Single-machine Group Scheduling Problems Research Articles

Group Scheduling With Nonperiodical Maintenance and Deteriorating Effects

In this paper, we consider single-machine group scheduling with nonperiodical maintenance and deteriorating effects. Nonperiodical maintenance, which has unfixed maintaining interval or the number of jobs in each group is unfixed, results in a variable number of groups. Deteriorating effects lead to longer processing times of which the deterioration index depends on job grouping. This problem is of significance in different production settings and is much more difficult than and general that other simpler single-machine group scheduling problems. Making use of historical processing times, we construct the actual processing time model for jobs. We prove that the problem under study is NP-hard. By transforming the optimization objective, properties are discovered and two batch-based heuristics are presented for small size problems. To further improve the effectiveness for large size problems, an iterated greedy algorithm is proposed being its main advantages simplicity and effectiveness. The proposed methods are evaluated over a large number of random instances with calibrated parameters and components. Comprehensive computational and statistical analyses demonstrate the superiority of the methods proposed over adapted existing approaches.

Single-machine group scheduling with new models of position-dependent processing times

Abstract We propose new models of position-dependent processing times for single-machine group scheduling problems. The two objectives of the scheduling problems are to minimize the makespan and the total completion time, respectively. We present polynomial-time algorithms to solve the problems.

Single-machine group scheduling problems with deteriorating and learning effect

The concepts of deteriorating jobs and learning effects have been individually studied in many scheduling problems. However, most studies considering the deteriorating and learning effects ignore the fact that production efficiency can be increased by grouping various parts and products with similar designs and/or production processes. This phenomenon is known as ‘group technology’ in the literature. In this paper, a new group scheduling model with deteriorating and learning effects is proposed, where learning effect depends not only on job position, but also on the position of the corresponding job group; deteriorating effect depends on its starting time of the job. This paper shows that the makespan and the total completion time problems remain polynomial optimal solvable under the proposed model. In addition, a polynomial optimal solution is also presented to minimise the maximum lateness problem under certain agreeable restriction.

Single-Machine Group Scheduling Problems with Variable Job Processing Times

This paper considers two resource constrained single-machine group scheduling problems. These problems involve variable job processing times (general position-dependent learning effects and deteriorating jobs); that is, the processing time of a job is defined by the function that involves its starting time and position in the group, and groups’ setup time is a positive strictly decreasing continuous function of the amount of consumed resource. Polynomial time algorithms are proposed to optimally solve the makespan minimization problem under the constraint that the total resource consumption does not exceed a given limit and the total resource consumption minimization problem under the constraint that the makespan does not exceed a given limit, respectively.

Single-machine bicriterion group scheduling with deteriorating setup times and job processing times

This paper considers a group scheduling problem with two ordered criteria where both setup times and job-processing times are increasing functions of their starting times. It is assumed that the jobs be classified into several groups and the jobs of the same group have to be processed contiguously. We consider two objectives where the primary criterion is the total weighted completion time and the secondary criterion is the maximum cost. A polynomial time algorithm is presented to solve this bicriterion group scheduling problem with deteriorating setup times and job-processing times. This algorithm can also solve single-machine group scheduling problems with deteriorating setup times and job-processing times in several ordered maximum cost and arbitrary precedence.

Single machine group scheduling with time and position dependent processing times

In the paper two resource constrained single-machine group scheduling problems with time and position dependent processing times are considered. By time and position dependent processing times and group technology assumption, we mean that the processing time of a job is defined by the function of its starting time and position in the group, and the group setup times of a group is a positive strictly decreasing continuous function of the amount of consumed resource. We present polynomial solutions for the makespan minimization problem under the constraint that the total resource consumption does not exceed a given limit, and the total resource consumption minimization problem under the constraint that the makespan does not exceed a given limit, respectively.

Single-machine group scheduling with time-dependent learning effect and position-based setup time learning effect

As to learning effect, it may be more appropriate to assume that position-based learning takes place during machine setups only, while sum-of-processing-time-based learning occurs in considering the experience that workers have gained from producing jobs. Thus, in this paper, we consider sum-of-processing-time-based learning on job processing time and position-based learning on setup time in single-machine group scheduling problems. The objectives are to minimize the makespan and the total completion time, respectively. We provide two polynomial time algorithms to solve the makespan minimization problems. On the other hand, we also provide two polynomial time algorithms to solve the total completion time minimization problems under certain conditions.

Single-machine group scheduling with general deterioration and learning effects

In this paper we consider single-machine group scheduling problems with effects of learning and deterioration at the same time. By effects of learning and deterioration, we mean that the group setup times are general linear functions of their starting times and the jobs in the same group have general position-dependent and time-dependent learning effects. The objective of scheduling problems is to minimize the makespan and the sum of completion times, respectively. We show that the problems remain solvable in polynomial time under the proposed model.

Single-machine group scheduling with both learning effects and deteriorating jobs

In the paper two resource constrained single-machine group scheduling problems with both learning effects and deteriorating jobs are considered. By learning effects, deteriorating jobs and group technology assumption, we mean that the processing time of a job is defined by the function of its starting time and position in the group, and the group setup times of a group is a positive strictly decreasing continuous function of the amount of consumed resource. We present polynomial solutions for the makespan minimization problem under the constraint that the total resource consumption does not exceed a given limit, and the total resource consumption minimization problem under the constraint that the makespan does not exceed a given limit, respectively.

