Xingye Dong

An iterated local search algorithm for the permutation flowshop problem with total flowtime criterion

An ILS algorithm is proposed to solve the permutation flowshop sequencing problem with total flowtime criterion. The effects of different initial permutations and different perturbation strengths are studied. Comparisons are carried out with three constructive heuristics, three ant-colony algorithms and a particle swarm optimization algorithm. Experiments on benchmarks and a set of random instances show that the proposed algorithm is more effective. The presented ILS improves the best known permutations by a significant margin.

Iterated variable neighborhood descent algorithm for the capacitated vehicle routing problem

The capacitated vehicle routing problem (CVRP) aims to determine the minimum total cost routes for a fleet of homogeneous vehicles to serve a set of customers. A wide spectrum of applications outlines the relevance of this problem. In this paper, a hybrid heuristic method IVND with variable neighborhood descent based on multi-operator optimization is proposed for solving the CVRP. A perturbation strategy has been designed by cross-exchange operator to help optimization escape from local minima. The performance of our algorithm has been tested on 34 CVRP benchmark problems and it shows that the proposed IVND performs well and is quite competitive with other state-of-the-art heuristics. Additionally, the proposed IVND is flexible and problem dependent, as well as easy to implement.

An improved NEH-based heuristic for the permutation flowshop problem

NEH is an effective heuristic for solving the permutation flowshop problem with the objective of makespan. It includes two phases: generate an initial sequence and then construct a solution. The initial sequence is studied and a strategy is proposed to solve job insertion ties which may arise in the construct process. The initial sequence which is generated by combining the average processing time of jobs and their standard deviations shows better performance. The proposed strategy is based on the idea of balancing the utilization among all machines. Experiments show that using this strategy can improve the performance of NEH significantly. Based on the above ideas, a heuristic NEH-D (NEH based on Deviation) is proposed, whose time complexity is O(mn^2), the same as that of NEH. Computational results on benchmarks show that the NEH-D is significantly better than the original NEH.

A multi-restart iterated local search algorithm for the permutation flow shop problem minimizing total flow time

A variety of metaheuristics have been developed to solve the permutation flow shop problem minimizing total flow time. Iterated local search (ILS) is a simple but powerful metaheuristic used to solve this problem. Fundamentally, ILS is a procedure that needs to be restarted from another solution when it is trapped in a local optimum. A new solution is often generated by only slightly perturbing the best known solution, narrowing the search space and leading to a stagnant state. In this paper, a strategy is proposed to allow the restart solution to be generated from a group of solutions drawn from local optima. This allows an extension of the search space, while maintaining the quality of the restart solution. A multi-restart ILS (MRSILS) is proposed, with the performance evaluated on a set of benchmark instances and compared with six state of the art metaheuristics. The results show that the easily implementable MRSILS is significantly better than five of the other metaheuristics and comparable to or slightly better than the remaining one.

Self-adaptive perturbation and multi-neighborhood search for iterated local search on the permutation flow shop problem

A self-adaptive perturbation for iterated local search is developed and found to be effective.Multi-neighborhood search performance is improved through the application of self-adaptive perturbation.The performance of the proposed methods are found to be superior to other compared algorithms.New best solutions are found for 20 benchmark instances. The flow shop scheduling problem minimizing total flow time is a famous combinatorial optimization problem. Among the many algorithms proposed to solve this problem, iterated local search (ILS) is a simple, effective and efficient one. Research shows that the perturbation method and neighborhood structure play key roles in the performance of ILS. However, existing ILS lacks the self-adaptive ability to adjust the degree of perturbation relative to the search status. Also, only one basic insertion neighborhood is often used, greatly limiting the size of the search space and the ability to escape from a local optimum. In this work, a self-adaptive strategy is proposed, evaluating the neighborhoods around the local optimum and adjusting the perturbation strength according to this evaluation. If neighboring solutions are found to be considerably worse than the best known solution, indicating that it may be hard to escape from the local optimum, then the perturbation strength is amplified. Additionally, a swap neighborhood is incorporated with an insertion neighborhood to form a new version of multi-neighborhood search. Experimental results on benchmark instances show that the self-adaptive search performs significantly better than three state of the art algorithms, particularly when tested with extended CPU time. The multi-neighborhood search performs even better, also outperforming two state of the art variable neighborhood search algorithms, indicating that the hybrid use of insertion and swap neighborhoods is effective for the discussed problem.

