Variable Neighborhood Algorithm Research Articles

A lightweight genetic algorithm with variable neighborhood search for multi-depot vehicle routing problem with time windows

A multi-depot green vehicle routing problem with time windows considering customer satisfaction (MDGVRPTW-CS) was constructed. To optimize it, we proposed a lightweight genetic algorithm with variable neighborhood search (LGAVNS), which takes the genetic algorithm as the upper algorithm and the variable neighborhood algorithm as the neighborhood search algorithm. In our constructed algorithm, the neighborhood search operators were applied to the optimal gene instead of all genes. Additionally, an enhanced crossover operator was developed to facilitate the effective transmission of gene information. Finally, we extended and optimized the parameters of the algorithm. Experimental results demonstrated that our approach exhibited significant advantages in terms of optimization effectiveness and convergence speed compared to six state-of-the-art algorithms, particularly when addressing MDGVRPTW-CS in large-scale scenarios. Moreover, the lightweight nature of our algorithm can be utilized for optimizing both the number and location of depots while further exploring the optimization potential of MDGVRPTW-CS.

Exact likelihood ratio and Wald tests for the balanced joint progressive censoring scheme

AbstractRecently, the balanced joint progressive censoring (BJPC) for two samples has been introduced by the authors. This work comprises of developing testing of hypothesis for the BJPC scheme. An exact likelihood ratio test is developed to test the ratio of mean life times of two different products through joint life testing experiment. Along with this development, the Wald test has also been discussed, and an approximate power function has been used for power computation. Along with the study of testing of hypothesis, the optimal censoring scheme based on the power of the proposed tests has been determined. Meta heuristic variable neighborhood algorithm is exploited in this optimization task.

Dynamic scheduling method for integrated process planning and scheduling problem with machine fault

• A pre-reactive scheduling method is designed to deal with the integrated process planning and scheduling problem with machine fault. • The process adjustment method based on jobs classification is proposed to improve the stability of rescheduling. • Two hybrid algorithms combining genetic algorithm and variable neighborhood algorithm are proposed to solve the initial scheduling scheme and the rescheduling scheme respectively. The integration of process planning and job shop scheduling is of great significance to improve the performance of manufacturing system. Many studies on integrating process planning and scheduling problem focused on static workshop environment. However, there are a lot of uncertain factors in the workshop that need to be dealt with. Therefore, this paper studies the dynamic scheduling method of dynamic integrated process planning and scheduling problem under machine fault. To solve the dynamic integrated process planning and scheduling problem, two hybrid algorithms combining genetic algorithm with neighborhood search algorithm are designed. To improve the stability of rescheduling scheme, according to the characteristics of sequencing flexibility, processing flexibility and machine flexibility of the integrated process planning and scheduling problem, a process adjustment method based on job classification is proposed. To dynamically adjust the diversity and convergence of population, an adaptive hierarchical migration strategy is proposed. To decode dynamic scheduling scheme, the greedy decoding method is improved. The designed dynamic scheduling method is applied to the famous benchmark to verify the effectiveness of the proposed method in solving the dynamic integrated process planning and scheduling problem with machine fault.

Optimization of a molten iron scheduling problem with uncertain processing time using variable neighborhood search algorithm

Punctuality of the steel-making scheduling is important to save steel production costs, but the processing time of the pretreatment process, which connects the iron- and steel-making stages, is usually uncertain. This paper presents a distributionally robust iron-steel allocation (DRISA) model to obtain a robust scheduling plan, where the distribution of the pretreatment time vector is assumed to belong to an ambiguity set which contains all the distributions with given first and second moments. This model aims to minimize the production objective by determining the iron-steel allocation and the completion time of each charge, while the constraints should hold with a certain probability under the worst-case distribution. To solve problems in large-scale efficiently, a variable neighborhood algorithm is developed to obtain a near-optimal solution in a short time. Experiments based on actual production data demonstrate its efficiency. Results also show the robustness of the DRISA model, i.e., the adjustment and delay of the robust schedule derived from the DRISA model are less than the nominal one.

