Showing papers by "Quan-Ke Pan published in 2017"

An improved fruit fly optimization algorithm for solving the multidimensional knapsack problem

Abstract: Display Omitted An improved fruit fly optimization algorithm (IFFOA) for solving the multidimensional knapsack problem (MKP) is proposed.The parallel search is employed to balance exploitation and exploration.A modified harmony search algorithm (MHS) is presented to add cooperation among swarms in IFFOA.A novel vertical crossover is designed to guide stagnant dimensions out of local optima.Experimental results indicate that IFFOA is an effective alternative for solving the MKP. This paper presents an improved fruit fly optimization algorithm (IFFOA) for solving the multidimensional knapsack problem (MKP). In IFFOA, the parallel search is employed to balance exploitation and exploration. To make full use of swarm intelligence, a modified harmony search algorithm (MHS) is proposed and applied to add cooperation among swarms in IFFOA. In MHS, novel pitch adjustment scheme and random selection rule are developed by considering specific characters of MKP and FOA. Moreover, a vertical crossover is designed to guide stagnant dimensions out of local optima and further improve the performance. Extensive numerical simulations are conducted and comparisons with other state-of-the-art algorithms verify that the proposed algorithm is an effective alternative for solving the MKP.

An effective modified migrating birds optimization for hybrid flowshop scheduling problem with lot streaming

Abstract: This above figure illustrates the flowchart of our proposed algorithm (EMBO). The proposed algorithm starts with a number of initial solutions randomly generated in the solution space, including a leader solution and the other members in left and right lines. Then, an evolving loop involving a number of tours proceeds, and each tour evolves beginning with the leader and processing along the left and right lines in parallel by exploring their neighborhood and using the dynamic solution acceptance criteria. The insertion and the pairwise exchange neighborhood operators are respectively applied for the individuals in PL and PR. And two competitive mechanisms are used to modify the solutions order when a tour is finished. Finally, when a loop is finished, the scout phase for the solutions is conducted, the leader is to be changed, and another loop starts.Display Omitted The problem of hybrid flowshop hybridizing with lot streaming is addressed.A shortest waiting time rule is introduced to schedule the jobs concurrently arriving.The dynamic solution acceptance criteria is developed. In this paper, the problem of hybrid flowshop hybridizing with lot streaming (HLFS) with the objective of minimizing the total flow time is addressed. We propose a mathematical model and an effective modified migrating birds optimization (EMBO) to solve this problem within an acceptable computational time. A so-called shortest waiting time rule (SWT) is introduced to schedule the jobs concurrently arriving at stages more reasonably. A combined neighborhood search strategy is developed that unites two different neighborhood operators during evolution, not only taking full advantage of their specializations but also promoting their joint efforts. Two competitive mechanisms are respectively used to increase the probability of locating better solutions at the front of the flock and enhance the interaction between two lines. The scout phase on the basis of the Glover operator and a well-designed local search is applied to the individuals trapped into local optimums and helps the algorithm explore potential promising domains. The dynamic solution acceptance criteria is developed to strike a compromise between intensification and diversification mechanisms. The performance of our proposed algorithm is evaluated by comparisons with seven other efficient algorithms in the literature. And the extensive numerical illustrations demonstrate that the proposed algorithm performs much more effectively for the addressed problem.

A hybrid artificial bee colony for optimizing a reverse logistics network system

Abstract: This paper proposes a hybrid discrete artificial bee colony (HDABC) algorithm for solving the location allocation problem in reverse logistics network system. In the proposed algorithm, each solution is represented by two vectors, i.e., a collection point vector and a repair center vector. Eight well-designed neighborhood structures are proposed to utilize the problem structure and can thus enhance the exploitation capability of the algorithm. A simple but efficient selection and update approach is applied to the onlooker bee to enhance the exploitation process. A scout bee applies different local search methods to the abandoned solution and the best solution found so far, which can increase the convergence and the exploration capabilities of the proposed algorithm. In addition, an enhanced local search procedure is developed to further improve the search capability. Finally, the proposed algorithm is tested on sets of large-scale randomly generated benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed HDBAC algorithm is shown against several efficient algorithms from the literature.

