Neighborhood Search Technique Research Articles

Bacterial foraging optimization algorithm in robotic cells with sequence-dependent setup times

In this paper, we propose an improved discrete bacterial foraging algorithm to determine the optimal sequence of parts and robot moves in order to minimize the cycle time for the 2-machine robotic cell scheduling problem with sequence-dependent setup times. We present a method to convert the solutions from continuous to discrete form. In addition, two neighborhood search techniques are employed to updating the positions of each bacterium during chemotaxis and elimination–dispersal operations in order to accelerate the search procedure and to improve the solution. Moreover, a multi-objective optimization algorithm based on NSGA-II combined with the response surface methodology and the desirability technique is applied to tune the parameters as well as to enhance the convergence speed of the proposed algorithm. Finally, a design of experiment based on central composite design is used to determine the optimal settings of the operating parameters of the proposed algorithm. The results of the computational experimentation with a large number of randomly generated test problems demonstrate that the proposed method is relatively more effective and efficient than the state-of-the-art algorithms in minimizing the cycle time in the robotic cell scheduling.

Realizing Energy Savings in Integrated Process Planning and Scheduling

The integration of scheduling and process planning can eliminate resource conflicts and hence improve the performance of a manufacturing system. However, the focus of most existing works is mainly on the optimization techniques to improve the makespan criterion instead of more efficient uses of energy. In fact, with a deteriorating global climate caused by massive coal-fired power consumption, carbon emission reduction in the manufacturing sector is becoming increasingly imperative. To ease the environmental burden caused by energy consumption, e.g., coal-fired power consumption in use of machine tools, this research considers both makespan as well as environmental performance criteria, e.g., total power consumption, in integrated process planning and scheduling using a novel multi-objective memetic algorithm to facilitate a potential amount of energy savings; this can be realized through a better use of resources with more efficient scheduling schemes. A mixed-integer linear programming (MILP) model based on the network graph is formulated with both makespan as well as total power consumption criteria. Due to the complexity of the problem, a multi-objective memetic algorithm with variable neighborhood search (VNS) technique is then developed for this problem. The Kim’s benchmark instances are employed to test the proposed algorithm. Moreover, the TOPSIS decision method is used to determine the most satisfactory non-dominated solution. Several scenarios are considered to simulate different machine automation levels and different machine workload levels. Computational results show that the proposed algorithm can strike a balance between the makespan criterion and the total power consumption criterion, and the total power consumption can be affected by machine tools with different automation levels and different workloads. More importantly, results also show that energy saving can be realized by completing machining as early as possible on a machine tool and taking advantage of machine flexibility.

A Parallel Heuristic Method for Optimizing a Real Life Problem (Agricultural Land Investment Problem)

This paper proposed a parallel method for solving the Agricultural Land Investment Problem (ALIP), the problem that has an important impact on the agriculture issues. The author is first represent mathematically the problem by introducing a mathematical programming model. Then, a parallel method is proposed for optimizing the problem. The proposed method based on principles of parallel computing and neighborhood search methods. Neighborhood search techniques explore a series of solutions spaces with the aim of finding the best one. This is exploited in parallel computing, where several search processes are performed simultaneously. The parallel computing is designed using Message Passing Interface (MPI) which allows to build a flexible parallel program that can be executed in multicore and/or distributed environment. The method is competitive since it is able to solve a real life problem and yield high quality results in a fast solution runtime.

A hybrid heuristic for the multi-activity tour scheduling problem

This work investigates the tour scheduling problem with a multi-activity context, a challenging problem that often arises in personnel scheduling. We propose a hybrid heuristic, which combines tabu search and large neighborhood search techniques. We present computational experiments on weekly time horizon problems dealing with up to five work activities. The results show that the proposed approach is able to find good quality solutions.

