Johnson’s rule,composite jobs and the relocation problem

Two-machine flowshop scheduling to minimize makespan is one of the most well-known classical scheduling problems. Johnson’s rule for solving this problem has been widely cited in the literature. We introduce in this paper the concept of composite job, which is an artificially constructed job with processing times such that it will incur the same amount of idle time on the second machine as that incurred by a chain of jobs in a given processing sequence. This concept due to Kurisu first appeared in 1976 to deal with the two-machine flowshop scheduling problem involving precedence constraints among the jobs. We show that this concept can be applied to reduce the computational time to solve some related scheduling problems. We also establish a link between solving the two-machine flowshop makespan minimization problem using Johnson’s rule and the relocation problem introduced by Kaplan. We present an intuitive interpretation of Johnson’s rule in the context of the relocation problem.     

Scheduling alternative operations in two-machine flow-shops

Most of the previous studies on scheduling problems assume that each machine is used exclusively for one operation although it has, in practice, potential to carry out some others. This paper studies two-machine flow-shop scheduling problems in which either or both machines are versatile so that alternative operations are possible. Branch-and-bound algorithms are developed to minimize the makespan of jobs for these problems and computational experiments are conducted to illustrate the effectiveness of these algorithms.     

A branch-and-bound algorithm for a two-machine flowshop scheduling problem with limited waiting time constraints

In this paper, we consider a two-machine flowshop scheduling problem in which the waiting time of each job between the two machines cannot be greater than a certain time period. For the problem with the objective of minimizing makespan, we identify several dominance properties of the problem and develop a branch-and-bound (B&B) algorithm using the dominance properties. Computational tests are performed on randomly generated test problems for evaluation of performance of the B&B algorithm, and results show that the algorithm can solve problems with up to 150 jobs in a reasonable amount of CPU time.     

Minimizing makespan on a two-machine re-entrant flowshop

This paper focuses on a two-machine re-entrant flowshop scheduling problem with the objective of minimizing makespan. In the re-entrant flowshop considered here, all jobs must be processed twice on each machine, that is, each job should be processed on machine 1, machine 2 and then machine 1 and machine 2. We develop dominance properties, lower bounds and heuristic algorithms for the problem, and use these to develop a branch and bound algorithm. For evaluation of the performance of the algorithms, computational experiments are performed on randomly generated test problems. Results of the experiments show that the suggested branch and bound algorithm can solve problems with up to 200 jobs in a reasonable amount of CPU time.     

The two-machine flowshop scheduling problem with sequence-independent setup times: New lower bounding strategies

The two-machine flowshop environment with sequence-independent setup times has been intensely investigated both from theoretical and practical perspectives in the scheduling literature. Nevertheless, very scant attention has been devoted to deriving effective lower bounding strategies. In this paper, we propose new lower bounds for the total completion time minimization criterion. These bounds are based on three relaxation schemes, namely the waiting time-based relaxation scheme, the single machine-based relaxation scheme, and the Lagrangian relaxation scheme. Extensive computational study carried on instances with up to 500 jobs reveals that embedding the waiting time-based bounding strategy within the Lagrangian relaxation framework yields the best performance while requiring negligible CPU time.     

Single-machine and two-machine flowshop scheduling problems with truncated position-based learning functions

Scheduling with learning effects has received growing attention nowadays. A well-known learning model is called ‘position-based learning’ in which the actual processing time of a job is a non-increasing function of its position to be processed. However, the actual processing time of a given job drops to zero precipitously as the number of jobs increases. Motivated by this observation, we propose two truncated learning models in single-machine scheduling problems and two-machine flowshop scheduling problems with ordered job processing times, respectively, where the actual processing time of a job is a function of its position and a control parameter. Under the proposed learning models, we show that some scheduling problems can be solved in polynomial time. In addition, we further analyse the worst-case error bounds for the problems to minimize the total weighted completion time, discounted total weighted completion time and maximum lateness.     

