnsor Data Processing Framework (REDPF) is proposed to enhance reliability of data transmission and processing speed. Functionalities of REDPF include fault-tolerant data transmission, self-adaptive filtering and data-load-reduction processing. Specifically, a reliable transmission mechanism, managed by a self-adaptive filter, will recollect lost or inaccurate data automatically. Then, a new scheme is designed to evaluate the health status of the elderly people. Through extensive simulations, we show that our proposed scheme improves network reliability, and provides a faster processing speed.

Optimization of the Shunting Operation Plan at Electric Multiple Units Depots

The number of standard electric multiple units (EMUs) in China has increased from 1003 in 2013 to 3256 in 2018. For maintaining all EMUs in time, the high-speed rail system with the fast-developing number of EMUs is facing growing pressure. The maintenance and cleaning capacity of an EMU depot can be improved by a better shunting operation planning (SOP). This paper considers an SOP problem at EMU depots, which may have two types of yards, namely, stub-end and through. Every track at an EMU depot has two sections and can accommodate two short standard EMUs of 8 railcars or one long EMU of 16 railcars. As the SOP is currently handled manually by dispatchers, this paper proposes two integer linear programming models for two types of yards for daily planning and dispatching, which aim at minimizing the total delay time of all EMUs during the planning horizon. A Reduced Variable Neighborhood Search (RVNS) algorithm is designed to improve the solution efficiency. The results of the numerical experiment show that the RVNS algorithm can yield an optimal maintenance plan in a few seconds for depots of different layout types and can be applied to a computer-aided planning system. The track utilization rate of the maintenance yard with the stub-end type is higher than that of the through type. The stub-end type may be more suitable for the current schedule, as its total track utilization rate is much lower than the through type.

A modified variable neighborhood search algorithm for manufacturer selection and order acceptance in distributed virtual manufacturing network

In this paper, we investigate a manufacturer selection and composition problem for Distributed Virtual Manufacturing Network (DVMN) with order acceptance and scheduling of deteriorating jobs, where potential manufacturers include proprietary plants and outsourced co-manufacturers. In such a problem, at the beginning of planning horizon, a manufacturing company (MC) receives a series of order requests from its customers. Due to the limited production capacity, the MC is not able to fulfill all the orders requested by the customers on time. To satisfy the order demand as much as possible, one of the MC’s choices is to establish a collaborative manufacturing platform (i.e., DVMN) that leverages the advantages of a social manufacturing network to expand their production capabilities. However, the increase in production capacity does not mean that all orders from the customers are able to be accepted and produced. In fact, standing at the profit-maximizing point of view, the order requests that do not bring profits to the company need to be rejected at the beginning of planning horizon. For the accepted orders, the MC will produce a part of them by its plants in the DVMN, and the remaining orders will be outsourced to other co-manufacturers in the DVMN. Based on some structural properties, we develop a novel Reduced Variable Neighborhood Search (RVNS) algorithm incorporated multiple random mutations neighborhood structures (NSs-MRM) to determine the composition of the DVMN, and further determine which orders should be rejected, which accepted orders should be processed by the company-owned plants in the DVMN, and which accepted orders should be outsourced to the co-manufacturers in the DVMN. In particular, the processing sequence of the orders produced in the company-owned plants are also determined. Finally, a reasonable composition of the DVMN and corresponding order acceptance as well as order scheduling on each member of the DVMN are given. To evaluate the proposed algorithm, a series of computational experiments are conducted, and the experimental results show that the proposed algorithm outperforms other existing baseline algorithms in solving both small-scale and large-scale problem instances.

Minimum common string partition: on solving large‐scale problem instances

AbstractMinimum common string partition is an NP‐hard combinatorial optimization problem from the bioinformatics field. The current state‐of‐the‐art algorithm is a hybrid technique known as construct, merge, solve, and adapt (CMSA). This algorithm combines two main algorithmic components: generating solutions in a probabilistic way and solving reduced subinstances obtained from the tackled problem instances, if possible, to optimality. However, the CMSA algorithm was not intended for application to very large problem instances. Therefore, in this paper we present a technique that makes CMSA, and other available algorithms for this problem, applicable to problem instances that are about one order of magnitude larger than the largest problem instances considered so far. Moreover, a reduced variable neighborhood search (RVNS) for solving the tackled problem, based on integer programming, is introduced. The experimental results show that the modified CMSA algorithm is very strong for problem instances based on rather small alphabets. With growing alphabet size, it turns out that RVNS has a growing advantage over CMSA.