A VNS Algorithm for PID Controller: Hardware-In-The-Loop Approach

Tuning the Proportional Integral Derivative, or PID, controller in cyber-physical systems is a major challenge as it requires advanced mathematical skills. Several authors in the literature have shown that optimization algorithms are efficient for auto-adjust PID controller constants, especially when there is no mathematical modeling. However, the literature lacks works that show the efficiency of the Variable Neighborhood Search (VNS) algorithm to auto-adjust the PID. In this work, we investigate the efficiency of the Variable Neighborhood Algorithm to fine-tune a PID controller of a real cyber physical-system: a birotor flying drone. The approach consists of applying a numerical neighborhood structure to optimize the three constants of the PID, according to a proposed fitness function. Experiments reveal the feasibility of fine-tuning the PID controller and the birotor balancing with the Variable Neighborhood Algorithm with reduced time. We compared the VNS-approach against one based on genetic algorithms, and on average, the VNS-approach achieves better results with lower computational and memory costs. Results suggest that the approach may be used in real or commercial systems, helping to fine-tune the controller to new environment changes or even last-minute project modifications.

Low Carbon Multi-Objective Shop Scheduling Based On Genetic and Variable Neighborhood Algorithm

Aiming at the low-carbon scheduling problem of flexible job shop, the carbon emission and key scheduling objectives of machining system are studied. Firstly, the carbon emissions directly generated by machines and indirectly generated by waste treatment are considered in the calculation of carbon emissions, and then a multi-objective optimization model is established with completion time, total machine load and carbon emissions as the objective function; secondly, the hybrid algorithm of genetic variable neighborhood is designed to solve the optimization model, and the completion time, total machine load. Finally, the production example is simulated in Matlab environment. The experimental results show that the multi-objective scheduling model is feasible and effective. According to the production situation of the workshop, the producer can allocate the weight of each objective, and reduce the carbon emissions in the production process on the premise of ensuring the balance of the completion time and equipment utilization.

A Noise Suppression Method for Underwater Vehicle Flow

Tian, J., 2019. A noise suppression method for underwater vehicle flow. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 125–128. Coconut Creek (Florida), ISSN 0749-0208.In order to ensure the compatibility and balance of low noise and small leakage of underwater vehicle flow seal, a new method of underwater vehicle flow noise suppression based on Bivariate Function Algorithms (BFA) is proposed. The algorithm combines the variable neighborhood algorithm with underwater vehicle flow noise suppression to overcome the shortcomings of traditional underwater vehicle flow noise suppression. Finally, the algorithm and VNM algorithm are used to suppress underwater vehicle flow noise. The simulation results show that the algorithm can quickly suppress the noise suppression of underwater vehicle in the working process of flow seal.

Variable neighborhood search algorithms for the permutation flowshop scheduling problem with the preventive maintenance

This paper addresses to the permutation flowshop scheduling problem while considering the preventive maintenance in the non-resumable case. The criterion to be optimized is the makespan. Two variable neighborhood search algorithms are proposed. In the first algorithm, only one initial solution is generated according to a constructive heuristic. In the second algorithm, a learning process using a probabilistic model is introduced to the variable neighborhood algorithm in order to generate the initial solution. The computational results show the high performance of the proposed algorithms according to the compared approaches. Besides, the change of the initial solution during the optimization procedure may improve the performance of the variable neighborhood search algorithm.

<b>A genetic algorithm and variable neighborhood search for the unrelated parallel machine scheduling problem with sequence dependent setup time

This paper presents the evaluation of two metaheuristics to solve the Unrelated Parallel Machine Scheduling Problem with Sequence Machine Dependent Setup Time. Considering such a problem, there is no relation between the time to process each task and the machine; and this is why the machines are referred to as unrelated. Furthermore, the setup time between the executions of two tasks depends on both, the task sequence and its associated machine. A metaheuristic genetic algorithm and a variable neighborhood search were used in order to solve the problem due to the difference among their characteristics. The maximal time for the schedule to be completed, also called makespan, was the performance measure used to evaluate the solutions. The results obtained by both metaheuristics were directly compared according to their performance to try to reduce this makespan. The results showed that the variable neighborhood algorithm search outperformed the genetic algorithm regarding the solutions quality and execution time.

