Hybrid Variable Neighborhood Search Algorithm Research Articles

Integrated optimization of worker assignment, batch splitting and scheduling for a hybrid assembly line-seru production system

This paper studies an integrated optimization problem of worker assignment, batch splitting and scheduling for a hybrid assembly line-seru production system, which includes multiple serus and a short assembly line. The worker assignment involves assigning each multi-skilled worker to a seru or the assembly line, and assigning the worker tasks to perform. The batch splitting determines the number of subbatches split for each batch, the size of each subbatch, and in which seru the production of each subbatch is performed. The batch scheduling aims to optimize the process sequences of batches in serus and on the assembly line. For the integrated optimization problem, we propose a mixed-integer nonlinear programming model that minimizes the makespan. To solve large-scale instances of the problem, we develop a tailored hybrid variable neighborhood search algorithm with a four-layer solution coding scheme and four novel neighborhood structures. We validate the effectiveness of our model and solution algorithm on two sets of instances. The experimental results indicate that the proposed hybrid metaheuristic achieves a significant reduction in makespan, with an improvement of up to 20.94% compared to the solutions obtained by the Gurobi optimization solver within one hour. In addition, we demonstrate the advantage of optimized batch splitting by comparing it with no batch splitting and equal-size batch splitting strategies in the literature.

The paired pickup and delivery problem with profit in a two-echelon delivery system with multiple trucks and drones

ABSTRACT Recently researchers proposed truck and drone coordination to increase delivery efficiency and suggested various truck-drone routing problems. In this paper, we also focused on truck and drone coordination and introduced the paired pickup and delivery problem with profit in a two-echelon delivery system. To solve the problem, we propose a hybrid variable neighborhood search algorithm. For this algorithm, we adapted existing neighborhood search operators from the literature and considering the structure of the proposed problem, developed new neighborhood search operators. Also, we have carried out numerous computational experiments to evaluate the proposed solution methods’ performance, where the results show the efficiency of the proposed algorithms. The results highlight that in the paired pickup and delivery problem, for small values of drone operational costs, employing the two-echelon truck and drone routing system increases the profit by up to 5.6 percent in comparison to the vehicle routing system with drones.

Optimal allocation with known covariates into two treatments under generalized linear model through Hybrid VNS algorithm

The problem of optimal allocation of experimental units with known covariates into several treatment groups under linear ANCOVA model has already been discussed by the same authors and the optimal allocation design has been derived through an efficient algorithm named as hybrid variable neighborhood search (VNS) algorithm. In this work, we have addressed the same issue with regard to D- and A-optimality under the generalized linear model (GLM) set-up, assuming the response variable to be count or binary. The issue of mean covariate balance is also addressed through a constrained optimal approach in which a compromise between balance and optimality is maintained. The performance of the optimal and constrained optimal allocation design obtained through the hybrid VNS algorithm, under various GLM frameworks, has been investigated through simulation studies and real-life example.

An adaptive hybrid variable-large neighborhood search algorithm for profit maximization problem in designing production lines

Designing production lines is an important research issue for both academy as well as industry since it should consider both system efficiency and production costs. In this study, the buffer allocation problem (BAP) is solved to maximize the profit of unreliable lines. The profit of a production line is a function of the throughput and various costs, such as WIP, holding and buffer space costs. In this respect, a crucial decision-making problem, i.e., BAP, is how to allocate the finite buffers to deal with the trade-off between maximizing the throughput of the line and production costs. In this study, profit maximization problem is formulated considering two different objective functions and solved under different constraint sets. An adaptive hybrid variable neighborhood search algorithm that incorporates large neighborhood search as a part of the diversification strategy is proposed to solve the problem. Moreover, a new initialization procedure based on the buffer location providing more profit is proposed to reduce the search effort. The efficiency of the proposed algorithm is tested on existing benchmark problems as well as the newly introduced large-sized data sets. In addition to these experiments, a comprehensive experimental design is conducted to determine the influencing factors on the problem at hand. The experimental study reveals that the proposed solution algorithm is capable to solve the profit maximization problem for large production lines, and the number of machines and the reliability parameters are the most influential factors in solving the BAP for profit maximization. Moreover, it has been observed that the proposed initialization procedure significantly reduces the search effort.

Ring hierarchical hub network design problem: Exact and heuristic solution methods

In this paper, we study the hierarchical hub network design problem with a ring-star-star structure. In this problem, the hubs are located in two layers. In the first layer, central hubs (main hubs) are located in a ring structure, and in the second layer, secondary hubs are located. Each secondary hub is allocated to a central hub. Other demand points can also be allocated to each of these hubs (central hubs or secondary hubs). The objective is to minimize transportation costs on the network. This problem applies to communication networks when establishing direct links between demand nodes is not cost-effective, and there are two levels of service for customers. Also, the ring structure is used to reduce costs associated with full communication between central hubs. We present a mixed-integer programming model for the problem and report the results of the problem solving for a numerical example on the CAB dataset. Also, we present three solution methods for the problem. First, by exploiting the decomposable structure of the proposed model, we introduce an accelerated Benders decomposition algorithm. Then, we present a hybrid genetic algorithm that uses the Dijkstra algorithm to evaluate solutions. Next, we present a hybrid variable neighborhood search algorithm that uses the Dijkstra algorithm to calculate the cost of each solution. Also, a relax-and-fix algorithm is proposed to find near-optimal feasible solutions for the problem. Computational results are presented on the USA423 dataset. The results show that the proposed relax-and-fix algorithm can solve instances with up to 100 nodes. Also, the proposed hybrid algorithms can solve instances with up to 423 nodes. The performance of the proposed hybrid variable neighborhood search is better than the proposed hybrid genetic algorithm in solving large-sized instances. • The paper formulates the hierarchical hub network using a ring structure for the central hubs. • Minimizing the total flow cost while satisfying the demands is considered. • The paper develops solution methodologies based on Accelerated Benders Decomposition, Hybrid Genetic Algorithm, and Hybrid Variable Neighborhood Search Algorithm. • The paper presents computational studies using CAB and USA423 datasets.

