Showing papers by "Quan-Ke Pan published in 2010"

Minimizing the total flow time in a flow shop with blocking by using hybrid harmony search algorithms

Abstract: In this paper, three hybrid harmony search (HS) algorithms, namely, hybrid harmony search (hHS) algorithm, hybrid globalbest harmony search (hgHS) algorithm and hybrid modified globalbest harmony search (hmgHS) algorithm, are developed for solving the flow shop scheduling with blocking to minimize the total flow time. Firstly, a largest position value (LPV) rule is proposed to convert continuous harmony vectors into job permutations. Secondly, an initialization scheme based on a variant of the NEH heuristic is presented to construct the initial harmony memory with certain quality and diversity. Thirdly, HS is employed to evolve harmony vectors in the harmony memory to perform exploration, whereas a local search algorithm based on the insert neighborhood is embedded to enhance the local exploitation ability. In addition, some new pitch adjustment rules are developed to well inherit good structures from the globalbest harmony vector. Based on a set of well-known benchmark instances, extensive computational experiments are carried out. Computational results show the effectiveness of the hybrid harmony search algorithms, especially the (hmgHS) algorithm, in solving the blocking flow shop scheduling with total flow time criterion.

A local-best harmony search algorithm with dynamic subpopulations

Abstract: This article presents a local-best harmony search algorithm with dynamic subpopulations (DLHS) for solving the bound-constrained continuous optimization problems. Unlike existing harmony search algorithms, the DLHS algorithm divides the whole harmony memory (HM) into many small-sized sub-HMs and the evolution is performed in each sub-HM independently. To maintain the diversity of the population and to improve the accuracy of the final solution, information exchange among the sub-HMs is achieved by using a periodic regrouping schedule. Furthermore, a novel harmony improvisation scheme is employed to benefit from good information captured in the local best harmony vector. In addition, an adaptive strategy is developed to adjust the parameters to suit the particular problems or the particular phases of search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from the literature. The computational results show that, overall, the proposed D...

An ensemble of differential evolution algorithms for constrained function optimization

Abstract: This paper presents an ensemble of differential evolution algorithms employing the variable parameter search and two distinct mutation strategies in the ensemble to solve real-parameter constrained optimization problems. It is well known that the performance of DE is sensitive to the choice of mutation strategies and associated control parameters. For these reasons, the ensemble is achieved in such a way that each individual is assigned to one of the two distinct mutation strategies or a variable parameter search (VPS). The algorithm was tested using benchmark instances in Congress on Evolutionary Computation 2010. For these benchmark problems, the problem definition file, codes and evaluation criteria are available in http://www.ntu.edu.sg/home/EPNSugan. Since the optimal or best known solutions are not available in the literature, the detailed computational results required in line with the special session format are provided for the competition.

A Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem

Abstract: —Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, a hybrid of particle swarm optimization (PSO) algorithm and tabu search (TS) algorithm are presented to solve the FJSP with the criterion to minimize the maximum completion time (makespan). In the novel hybrid algorithm, PSO was used to produce a swarm of high quality candidate solutions, while TS was used to obtain a near optimal solution around the given good solution. The computational results have proved that the proposed hybrid algorithm is efficient and effective for solving FJSP, especially for the problems with large scale.

A discrete artificial bee colony algorithm for the permutation flow shop scheduling problem with total flowtime criterion

Abstract: Very recently, Jarboui et al. [1] (Computers & Operations Research 36 (2009) 2638–2646) and Tseng and Lin [2] (European Journal of Operational Research 198 (2009) 84–92) presented a novel estimation distribution algorithm (EDA) and a hybrid genetic local search (hGLS) algorithm for the permutation flowshop scheduling (PFSP) with the total flowtime (TFT) criterion, respectively. Both algorithms generated excellent results, thus improving all the best known solutions reported in the literature so far. However, in this paper, we present a discrete artificial bee colony (DABC) algorithm hybridized with an iterated greedy (IG) and iterated local search (ILS) algorithms embedded in a variable neighborhood search (VNS) procedure based on swap and insertion neighborhood structures. We also present a hybrid version of our previous discrete differential evolution (hDDE) algorithm employing the IG and VNS structure too. The performance of the DABC and hDDE is highly competitive to the EDA and hGLS algorithms in terms of both solution quality and CPU times. Ultimately, 43 out of 60 best known solutions provided very recently by the EDA and hGLS algorithms are further improved by the DABC and hDDE algorithms with short-term search.

