Total Completion Time Minimization Problem Research Articles

Permutation flowshop manufacturing cell scheduling problems with deteriorating jobs and sequence dependent setup times under dominant machines

This paper investigates permutation flowshop manufacturing cell scheduling problems with deteriorating jobs and sequence dependent setup times under dominant machines. In the proposed models, we need to make joint decisions on part families sequencing and jobs sequencing within each family. To solve the makespan minimization problem, the structural properties of the optimal solutions are derived, based on which an optimization algorithm is developed. Then, we consider the total completion time minimization problem and propose a useful lemma for the optimal solutions. Finally, we discuss two special cases of the problem and propose optimization algorithms to solve them respectively.

Complexity of Scheduling with Proportional Deterioration and Release Dates

In this paper, we analyze the computational complexity of single-machine scheduling problems with deteriorating jobs having non-zero release dates. The processing time of each job is a proportional increasing function of its starting time. The objectives are to minimize the total weighted completion time, the number of tardy jobs and the total completion time, respectively. We first show that the total weighted completion time minimization problem and the number of tardy jobs minimization problem are binary NP-hard. Then, we present some optimal properties for the two problems under the restriction that the jobs have identical deteriorating rates. Finally, we show that the total completion time minimization problem with deadline is binary NP-hard, and in the case when the jobs have identical deadlines, agreeable release dates and deterioration rates, it can be solved in \(O(n\log n)\) time.

The Study of Group Scheduling Problems with General Dual-Position-Based Job Processing Times

Scheduling with group technology has been a vivid research area in the past decades. However, group technology with general dual-effect variable processing times needs to be further explored although this kind of group technology plays an important role in some actual manufacturing scenarios. Accordingly, this paper considers group scheduling problems with a kind of general group variable processing times model, where the actual processing time of each job in group is variable due to the dual effect of both the job position and the group position. The objectives of two types of considered problems are to minimize the makespan and the total completion time, respectively. Based on the decomposition analysis, the mathematical logic analysis and the computational complexity proof, it is obtained that the makespan minimization problem and the total completion time minimization problem are both polynomially solvable under the condition that the group number is constant. For three special cases of considered problems, polynomial solving algorithms with lower computational complexity are proposed.

Scheduling with non-decreasing deterioration jobs and variable maintenance activities on a single machine

ABSTRACTThere is a situation found in many manufacturing systems, such as steel rolling mills, fire fighting or single-server cycle-queues, where a job that is processed later consumes more time than that same job when processed earlier. The research finds that machine maintenance can improve the worsening of processing conditions. After maintenance activity, the machine will be restored. The maintenance duration is a positive and non-decreasing differentiable convex function of the total processing times of the jobs between maintenance activities. Motivated by this observation, the makespan and the total completion time minimization problems in the scheduling of jobs with non-decreasing rates of job processing time on a single machine are considered in this article. It is shown that both the makespan and the total completion time minimization problems are NP-hard in the strong sense when the number of maintenance activities is arbitrary, while the makespan minimization problem is NP-hard in the ordinary sense when the number of maintenance activities is fixed. If the deterioration rates of the jobs are identical and the maintenance duration is a linear function of the total processing times of the jobs between maintenance activities, then this article shows that the group balance principle is satisfied for the makespan minimization problem. Furthermore, two polynomial-time algorithms are presented for solving the makespan problem and the total completion time problem under identical deterioration rates, respectively.

Scheduling deteriorating jobs on a single serial-batching machine with multiple job types and sequence-dependent setup times

In this paper, we study a scheduling model in which the features of deteriorating jobs, serial batches, multiple job types, and setup times are considered simultaneously. In this proposed model, the jobs of each type are first partitioned into serial batches, and then all batches of different job types are processed on a single serial-batching machine. The actual job processing time is an increasing function of its starting time, and the setup time of the batches is sequence-dependent, i.e., setup time is required only when a new batch is processed first on the machine or immediately after a batch belonging to another job type. We develop optimization algorithms to solve the makespan minimization problem, the maximum tardiness minimization problem, the maximum lateness minimization problem, and the maximum earliness minimization problem, respectively. We also propose optimization algorithms to solve the problem of minimizing the number of tardy jobs under a certain agreeable condition. Finally, we discuss two special cases of the total completion time minimization problem and develop optimization algorithms to solve them.

