Quan-Ke Pan

A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem

In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the no-wait flowshop scheduling problem with both makespan and total flowtime criteria. The main contribution of this study is due to the fact that particles are represented as discrete job permutations and a new position update method is developed based on the discrete domain. In addition, the DPSO algorithm is hybridized with the variable neighborhood descent (VND) algorithm to further improve the solution quality. Several speed-up methods are proposed for both the swap and insert neighborhood structures. The DPSO algorithm is applied to both 110 benchmark instances of Taillard [Benchmarks for basic scheduling problems. European Journal of Operational Research 1993;64:278-85] by treating them as the no-wait flowshop problem instances with the total flowtime criterion, and to 31 benchmark instances provided by Carlier [Ordonnancements a contraintes disjonctives. RAIRO Recherche operationelle 1978;12:333-51], Heller [Some numerical experiments for an MxJ flow shop and its decision-theoretical aspects. Operations Research 1960;8:178-84], and Revees [A genetic algorithm for flowshop sequencing. Computers and Operations Research 1995;22:5-13] for the makespan criterion. For the makespan criterion, the solution quality is evaluated according to the reference makespans generated by Rajendran [A no-wait flowshop scheduling heuristic to minimize makespan. Journal of the Operational Research Society 1994;45:472-8] whereas for the total flowtime criterion, it is evaluated with the optimal solutions, lower bounds and best known solutions provided by Fink and Vosz [Solving the continuous flow-shop scheduling problem by metaheuristics. European Journal of Operational Research 2003;151:400-14]. The computational results show that the DPSO algorithm generated either competitive or better results than those reported in the literature. Ultimately, 74 out of 80 best known solutions provided by Fink and Vosz [Solving the continuous flow-shop scheduling problem by metaheuristics. European Journal of Operational Research 2003;151:400-14] were improved by the VND version of the DPSO algorithm.

A discrete differential evolution algorithm for the permutation flowshop scheduling problem

Very recently, Pan et al. [Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO07, pp. 126-33] presented a new and novel discrete differential evolution algorithm for the permutation flowshop scheduling problem with the makespan criterion. On the other hand, the iterated greedy algorithm is proposed by [Ruiz, R., & Stutzle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-49] for the permutation flowshop scheduling problem with the makespan criterion. However, both algorithms are not applied to the permutation flowshop scheduling problem with the total flowtime criterion. Based on their excellent performance with the makespan criterion, we extend both algorithms in this paper to the total flowtime objective. Furthermore, we propose a new and novel referenced local search procedure hybridized with both algorithms to further improve the solution quality. The referenced local search exploits the space based on reference positions taken from a reference solution in the hope of finding better positions for jobs when performing insertion operation. Computational results show that both algorithms with the referenced local search are either better or highly competitive to all the existing approaches in the literature for both objectives of makespan and total flowtime. Especially for the total flowtime criterion, their performance is superior to the particle swarm optimization algorithms proposed by [Tasgetiren, M. F., Liang, Y. -C., Sevkli, M., Gencyilmaz, G. (2007). Particle swarm optimization algorithm for makespan and total flowtime minimization in permutation flowshop sequencing problem. European Journal of Operational Research, 177(3), 1930-47] and [Jarboui, B., Ibrahim, S., Siarry, P., Rebai, A. (2007). A combinatorial particle swarm optimisation for solving permutation flowshop problems. Computers &Industrial Engineering, doi:10.1016/j.cie.2007.09.006]. Ultimately, for Taillards benchmark suite, four best known solutions for the makespan criterion as well as 40 out of the 90 best known solutions for the total flowtime criterion are further improved by either one of the algorithms presented in this paper.

A Discrete Differential Evolution Algorithm for the No-Wait Flowshop Scheduling Problem with Total Flowtime Criterion

In this paper, a discrete differential evolution (DDE) algorithm is presented to solve the no-wait flowshop scheduling problem with the total flowtime criterion. The DDE algorithm is hybridized with the variable neighborhood descent (VND) algorithm to solve the well-known benchmark suites in the literature. The DDE algorithm is applied to the 110 benchmark instances of Taillard (1993) by treating them as the no-wait flowshop problem instances with the total flowtime criterion. The solution quality is evaluated with optimal solutions, lower bounds and best known solutions provided by Fink & Voss (2003). The computational results show that the DDE algorithm generated better results than those in Fink & Voss (2003).

A Discrete Differential Evolution Algorithm for the Total Earliness and Tardiness Penalties with a Common Due Date on a Single-Machine

In this paper, a discrete differential evolution (DDE) algorithm is presented to solve the single machine total earliness and tardiness penalties with a common due date. A new binary swap mutation operator called Bswap is presented. In addition, the DDE algorithm is hybridized with a local search algorithm to further improve the performance of the DDE algorithm. The performance of the proposed DDE algorithm is tested on 280 benchmark instances ranging from 10 to 1000 jobs from the OR Library. The computational experiments showed that the proposed DDE algorithm has generated better results than those in the literature in terms of both solution quality and computational time

Minimizing total earliness and tardiness penalties with a common due date on a single-machine using a discrete particle swarm optimization algorithm

In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the single machine total earliness and tardiness penalties with a common due date. A modified version of HRM heuristic presented by Hino et al. in [1], here we call it MHRM, is also presented to solve the problem. In addition, the DPSO algorithm is hybridized with the iterated local search (ILS) algorithm to further improve the solution quality. The performance of the proposed DPSO algorithm is tested on 280 benchmark instances ranging from 10 to 1000 jobs from the OR Library. The computational experiments showed that the proposed DPSO algorithm has generated better results, in terms of both percentage relative deviations from the upper bounds in Biskup and Feldmann and computational time, than Hino et al. [1].