A variable neighborhood search for the network design problem with relays

Given a set of commodities to be routed over a network, the network design problem with relays involves selecting a route for each commodity and determining the location of relays where the commodities must be reprocessed at certain distance intervals. We propose a hybrid approach based on variable neighborhood search. The variable neighborhood algorithm searches for the route for each commodity and the optimal relay locations for a given set of routes are determined by an implicit enumeration algorithm. We show that dynamic programming can be used to determine the optimal relay locations for a single commodity. Dynamic programming is embedded into the implicit enumeration algorithm to solve the relay location problem optimally for multiple commodities. The special structure of the problem is leveraged for computational efficiency. In the variable neighborhood search algorithm, the routes of the current solution are perturbed and reconstructed to generate neighbor solutions using random and greedy construction heuristics. Computational experiments on three sets of problems (80 instances) show that the variable neighborhood search algorithm with optimal relay allocations outperforms all existing algorithms in the literature.

Carbon reduction in the location routing problem with heterogeneous fleet, simultaneous pickup-delivery and time windows

Along with the concerns about global climate change, more and more researchers pay attention to the low carbon logistics. Since the logistics industry is an important source of carbon emissions, it is becoming more and more important to reduce carbon emissions in logistics operation. In this paper, we study a low carbon for location routing problem with heterogeneous fleet, simultaneous pickup-delivery and time windows and design a two-phased hybrid heuristic algorithm to solve the problem. Firstly, we introduce the concept of temporal-spatial distance and use genetic algorithm to cluster the customer points to construct the initial path. Then, we use variable neighborhood search algorithm for local search. By incorporating the idea of simulated annealing algorithm into the framework of variable neighborhood algorithm, the global optimization ability of the algorithm is improved. At the same time, the vehicle adjustment strategy is added in the optimization process. The computational experiments are implemented to investigate the performance of the proposed heuristic algorithm. Computational results show that the initial solution considering temporal-spatial distance has obvious advantages in the efficiency of the algorithm and the quality of the solution.

Demand Response Control of a Townhouse Thermal Storage by Greedy Variable Neighborhood Algorithm

Residential buildings energy cost optimization requires demand response (DR) scheduling when employing electric storage space heating in a market that hourly tariff electricity price is applied for customers. Thus, a suitable optimization algorithm for the thermal storage controller causes the household energy expenditure for heating purpose to be minimized. This study proposes greedy variable neighborhood algorithm (GVNA) that is composed of greedy algorithm and variable neighborhood search as the DR control algorithm. The case study outcomes illustrate that the daily heating electricity price decreases by 3,5 % to 11,4 % and monthly heating electricity cost reduces by 9 % to 20 % when employing different storage degrees from 10 % to 100 % of the total daily or monthly heat demand, respectively. Furthermore, this method enables both dayahead and realtime scheduling of a residential thermal storage. Thus, GVNA can be a feasible approach at least for residential houses energy cost minimization problem due to its simplicity and small computational burden compared to other techniques in the literature.

A New Recursive Dynamic Lot-Sizing Model with Multi-Level Discount

<p><em>The optimal solution of dynamic lot-sizing problem with multi-level discount is solved by integer programming model in the past papers. However, a complex computation and a large computer memory are generated.</em><em> Due to the complexity and the big computer memory, the heuristic approaches and the variable neighborhood algorithm are usually adopted in the large-scale multi-level lot-sizing problem. This paper develops a model with a recursive relation between the adjacent periods, and to obtain an optimal solution when multi-level discount is considered. Four types of the feasible policies in the </em><em>D</em><em>ynamic </em><em>L</em><em>ot-</em><em>S</em><em>izing </em><em>m</em><em>odel with multi-level discount are classified to develop the recursive relations. A few properties, theorems and algorithms are developed to show the recursion between the adjacent periods. The number of addition items will significantly be reduced. The recursive algorithm can significantly decrease the computational entries, comparison entries and computer memory and improve the computation efficiency. </em></p>

A parallel competitive colonial algorithm for JIT flowshop scheduling

This paper proposes two parallel algorithms which are improved by heuristics for a bi-objective flowshop scheduling problem with sequence-dependent setup times in a just-in-time environment. In the proposed algorithms, the population will be decomposed into the several sub-populations in parallel. Multiple objectives are combined with min–max method then each sub-population evolves separately in order to obtain a good approximation of the Pareto-front. After unifying the obtained results, we propose a variable neighborhood algorithm and a hybrid variable neighborhood search/tabu search algorithm to improve the Pareto-front. The non-dominated sets obtained from our proposed algorithms, a genetic local search and restarted iterated Pareto greedy algorithm are compared. It is found that most of the solutions in the net non-dominated front are yielded by our proposed algorithms.