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

Abstract: 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.

Solving manpower scheduling problem in manufacturing using mixed-integer programming with a two-stage heuristic algorithm.

Abstract: Manpower scheduling problem is one of the key scheduling problems with extensive applications in manufacturing. This paper presents a mixed-integer programming model with a two-stage heuristic algorithm for solving the manpower scheduling problem in the precision engineering industry. Firstly, a mixed-integer programming formulation is developed to model the manpower scheduling problem in this high-mix low-volume manufacturing environment. Secondly, a two-stage heuristic algorithm is proposed where the first stage is deployed to calculate the skill requirements for each shift by considering the jobs, machines, and their production schedule and the second stage is designed to assign operators to the machines by considering the skill set requirements and the operator's expressed preferences. Lastly, the computational results based on problem instances emulating real-world scenarios demonstrated the feasibility and effectiveness of the proposed heuristic.

A hybrid pareto-based tabu search for multi-objective flexible job shop scheduling problem with e/t penalty

Abstract: In this paper, we propose a Pareto-based tabu search algorithm for multi-objective FJSP with Earliness/Tardiness (E/T) penalty In the hybrid algorithm, several neighboring structure based approaches were proposed to improve the convergence capability of the algorithm while keep population diversity of the last Pareto archive set In addition, an external Pareto archive was developed to record the non-dominated solutions found so far In the hybrid algorithm, dynamic parameters were introduced to adapt to the searching process Experimental on several well-known benchmark instances show that the proposed algorithm is superior to several existing approaches in both solution quality and convergence ability.

A novel discrete harmony search algorithm for scheduling lot-streaming flow shops

Abstract: This paper proposes a novel discrete harmony search (DHS) algorithm to minimize makespan for a lot-streaming flow shop scheduling problem with sequence dependent setup times. Unlike the traditional harmony search (HS) algorithm, the proposed DHS algorithm utilizes job permutations to represent harmonies and applies a job-permutation-based improvisation to generate new harmonies. To enhance the algorithm's searching ability, an effective initialization scheme based on the NEH heuristic is developed to construct an initial harmony memory with certain quality and diversity, and an efficient local search algorithm based on the insert neighborhood structures is fused to stress the local exploitation. Extensive computational simulations and comparisons are provided, which demonstrate the effectiveness of the proposed DHS against the best performing algorithms from the literature.

Harmony search algorithm with dynamic subpopulations for scheduling identical parallel machines

Abstract: In this paper, we study the identical parallel machines scheduling problem for minimizing the makespan. A novel harmony search (HS) algorithm with dynamic subpopulations is proposed to tackle this problem. First, an encoding scheme based on random key representation and list schedule rule is developed, which constructs a mapping scheme between the real-valued harmony vectors and job assignments. Second, the whole harmony memory is divided into many small-sized subpopulations. Each subpopulation performs evolution independently and exchanges information with the other subpopulations periodically by using a regrouping schedule. Moreover, a novel improvisation process is applied to generate new harmonies by making use of the information of the local best harmony in each subpopulation. Simulation results demonstrate that the proposed HS algorithm is more effective when compared with the other two heuristics.

A Hybrid Discrete Harmony Search Algorithm for blocking flow shop Scheduling

Abstract: This paper presents a hybrid discrete harmony search (DUS) algorithm for solving the blocking flow shop scheduling problem with the objective to minimize makespan. The proposed hybrid DUS algorithm utilizes discrete job permutations to represent harmonies and applies a job-permutation-based improvisation scheme to generate new harmonies. An initialization scheme based on a variant of the Minimum Blocking Tardiness (MBT) heuristic is presented to construct the initial harmony memory with a certain level of quality and diversity. The DUS algorithm is employed to evolve harmony vectors in the harmony memory to perform exploration, whereas a local search algorithm based on the insert neighborhood is embedded to enhance the local exploitation ability. Computational simulations and comparisons demonstrate that the proposed algorithm (DUS) is effective and efficient for the blocking flow shop scheduling problems with makes pan criterion.