An Ant Colony System Based Heuristic Algorithm for the Vehicle Routing Problem with Simultaneous Delivery and Pickup

The vehicle routing problem with simultaneous delivery and pickup(VRPSDP) is a general variant of Vehicle Routing Problem(VRP). Although there is a vast literature related to the VRP, little is dealing with the VRPSDP. In this paper, we propose a heuristic algorithm for solving the VRPSDP, based on the Ant Colony System(ACS). In our algorithm, the classical construction phase of the ACS is replaced by an alternative insertion procedure. Numerical experimental results show that our algorithm is effective for solving the VRPSDP, and it gets better solutions than those reported in the literature.

A New Hybrid Iterated Local Search for the Open Vehicle Routing Problem

Open vehicle routing problem (OVRP) aims to design a set of open vehicle routes with the least number of vehicles and the shortest total travel time, for serving a set of geographically distributed customers with known coordinates and demands. In this paper, a new hybrid iterated local search algorithm IVND is proposed for solving the OVRP. The IVND integrates a variable neighborhood descent (VND) procedure into the framework of iterated local search (ILS). Four different neighborhood structures, i.e., relocation, swap, 2-opt*, and 2-opt, are used in a VND procedure to improve the incumbent solution iteratively. A perturbation strategy is designed to help the search process jump from the local optima. Computational results on 16 benchmark problems instances show that the proposed algorithm can find the best known solutions for most of the problems within a short time, which indicates that the proposed hybrid metaheuristic algorithm is competitive with other state-of-the-art metaheuristics for solving the OVRP in terms of solution quality and efficiency.

Study on heuristics for the permutation flowshop with sequence dependent setup times

It is studied that several constructive heuristics for solving the sequence dependent setup time flowshop problem with the objective of minimizing makespan. Three priority rules imbedded in the heuristics are tested and a tie-breaking strategy is examined. The experimental results on benchmarks show that the priority rules are helpful to improve the performance, especially for the instances in which setup times are averagely smaller than the average processing time. The results also show that the setup times have a large effect on the performance of the heuristics.

A self-adaptive iterated local search algorithm on the permutation flow shop scheduling problem

Iterated local search (ILS) is a simple, effective and efficient metaheuristic, displaying strong performance on the permutation flow shop scheduling problem minimizing total flow time. Its perturbation method plays an important role in practice. However, in ILS, current methodology does not use an evaluation of the search status to adjust the perturbation strength. In this work, a method is proposed that evaluates the neighborhoods around the local optimum and adjusts the perturbation strength according to this evaluation using a technique derived from simulated-annealing. Basically, if the neighboring solutions are considerably worse than the best solution found so far, indicating that it is hard to escape from the local optimum, then the perturbation strength is likely to increase. A self-adaptive ILS named SAILS is proposed by incorporating this perturbation strategy. Experimental results on benchmark instances show that the proposed perturbation strategy is effective and SAILS performs better than three state of the art algorithms.

Iterated Local Search Algorithms for the Sequence-Dependent Setup Times Flow Shop Scheduling Problem Minimizing Makespan

Iterated Local Search (ILS) algorithm is a simple and effective metaheuristic for permutation flow shop scheduling problem (PFSP) minimizing the total flow time. In this work, the ILS algorithms are studied to deal with the PFSP with sequence-dependent setup times (SDST-PFSP) minimizing makespan. The first two methods, originally proposed for the PFSP minimizing total flow time, are adapted for the discussed problem. Four other ILS versions are also designed using different perturbation methods. Experimental results on a benchmark set show that the proposed ILSs can solve the discussed problem more effectively, and much better than the iterated greedy algorithm, one of the existing state-of-the-art algorithms. This work shows that the ILS is a promising method for extended types of scheduling problems.