TOPAZ: asymmetric suffix array neighbourhood search for massive protein databases

BackgroundProtein homology search is an important, yet time-consuming, step in everything from protein annotation to metagenomics. Its application, however, has become increasingly challenging, due to the exponential growth of protein databases. In order to perform homology search at the required scale, many methods have been proposed as alternatives to BLAST that make an explicit trade-off between sensitivity and speed. One such method, SANSparallel, uses a parallel implementation of the suffix array neighbourhood search (SANS) technique to achieve high speed and provides several modes to allow for greater sensitivity at the expense of performance.ResultsWe present a new approach called asymmetric SANS together with scored seeds and an alternative suffix array ordering scheme called optimal substitution ordering. These techniques dramatically improve both the sensitivity and speed of the SANS approach. Our implementation, TOPAZ, is one of the top performing methods in terms of speed, sensitivity and scalability. In our benchmark, searching UniProtKB for homologous proteins to the Dickeya solani proteome, TOPAZ took less than 3 minutes to achieve a sensitivity of 0.84 compared to BLAST.ConclusionsDespite the trade-off homology search methods have to make between sensitivity and speed, TOPAZ stands out as one of the most sensitive and highest performance methods currently available.

An approximate/exact objective based search technique for solving general scheduling problems

In this paper, we analyze single machine scheduling problems under the following minimization objectives: the maximum completion time (makespan), the total completion time and the maximum lateness, including fundamental practical aspects, which often occur in industrial or manufacturing reality: release dates, due dates, setup times, precedence constraints, deterioration (aging) of machines, as well as maintenance activities. To solve the problems, we propose an efficient representation of a solution and a fast neighborhood search technique, which calculates an approximation of criterion values in a constant time per solution in a neighborhood. On this basis, a novel approximate/exact search technique, using exact as well as approximate criterion values during search process, is introduced and used to develop efficient metaheuristic algorithms dedicated to the considered problems. Their efficiency is verified during computational experiments.

Hybrid approaches to optimize mixed-model assembly lines in low-volume manufacturing

In this paper, a production planning problem for mixed-model assembly lines in low-volume manufacturing as can be found in aircraft manufacturing is considered. This type of manufacturing is labor-intensive. Low-volume production of huge-sized jobs, i.e. airplanes, is typical. Balancing labor costs and inventory holding costs assuming a given job sequence is the purpose of this paper. Therefore, worker assignments to each station and start times and processing times for each job on each station are determined. Two different mathematical models are proposed. The first formulation is a time-indexed linear formulation that allows for a flexible allocation of workers to periods and stations while the second one has a non-linear objective function and allows only for a fixed assignment of workers to stations. It is proven that the second formulation leads to a linear program with continuous decision variables if the values of the decision variables that determine the number of workers assigned to a station are given, while the first formulation contains even in this situation binary decision variables. Heuristics that hybridize the mathematical formulations with variable neighborhood search techniques are proposed. Computational experiments on randomly generated problem instances and on real-world instances demonstrate the high performance of the heuristics.

A FAST METHOD FOR OPTIMIZING THE K-CLUSTERING BI-CLIQUE COMPLETION PROBLEM IN TELECOMMUNICATION

In this work, we present a fast approximate method for solving the k-Clustering Minimum Bi-clique Completion Problem (KBCP), a problem belonging to the telecommunication and transportation do- mains. In KBCP, we are given a set of demands of services from customers and its goal is to determine a subset of k multicast sessions that is able to partition the set of the starting demands. Each of the considered service has to belong to a multicast session while each costumer can appear in more sessions. The KBCP is tackled by using a fast approximate method which is based on the principle of neighborhood search techniques. The method can search several solutions belonging to diversified sub-spaces aiming to find the best solution. The performance of the presented method is evaluated on benchmark instances taken from the literature, whereby the provided results are compared to those reached by the Cplex solver and recent methods described in the literature. The results show that, the proposed method is fast and competitive and it is able to reach new bounds.