Outsourcing and rescheduling for a two-machine flow shop with the disruption of new arriving jobs: A hybrid variable neighborhood search algorithm

This paper addresses the two-machine flow shop outsourcing and rescheduling problem (TFSORP) in response to an unexpected arrival of new jobs, in which each job can be processed in the in-house shop or outsourced to a single subcontractor. An efficient schedule is constructed for the in-house jobs, and its performance is measured by the makespan. Each of the outsourced jobs requires paying an outsourcing cost. The objective is to minimize the sum of the in-house makespan and total outsourcing cost, subject to a limit on the number of original jobs processed by the subcontractor after disruption. To solve this NP-hard problem, a mixed integer programming formulation and helpful optimization properties are first established, and then a hybrid variable neighborhood search algorithm (HVNS) that incorporates one-dimensional array representation and three problem-specific neighborhood structures is developed. At the initialization stage, either of two well-designed constructive heuristics, i.e., job addition heuristic (JAH) and job removal heuristic (JRH) is employed. At the iterative search stage, an adaptive best-improvement procedure based on three neighborhood structures is devised to play the role of local search. An extensive experimental calibration for main factors in HVNS is carried out by a full-factorial design of experiments. To validate the calibrated HVNS, computational experiments are conducted upon the test suite of various instances. As demonstrated in the results, the calibrated HVNS gains the advantage over both the commercial optimizer and exhaustive approach in terms of computation time for small-sized instances, while it also outperforms an existing meta-heuristic algorithm and two canonical ones (based on the principles of genetic algorithm and simulated annealing) in the optimizing capability.

A hybrid variable neighborhood search algorithm for the hot rolling batch scheduling problem in compact strip production

Abstract This paper deals with a hot rolling batch scheduling (HRBS) problem arising from the compact strip production (CSP) process, which is one of the most popular production systems in the modern iron-steel industry to produce sheet strips. The HRBS problem aims to determine a sequence of the sheet strips in a predetermined number of rolling turns with the objective of minimizing average thickness change. In this paper, a mathematical model based on a comprehensive investigation is first given. Then a constructive heuristic based on the problem-specific characteristics is presented to generate initial feasible solutions. The heuristic can guarantee a minimum number of rolling turns to accommodate all the ordered sheet strips, but generally performs poorly in the objective of average thickness change. To improve the objective, a hybrid variable neighborhood search algorithm (HVNS) is proposed. In the HVNS, a thickness value permutation is used to encode the solution and four neighborhood structures are well designed. The fruit fly optimization algorithm, is integrated to improve search efficiency. Correspondingly, a neighborhood switching strategy is developed to improve local search ability. Moreover, a restart strategy based on the block swapping operator is used to help the algorithm escape from local optima. To investigate the effectiveness of the solution approach, two sets of instances are tested, including real-world instances and randomly generated instances. The performance of the proposed HVNS is evaluated by comparing with the other existing algorithms and the experimental results demonstrate that the proposed algorithm performs much better.

A hybrid variable neighborhood search algorithm for targeted offers in direct marketing

This paper focuses on the targeted offers problem in direct marketing campaigns. The main objective is to maximize the feedback of customers purchases, offering products for the set of customers with the highest probability of positively accepting the offer and, at the same time, minimizing the operational costs of the campaign. Given the combinatorial nature of the problem and the large volume of data, involving instances with up to one million customers, approaches solely based on mathematical programming methods, said exact, appear limited and infeasible. In this paper, the use of a hybrid heuristic algorithm, based on the Greedy Randomized Adaptive Search Procedures and General Variable Neighborhood Search, is proposed. Computational experiments performed on a set of test problems from the literature show that the proposed algorithm was able to produce competitive solutions.

A hybrid variable neighborhood search algorithm for solving the limited-buffer permutation flow shop scheduling problem with the makespan criterion

This paper investigates the limited-buffer permutation flow shop scheduling problem (LBPFSP) with the makespan criterion. A hybrid variable neighborhood search (HVNS) algorithm hybridized with the simulated annealing algorithm is used to solve the problem. A method is also developed to decrease the computational effort needed to implement different types of local search approaches used in the HVNS algorithm. Computational results show the higher efficiency of the HVNS algorithm as compared with the state-of-the-art algorithms. In addition, the HVNS algorithm is competitive with the algorithms proposed in the literature for solving the blocking flow shop scheduling problem (i.e., LBPFSP with zero-capacity buffers), and finds 54 new upper bounds for the Taillard's benchmark instances.

A hybrid variable neighborhood search algorithm for solving multi-objective flexible job shop problems

In this paper, we propose a novel hybrid variable neighborhood search algorithm combining with the genetic algorithm (VNS+GA) for solving the multi-objective flexible job shop scheduling problems (FJSPs) to minimize the makespan, the total workload of all machines, and the workload of the busiest machine. Firstly, a mix of two machine assignment rules and two operation sequencing rules are developed to create high quality initial solutions. Secondly, two adaptive mutation rules are used in the hybrid algorithm to produce effective perturbations in machine assignment component. Thirdly, a speed-up local search method based on public critical blocks theory is proposed to produce perturbation in operation sequencing component. Simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is concluded that the proposed VNS+GA algorithm is superior to the three existing algorithms, i.e., AL+CGA algorithm, PSO+SA algorithm and PSO+TS algorithm, in terms of searching quality and efficiency.