Scheduling the blocking flow shop problem using a harmony search algorithm

Abstract: This paper propose a harmony search (HS) algorithm to minimize makespan for the blocking flow shop scheduling problem. In the proposed algorithm, the HS-based search is employed to evolve harmony vectors to perform exploration, whereas a local search procedure based on the insert neighborhood is used to enhance the local exploitation ability. Extensive computational experiments and comparisons are carried out. Computational results show that the proposed HS algorithm is effective in finding optimal and near-optimal solutions.

Solving lot-streaming flow shop scheduling problems using a discrete harmony search algorithm

Abstract: The harmony search (HS) algorithm is one of the recent evolutionary computation techniques to solve optimization problems. To make it applicable for lot-streaming flow shop problems, a discrete variant of the HS algorithm (DHS) with job permutations representation is proposed. In the proposed DHS algorithm, a new improvisation scheme is designed to generate feasible job sequences. A local search algorithm based on the insert neighborhood structure is fused to stress the further enhancement capability of the algorithm proposed whereas a restart scheme is employed to avoid the stagnation of the evolution. Extensive computational simulations and comparisons are provided, which demonstrate the effectiveness of the proposed DHS against the best performing algorithms from the literature.

A Dynamic Multi-swarm Particle Swarm Optimizer for blocking flow shop scheduling

Abstract: This paper presents a Dynamic Multi-Swarm Particle Swarm Optimizer (DMS-PSO) for solving blocking flow shop scheduling problems with makespan criterion. Using small swarms and a regrouping schedule, DMS-PSO has a better global search ability. In order to improve its local search ability, a special designed local search phase is added into the algorithm. The experimental results indicate that the proposed DMS-PSO has a better performance on the blocking flow shop scheduling problems comparing some other algorithms.

A hybrid Pareto-based local search for multi-objective flexible job shop scheduling problem

Abstract: This paper presents a hybrid Pareto-based local search (PLS) algorithm for solving the multi-objective flexible job shop scheduling problem. Three minimization objectives-the maximum completion time (makespan), the total workload of all machines, and the workload of the critical machine are considered simultaneously. In this study, several well-designed local search approaches are proposed, which consider the problem characteristics and thus can hold fast convergence ability while keep rich population diversity. Then, an external Pareto archive is developed to memory the Pareto optimal solutions found so far. In addition, to improve the efficiency of the scheduling algorithm, a speed-up method is devised to decide the domination status of a solution with the archive set. Experimental results on two well-known benchmarks show the efficiency of the proposed hybrid algorithm. It is concluded that the PLS algorithm is superior to the very recent algorithms in term of both search quality and computational efficiency.

A harmony search algorithm for the no-wait flow shop optimization scheduling

Abstract: This paper proposes an effective method for the no-wait flow-shop scheduling optimization based on harmony search (HS). Flow-shop scheduling problem (FSSP) is a typical NP-hard combinational optimization. The purpose of this paper is the total flow time criterion. Firstly, the HS-based optimization mechanism and framework is presented. Secondly, the total flow time is calculated by a novel method. Thirdly, a largest-order-value rule is used to transform harmony in harmony memory from real vectors to job sequence so that the harmony search can be applied for FSSP. Improvising rule of new harmony is expatiated and high effective algorithm parameters are set for optimization object. At last, simulations and comparisons demonstrate the efficiency, effectiveness and robustness of harmony search algorithm for no-wait FSSP.

Scheduling the lot-streaming flow shop problem using a shuffled frog-leaping algorithm

Abstract: This paper addresses to the lot-streaming flow shop scheduling problem with makespan criterion. An effective shuffled frog-leaping algorithm (SFLA) with job permutation based representation is proposed. Extensive computational experiments and comparisons demonstrate the effectiveness of the proposed SFLA.

Tabu search algorithm for solving No-idle permutation Flow Shop Scheduling Problem

Abstract: An effective tabu search algorithm is proposed for solving No-idle permutation Flow Shop Scheduling Problem in this paper. The objective is to minimize the maximum completion time (MakeSpan) of the problem. The Tabu Search Algorithm starts to search from an initial solution generated by the famous NEH heuristic. The dynamic feature of the tabu list length is applied to improve the robustness of the algorithm. In order to further improve the capability of the algorithm, a detailed local search method is introduced. Simulation results show the effectiveness and superiority of the algorithm.

