scholarly journals Batch Scheduling of Deteriorating Products

2007 ◽  
Vol 1 (2) ◽  
pp. 25-34
Author(s):  
Maksim S. Barketau ◽  
T.C. Edwin Cheng ◽  
Mikhail Y. Kovalyov ◽  
C.T. Daniel Ng
Keyword(s):  
Completion Time ◽  
Processing Time ◽  
Setup Time ◽  
Time Algorithm ◽  
Batch Scheduling ◽  
Performance Guarantee ◽  
Negative Number ◽  
Time Approximation ◽  
Starting Time ◽  
Increasing Function

In this paper we consider the problem of scheduling N jobs on a single machine, where the jobs are processed in batches and the processing time of each job is a simple linear increasing function depending on job’s waiting time, which is the time between the start of the processing of the batch to which the job belongs and the start of the processing of the job. Each batch starts from the setup time S. Jobs which are assigned to the batch are being prepared for the processing during time S0 S. After this preparation they are ready to be processed one by one. The non-negative number bi is associated with job i. The processing time of the i-th job is equal to bi(si − (sib + S0)), where sib and si are the starting time of the b-th batch to which the i-th job belongs and the starting time of this job, respectively. The objective is to minimize the completion time of the last job. We show that the problem is NP-hard. After that we present an O(N) time algorithm solving the problem optimally for the case bi = b. We further present an O(N2) time approximation algorithm with a performance guarantee 2.

scholarly journals Parallel Batch Scheduling of Deteriorating Jobs with Release Dates and Rejection

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Juan Zou ◽  
Cuixia Miao
Keyword(s):  
Approximation Scheme ◽  
Deteriorating Jobs ◽  
Time Algorithm ◽  
Batch Scheduling ◽  
Release Dates ◽  
Ordinary Sense ◽  
Starting Time ◽  
Total Penalty ◽  
Batching Machine ◽  
Increasing Function

We consider the unbounded parallel batch scheduling with deterioration, release dates, and rejection. Each job is either accepted and processed on a single batching machine, or rejected by paying penalties. The processing time of a job is a simple linear increasing function of its starting time. The objective is to minimize the sum of the makespan of the accepted jobs and the total penalty of the rejected jobs. First, we show that the problem is NP-hard in the ordinary sense. Then, we present two pseudopolynomial time algorithms and a fully polynomial-time approximation scheme to solve this problem. Furthermore, we provide an optimalO(nlog⁡n)time algorithm for the case where jobs have identical release dates.

A PTAS FOR MINIMIZING TOTAL COMPLETION TIME OF BOUNDED BATCH SCHEDULING

2002 ◽  
Vol 13 (06) ◽  
pp. 817-827 ◽  
Author(s):  
XIAOTIE DENG ◽  
HAODI FENG ◽  
GUOJUN LI ◽  
GUIZHEN LIU
Keyword(s):  
Completion Time ◽  
Processing Time ◽  
Approximation Scheme ◽  
Processing System ◽  
Handling Time ◽  
Batch Scheduling ◽  
Total Completion Time ◽  
Polynomial Time Approximation Scheme ◽  
Time Approximation ◽  
System P

We consider a batch processing system {pi : i = 1, 2,…,n} where pi is the processing time of job i, and up to B jobs can be processed together such that the handling time of a batch is the longest processing time among jobs in the batch. The number of job types m is not fixed and all the jobs are released at the same time. Jobs are executed non-preemptively. Our objective is to assign jobs to batches and sequence the batches so as to minimize the total completion time. The best previously known result is a 2–approximation algorithm. In this paper, we establish the first polynomial time approximation scheme (PTAS) for the problem.

A STOCHASTIC BATCHING AND SCHEDULING PROBLEM

2001 ◽  
Vol 15 (4) ◽  
pp. 465-479 ◽  
Author(s):  
Ger Koole ◽  
Rhonda Righter
Keyword(s):  
Optimal Policy ◽  
Semiconductor Manufacturing ◽  
Processing Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Flow Time ◽  
Batch Scheduling ◽  
Scheduling Problem ◽  
Processing Times ◽  
Deterministic Problem

We consider a batch scheduling problem in which the processing time of a batch of jobs equals the maximum of the processing times of all jobs in the batch. This is the case, for example, for burn-in operations in semiconductor manufacturing and other testing operations. Processing times are assumed to be random, and we consider minimizing the makespan and the flow time. The problem is much more difficult than the corresponding deterministic problem, and the optimal policy may have many counterintuitive properties. We prove various structural properties of the optimal policy and use these to develop a polynomial-time algorithm to compute the optimal policy.

scholarly journals Parallel-Batch Scheduling with Two Models of Deterioration to Minimize the Makespan

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Cuixia Miao
Keyword(s):  
Polynomial Time ◽  
Single Machine ◽  
Processing Time ◽  
Parallel Machine ◽  
Batch Scheduling ◽  
Approximation Schemes ◽  
Time Approximation ◽  
Identical Parallel Machine ◽  
Parallel Machine Problem ◽  
Machine Problem

We consider the bounded parallel-batch scheduling with two models of deterioration, in which the processing time of the first model ispj=aj+αtand of the second model ispj=a+αjt. The objective is to minimize the makespan. We presentO(n log n)time algorithms for the single-machine problems, respectively. And we propose fully polynomial time approximation schemes to solve the identical-parallel-machine problem and uniform-parallel-machine problem, respectively.