Bike-Sharing Static Rebalancing by Considering the Collection of Bicycles in Need of Repair

Bike-sharing systems, which are used in many cities worldwide, need to maintain a balance between the availability of bicycles and the availability of unoccupied bicycle slots. This paper presents an investigation of the net flow of each bike-sharing station in Jersey City. The data was recorded at 1-minute intervals. The sum of the initial bicycle number and the minimum net flow value was determined to be the demand for static rebalancing, and this led to the proposal of a bike-sharing demand prediction method based on autoregressive integrated moving average models. Considering that the existence of bicycles in a state of disrepair may adversely affect demand prediction and routine planning, we present an integer linear programming formulation to model bike-sharing static rebalancing. The proposed formulation takes into account the problem introduced by the need to collect bicycles in need of repair. A hybrid Discrete Particle Swarm Optimization (DPSO) algorithm was proposed to solve the model, which incorporates a reduced variable neighborhood search (RVNS) functionality together with DPSO to improve the global optimization performance. The effectiveness of the algorithms was verified by a detailed numerical example.

A Two-Phase Optimization Method for Solving the Multi-Type Maximal Covering Location Problem in Emergency Service Networks

This study introduces the Multi-Type Maximal Covering Location Problem (MTMCLP) that arises from the design of emergency service networks, and represents a generalization of the well-known Maximal Covering Location Problem (MCLP). Differently from the basic MCLP, several types of incidents and emergency units are considered and hierarchy of emergency units of different types is assumed in the MTMCLP. The numbers of available emergency units of each type are limited to some constants. The objective of the MTMCLP is to choose locations for establishing emergency units of each type, such that the total number of covered incidents is maximized. In order to provide a decision maker with optimal solutions in an efficient manner, a two-phase optimization approach to the MTMCLP is designed. In the first phase, a variant of Reduced Variable Neighborhood Search (RVNS) is applied to quickly find a high-quality solution. The obtained RVNS solution is used as a good starting point for the Linear Programming method in the second phase, which returns the optimal solution to the MTMCLP. All constructive elements of the proposed two-phase method, denoted as RVNS-LP, are adapted to the characteristics of the considered problem. The RVNS-LP approach is evaluated on real-life instances obtained from two networks of police units in Montenegro and Serbia, and randomly generated test instances of larger dimensions. Experimental evaluation shows that the proposed RVNS-LP reached all optimal solutions on all real-life test instances in very short CPU time. On generated test instances, the RVNS-LP also returned optimal solutions in all cases, within short running times and significant time savings compared to CPLEX solver. The mathematical model and the proposed two-phase optimization method may be applicable in the design and management of various emergency-service networks.DOI: http://dx.doi.org/10.5755/j01.itc.46.1.13853

Hybrid metaheuristic method for solving a multi-period emergency service location problem

This study deals with the problem of establishing the network of emergency service units. The goal of the basic problem proposed in the literature is to locate certain number of units at given discrete points of the region and to allocate cities to established units, in order to balance the load of established emergency units. Having in mind that emergency units work in shifts, we extend the basic model to a multi-period model and involve additional constraints on the number of units to be located. Since, in practice, the number of emergency incidents varies on daily or monthly basis, we consider the uncertainty of the number of incidents and propose a robust integer programming formulation of the multi-period model, which controls the deviation of objective value under uncertainty of input data. In order to solve both deterministic and robust variant of the problem, we design an efficient hybrid metaheuristic method based on combination of Particle Swarm Optimization method (PSO) and Reduced Variable Neighborhood Search (RVNS). Computational results show that the proposed hybrid PSO-RVNS method quickly reaches all known optimal solutions obtained by CPLEX solver, and provides solutions for instances that remained out of reach of CPLEX. In the single-period case, PSO-RVNS outperforms existing metaheuristic method from the literature in the sense of CPU time. Short running times of PSO-RVNS and high-quality solutions indicate the efficiency of the proposed hybrid metaheuristic approach when solving the considered problem. Results presented in this study may help security experts and emergency managers to design an efficient and sustainable emergency system.DOI: http://dx.doi.org/10.5755/j01.itc.45.3.14041

Mobile big data fault-tolerant processing for ehealth networks

In daily life, people tend to use mobile networks for more accurate overall data. With intelligent mobile devices, almost all kinds of data can be collected automatically, which contributes directly to the blooming of eHealth. However, large amounts of data are also leading us into the era of big data, in which new data collection, transmission, and processing techniques are required. To ensure ubiquitous data collection, the scale of mobile eHealth networks has to be expanded. Also, networks will face more pressure to transmit large amounts of eHealth data. In addition, because the processing time increases with data volume, even powerful processors cannot always be regarded as efficient for big data. To solve these problems, in this article, an interests-based reduced variable neighborhood search (RVNS) queue architecture (IRQA) is proposed. In this three-layer architecture, a fault-tolerant mechanism based on interests matching is designed to ensure the completeness of eHealth data in the data gathering layer. Then the data integrating layer checks the accuracy of data, and also prepares for data processing. In the end, an RVNS queue is adopted for rapid data processing in the data analyzing layer. After processing with relevant rules, only valuable data will be reported to health care providers, which saves their effort to identify these data. Simulation shows that IRQA is steady and fast enough to process large amounts of data.