An Effective Trust-Based Search Approach in Peer-to-Peer Network

Abstract: In this paper, a novel optimized trust-based search approach was proposed for the peer-to-peer (P2P) platform. In traditional trust-based or recommendation P2P system, the trust or recommendation value of a peer is considered when selecting it for forwarding messages or downloading resources, while the other factors are ignored, such as good download rate, good response rate, and on-line time similarity. In the proposed optimized approach, we consider all possible factors which affect the search quality in the P2P system. The proposed algorithm is developed on the well-known QueryCycle platform. Experiment results show the efficiency of our approach.

An improved harmony search algorithm for multi-dimensional function optimization problem

Abstract: An Improved Harmony search algorithm (IHS) is proposed for solving multi-dimensional and multi-extremal function optimization problems. The IHS algorithm uses a common cross-operation to generate new solutions instead of the traditional improvisation mechanism presented in the basic Harmony search. The number of the new solutions varies from 1 to HMS. In each iteration, IGHS uses a one-on-one selection scheme to compare the old solutions with the new ones, and the winner will enter the next generation. Simulation results show that the proposed algorithm is more quickly and accurately to solve function optimization problems than the compared algorithms.

A Novel Time Decaying Model for P2P Networks

Abstract: In this paper, a novel time decaying model was proposed for peer-to-peer (P2P) network. In the proposed approach, the direct trust value and the recommendation trust value were considered concurrently for the P2P network. The direct trust value for all other peers were recorded in a dynamic vector, and then decreased with the time. Two different time-decaying functions were presented in this study to consider both efficiency and balance for the system, named exponential time decaying function and slow start and malicious avoid (SSMA) function, respectively. The two time decaying model were formulated and illustrated. Then, comparison was also made between the two time decaying models.

Memetic algorithm based on improved inver-over operator for TSP

Abstract: The Inver-over operator is always stuck to local optima in solving the Traveling Salesman Problem(TSP). In this paper, two improved Inver-over operators are proposed which contain the noise method(NM) based local search with multiple different neighboring structures. An effective memetic algorithm(MA) based on two improved Inver-over operators is implemented, which conduct different operators in different stages, and then improve the convergence speed of the proposed algorithm while maintain the popular diversification. In addition, the adaptive Meta-Lamarckian learning strategy is applied in the local search, which decides the different neighboring structure in the evolution. Experimental results show that the proposed algorithm is efficient.

Solving the single machine total earliness and tardiness problem with a common due date by differential evolution

Abstract: A differential evolution (DE) is presented for Minimizing earliness and tardiness penalties in a single machine problem with a common due date. Some control parameters of DE such as population, termination, and crossover factor, are selected according to the dynamic process of evolution, so the DE is very effective and efficient on finding optimum or near-optimal solutions. In order to improve solution quality, we combine DE with simulated annealing, local search and iterated local search respectively, and three hybrid heuristics, DE1, DE2 and DE3, are derived. Computational results based on the well known benchmark suites in the literature show that all the hybrid heuristics produce slightly better results than the GA of Hino et al.

A hybrid particle swarm optimization algorithm for bi-criteria flexible job-shop scheduling problem

Abstract: This paper presents a hybrid particle swarm optimization algorithm (HPSO) for solving the bi-criteria flexible job shop scheduling problem. Two minimization objectives- the maximum completion time (makespan) and the total workload of all machines are considered simultaneously. In this study, a novel discrete particle swarm optimization (PSO) algorithm was proposed, which incorporates well-designed crossover and mutation operators concurrently. Then, an external Parteo archive was developed to memory the Pareto optimal solutions found so far. In addition, to improve the efficiency of the scheduling algorithm, a speed-up method was devised to decide the domination status of a solution with the archive set. Experimental results on two well-known benchmarks show the efficiency of the proposed hybrid algorithm. It is concluded that the HPSO algorithm is superior to the existing present algorithms in term of both search quality and computational efficiency.