scholarly journals Explorable Uncertainty in Scheduling with Non-uniform Testing Times

2021 ◽  
pp. 127-142
Author(s):  
Susanne Albers ◽  
Alexander Eckl
Keyword(s):  
Processing Time ◽  
Randomized Algorithm ◽  
General Setting ◽  
Unit Size ◽  
Deterministic Case ◽  
Upper Limit ◽  
Uncertainty Models ◽  
Competitive Algorithms ◽  
Completion Times ◽  
Sum Of Completion Times

AbstractThe problem of scheduling with testing in the framework of explorable uncertainty models environments where some preliminary action can influence the duration of a task. In the model, each job has an unknown processing time that can be revealed by running a test. Alternatively, jobs may be run untested for the duration of a given upper limit. Recently, Dürr et al. [4] have studied the setting where all testing times are of unit size and have given lower and upper bounds for the objectives of minimizing the sum of completion times and the makespan on a single machine. In this paper, we extend the problem to non-uniform testing times and present the first competitive algorithms. The general setting is motivated for example by online user surveys for market prediction or querying centralized databases in distributed computing. Introducing general testing times gives the problem a new flavor and requires updated methods with new techniques in the analysis. We present constant competitive ratios for the objective of minimizing the sum of completion times in the deterministic case, both in the non-preemptive and preemptive setting. For the preemptive setting, we additionally give a first lower bound. We also present a randomized algorithm with improved competitive ratio. Furthermore, we give tight competitive ratios for the objective of minimizing the makespan, both in the deterministic and the randomized setting.

Stochastic flowshop no-wait scheduling

1985 ◽  
Vol 22 (1) ◽  
pp. 240-246 ◽  
Author(s):  
E. Frostig ◽  
I. Adiri
Keyword(s):  
Independent Random Variable ◽  
Processing Time ◽  
Random Variables ◽  
Random Variable ◽  
Schedule Length ◽  
Special Cases ◽  
No Wait ◽  
Completion Times ◽  
Sum Of Completion Times

This paper deals with special cases of stochastic flowshop, no-wait, scheduling. n jobs have to be processed by m machines . The processing time of job Ji on machine Mj is an independent random variable Ti. It is possible to sequence the jobs so that , . At time 0 the realizations of the random variables Ti, (i are known. For m (m ≧ 2) machines it is proved that a special SEPT–LEPT sequence minimizes the expected schedule length; for two (m = 2) machines it is proved that the SEPT sequence minimizes the expected sum of completion times.

Stochastic flowshop no-wait scheduling

1985 ◽  
Vol 22 (01) ◽  
pp. 240-246
Author(s):  
E. Frostig ◽  
I. Adiri
Keyword(s):  
Independent Random Variable ◽  
Processing Time ◽  
Random Variables ◽  
Random Variable ◽  
Schedule Length ◽  
Special Cases ◽  
No Wait ◽  
Completion Times ◽  
Sum Of Completion Times

This paper deals with special cases of stochastic flowshop, no-wait, scheduling. n jobs have to be processed by m machines . The processing time of job Ji on machine Mj is an independent random variable Ti . It is possible to sequence the jobs so that , . At time 0 the realizations of the random variables Ti , ( i are known. For m (m ≧ 2) machines it is proved that a special SEPT–LEPT sequence minimizes the expected schedule length; for two (m = 2) machines it is proved that the SEPT sequence minimizes the expected sum of completion times.

Stochastically Minimizing Total Delay of Jobs Subject to Random Deadlines

1991 ◽  
Vol 5 (3) ◽  
pp. 333-348 ◽  
Author(s):  
Susan H. Xu
Keyword(s):  
Time Distribution ◽  
Processing Time ◽  
Fixed Number ◽  
Parallel Processors ◽  
Threshold Strategy ◽  
Total Delay ◽  
Scheduling System ◽  
Processing Times ◽  
Completion Times ◽  
Sum Of Completion Times

This paper analyzes a scheduling system where a fixed number of nonpreemptive jobs is to be processed on multiple parallel processors with different processing speeds. Each processor has an exponential processing time distribution and the processors are ordered in ascending order of their mean processing times. Each job has its own deadline that is exponentially distributed with rate ß1, independent of the deadlines of other jobs and also independent of job processing times. A job departs the system as soon as either its processing completes or its deadline occurs. We show that there exists a simple threshold strategy that slochastically minimizes the total delay of all jobs. The policy depends on distributions of processing times and deadlines, but is independent of the rate of deadlines. When the rate of the deadline distribution is 0 (no deadlines), the total delay reduces to the flowtime (the sum of completion times of all jobs). If each job has its own probability of being correctly processed, then an extension of this policy stochastically maximizes the total number of correctly processed, nontardy jobs. We discuss possible generalizations and limitations of this result.

An Efficient Batch Scheduling Model for Hospital Sterilization Services Using Genetic Algorithm

2021 ◽  
pp. 928-946
Author(s):  
Shubin Xu ◽  
John Wang
Keyword(s):  
Genetic Algorithm ◽  
Medical Devices ◽  
Programming Model ◽  
Flow Time ◽  
Nonlinear Integer Programming ◽  
Batch Scheduling ◽  
Scheduling Problem ◽  
Work In Process ◽  
Completion Times ◽  
Sum Of Completion Times

A major challenge faced by hospitals is to provide efficient medical services. The problem studied in this article is motivated by the hospital sterilization services where the washing step generally constitutes a bottleneck in the sterilization services. Therefore, an efficient scheduling of the washing operations to reduce flow time and work-in-process inventories is of great concern to management. In the washing step, different sets of reusable medical devices may be washed together as long as the washer capacity is not exceeded. Thus, the washing step is modeled as a batch scheduling problem where washers have nonidentical capacities and reusable medical device sets have different sizes and different ready times. The objective is to minimize the sum of completion times for washing operations. The problem is first formulated as a nonlinear integer programming model. Given that this problem is NP-hard, a genetic algorithm is then proposed to heuristically solve the problem. Computational experiments show that the proposed algorithm is capable of consistently obtaining high-quality solutions in short computation times.

scholarly journals Tabu search for a parallel-machine scheduling problem with periodic maintenance, job rejection and weighted sum of completion times

2021 ◽  
Author(s):  
Hanane Krim ◽  
Nicolas Zufferey ◽  
Jean-Yves Potvin ◽  
Rachid Benmansour ◽  
David Duvivier
Keyword(s):  
Tabu Search ◽  
Parallel Machines ◽  
Mixed Integer ◽  
Weighted Sum ◽  
Scheduling Problem ◽  
Maintenance Policy ◽  
Mixed Integer Linear Program ◽  
Job Rejection ◽  
Completion Times ◽  
Sum Of Completion Times

AbstractWe consider in this work a bicriteria scheduling problem on two different parallel machines with a periodic preventive maintenance policy. The two objectives considered involve minimization of job rejection costs and weighted sum of completion times. They are handled through a lexicographic approach, due to a natural hierarchy among the two objectives in the applications considered. The main contributions of this paper are first to present a new problem relevant to practice, second, to develop a mixed-integer-linear-program model for the problem, and third, to introduce two generalizable tabu-search metaheuristics relying on different neighborhood structures and solution spaces. Computational results for 120 instances (generated from a real case) are reported to empirically demonstrate the effectiveness of the proposed metaheuristics.

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