A fast hybrid tabu search algorithm for the no-wait job shop problem

This paper describes a hybrid tabu search algorithm dedicated to a job shop problem with a no-wait constraint with a makespan criterion. The proposed here algorithm complexity is that the superior algorithm based on the tabu search technique selects parameters controlling the work of a certain constructional algorithm. This approach limits the checked solutions only to a group of solutions being able to be generated by the structural algorithm in question. It bears serious consequences both positive, for example it limits the research scope for a small fraction of relatively extremely well quality of acceptable solutions, and negative that is the lack of possibility of finding the optimal solution. In this paper numerical researches of the proposed algorithm are conducted as well as a comparative analysis with reference to the literature algorithms of the algorithm in question is made.     

An efficient simple metaheuristic for minimizing the makespan in two-machine no-wait job shops

This paper examines the problem of scheduling two-machine no-wait job shops to minimize makespan. The problem is known to be strongly NP-hard. A two-phase heuristic is developed to solve the problem. Phase 1 of the heuristic transforms the problem into a no-wait flow shop problem and solves it using the well known Gilmore and Gomory algorithm. Phase 2 of the heuristic improves the solution obtained in phase 1 using a simple tabu search algorithm. Computational results show that the proposed heuristic performs extremely well in terms of both solution quality and computation time. It finds an optimal solution to about 90% of the problem instances and the average deviation from the lower bond for the other problem instances is infinitesimal.     

A tabu search/path relinking algorithm to solve the job shop scheduling problem

We present an algorithm that incorporates a tabu search procedure into the framework of path relinking to generate solutions to the job shop scheduling problem (JSP). This tabu search/path relinking (TS/PR) algorithm comprises several distinguishing features, such as a specific relinking procedure to effectively construct a path linking the initiating solution and the guiding solution, and a reference solution determination mechanism based on two kinds of improvement methods. We evaluate the performance of TS/PR on almost all of the benchmark JSP instances available in the literature. The test results show that TS/PR obtains competitive results compared with state-of-the-art algorithms for JSP in the literature, demonstrating its efficacy in terms of both solution quality and computational efficiency. In particular, TS/PR is able to improve the upper bounds for 49 out of the 205 tested instances and it solves a challenging instance that has remained unsolved for over 20 years.     

Tardiness minimization in a flexible job shop: A tabu search approach

This paper addresses the problem of scheduling jobs in a flexible job shop with the objective of minimizing total tardiness. The flexible job shop differs from the classical job shop in that each of the operations associated with a job can be processed on any of a set of alternative machines. Two heuristics based on tabu search are developed for this problem: a hierarchical procedure and a multiple start procedure. The procedures use dispatching rules to obtain an initial solution and then search for improved solutions in neighborhoods generated by the critical paths of the jobs in a disjunctive graph representation. Diversification strategies are also implemented and tested. The outcomes of extensive computational results are reported.     

An improved heuristic to minimize total flow time for scheduling in the m-machine no-wait flow shop

In this paper, we present a constructive heuristic to minimize total flow time criterion for the well-known NP-hard no-wait flow shop scheduling problem. It is based on the assumption that the priority of a job in the initial sequence is given by the sum of its processing times on the bottleneck machines. The initial sequence of jobs thus generated is further improved using a new job insertion technique. We show, through computational experimentation, that the proposed method significantly outperforms the best-known heuristics while retaining its time complexity of O(n²). Statistical tests of significance are used to confirm the improvement in solution quality.     

Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan

The multistage hybrid flow shop (HFS) scheduling problems are considered in this paper. Hybrid flowshop scheduling problems were proved to be NP-hard. A recently developed cuckoo search (CS) metaheuristic algorithm is presented in this paper to minimize the makespan for the HFS scheduling problems. A constructive heuristic called NEH heuristic is incorporated with the initial solutions to obtain the optimal or near optimal solutions rapidly in the improved cuckoo search (ICS) algorithm. The proposed algorithm is validated with the data from a leading furniture manufacturing company. Computational results show that the ICS algorithm outperforms many other metaheuristics.     