A hybrid scheduling approach for a two-stage flexible flow shop with batch processing machines

In this paper, we discuss a flexible flow shop scheduling problem with batch processing machines at each stage and with jobs that have unequal ready times. Scheduling problems of this type can be found in semiconductor wafer fabrication facilities (wafer fabs). We are interested in minimizing the total weighted tardiness of the jobs. We present a mixed integer programming formulation. The batch scheduling problem is NP-hard. Therefore, an iterative stage-based decomposition approach is proposed that is hybridized with neighborhood search techniques. The decomposition scheme provides internal due dates and ready times for the jobs on the first and second stage, respectively. Each of the resulting parallel machine batch scheduling problems is solved by variable neighborhood search in each iteration. Based on the schedules of the subproblems, the internal due dates and ready times are updated. We present the results of designed computational experiments that also consider the number of machines assigned to each stage as a design factor. It turns out that the proposed hybrid approach outperforms an iterative decomposition scheme where a fairly simple heuristic based on time window decomposition and the apparent tardiness cost dispatching rule is used to solve the subproblems. Recommendations for the design of the two stages with respect to the number of parallel machines on each stage are given.

Dubins Orienteering Problem

In this letter, we address the orienteering problem (OP) for curvature constrained vehicle. For a given set of target locations, each with associated reward, the OP stands to find a tour from a prescribed starting location to a given ending location such that it maximizes collected rewards while the tour length is within a given travel budget constraint. The addressed generalization of the Euclidean OP is called the Dubins Orienteering Problem (DOP) in which the reward collecting tour has to satisfy the limited turning radius of the Dubins vehicle. The DOP consists not only of selecting the most valuable targets and determination of the optimal sequence to visit them, but it also involves the determination of the vehicle's heading angle at each target location. The proposed solution is based on the Variable neighborhood search technique, and its feasibility is supported by an empirical evaluation in existing OP benchmarks. Moreover, an experimental verification in a real practical scenario further demonstrates the necessity of the proposed direct solution of the Dubins Orienteering Problem.

A hybrid metaheuristic for the time-dependent vehicle routing problem with hard time windows

This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries. The algorithm presented in this paper makes use of Large Neighbourhood Search approaches and Variable Neighbourhood Search techniques to guide the search. A first stage is specifically designed to reduce the number of vehicles required in a search space by the reduction of penalties generated by time-window violations with Large Neighbourhood Search procedures. A second stage minimises the travel distance and travel time in an ‘always feasible’ search space. Comparison of results with available test instances shows that the proposed algorithm is capable of obtaining a reduction in the number of vehicles (4.15%), travel distance (10.88%) and travel time (12.00%) compared to previous implementations in reasonable time

A large neighborhood search heuristic for supply chain network design

Many exact and approximate solution techniques have been used to solve facility location problems and, more generally, supply chain network design problems. Yet, the Large Neighborhood Search technique (LNS) has almost never been suggested for solving such problems, although it has proven its efficiency and flexibility in solving other complex combinatorial optimization problems. In this paper, we propose an LNS framework for solving a four-layer single period multi-product supply chain network design problem. One important feature of the model is that it includes inter-modality: the itinerary followed by the cargo from origin to destination may take several transportation modes. Moreover, several modes may compete on some arcs. Location decisions for intermediate facilities (e.g. plants and distribution centers) are determined by the LNS while transportation modes and product flow decisions are determined by a greedy heuristic. As a post-optimization step, linear programming is used to optimize product flows once the structure of the logistics network is fixed. Extensive experiments, based on randomly generated instances of different sizes and characteristics, show the effectiveness of the method compared with a state-of-the-art solver.

Visualizing proportions and dissimilarities by Space-filling maps: A Large Neighborhood Search approach

In this paper we address the problem of visualizing a set of individuals, which have attached a statistical value given as a proportion, and a dissimilarity measure. Each individual is represented as a region within the unit square, in such a way that the area of the regions represent the proportions and the distances between them represent the dissimilarities. To enhance the interpretability of the representation, the regions are required to satisfy two properties. First, they must form a partition of the unit square, namely, the portions in which it is divided must cover its area without overlapping. Second, the portions must be made of a connected union of rectangles which verify the so-called box-connectivity constraints, yielding a visualization map called Space-filling Box-connected Map (SBM). The construction of an SBM is formally stated as a mathematical optimization problem, which is solved heuristically by using the Large Neighborhood Search technique. The methodology proposed in this paper is applied to three real-world datasets: the first one concerning financial markets in Europe and Asia, the second one about the letters in the English alphabet, and finally the provinces of The Netherlands as a geographical application.

