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

A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems

This paper proposes a tabu mechanism improved iterated greedy (TMIIG) algorithm to solve the no-wait flow-shop scheduling problem with makespan criterion. The motivation of seeking for further improvement in the iterated greedy (IG) algorithm framework is based on the observation that the construction phase of the original IG algorithm may lead to repeated search when applying the insertion neighborhood search. To overcome the drawback, we modified the IG algorithm by a tabu-based reconstruction strategy to enhance its exploitation ability. A powerful neighborhood search method which involves insert, swap, and double-insert moves is then applied to obtain better soluions from the reconstructed solution in the previous step. Numerical computations verified the advantages of utilizing the new reconstruction scheme. In addition, comparisons with other high-performing algorithms demonstrated the effectiveness and robustness of the proposed algorithm.

A variable iterated greedy algorithm with differential evolution for the no-idle permutation flowshop scheduling problem

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.

A hybrid discrete differential evolution algorithm for the no-idle permutation flow shop scheduling problem with makespan criterion

This paper deals with the total weighted tardiness and earliness penalties for lot-streaming flow shop scheduling problems. A discrete differential evolution (DDE) algorithm with job permutations based representation is proposed. In the proposed DDE algorithm, the DE-based evolution is used to perform global exploitation, and a local search procedure based on the insert and swap neighborhood structure is used to enhance the exploration capability, and 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 DDE algorithm.

This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem. Highlights The blocking flowshop scheduling problem is very attractive problem in engineering. A particle swarm optimization approach is proposed. Extensive experiments are conducted to choose the initialization way. Improvement procedure is introduced based on iterated local search algorithm. A real-world case is solved by the proposed algorithm.

Lot-streaming scheduling problem has been an active area of research due to its important applications in modern industries. This paper deals with the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion. An effective discrete invasive weed optimization (DIWO) algorithm is presented with new characteristics. A job permutation representation is utilized and an adapted Nawaz–Enscore–Ham heuristic is employed to ensure an initial weed colony with a certain level of quality. A new spatial dispersal model is designed based on the normal distribution and the property of tangent function to enhance global search. A local search procedure based on the insertion neighborhood is employed to perform local exploitation. The presented DIWO is calibrated by means of the design of experiments approach. A comparative evaluation is carried out with several best performing algorithms based on a total of 280 randomly generated instances. The numerical experiments show that the presented DIWO algorithm produces significantly better results than the competing algorithms and it constitutes a new state-of-the-art solution for the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion.

Memetic algorithm with node and edge histogram for no-idle flow shop scheduling problem to minimize the makespan criterion

Most of the metaheuristic algorithms for the no-wait flowshop scheduling problem with makespan criterion have adopted the O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) size insertion neighborhoods, and higher order (polynomial size) neighborhoods are seldom tried. However, higher order neighborhoods can improve the solution quality of metaheuristic algorithms. The paper presents a high order neighborhood with O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) size called nonadjacent job block exchange neighborhood and develops a fast search algorithms with O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) time complexity for it. An iterated local search algorithm is further presented for the considered problem, where the new neighborhood along with the insertion neighborhood is used in variable neighborhood decent to provide a local search procedure for iterated local search. Experimental comparison shows that the higher order neighborhood based iterated local search algorithm is both fast and effective.

This paper presents an efficient hybrid algorithms (EHA) based on harmony search (HS) algorithms and genetic algorithm (GA) for solving blocking flow shop scheduling problem. An improved GA is used to get better results. The computational result shows that EHA is not only better than GA , but also better than HS algorithm.

In this paper, a problem arising in the planning of specialized diagnostic services in a segmented public healthcare system is addressed. The problem consists of deciding which hospitals will provide the service and their capacity levels, the allocation of demand in each institution, and the reallocation of uncovered demand to other institutions or private providers, while minimizing the total equivalent annual cost of investment and operating cost required to satisfy all the demand. An associated mixed-integer linear programming model can be solved by conventional branch and bound for relatively small instances; however, for larger instances the problem becomes intractable. To effectively address larger instances, a hybrid metaheuristic framework combining iterated greedy (IGA) and variable neighborhood descent (VND) components for this problem is proposed. Two greedy construction heuristics are developed, one starting with an infeasible solution and iteratively adding capacity and the other starting with a feasible, but expensive, solution and iteratively decrease capacity. The iterated greedy algorithm includes destruction and reconstruction procedures. Four different neighborhood structures are designed and tested within a VND procedure. In addition, the computation of local search components benefit from an intelligent exploitation of problem structure since, when the first-level location variables (hospital location and capacity) are fixed, the remaining subproblem can be solved efficiently as it is very close to a transshipment problem. All components and different strategies were empirically assessed both individually and within the IGA-VND framework. The resulting metaheuristic is able to obtain near optimal solutions, within 3% of optimality, when tested over a data base of 60- to 300-hospital instances.

This paper presents an innovative hybrid estimation of distribution algorithm, named HEDA, for blocking flow-shop scheduling problem (BFSP) with sequence-dependent setup times (SDSTs) to minimize the makespan criterion, which has been proved to be typically NP-hard combinatorial optimization problem with strong engineering background. Firstly, several efficient heuristics are proposed according to the property of BFSP with SDSTs. Secondly, the genetic information of both the order of jobs and the promising blocks of jobs are concerned to generate the guided probabilistic model. Thirdly, after the HEDA-based global exploration, a reference sequence-based local search with path relinking technique is developed and incorporated into local exploitation to escape from local optima and improve the convergence property. Due to the reasonable balance between EDA-based global exploration and sequence dependent local exploitation as well as comprehensive utilization of the speedup evaluation method, the BFSP with SDSTs can be solved effectively and efficiently. Finally, computational results and comparisons with the existing state-of-the-art algorithms are carried out, which demonstrate the superiority of the proposed HEDA in terms of searching quality, robustness, and efficiency.

The blocking flow shop scheduling problem: A comprehensive and conceptual review

The energy problem in green manufacturing has attracted enormous attention from researchers and practitioners in the manufacturing domain with the global energy crisis and the aggravation of environmental pollution. The distributed blocking flow shop scheduling problem (DBFSP) has considerable application scenarios in connection with its widespread application in the industry under the background of intelligent manufacturing. A multi-objective discrete differential evolution (MODE) algorithm is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with the objectives of the makespan and total energy consumption (TEC) in this paper. The cooperative initialisation strategy is proposed to generate the initial population of the EEDBFSP. The mutation, crossover, and selection operators are redesigned to enable the MODE algorithm as applied to discrete space. A local search strategy based on the knowledge of five operators is introduced to enhance the exploitation capability of the MODE algorithm in the EEDBFSP. The non-critical path energy-efficient strategy is proposed to reduce energy consumption according to the specific constraints in the EEDBFSP. The effectiveness of each strategy in the MODE algorithm is verified and compared with the state-of-the-art algorithms. The numerical results demonstrate that the MODE algorithm is the efficient optimiser for solving the EEDBFSP.