An evolutionary clustering search for the no-wait flow shop problem with sequence dependent setup times

This paper examines the m machine no-wait flow shop problem with setup times of a job separated from its processing time. The performance measure considered is the makespan. The hybrid metaheuristic Evolutionary Cluster Search (ECS_NSL) proposed in Nagano et al. (2012) is extended to solve the scheduling problem. The ECS_NSL performance is evaluated and the results are compared with the best method reported in the literature. Experimental tests show superiority of the ECS_NSL regarding the solution quality.     

A tabu search algorithm for the job shop problem with sequence dependent setup times

This paper addresses the classic job shop scheduling problem where sequence dependent setup times are present. Based on a modified disjunctive graph, we further investigate and generalize structural properties for the problem under study. A tabu search algorithm with a sophisticated neighbourhood structure is then developed. Compared to most studies in this research area, we are interested in moving internal critical operations rather than merely focusing on non-internal ones. Moreover, neighbourhood functions are defined using insertion techniques instead of simple swaps. Test results show that our algorithm outperforms a simulated annealing algorithm which is recently published. We have also conducted experiments considering the efficiency of developed propositions.     

Parallel tabu search algorithm for the hybrid flow shop problem

The paper deals with the parallel variant of the scheduling algorithm dedicated to the hybrid flow shop problem. The problem derives from practice of automated manufacturing lines, e.g. for printed packages. The overall goal is to design a new algorithm which merges the performance of the best known sequential approach with the efficient exploitation of parallel calculation environments. In order to fulfill the above aim, there are two methods proposed in this paper: the original fast method of parallel calculation of the criterion function and the local neighborhood parallel search method embedded in the tabu search approach. The theoretical analysis, as well as the original implementation, with the use of vector processing instructions SSE2 supported by suitable data organization, are presented below. Numerical properties of the proposed algorithm are empirically verified on the multi-core processor.     

Solving two-stage hybrid flow shop using climbing depth-bounded discrepancy search

This paper investigates how to adapt a discrepancy-based search method to solve two-stage hybrid flowshop scheduling problems in which each stage consists of several identical machines operating in parallel. The objective is to determine a schedule that minimizes the makespan. We present an adaptation of the Climbing Depth-bounded Discrepancy Search (CDDS) method based on Johnson’s rule and on dedicated lower bounds for the two-stage hybrid flow shop problem. We report the results of extensive computational experiments, which show that the proposed adaptation of the CDDS method solves instances in restrained CPU time and with high quality of makespan.     

Population-based neighborhood search for job shop scheduling with interval processing time

This paper applies interval number theory to production scheduling for its advantage in uncertainty modeling. A job shop scheduling problem with interval processing time is first described and then a population-based neighborhood search (PNS) is presented to optimize the interval makespan of the problem. In PNS, an ordered operation-based representation is used and a decoding procedure is constructed by using operations of interval numbers, in which there are no approximate treatments. It is proved that the possible actual makespan of each schedule are contained in its interval makespan. A swap operation and binary tournament selection are applied to update the population. PNS is finally tested by using some instances and computational results show that PNS can provide better results than some methods from the literature.     

A hybrid particle swarm optimization for job shop scheduling problem

A hybrid particle swarm optimization (PSO) for the job shop problem (JSP) is proposed in this paper. In previous research, PSO particles search solutions in a continuous solution space. Since the solution space of the JSP is discrete, we modified the particle position representation, particle movement, and particle velocity to better suit PSO for the JSP. We modified the particle position based on preference list-based representation, particle movement based on swap operator, and particle velocity based on the tabu list concept in our algorithm. Giffler and Thompson’s heuristic is used to decode a particle position into a schedule. Furthermore, we applied tabu search to improve the solution quality. The computational results show that the modified PSO performs better than the original design, and that the hybrid PSO is better than other traditional metaheuristics.     

Total flow time minimization in a flowshop sequence-dependent group scheduling problem

We have developed a mathematical programming model for minimizing total flow time of the flow shop sequence dependent group scheduling (FSDGS) problem, typically classified as F_m|fmls, S_plk, prmu|∑C_j. As the problem is shown to be strongly NP-hard, a tabu search (TS) algorithm as well as a hybrid ant colony optimization (HACO) algorithm have been developed to heuristically solve the problem. A lower bounding (LB) method based on the Branch-and-Price algorithm is also developed to evaluate the quality of the metaheuristic algorithms. In order to compare the performance of metaheuristic algorithms, random test problems, ranging in size from small, medium, to large are created and solved by both the TS and the HACO algorithms. A comparison shows that the HACO algorithm has a better performance than the TS algorithm. The results of the heuristic algorithms are also compared with the results of the LB method to evaluate the quality of the solutions. The LB method presented in this paper can be generalized to solve the FSDGS problem with other objective functions.     