Parallel-Machine Scheduling with Time-Dependent and Machine Availability Constraints

We consider the parallel-machine scheduling problem in which the machines have availability constraints and the processing time of each job is simple linear increasing function of its starting times. For the makespan minimization problem, which is NP-hard in the strong sense, we discuss the Longest Deteriorating Rate algorithm and List Scheduling algorithm; we also provide a lower bound of any optimal schedule. For the total completion time minimization problem, we analyze the strong NP-hardness, and we present a dynamic programming algorithm and a fully polynomial time approximation scheme for the two-machine problem. Furthermore, we extended the dynamic programming algorithm to the total weighted completion time minimization problem.

Scheduling jobs with position and sum-of-processing-time based processing times

The paper is devoted to some single-machine scheduling problems with variable job processing times. The objectives are to minimize the makespan (i.e., the maximum completion time of all jobs), and to minimize the total completion time. For some special cases, we show that these problems can be solved in polynomial time. For some another special cases of the makespan and the total completion time minimization problems, we prove that an optimal schedule has an V-shape property in terms of processing times. We also propose a heuristic algorithm by utilizing the V-shape property.

Scheduling a variable maintenance and linear deteriorating jobs on a single machine

We investigate a single machine scheduling problem in which the processing time of a job is a linear function of its starting time and a variable maintenance on the machine must be performed prior to a given deadline. The goals are to minimize the makespan and the total completion time. We prove that both problems are NP-hard. Furthermore, we show that there exists a fully polynomial time approximation scheme for the makespan minimization problem. For the total completion time minimization problem we point out that there exists a fully polynomial time approximation scheme for a special case.

Single machine scheduling with sum-of-logarithm-processing-times based deterioration

In this study we consider the single machine scheduling problems with sum-of-logarithm-processing-times based deterioration, i.e., the actual job processing time is a function of the sum of the logarithm of the processing times of the jobs already processed. We show that even with the introduction of the sum-of-logarithm-processing-times based deterioration to job processing times, single machine makespan minimization problem remain polynomially solvable. But for the total completion time minimization problem, we show that the optimal schedule is not always V-shaped with respect to job normal processing times. Heuristic algorithms and computational results are presented for the total completion time minimization problem.

Some single-machine scheduling with sum-of-processing-time-based and job-position-based processing times

The single-machine scheduling problems with position and sum-of-processing-time based processing times are considered. The actual processing time of a job is defined by function of its scheduled position and total normal processing time of jobs in front of it in the sequence. We provide optimal solutions in polynomial time for some special cases of the makespan minimization and the total completion time minimization. We also show that an optimal schedule to be a V-shaped schedule in terms of the normal processing times of jobs for the total completion time minimization problem and the makespan minimization problem.

Single-machine scheduling problems with an actual time-dependent deterioration

In this paper, we consider the single machine scheduling problems with an actual time-dependent deterioration effect. By the actual time-dependent deterioration effect, we mean that the processing time of a job is defined by increasing function of total actual processing time of jobs in front of it in the sequence. We show that even with the introduction of an actual time-dependent deterioration to job processing times, makespan minimization problem, total completion time minimization problem, the total lateness, and the sum of the quadratic job completion times minimization problem remain polynomially solvable, respectively. We also show that the total weighted completion time minimization problem, the discounted total weighted completion time minimization problem, the maximum lateness minimization problem, and the total tardiness minimization problem can be solved in polynomial time under certain conditions.

Single machine scheduling with general time-dependent deterioration, position-dependent learning and past-sequence-dependent setup times

The paper deals with single machine scheduling problems with setup time considerations where the actual processing time of a job is not only a non-decreasing function of the total normal processing times of the jobs already processed, but also a non-increasing function of the job’s position in the sequence. The setup times are proportional to the length of the already processed jobs, i.e., the setup times are past-sequence-dependent (p-s-d). We consider the following objective functions: the makespan, the total completion time, the sum of the δth (δ ≥ 0) power of job completion times, the total weighted completion time and the maximum lateness. We show that the makespan minimization problem, the total completion time minimization problem and the sum of the δ th (δ ≥ 0) power of job completion times minimization problem can be solved by the smallest (normal) processing time first (SPT) rule, respectively. We also show that the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time under certain conditions.

