Batch Scheduling Algorithm with Approximation of Job Completion Times and Case Studies

Author(s):  
Song-Eun Kim ◽  
◽  
Seong-Hyeon Park ◽  
Su-Min Kim ◽  
Kyungsu Park ◽  
...  
Algorithmica ◽  
2021 ◽  
Author(s):  
Matthias Englert ◽  
David Mezlaf ◽  
Matthias Westermann

AbstractIn the classic minimum makespan scheduling problem, we are given an input sequence of n jobs with sizes. A scheduling algorithm has to assign the jobs to m parallel machines. The objective is to minimize the makespan, which is the time it takes until all jobs are processed. In this paper, we consider online scheduling algorithms without preemption. However, we allow the online algorithm to change the assignment of up to k jobs at the end for some limited number k. For m identical machines, Albers and Hellwig (Algorithmica 79(2):598–623, 2017) give tight bounds on the competitive ratio in this model. The precise ratio depends on, and increases with, m. It lies between 4/3 and $$\approx 1.4659$$ ≈ 1.4659 . They show that $$k = O(m)$$ k = O ( m ) is sufficient to achieve this bound and no $$k = o(n)$$ k = o ( n ) can result in a better bound. We study m uniform machines, i.e., machines with different speeds, and show that this setting is strictly harder. For sufficiently large m, there is a $$\delta = \varTheta (1)$$ δ = Θ ( 1 ) such that, for m machines with only two different machine speeds, no online algorithm can achieve a competitive ratio of less than $$1.4659 + \delta $$ 1.4659 + δ with $$k = o(n)$$ k = o ( n ) . We present a new algorithm for the uniform machine setting. Depending on the speeds of the machines, our scheduling algorithm achieves a competitive ratio that lies between 4/3 and $$\approx 1.7992$$ ≈ 1.7992 with $$k = O(m)$$ k = O ( m ) . We also show that $$k = \varOmega (m)$$ k = Ω ( m ) is necessary to achieve a competitive ratio below 2. Our algorithm is based on maintaining a specific imbalance with respect to the completion times of the machines, complemented by a bicriteria approximation algorithm that minimizes the makespan and maximizes the average completion time for certain sets of machines.


Author(s):  
Shubin Xu ◽  
John Wang

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.


2017 ◽  
Vol 2 (9) ◽  
pp. 35-39
Author(s):  
Afaf Abd Elkader Abd Elhafiz

The process of assigning independent tasks to resources with the aim of optimizing some objective functions is known as scheduling. The efficient scheduling of independent tasks to improve the performance of a system is an important problem. Several algorithms are developed to schedule tasks on their resources to minimize the makespan. One of these algorithms is ACTA (Average of Completion Times Algorithm). This paper proposes an algorithm HASA (Half the Average Scheduling Algorithm) that ameliorates the makespan produced by ACTA. Experimental results show that the proposed algorithm gives makespan smaller than ACTA.


2011 ◽  
Vol 10 (04) ◽  
pp. 315-326 ◽  
Author(s):  
Paul Oluikpe ◽  
Muhammad Sohail ◽  
Frank Odhiambo

The paper investigates the role of knowledge management in enabling project success, innovation, completion times, operational efficiency and the generation of new knowledge in development projects. Four projects in Uganda, Nigeria, and Cote d'Ivoire were used as case studies. The objective was to explore the nature of knowledge management practices in these projects in order to see how they could be improved. The research found that knowledge management is a significant factor in speeding up completion times, achieving project success, innovation, operational efficiency and the generation of new knowledge. Knowledge sharing practices were identified within case studies and difficulties relating to managing knowledge generated during the project were highlighted.


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