Discrete harmony search algorithm for scheduling and rescheduling the reprocessing problems in remanufacturing: a case study

Abstract: In this article, scheduling and rescheduling problems with increasing processing time and new job insertion are studied for reprocessing problems in the remanufacturing process. To handle the unpre...

Multi-objective inverse scheduling optimization of single-machine shop system with uncertain due-dates and processing times

Abstract: Generally, ideal manufacturing system environments are assumed before determining effective scheduling. However, the original schedule is no longer optimal or even to be infeasible due to many uncertain events. This paper investigates a multi-objective inverse scheduling problem in single-machine shop system with due-dates and uncertain processing parameters. Moreover, in order to more close the addressed problem into the situations encountered in real world, the processing parameters are considered to be uncertain stochastic parameters. First, a comprehensive mathematical model for multi-objective single-machine inverse scheduling problem (MSMISP) is addressed. Second, an effective hybrid multi-objective evolutionary algorithm (HMNL) is proposed to handle uncertain processing parameters (uncertainties) and multiple objectives at the same time. In HMNL, using an effective decimal system encoding scheme and genetic operators, the non-dominated sorting based on NSGA-II is adapted for the MSMISP. In addition, hybrid HMNL are proposed by incorporating an adaptive local search scheme into the well-known NSGA-II, where applies a separate local search process, total six strategies, to improve quality of solutions. Furthermore, an on-demand layered strategy is embedded into the elitism strategy to keep the population diversity. Afterwards, an external archive set is dynamically updated, where a non-dominated solution is selected to participate in the creation of the new population. Finally, 36 public problem instances with different scales and statistical performance comparisons are provided for the HMNL algorithm. This paper is the first to propose a mathematical model and develop a hybrid MOEA algorithm to solve MSMISP in inverse scheduling domain.

A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production

Abstract: A Lagrangian relaxation (LR) approach was presented which is with machine capacity relaxation and operation precedence relaxation for solving a flexible job shop (FJS) scheduling problem from the steelmaking-refining-continuous casting process. Unlike the full optimization of LR problems in traditional LR approaches, the machine capacity relaxation is optimized asymptotically, while the precedence relaxation is optimized approximately due to the NP-hard nature of its LR problem. Because the standard subgradient algorithm (SSA) cannot solve the Lagrangian dual (LD) problem within the partial optimization of LR problem, an effective deflected-conditional approximate subgradient level algorithm (DCASLA) was developed, named as Lagrangian relaxation level approach. The efficiency of the DCASLA is enhanced by a deflected-conditional epsilon-subgradient to weaken the possible zigzagging phenomena. Computational results and comparisons show that the proposed methods improve significantly the efficiency of the LR approach and the DCASLA adopting capacity relaxation strategy performs best among eight methods in terms of solution quality and running time.

Differential evolution algorithm-based range image registration for free-form surface parts quality inspection

Abstract: Increasing demands on precision manufacturing of complex free-form surface parts have been observed in the past several years. Although some advanced techniques have been employed to solve the design and machining problems for such parts, quality inspection remains a difficult problem. Registration is a crucial issue in surface inspection; it is used to transform the design model and measurement model into a common coordinate system. The comparison results are then outputted in a report and displayed visually by color gradients. This paper presents a design model-based inspection method with range image registration, in which the measurement model is represented by a series of 3D discrete points. In the model preprocessing, the directed Hausdorff distance (DHD) method is employed for point cloud simplification, and a novel point descriptor is designed to evaluate the property of each point. Subsequently, a differential evolution (DE) algorithm-based optimizer is proposed for error evaluation. Combined with the properties of 3D points, the optimizer can measure the similarity between the design model and the measurement model with a recursive process. The proposed algorithms have been implemented and tested with several sets of simulated and real data. The experiment results illustrate that they are effective and efficient for free-form surface part quality inspection.

A novel simplification method of point cloud with directed Hausdorff distance

Abstract: Three dimensional (3D) point clouds are typically used in computer vision and pattern recognition areas. In general, the raw point cloud has large numbers of redundant points which require excessively large storage space and lots of time for post-processing. This paper presents a synthetic point cloud simplification method to obtain computationally manageable point sets. First, a coarse-to-fine feature extraction manner is designed with normal vectors deviation and k-means clustering methods, which can concentrate more sample points in regions of high curvature. Additionally, the directed Hausdorff distance is performed directly on the point cloud which samples the point cloud judiciously with an edge-preserving manner. Experimental results demonstrate that the proposed method is effective for point cloud simplification, and it exhibited superior performance compared to existing techniques.