MaxSAT-based large neighborhood search for high school timetabling

High School Timetabling (HSTT) is a well known and wide spread problem. The problem consists of coordinating resources (e.g. teachers, rooms), times, and events (e.g. lectures) with respect to various constraints. Unfortunately, HSTT is hard to solve and just finding a feasible solution for simple variants of HSTT have been proven to be NP-complete. We propose a new algorithm for HSTT which combines local search with a novel maxSAT-based large neighborhood search. A local search algorithm is used to drive an initial solution into a local optimum and then more powerful large neighborhood search (LNS) techniques based on maxSAT are used to further improve the solution. We prove the effectiveness of our approach with experimental results on instances taken from the Third International Timetabling Competition 2011 and the XHSTT-2014 benchmark archive. We were able to model 27 out of 39 instances. The remaining 12 instances were not modeled because the currently used maxSAT formulation for XHSTT does not support resource assignments in general. For the instances which could be modeled, our algorithm shows good performance when compared to other XHSTT state-of-the-art solvers and for several instances new best known upper bounds have been computed.

A new cuckoo search algorithm for 2-machine robotic cell scheduling problem with sequence-dependent setup times

Abstract The paper addresses the problem of 2-machine robotic cell scheduling of one-unit cycle with sequence-dependent setup times and different loading/unloading times of the parts. As an alternative metaheuristic algorithm, the cuckoo search algorithm has recently attracted growing interests of researchers. It has the capability to search globally as well as locally to converge to the global optimality by exploring the search space more efficiently due to its global random walk governed by Levy flights, rather than standard isotropic random walk. In this study, a discrete cuckoo search algorithm is proposed to determine the sequence of robot moves along with the sequence of parts so that the cycle time is minimized. In the proposed algorithm, the fractional scaling factor based procedure is presented to determine the step length of Levy flights distribution in discrete from and then, using this step length, two neighborhood search techniques, interchange and cyclical shift methods are applied to the current solution to obtain improved solution. A response surface methodology based on desirability function is used to enhance the convergence speed of the proposed algorithm. Also, a design of experiment is employed to tune the operating parameters of the algorithm. Finally, empirical results with a large number of randomly generated problem instances involving large part sizes varying from 200 to 500 under different operating conditions are compared with two well-known algorithms in the literature and demonstrate the effectiveness of the proposed algorithm.

An automated negotiation approach to solve single machine scheduling problems with interfering job sets

In this paper, we propose a decentralized approach to solve multiple-criteria single machine scheduling problems. We consider a single machine that is utilized by two agents where each of them has its own jobs and a private objective. The objective function of the first agent is the total completion time or the total weighted completion time of its jobs, respectively, whereas the objective function of the second agent is the maximum lateness or the total weighted completion time of its jobs. The problem is motivated by scheduling problems found in semiconductor manufacturing. An automated negotiation mechanism is presented to solve the scheduling problems. The basic ingredient of the mechanism is a mediator that proposes contracts using a variable neighborhood search (VNS) technique. We study the behavior of both greedy and cooperative agents. In addition, a hybrid strategy is developed where the mediator accepts deteriorations of the objective values with a certain probability. The performance of the negotiation schemes is assessed using randomly generated problem instances. It turns out that the solutions determined by the negotiation mechanism are close to the Pareto frontier from a centralized approach with full information. We use a problem-specific Non-Dominated Sorting Genetic Algorithm (NSGA)-II scheme to determine the solutions on the Pareto frontier. It is shown that the degree of cooperation of the two agents depends on the objective function used.