Minimizing total tardiness on a two-machine re-entrant flowshop

We present a branch and bound algorithm for a two-machine re-entrant flowshop scheduling problem with the objective of minimizing total tardiness. In the re-entrant flowshop considered here, all jobs must be processed twice on each machine, that is, each job should be processed on machine 1, machine 2 and then machine 1 and machine 2. By regarding a job as a pair of sub-jobs, each of which represents a pass through the two machines, we develop dominance properties, a lower bound and heuristic algorithms for the problem, and use these to develop a branch and bound algorithm. For evaluation of the performance of the algorithms, computational experiments are performed on randomly generated test problems and results are reported. Results of the experiments show that the suggested branch and bound algorithm can solve problems with up to 20 sub-jobs in a reasonable amount of CPU time, and the average percentage gap of the heuristic solutions is about 13%.     

Minimizing due date related performance measures on two batch processing machines

This paper considers a scheduling problem in a two-machine flowshop of two batch processing machines. On each batch processing machine, jobs are processed in a batch, and each batch is allowed to contain jobs up to the maximum capacity of the associated machine. The scheduling problem is analyzed with respect to three due date related objectives including maximum tardiness, number of tardy jobs and total tardiness. In the analysis, several solution properties are characterized and based upon these properties, three efficient polynomial time algorithms are developed for minimizing the due date related measures.     

On the complexity of two-machine flowshop problems with due date related objectives

The complexity of two-machine flowshop problems with due date related criteria is investigated. It is shown that when certain restrictions are imposed on the job processing times and their due dates these problems become polynomially solvable while when these restrictions are slightly weakened the problems remain NP-complete.     

A bicriteria flowshop scheduling with a learning effect

In many situations, a worker’s ability improves as a result of repeating the same or similar tasks; this phenomenon is known as the learning effect. In this paper the learning effect is considered in a two-machine flowshop. The objective is to find a sequence that minimizes a weighted sum of total completion time and makespan. Total completion time and makespan are widely used performance measures in scheduling literature. To solve this scheduling problem, an integer programming model with n² + 6n variables and 7n constraints where n is the number of jobs is formulated. Because of the lengthy computing time and high computing complexity of the integer programming model, the problem with up to 30 jobs can be solved. A heuristic algorithm and a tabu search based heuristic algorithm are presented to solve large size problems. Experimental results show that the proposed heuristic methods can solve this problem with up to 300 jobs rapidly. According to the best of our knowledge, no work exists on the bicriteria flowshop with a learning effect.     

Two-machine flowshop scheduling problems involving a batching machine with transportation or deterioration consideration

This paper considers two scheduling problems for a two-machine flowshop where a single machine is followed by a batching machine. The first problem is that there is a transporter to carry the jobs between machines. The second problem is that there are deteriorating jobs to be processed on the single machine. For the first problem with minimizing the makespan, we formulate it as a mixed integer programming model and then prove that it is strongly NP-hard. A heuristic algorithm is proposed for solving this problem and its worst case performance is analyzed. The computational experiments are carried out and the numerical results show that the heuristic algorithm is effective. For the second problem, we derive the optimal algorithms with polynomial time for minimizing the makespan, the total completion time and the maximum lateness, respectively.     

Two-machine flowshop scheduling to minimize the number of tardy jobs

We consider the two-machine flowshop problem with the objective of minimizing the total number of tardy jobs. Since this problem is known to be strongly NP-hard, algorithms are described for four polynomially solvable special cases. In addition, several heuristic algorithms are developed to find optimal or near optimal schedules. Results of computational tests in solving problems up to 60 jobs are reported and directions for future research are provided.     