Variable neighborhood search for the pharmacy duty scheduling problem

In this paper, we study on the Pharmacy Duty Scheduling (PDS) problem, where a subset of pharmacies should be on duty on national holidays, at weekends and at nights in order to be able to satisfy the emergency drug needs of the society. PDS problem is a multi-period p-median problem with special side constraints and it is an NP-Hard problem. We propose four Variable Neighborhood Search (VNS) heuristics. The first one is the basic version, BVNS. The latter two, Variable Neighborhood Decomposition Search (VNDS) and Variable Neighborhood Restricted Search (VNRS), aim to obtain better results in less computing time by decomposing or restricting the search space. The last one, Reduced VNS (RVNS), is for obtaining good initial solutions rapidly for BVNS, VNDS and VNRS. We test BVNS, VNRS and VNDS heuristics on randomly generated instances and report the computational test results. We also use VNS heuristics on real data for the pharmacies in central İzmir and obtain significant improvements.

Combining intensification and diversification strategies in VNS. An application to the Vertex Separation problem

The Vertex Separation problem (VSP) is an NP-hard problem with practical applications in VLSI design, graph drawing and computer language compiler design. VSP belongs to a family of optimization problems in which the objective is to find the best separator of vertices or edges in a generic graph. In this paper, we propose different heuristic methods and embed them into a Variable Neighborhood Search scheme to solve this problem. More precisely, we propose (i) a constructive algorithm, (ii) four shake procedures, (iii) two neighborhood structures, (iv) efficient algorithmic strategies to explore them, (v) an extended version of the objective function to facilitate the search process and finally, (vi) we embed these strategies in a Reduced Variable Neighborhood Search (RVNS), a Variable Neighborhood Descent (VND) and a General Variable Neighborhood Search (GVNS). Additionally, we provide an extensive experimental comparison among them and with the best previous method of the literature. We consider three different benchmarks, totalizing 162 representative instances. The experimentation reveals that our best procedure (GVNS) improves the state of the art in both quality and computing time. This fact is confirmed by non-parametric statistical tests. In addition, when considering only the largest instances, the other two proposed variants (RVNS and VND) also obtain statistically significant differences with respect to the best previous method identified in the state of the art.

Variable neighborhood search method for optimizing the emergency service network of police special forces units

Abstract In this study we consider mathematical models that arise from the problem of establishing and optimally utilizing the network of Police Special Forces Units (PSFUs) in the Republic of Serbia. The goal is to locate certain number of PSFUs in the given network in order to minimize the maximal load of established emergency units. We have designed an efficient metaheuristic method, based on the combination of the Reduced Variable Neighborhood Search (RVNS) and the basic Variable Neighborhood Search (VNS). The proposed approach, named RVNS-VNS, involves two efficient improvement procedures: Local Search Deletion and Steepest-descent Hill Climbing, which help the algorithm converge to high-quality solutions. Computational experiments show that the proposed RVNS-VNS method quickly reaches all known optimal solutions. Furthermore, it provides solutions for the instances that CPLEX solver was unable to obtain.

Multi-objective optimization with fuzzy measures and its application to flow-shop scheduling

Most of the research in multi-objective scheduling optimization uses the classical weighted arithmetic mean operator to aggregate the various optimization criteria. However, there are scheduling problems where criteria are considered interact and thus a different operator should be adopted. This paper is devoted to the search of Pareto-optimal solutions in a tri-criterion flow-shop scheduling problem (FSSP) considering the interactions among the objectives. A new hybrid meta-heuristic is proposed to solve the problem which combines a genetic algorithm (GA) for solutions evolution and a reduced variable neighborhood search (RVNS) technique for fast solution improvement. To deal with the interactions among the three criteria the discrete Choquet integral method is adopted as a means to aggregate the criteria in the fitness function of each individual solution. Experimental comparisons (over public available FSSP test instances) with five existing multi-objective evolutionary algorithms (including the well known SPEA2 and NSGAII algorithms as well as the recently published L-NSGA algorithm) showed a superior performance for the developed approach in terms of diversity and domination of solutions.

A reduced variable neighborhood search algorithm for uncapacitated multilevel lot-sizing problems

Multilevel lot-sizing (MLLS) problems, which involve complicated product structures with interdependence among the items, play an important role in the material requirement planning (MRP) system of modern manufacturing/assembling lines. In this paper, we present a reduced variable neighborhood search (RVNS) algorithm and several implemental techniques for solving uncapacitated MLLS problems. Computational experiments are carried out on three classes of benchmark instances under different scales (small, medium, and large). Compared with the existing literature, RVNS shows good performance and robustness on a total of 176 tested instances. For the 96 small-sized instances, the RVNS algorithm can find 100% of the optimal solutions in less computational time; for the 40 medium-sized and the 40 large-sized instances, the RVNS algorithm is competitive against other methods, enjoying good effectiveness as well as high computational efficiency. In the calculations, RVNS updated 7 (17.5%) best known solutions for the medium-sized instances and 16 (40%) best known solutions for the large-sized instances.