Makespan minimization in a no-wait flow shop problem with two batching machines

This paper deals with the problem of task scheduling in a no-wait flowshop with two batching machines. Each task has to be processed by both machines. All tasks visit the machines in the same order. Batching machines can process several tasks per batch so that all tasks of the same batch start and complete together. The batch processing time for the first machine is equal to the maximal processing time of the tasks in this batch, and for the second machine is equal to the sum of the processing times of the tasks in this batch. We assume that the capacity of any batch on the first machine is bounded, and that when a batch is completed on the first machine it is immediately transferred to the second machine. The aim is to make batching and sequencing decisions that allow the makespan to be minimized.     

An improved heuristic for no-wait flow shop to minimize makespan

No-wait flow shop production has been widely applied in manufacturing, where no waiting time is allowed between intermediate operations. However, minimization of makespan for no-wait flow shop production is NP-hard. In this paper, we propose an average idle time (AIT) heuristic to minimize makespan in no-wait flow shop production. First, we take the current idle times and future idle times into consideration, proposing an initial sequence algorithm, and then use the insertion and neighborhood exchanging methods to further improve solutions. Compared with three existing best-known heuristics, our AIT heuristic can achieve the smallest deviations of 0.23% from optimum, based on Taillard’s benchmarks and 600 randomly generated instances, in the same computational complexity.     

A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem

This paper proposes a hybrid modified global-best harmony search (hmgHS) algorithm for solving the blocking permutation flow shop scheduling problem with the makespan criterion. First of all, the largest position value (LPV) rule is proposed to convert continuous harmony vectors into job permutations. Second, an efficient initialization scheme based on the Nawaz-Enscore-Ham (NEH) heuristic is presented to construct the initial harmony memory with a certain level of quality and diversity. Third, harmony search 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. Moreover, a new pitch adjustment rule is developed to well inherit good structures from the global-best harmony vector. Computational simulations and comparisons demonstrated the superiority of the proposed hybrid harmony search algorithm in terms of solution quality.     

Flexible job shop scheduling using hybrid differential evolution algorithms

This paper proposes hybrid differential evolution (HDE) algorithms for solving the flexible job shop scheduling problem (FJSP) with the criterion to minimize the makespan. Firstly, a novel conversion mechanism is developed to make the differential evolution (DE) algorithm that works on the continuous domain adaptive to explore the problem space of the discrete FJSP. Secondly, a local search algorithm based on the critical path is embedded in the DE framework to balance the exploration and exploitation by enhancing the local searching ability. In addition, in the local search phase, the speed-up method to find an acceptable schedule within the neighborhood structure is presented to improve the efficiency of whole algorithms. Extensive computational results and comparisons show that the proposed algorithms are very competitive with the state of the art, some new best known solutions for well known benchmark instances have even been found.     

A GES/TS algorithm for the job shop scheduling

The job shop scheduling problem is a difficult combinatorial optimization problem. This paper presents a hybrid algorithm which combines global equilibrium search, path relinking and tabu search to solve the job shop scheduling problem. The proposed algorithm used biased random sampling to have a better covering of the solution space. In addition, a new version of N6 neighborhood is applied in a tabu search framework. In order to evaluate the algorithm, comprehensive tests are applied to it using various standard benchmark sets. Computational results confirm the effectiveness of the algorithm and its high speed. Besides, 19 new upper bounds among the unsolved problems are found.     

Scatter search with path relinking for the job shop with time lags and setup times

This paper addresses the job shop scheduling problem with time lags and sequence-dependent setup times. This is an extension of the job shop scheduling problem with many applications in real production environments. We propose a scatter search algorithm which uses path relinking and tabu search in its core. We consider both feasible and unfeasible schedules in the execution, and we propose effective neighborhood structures with the objectives of reducing the makespan and regain feasibility. We also define new procedures for estimating the quality of the neighbors. We conducted an experimental study to compare the proposed algorithm with the state-of-the-art, in benchmarks both with and without setups. In this study, our algorithm has obtained very competitive results in a reduced run time.