A Discrete Particle Swarm Optimization Algorithm for Single Machine Total Earliness and Tardiness Problem with a Common Due Date

In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the single machine total earliness and tardiness penalties with a common due date. A modified version of HRM heuristic presented by Hino et al. in [7], here we call it M_HRM, is also presented to solve the problem. In addition, the DPSO algorithm is hybridized with the neighborhood search algorithm to further improve the solution quality. The performance of the proposed DPSO algorithm is tested on 280 benchmark instances up to 1000 jobs from the OR Library. The computational experiments showed that the proposed DPSO algorithm has generated better results, in terms of both percent deviations from the upper bounds in Biskup and Feldmann [1] and computational time, than the existing approaches in the literature.

Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion

Recently, iterated greedy algorithms have been successfully applied to solve a variety of combinatorial optimization problems. This paper presents iterated greedy algorithms for solving the blocking flowshop scheduling problem (BFSP) with the makespan criterion. Main contributions of this paper can be summed up as follows. We propose a constructive heuristic to generate an initial solution. The constructive heuristic generates better results than those currently in the literature. We employ and adopt well-known speed-up methods from the literature for both insertion and swap neighborhood structures. In addition, an iteration jumping probability is proposed to change the neighborhood structure from insertion neighborhood to swap neighborhood. Generally speaking, the insertion neighborhood is much more effective than the swap neighborhood for the permutation flowshop scheduling problems. Instead of considering the use of these neighborhood structures in a framework of the variable neighborhood search algorithm, two powerful local search algorithms are designed in such a way that the search process is guided by an iteration jumping probability determining which neighborhood structure will be employed. By doing so, it is shown that some additional enhancements can be achieved by employing the swap neighborhood structure with a speed-up method without jeopardizing the effectiveness of the insertion neighborhood. We also show that the performance of the iterated greedy algorithm significantly depends on the speed-up method employed. The parameters of the proposed iterated greedy algorithms are tuned through a design of experiments on randomly generated benchmark instances. Extensive computational results on Taillards well-known benchmark suite show that the iterated greedy algorithms with speed-up methods are equivalent or superior to the best performing algorithms from the literature. Ultimately, 85 out of 120 problem instances are further improved with substantial margins. A new constructive heuristic is proposed.Speed-up methods from the literature are adapted very well.IG algorithm is superior with the speed-up method. But without a speed-up method, its performance is poor.We propose an iteration jumping probability to employ the swap neighborhood structure.Ultimately, 85 out of 90 problem instances are further improved.

A Harmony Search Algorithm with Ensemble of Parameter Sets

This paper presents a harmony search algorithm with ensemble of parameter sets, named EHS algorithm, for solving continuous optimization problems. In the proposed algorithm, an ensemble of parameter sets is adopted to self-adaptively choose the best control parameters during the evolution process. This method not only eliminates the need to perform the trail-and-error search for the best single parameter set, but enables us to benefit from the match between the parameter sets, the different search phases, and the specific problems as well. Extensive computational simulations and comparisons are carried out by employing a set of 10 benchmark problems from the literature. The computational results show that the proposed EHS algorithm is more effective in finding better solutions than the state-of-the-art harmony search (HS) variants [1,2,3].

A genetic algorithm for the generalized traveling salesman problem

In a traveling salesman problem, if the set of nodes is divided into clusters so that a single node from each cluster can be visited, then the problem is known as the generalized traveling salesman problem where the objective is to find a tour with minimum cost passing through only a single node from each cluster. In this paper, a genetic algorithm is presented to solve the problem on a set of benchmark instances. The genetic algorithm is hybridized with an iterated local search to further improve the solution quality. Some speed-up methods are presented to accelerate the greedy node insertions. The genetic algorithm is tested on a set of benchmark instances with symmetric distances ranging from 51 to 442 nodes from the literature. Computational results show that the proposed genetic algorithm is the best performing algorithm so far in the literature in terms of solution quality.

A hybrid dynamic harmony search algorithm for identical parallel machines scheduling

In this article, a dynamic harmony search (DHS) algorithm is proposed for the identical parallel machines scheduling problem with the objective to minimize makespan. First, an encoding scheme based on a list scheduling rule is developed to convert the continuous harmony vectors to discrete job assignments. Second, the whole harmony memory (HM) is divided into multiple small-sized sub-HMs, and each sub-HM performs evolution independently and exchanges information with others periodically by using a regrouping schedule. Third, a novel improvisation process is applied to generate a new harmony by making use of the information of harmony vectors in each sub-HM. Moreover, a local search strategy is presented and incorporated into the DHS algorithm to find promising solutions. Simulation results show that the hybrid DHS (DHS_LS) is very competitive in comparison to its competitors in terms of mean performance and average computational time.