Scheduling Deteriorating Jobs with Availability Constraints to Minimize the Makespan

2016 ◽  
Vol 33 (06) ◽  
pp. 1650048
Author(s):  
Chuanli Zhao ◽  
Hengyong Tang
Keyword(s):  
Polynomial Time ◽  
Approximation Scheme ◽  
Deteriorating Jobs ◽  
Worst Case ◽  
Time Approximation ◽  
Starting Time ◽  
Polynomial Time Approximation Algorithm ◽  
Availability Constraints ◽  
Increasing Function ◽  
Polynomial Time Approximation

In this paper, we consider the scheduling problem in which the processing time of a job is a linear increasing function of its starting time and machine with availability constraints. The objective is to minimize the makespan. We first present a fully polynomial-time approximation scheme (FPTAS) for the case with a single machine. We then show that there exists no polynomial time approximation algorithm with a constant worst-case bound for the case with two identical machines unless [Formula: see text].

scholarly journals Hierarchical minimization of two maximum costs on a bounded serial-batching machine

2020 ◽  
Author(s):  
Cheng He ◽  
Hao Lin ◽  
Li Li
Keyword(s):  
Processing Time ◽  
Optimization Problem ◽  
Setup Time ◽  
Time Algorithm ◽  
Hierarchical Optimization ◽  
Objective Functions ◽  
Processing Times ◽  
Hierarchical Minimization ◽  
Batching Machine ◽  
Special Case

This paper studies a hierarchical optimization problem of scheduling $n$ jobs on a serial-batching machine, in which two objective functions are maximum costs. By a hierarchical optimization problem, we mean the problem of optimizing the secondary criterion under the constraint that the primary criterion is optimized. A serial-batching machine is a machine that can handle up to $b$ jobs in a batch and jobs in a batch start and complete respectively at the same time and the processing time of a batch is equal to the sum of the processing times of jobs in the batch. When a new batch starts, a constant setup time $s$ occurs. We confine ourselves to the bounded model, where $b<n$. We present an $O(n^4)$-time algorithm for this hierarchical optimization problem. For the special case where two objective functions are maximum lateness, we give an $O(n^3\log n)$-time algorithm.

scholarly journals Optimization for Due-Window Assignment Scheduling with Position-Dependent Weights

2020 ◽  
Vol 2020 ◽  
pp. 1-7 ◽  
Author(s):  
Li-Yan Wang ◽  
Dan-Yang Lv ◽  
Bo Zhang ◽  
Wei-Wei Liu ◽  
Ji-Bo Wang
Keyword(s):  
Single Machine ◽  
General Position ◽  
Processing Time ◽  
Window Size ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Scheduling Problem ◽  
Starting Time ◽  
Due Window ◽  
Due Window Assignment

This paper considers a single-machine due-window assignment scheduling problem with position-dependent weights, where the weights only depend on their position in a sequence. The objective is to minimise the total weighted penalty of earliness, tardiness, due-window starting time, and due-window size of all jobs. Optimal properties of the problem are given, and then, a polynomial-time algorithm is provided to solve the problem. An extension to the problem is offered by assuming general position-dependent processing time.

scholarly journals Supply Chain Batching Problem with Identical Orders and Lifespan

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Shanlin Li ◽  
Maoqin Li ◽  
Hong Yan
Keyword(s):  
Dynamic Programming ◽  
Processing Time ◽  
Recursion Formula ◽  
Dynamic Programming Algorithm ◽  
Setup Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Programming Algorithm ◽  
Short Lifespan ◽  
The Difference

In the real world, there are a large number of supply chains that involve the short lifespan products. In this paper, we consider an integrated production and distribution batch scheduling problem on a single machine for the orders with a short lifespan, because it may be cheaper or faster to process and distribute orders in a batch than to process and distribute them individually. Assume that the orders have the identical processing time and come from the same location, and the batch setup time is a constant. The problem is to choose the number of batches and batch sizes to minimize the total delivery time without violating the order lifespan. We first give a backward dynamic programming algorithm, but it is not an actually polynomial-time algorithm. Then we propose a constant time partial dynamic programming algorithm by doing further research into the recursion formula in the algorithm. Further, using the difference characteristics of the optimal value function, a specific calculating formula to solve the problem with the setup time being integer times of the processing time is obtained.

STOCHASTIC BATCH SCHEDULING AND THE “SMALLEST VARIANCE FIRST” RULE

2007 ◽  
Vol 21 (4) ◽  
pp. 579-595 ◽  
Author(s):  
Michael Pinedo
Keyword(s):  
Completion Time ◽  
Processing Time ◽  
Random Variable ◽  
Combinatorial Problems ◽  
Set Partitioning ◽  
Batch Scheduling ◽  
Matching Problem ◽  
Batch Mode ◽  
Processing Times ◽  
Expected Makespan

Consider a single machine that can process multiple jobs in batch mode. We havenjobs and the processing time of jobjis a random variableXjwith distributionFj. Up tobjobs can be processed simultaneously by the machine. The jobs in a batch all have to start at the same time and the batch is completed when all jobs have finished their processing (i.e., at the maximum of the processing times of the jobs in that batch). We are interested in two objective functions, namely the minimization of the expected makespan and the minimization of the total expected completion time. We first show that under certain fairly general conditions, the minimization of the expected makespan is equivalent to specific deterministic combinatorial problems, namely the Weighted Matching problem and the Set Partitioning problem. We then consider the case when all jobs have the same mean processing time but different variances. We show that for certain special classes of processing time distributions theSmallest Variance Firstrule minimizes the expected makespan as well as the total expected completion time. In our conclusions we present various general rules that are suitable for the minimization of the expected makespan and the total expected completion time in batch scheduling.

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