Mathematical Problems in Engineering

Volume 2012 (2012), Article ID 275239, 9 pages

doi:10.1155/2012/275239

Research Article

Single-Machine Group Scheduling Problems with Deterioration to Minimize the Sum of Completion Times

Yong He¹ and Li Sun1,2

¹School of Economics and Management, Southeast University, Nanjing 210096, China

²Foundation Department, Huaiyin Advanced Vocational and Technical Health School, Huaian 223300, China

Received 14 August 2012; Accepted 11 November 2012

Academic Editor: Gradimir Milovanovic

Copyright © 2012 Yong He and Li Sun. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

We consider two single-machine group scheduling problems with deteriorating group setup and job processing times. That is, the job processing times and group setup times are linearly increasing (or decreasing) functions of their starting times. Jobs in each group have the same deteriorating rate. The objective of scheduling problems is to minimize the sum of completion times. We show that the sum of completion times minimization problems remains polynomially solvable under the agreeable conditions.

1. Introduction

In classical scheduling problems, the scheduling models routinely assume that job processing times are known and fixed throughout the period of job processing. However, this assumption may be unrealistic in many situations that the processing times of jobs may be prolonged due to deterioration or shortened due to learning over time. Scheduling with deteriorating jobs was first independently introduced by J. N. D. Gupta and S. K. Gupta [1] and Browne and Yechiali [2]. Since then, related models of scheduling with deteriorating jobs have been extensively studied from a variety of perspectives. Cheng et al. [3] give a detailed review of scheduling problems with deteriorating jobs. More recent papers which have considered scheduling jobs with deteriorating jobs include Wang [4], Voutsinas and Pappis [5], Lee et al. [6], Wang et al. [7], Zhu et al. [8], Cheng et al. [9, 10], Cheng et al. [11], Sun et al. [12], Wei et al. [13], Zhao and Tang [14], and Yin et al. [15, 16].

On the other hand, the 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. To the best of our knowledge, only a few results concerning scheduling problems with deteriorating effect under group technology are given. Wei and Wang [17] consider single-machine scheduling problems with group technology (GT) and deteriorating jobs. Yang [18] investigate group scheduling problems with simultaneous considerations of learning and deterioration effects on a single-machine setting. The learning phenomenon is implemented to model the setup time of groups. Three models of deteriorating for the job processing time within a group are examined. Bai et al. [19] consider single-machine group scheduling problems with effects of learning and deterioration at the same time. Lee and Lu [20] discuss a single-machine scheduling problem of minimizing the total weighted number of late jobs with deteriorating jobs and setup times. A branch-and-bound with several dominance properties and a lower bound is developed to solve the problem optimally. S. J. Yang and D. L. Yang [21] study two models of learning for the job processing time, they provide polynomial time solutions for the makespan minimization problems. Huang et al. [22] present a single-machine group scheduling problems with both learning effects and deteriorating jobs. Wu and Lee [23] consider a situation where both setup times and job-processing times are lengthened as jobs wait to be processed. 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.

In this paper, we study the group scheduling model proposed by Lee and Wu [24], who have considered the makespan problems. In our study, we investigate the sum of completion times minimization problem under the same model as Lee and Wu [24]. In addition, we suppose jobs in each group have the same deteriorating rate. Hence, the problems considered will be noted by , . We show that the problems remain polynomially solvable under the agreeable conditions.

The remainder of this paper is organized in four sections. The solution procedure for the linear increasing function problem is given in the next section, and the solution procedure for the linear decreasing function is described in Section 3. Then, some examples are offered in Section 4. The conclusion is given in the last section.

2. The , , Problem

In this section, we consider the single-machine group scheduling problem with deterioration to minimize the sum of completion times of all jobs. In our model, we follow the notation and terminology used by Lee and Wu [24]. In addition, is the basic (normal) processing time of job scheduled in the th position of group , and is the completion time scheduled in the th position of group . We suppose the jobs in each group have the same deterioration rate noted by .

Theorem 2.1. For the 1/GT, , problem, the optimal schedule is obtained by sequencing the jobs in each group in nondecreasing order of , that is, , , (the SPT rule).

Proof. In the same group, the result can be easily obtained by using interchanging technology and is omitted.