Flowshop scheduling problem to minimize total completion time with random and bounded processing times

The flowshop scheduling problems with n jobs processed on two or three machines, and with two jobs processed on k machines are addressed where jobs have random and bounded processing times. The probability distributions of random processing times are unknown, and only the lower and upper bounds of processing times are given before scheduling. In such cases, there may not exist a unique schedule that remains optimal for all feasible realizations of the processing times, and therefore, a set of schedules has to be considered which dominates all other schedules for the given criterion. We obtain sufficient conditions when transposition of two jobs minimizes total completion time for the cases of two and three machines. The geometrical approach is utilized for flowshop problem with two jobs and k machines.     

The Systems Approach to Hospitals

By exploiting the relationship between scheduling and sorting, this paper describes a functional heuristic algorithm for seeking a quick and approximate solution to the n-job, M-machine flowshop scheduling problem under the assumption that all jobs are processed on all machines in the same order and no passing of jobs is permitted. The proposed functional heuristic algorithm can be executed by hand for reasonably large size problems and yields solutions which are closer to optimal solutions than those obtained by Palmer's slope index algorithm.     

Stochastically minimizing total flowtime in flowshops with no waiting space

The problem of scheduling n jobs in a two-machine flowshop with constant and known processing times is considered with the total flowtime performance measure. The machines are subject to random breakdowns and there is no waiting space between them. The problem is formulated and an expression for the completion time of the jobs is obtained in terms of the processing times and the breakdown elements. Provided that the counting processes associated with both machines have stationary increments property, a sequence that stochastically minimizes the performance criterion is established for the cases when only the first or the second machine suffers breakdowns.     

A hybrid three-stage flowshop problem: Efficient heuristics to minimize makespan

We treat a problem of scheduling n jobs on a three stages hybrid flowshop of particular structure (one machine in the first and third stages and two dedicated machines in stage two). The objective is to minimize the makespan. This problem is NP-complete. We propose two heuristic procedures to cope with realistic problems. Extensive experimentation with various problem sizes are conducted and the computational results show excellent performance of the proposed heuristics.     

Scheduling under a Non-renewable Resource Constraint

This paper examines a single-machine, non-renewable-resource-constrained scheduling problem where jobs have arbitrary processing times and resource requirements. Unit supply of a resource is assumed at each time period. Performance criterion is makespan. It is proved that this problem is identical to the two-machine flowshop problem, enabling the use of Johnson's algorithm. Immediate extensions of this result are presented.     

Minimizing makespan with multiple-orders-per-job in a two-machine flowshop

We investigate a new scheduling problem, multiple-orders-per-job (MOJ), in the context of a two-machine flowshop. Lower bounds for the makespan performance measure are provided for combinations of lot-processing and item-processing machines. An optimization model is presented that addresses both job formation and job sequencing. We define a heuristic to minimize the makespan for the MOJ problem for two-machine item-processing flowshops. The heuristic obtains solutions within 2% of a tight lower bound and runs in O(HF) time, where H is the number of orders and F is the restricted number of jobs.     

A two-machine flow shop scheduling problem with controllable job processing times

The paper deals with a two-machine flow shop scheduling problem in which both the sequence of jobs and their processing times are decision variables. It is assumed that the cost of performing a job is a linear function of its processing time, and the schedule cost to be minimized is the total processing cost plus maximum completion time cost. In is shown that the decision form of this problem is NP-complete, even when the processing times on one machine only are controllable and all the processing cost units are identical. Two heuristic methods for solving the problem are proposed and their worst-case analysis is presented.     

A two-machine flowshop scheduling problem with a truncated sum of processing-times-based learning function

Scheduling with learning effects has received continuing attention in the recent days. However, it can be found that the actual processing time of a given job drops to zero precipitously as the job has a big processing time or the number of jobs increases. Moreover, most researchers paid more attention to single-machine settings, and the flowshop settings then are relatively unexplored. Motivated by these observations, we consider a two-machine total completion time flowshop problem in which the actual job processing time is a function depending on the jobs that have already been processed and a control parameter. In this paper, we develop a branch-and-bound and a genetic heuristic-based algorithm for the problem. In addition, the experimental results of all proposed algorithms are also provided.