A variable block insertion heuristic for permutation flowshops with makespan criterion

Abstract: This paper proposes a populated variable block insertion heuristic (PVBIH) algorithm for solving the permutation flowshop scheduling problem with the makespan criterion. The PVBIH algorithm starts with a minimum block size being equal to one. It removes a block from the current solution and inserts it into the partial solution randomly with a predetermined move size. A local search is applied to the solution found after several block moves. If the new solution generated after the local search is better than the current solution, it replaces the current solution. It retains the same block size as long as it improves. Otherwise, the block size is incremented by one and a simulated annealing-type of acceptance criterion is used to accept the new solution. This process is repeated until the block size reaches at the maximum block size. In addition, we present a randomized profile fitting heuristic with excellent results. Extensive computational results on the Taillard's well-known benchmark suite show that the proposed PVBIH algorithm substantially outperforms the differential evolution algorithm (NS-SGDE) recently proposed in the literature.

Variable block insertion heuristic for the quadratic assignment problem

Abstract: The aim of this paper is to apply the variable block insertion heuristic (VBIH) algorithm recently proposed in the literature for solving the quadratic assignment problem (QAP). The VBIH algorithm is concerned with making block moves in a given solution. As a local search in this paper, the VNST is employed from the literature to be applied to a solution obtained after several block moves. Besides the single-solution based VBIH, we also propose a populated VBIH (PVBIH) in this paper. The proposed algorithms were evaluated on quadratic assignment problem instances arising from real life problems as well as on a number of benchmark instances from the QAPLIB. The computational results show that the proposed algorithms are very effective in solving both types of instances. All PCB instances are further improved.

An improved migrating birds optimization for solving the multidimensional knapsack problem

Abstract: The multidimensional knapsack problem (MKP) is a famous NP-hard combinatorial optimization problem with strong engineering backgrounds. In this paper, we propose an improved migrating birds optimization (IMBO) to solve the MKP. In IMBO, to guarantee the initial swarm with a certain level of quality and diversity, we generate some meaningful solutions while other individuals are constructed randomly. In addition, considering the characteristics of MBO and MKP, an effective sharing scheme (NSS) is designed to deliver useful information to the following individual. Numerical experiments are performed and comparisons with state-of-the-art algorithms demonstrate the effectiveness of the proposed IMBO for solving the MKP.

A multi-swarm fruit fly optimization algorithm to minimize makespan for the hybrid flowshop problem

Abstract: This paper aims to minimize makespan for the hybrid flowshop scheduling problem. We present a novel Fruit Fly Optimization (FFO), called multi-swarm FFO, by introducing a multiple-swarm strategy and a competition-and-updating mechanism to the basic FFO. The parameters and operators for the presented MMFO algorithm are calibrated by means of a design of experiments approach. The numerical comparisons show that MFFO performs much better than several well-known metaheuristics in the literature for the considered HFS problem.

A two-stage invasive weed optimization algorithm for distributed assembly permutation flowshop problem

Abstract: Distributed assembly permutation flowshop scheduling problem (DAPFSP) has important applications in modern assembly systems. In this paper, we address the DAPFSP with total flowtime minimization. According to the problem knowledge, we present a two-level representation which consists of product permutation and job sequences. A two-stage discrete invasive weed optimization (DIWO) algorithm is proposed for optimizing product permutations and job sequences, respectively. A parameter is introduced to balance the product permutation based search and job sequence based search. The problem knowledge based operators, local search procedures, and heuristics are utilized to improve the DIWO. We carry out a comprehensive computational campaign based on the 900 benchmark instances in the literature. The numerical experiments show that the presented DIWO algorithm performs much better than the existing algorithms in the literature for solving the DPFSP with the total flowtime criterion.