A hybrid optimization algorithm for surgeries scheduling

This paper deals with the Operating Room (OR) planning problem at an operational planning level. The problem addressed consists of two interrelated sub-problems usually referred to as “advance scheduling” and “allocation scheduling”. In the first sub-problem, the decisions considered are the assignment of a surgery date and an OR block to a set of patients to be operated on over a given planning horizon. The second aims at determining the sequence of selected patients in each OR and day. We assume that the duration of surgeries are random variables with known probability distributions. For each sub-problem an integer linear stochastic formulation is given. A hybrid two-phase optimization algorithm which exploits the potentiality of neighborhood search techniques combined with Monte Carlo simulation is developed to solve the overall problem. The approach developed searches for a feasible and robust solution designed to balance the trade-off arising between the hospital and patient perspectives, i.e. maximizing the OR utilization and minimizing the number of patient cancellations. The contribution of this paper is twofold. The former, more methodological, is to provide an efficient algorithmic framework to solve the joint advance and allocation scheduling problem taking into account the inherent uncertainty of surgery durations. The latter, more practical, is to provide a tool to develop robust offline OR schedules which consider the trade-off between reducing surgery cancellations and postponements while maximizing the operating theater utilization. To evaluate the efficiency of the proposed algorithmic approach, in terms of quality of solutions and solution time, we provide a computational analysis on a set of instances based on real data.

Evaluating the neighborhood, hybrid and reversion search techniques of a simulated annealing algorithm in solving forest spatial harvest scheduling problems

Heuristic techniques have been increasingly used to address the complex forest planning problems over the last few decades. However, heuristics only can provide acceptable solutions to difficult problems, rather than guarantee that the optimal solution will be located. The strategies of neighborhood, hybrid and reversion search processes have been proved to be effective in improving the quality of heuristic results, as suggested recently in the literature. The overall aims of this paper were therefore to systematically evaluate the performances of these enhanced techniques when implemented with a simulated annealing algorithm. Five enhanced techniques were developed using different strategies for generating candidate solutions. These were then compared to the conventional search strategy that employed 1-opt moves (Strategy 1) alone. The five search strategies are classified into three categories: i) neighborhood search techniques that only used the change version of 2-opt moves (Strategy 2); ii) -hybrid search techniques that oscillate between 1-opt moves and the change version of 2-opt moves (Strategy 3), or the exchange version of 2-opt moves (Strategy 4); iii) reversion search techniques that utilize 1-opt moves and the change version of 2-opt moves (Strategy 5) or the exchange version of 2-opt moves (Strategy 6). We found that the performances of all the enhanced search techniques of simulated annealing were systematic and often clear better than conventional search strategy, however the required computational time was significantly increased. For a minimization planning problem, Strategy 6 produced the lowest mean objective function values, which were less than 1% of the means developed using conventional search strategy. Although Strategy 6 performed very well, the other search strategies should not be neglected because they also have the potential to produce high-quality solutions.self

A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems

Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications. The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex andNP-hard problems. This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan. In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules. In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation. A machine based precedence preserving order-based crossover was proposed to generate the offspring. Furthermore, a simulated annealing based neighborhood search technique was used to improve the local exploitation ability of the algorithm and to increase its population diversity. In order to prove the efficiency and effectiveness of the proposed algorithm, numerous benchmarked instances were collected from the Operations Research Library. Computational results of the proposed hybrid genetic algorithm demonstrate its effectiveness.

Moving towards an Optimal Sample using VNS Algorithm

In almost all random sampling schemes, we adopt different sampling designs with an objective of obtaining a better representative sample (optimal sample) for the population. Application of different randomization techniques were adopted for providing a supportive basis for this. Now the question arises, whether the final sample selected, on which all our efforts are utilized, from the population is an optimal sample or not? No where we are checking about the optimality of this sample, i.e ., whether this sample is the best one or there exists any other sample which is more optimal than the selected one satisfying all the constraints. In all these procedures, we only assume but, nowhere we are establishing a guarantee about the achievement of such a representative sample. The present paper emphasizes on achieving an optimal sample by using variable neighborhood search (VNS) technique.