Note on “A note on single-machine group scheduling problems with position-based learning effect”

In a recent paper, Lee and Wu [W.-C. Lee, C.-C. Wu, A note on single-machine group scheduling problems with position-based learning effect, Appl. Math. Model. 33 (2009) 2159–2163] proposed a new group scheduling learning model where the learning effect not only depends on the job position, but also depends on the group position. They investigate the makespan and the total completion time minimization problems on a single-machine. As for the total completion time minimization problem, they assumed that the numbers of jobs in each group are the same and the group normal setup and the job normal processing times are agreeable. Under the assumption conditions, they showed that the total completion time minimization problem can be optimally solved in polynomial time solution. However, the assumption conditions for the total completion time minimization problem do not reflect actual practice in many manufacturing processes. Hence, in this note, we propose other agreeable conditions and show that the total completion time minimization problem remains polynomially solvable under the agreeable conditions.

Single-machine group scheduling problems under the effects of deterioration and learning

This paper investigates single-machine group scheduling problems with simultaneous considerations of deteriorating and learning effects to minimize the makespan and the total completion time of all jobs. The group setup time is assumed to follow a simple linear time-dependent deteriorating model. Two models of learning for the job processing time are examined in this study. We provided polynomial time solutions for the makespan minimization problems. We also showed that the total completion time minimization problems remain polynomially solvable under agreeable conditions.

Single-machine group scheduling problems with deteriorated and learning effect

In many realistic situation, a job processed later consumes more time than the same job when it is processed earlier, this phenomenon is known as deteriorated effect. The skills of workers continuously improve when repeating the same or similar tasks, this phenomenon is as the ''learning effect'' in the literature. However, most studies considering the deteriorated and learning effect ignore the fact that production efficiency can be increased by grouping various parts and products with similar designs and/or production processes. This phenomenon is known ''group technology'' in the literature. In this paper, we propose a new group scheduling with deteriorated and learning model where the learning effect not only depends on job position, but also depends on the group position; the deteriorated effect depends on its starting time of the job. We then show that the single-machine makespan and the total completion time problems remain polynomial optimal solvable under the proposed model. In addition, we show the maximum lateness have a polynomial optimal solution under certain agreeable restriction.

Single-machine group-scheduling problems with deteriorating setup times and job-processing times

There is a growing interest in the research of deteriorating job-scheduling problems in recent years. However, the group technology is relatively unexplored in this field. In addition, the group setup times are assumed to be known and fixed. In reality, process setup or preparation often requires more time as food quality deteriorates or a patient's condition worsens. Therefore, this paper considers a situation where both setup times and job-processing times are lengthened as jobs wait to be processed. Specifically, two single-machine group-scheduling problems are investigated where the group setup times and the job-processing times are both increasing functions of their starting times. We first prove that the makespan minimization problem remains polynomially solvable when the deterioration is present. We then show that the sum of completion times problem is polynomially solvable when the numbers of jobs in each group are equal. For the case of unequal job sizes, a heuristic algorithm is proposed, and the computational experiments show that the performance of the heuristic is fairly accurately when the deterioration rate is small.

A note on single-machine group scheduling problems with position-based learning effect

In many situations, the skills of workers continuously improve when repeating the same or similar tasks. This phenomenon is known as the “learning effect” in the literature. However, most studies considering the learning effect ignore the fact that production efficiency can be increased by grouping various parts and products with similar designs and/or production processes. This phenomenon is known as “group technology” in the literature. In this paper, we propose a new group scheduling learning model where the learning effect not only depends on the job position, but also depends on the group position. We then show that the makespan and the total completion time problems remain polynomially solvable under the proposed model.

Single-machine group scheduling problems with deteriorating jobs

This paper investigates single-machine scheduling problems with deteriorating jobs and the group technology (GT) assumption. By deteriorating jobs and the group technology assumption, we mean that the group setup times and job processing times are both increasing functions of their starting times, i.e., the group setup times and job processing times are both described by a function which is proportional to a linear function of time. The two objectives of scheduling problems are to minimize the makespan and the total weighted completion time, respectively. We show that these problems remain solvable in polynomial time when deterioration and group technology are considered simultaneously.

Single-machine group scheduling problems with deterioration consideration

In many realistic situations, a job processed later consumes more time than the same job when it is processed earlier; this phenomenon is known as deteriorating jobs. However, job deterioration is relatively unexplored in the context of group technology. In this paper, we consider deterioration in the framework of minimizing the makespan and the total completion time in single-machine group scheduling problems. We show that these problems remain polynomially solvable when deterioration is considered.

Single-machine group scheduling with a time-dependent learning effect

In many realistic situations, the more time you practice, the better learning effect you obtain. Thus, we propose a time-dependent learning effect and introduce it into the single-machine group scheduling problems. The two objectives of scheduling problems are to minimize the makespan and the total completion time, respectively. We also provide two polynomial time algorithms to solve these problems.