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 scheduling with joint deterioration and learning effects under group technology and group availability assumptions

The goal of group technology in manufacturing process is to identify a set of products with similar processing requirements and minimize machine changeover and setup. Although different models of joint deterioration and learning effects have been extensively studied in various areas, it has rarely been studied in the context of group scheduling. In this article, we consider single-machine scheduling problems involving deterioration and learning effects simultaneously under the group technology and the group availability assumptions. We assume that the group setup time is a function of position-dependent learning while the actual processing time of a job is a function of joint time-dependent deterioration and position-dependent learning. We further assume that after the setup, the machine deterioration will reset first and then starts anew. We prove that the makespan and the total completion time minimization problems can be optimally solved in polynomial time algorithms with agreeable conditions.

Parallel machines scheduling with a deteriorating maintenance activity

In this paper, we consider parallel identical machines scheduling problems with a deteriorating maintenance activity. In this model, each machine has a deteriorating maintenance activity, that is, delaying the maintenance increases the time required to perform it. We need to make a decision on when to schedule the deteriorating maintenance activities and the sequence of jobs to minimize total completion time. We provide a polynomial time algorithm to solve the total completion time minimization problem.

Single machine scheduling with a general exponential learning effect

In this paper we consider the scheduling problem with a general exponential learning effect and past-sequence-dependent (p-s-d) setup times. By the general exponential learning effect, we mean that the processing time of a job is defined by an exponent function of the total weighted normal processing time of the already processed jobs and its position in a sequence, where the weight is a position-dependent weight. The setup times are proportional to the length of the already processed jobs. We consider the following objective functions: the makespan, the total completion time, the sum of the δ ⩾ 0th power of completion times, the total weighted completion time and the maximum lateness. We show that the makespan minimization problem, the total completion time minimization problem and the sum of the quadratic job completion times minimization problem can be solved by the smallest (normal) processing time first (SPT) rule, respectively. We also show that the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time under certain conditions.

A Note on Single-Machine Scheduling with Deteriorating Jobs

In many real life applications, jobs deteriorate at a certain rate while waiting to be processed. This paper introduces a new deterioration model where the actual processing time of a job depends not only on the starting time of the job but also on its scheduled position. We show that the single-machine makespan minimization problem remains polynomially solvable under the proposed model. We also show that an optimal schedule of the total completion time minimization problem is polynomially solvable under some cases and-shaped with respect to job deterioration rates for other cases.

Scheduling jobs with general learning functions

This paper deals with single-machine scheduling problems with a more general learning effect based on sum-of-processing-time. In this study, sum-of-processing-time-based learning effect means that the processing time of a job is defined by a decreasing function of the total normal processing time of jobs that come before it in the sequence. Results show that even with the introduction of the sum-of-processing-time-based learning effect to job processing times, single-machine makespan minimization problems remain polynomially solvable. The curves of the optimal schedule of a total completion time minimization problem are V-shaped with respect to job normal processing times.

Single-machine scheduling simultaneous with position-based and sum-of-processing-times-based learning considerations under group technology assumption

This paper considers the problems of scheduling with the effect of learning on a single-machine under group technology assumption. We propose a new learning model where the job actual processing time is linear combinations of the scheduled position of the job and the sum of the normal processing time of jobs already processed. We show that the makespan minimization problem is polynomially solvable. We also prove that the total completion time minimization problem with the group availability assumption remains polynomially solvable under agreeable conditions.

Single-machine scheduling with nonlinear deterioration

In this paper, we consider the single-machine scheduling problems with nonlinear deterioration. By the nonlinear deterioration effect, we mean that the processing times of jobs are nonlinear functions of their starting times. We show that even with the introduction of nonlinear deterioration to job processing times, single machine makespan minimization problem remains polynomially solvable. We also show that an optimal schedule of the total completion time minimization problem is V-shaped with respect to job normal processing times. A heuristic algorithm utilizing the V-shaped property is proposed, and computational experiments show that it performs effectively and efficiently in obtaining near-optimal solutions.