An enhanced migrating birds optimization for a lot-streaming flow shop scheduling problem

Abstract: Migrating birds optimization (MBO) is a newly reported metaheuristic that has been proved effective in dealing with combinatorial optimization problems. In this paper, we propose an enhanced MBO (EMBO) to solve a lot-streaming flow shop scheduling problem with setup times, in which job-splitting and job scheduling are considered simultaneously. The objective is to minimize the makespan. In EMBO, a two-stage vector is employed to represent solutions in the swarm. Borrowing idea from artificial bee colony, a special neighbor structure is designed to create new candidates. Moreover, attempting to jump out of the local best, a new solution update scheme is introduced. Numerical tests are conducted and comparisons with other recent algorithms show the superiority of the proposed EMBO.

Hot-rolling scheduling method for compact strip production

Abstract: The invention discloses a hot-rolling scheduling method for compact strip production. The method comprises the steps of constructing a hot-rolling production scheduling model including a non-outsourcing material optimization model and a plate blank thickness optimizing model with the target of minimizing the quantity of non-outsourcing materials, minimizing the maximum thickness change amount of two adjacent plate blanks in the same rolling unit, and minimizing the changing time of the thickness of the plate blanks; determining the constraint conditions of the non-outsourcing material optimization model and the plate blank thickness optimizing model according to technological constraints in an actual hot-rolling production process; adopting an improved heuristic algorithm to solve the non-outsourcing material optimization model to obtain the quantity of optimal rolling units and the quantity of the non-outsourcing materials; adopting a multi-objective evolutionary algorithm based on decomposition to solve the plate blank thickness optimizing model to obtain the optimal change value of the thicknesses of adjacent plate blanks and the optimal thickness changing time. According to the comparison between a scheduling plan obtained through the hot-rolling scheduling method and a production plan which is worked out manually, the plate blank specification skip cost and roll replacement cost can be effectively reduced, and thus the production cost is lowered.

Scheduling the hybrid flowshop problem using a global fruit fly optimizer

Abstract: This paper proposes an effective global Fruit Fly Optimization (GFFO) for the hybrid flowshop (HFS) problem with makespan criterion. To improve FFO's exploration ability, a global osphresis foraging and a global vision foraging is introduced. We calibrate the parameters and operators for the presented GFFO algorithm by means of a design of experiments approach. Comparative evaluations show that the proposed GFFO algorithm performs much better than the other algorithms in the literature in solving the HFS problem with makespan criterion.

A modified migrating birds optimization for solving the steelmaking-continuous casting problem with variable processing times

Abstract: In this paper, the steelmaking-continuous casting problem with variable processing times is addressed. We present a modified migrating birds optimization (MMBO) to deal with this problem within a reasonable time. For the addressed problem, we use the job permutation to represent the solution and give a detailed decoding process. For the employed algorithm, we introduce a various neighborhood strategy to explore the solution space more widely. And the benefit mechanism is modified to take full advantage of the promising solutions. To evaluate the performance of our proposed algorithm, the other three meta-heuristics are compared and the computational results demonstrate the effectiveness of the MMBO.

An effective fruit fly optimization algorithm for the hybrid flowshop scheduling problem

Abstract: Fruit Fly algorithm (FFO) is a recently reported metaheuristics originally designed for function optimization problems. This paper proposes an effective FFO to solve the hybrid flowshop scheduling (HFS) problem with the aim of makespan optimization (minimization). In presented FFO, we utilize the permutation-based representation and operators. The presented FFO can directly explore the discrete solution space of the HFS problem. Extensive comparisons are carried out with well-known meta-heuristics. The results show that the developed FFO is very effective for the considered HFS problem.

Intelligent Rescheduling System for Steelmaking and Continuous Casting Production

Abstract: There are many types of disturbances that upset the plan in steelmaking and continuous casting (SCC) production, including processing time variation, temperature variation, quality variation, machine failures, which could cause the static schedule to become inefficient and even infeasible. It is necessary to adjust the schedule or generate a new executable schedule upon the occurrence of unanticipated disruptions and changes. Two rescheduling methods are presented: the partial rescheduling and complete rescheduling. The former refers to the right shift partial rescheduling based on continuous casting, the partial rescheduling based on case-based reasoning and man-computer interaction. The latter refers to the complete rescheduling considering changeable processing time, the complete rescheduling considering changeable processing time and production path. The mathematical models of two class complete rescheduling are formulated, and the solving methods are also given. The intelligent rescheduling system for SCC production with these methods is successfully applied to one large steel plant, and rapidly optimal rescheduling is achieved when disturbances occur.