Theorem 2.2. For the 1/GT, , problem, the groups are arranged in nondecreasing order of , , if and have agreeable conditions, that is, if and only if , , .

Proof. Let and be two job schedules where the difference between and is a pairwise interchange of two adjacent groups and . That is, , , where and are partial sequences and and may be empty. Furthermore, we assume that denotes the completion time of the last job in . To show dominates , it suffices to show that

By definition, the completion times of jobs in are given by

And the completion times of jobs in are given by

Therefore, we have

By substituting

we can obtain

To have

when , if and only if

From the result of (2.8), we have , that is to say .

And from the case of (2.9), we can obtain .

Therefore, if and have agreeable conditions, the optimal sequence between groups are arranged in nondecreasing order of , This completes the proof.

From Theorems 2.1 and 2.2, if and have agreeable condition, the problem , , can be solved by the following algorithm.

Algorithm 2.3. Step  1. Jobs in each group are scheduled in nondecreasing order of the basic processing time , that is, , , (the SPT rule).

Step  2. Calculate .

Step  3. Groups are scheduled in nondecreasing order of , that is, .

Obviously, It is easy to show that the total time for Algorithm 2.3 is .

3. The , , Problem

In order to discuss the problem , , conveniently, we suppose and .

Theorem 3.1. For the 1/GT, , problem, the optimal schedule is obtained by sequencing the jobs in each group in nondecreasing order of basic processing time , that is, , , (the SPT rule).

Proof. In the same group, the result can be easily obtained by using interchanging technology and is omitted.

Theorem 3.2. For the 1/GT, , problem, the groups are arranged in nondecreasing order of , , if and have agreeable conditions, that is, if and only if , , and .

Proof. Here, we still use the same notations as that in Theorem 2.2. To show dominates , it suffices to show that

By definition, the completion times of jobs in are given by

And the completion times of jobs in are given by

Therefore, we have

By substituting

we can obtain

To have

when if and only if

From the result of (3.8), we have

that is to say .

And from the case of (3.9), we can obtain .

Therefore, if and have agreeable conditions, the optimal sequence between groups are arranged in nondecreasing order of , This completes the proof.

From Theorems 3.1 and 3.2, if and have agreeable condition, the problem , , can be solved by the following algorithm.

Algorithm 3.3. Step  1. Jobs in each group are scheduled in nondecreasing order of the basic processing time , that is, , , (the SPT rule).

Step  2. Calculate .

Step  3. Groups are scheduled in nondecreasing order of , that is, .

Obviously, It is easy to show that the total time for Algorithm 3.3 is .

4. Examples

Example 4.1. For the , , problem, we consider six jobs are divided into three groups. The basic job and group processing times, the job and group deterioration rates for each group are given, respectively, as follows:

Solution 1. According to Algorithm 2.3, we solve the example as follows.

Step 1. In group , the optimal job sequence is .

In group , the optimal job sequence is .

In group , the optimal job sequence is .

Step 2. calculate: , , , and , , . It is easy to say and . Therefore, the optimal group sequence is and the optimal schedule is . The completion times of jobs are , , , , , and , and the sum of completion times is .

Example 4.2. For the , , problem, we consider six jobs are divided into three groups as Example 4.1, the job and group deterioration rates for each group are given, respectively, as follows:

Solution 2. According to Algorithm 2.3, we solve the example as follows.

Step 1. In group , the optimal job sequence is .

In group , the optimal job sequence is .

In group , the optimal job sequence is .

Step 2. calculate: , , , and , , . It is easy to say and . Therefore, the optimal group sequence is and the optimal schedule is . The completion times of jobs are , , , , , , and the sum of completion times is .

5. Conclusions

In this paper, we investigate the single-machine group scheduling problems with deterioration which is studied by Lee and Wu [24]. We consider the sum of completion times minimization problem, and we show the problem to minimize total completion time of all jobs is polynomially solvable under agreeable conditions in our study.

Acknowledgments

The authors thank the valuable comments of the referees for an earlier version of this paper. Their comments have significantly improved the paper. This work is supported by the National Natural Science Foundation of China (no. 71001025). Also, this research is partly supported by the Program for New Century Excellent Talents in University (no. NCET-10-0327) and the Ministry of Education of China: Grant-in-aid for Humanity and Social Science Research (no. 11YJCZH139).

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