Minimising mean squared deviation of job completion times about a common due date in multimachine systems
03 Oct 2011-European Journal of Industrial Engineering (Inderscience Publishers Ltd)-Vol. 5, Iss: 4, pp 424-447
TL;DR: In this paper, the authors consider the problem of scheduling n jobs on two identical parallel machines in order to minimize the mean squared deviation (MSD) of job completion times about a given common due date.
Abstract: In this paper we consider the problem of scheduling n jobs on two identical parallel machines in order to minimise the mean squared deviation (MSD) of job completion times about a given common due date. When due dates are small and large deviations of job completion times from the due dates are undesirable, it becomes necessary to consider parallel machines. MSD comes under the category of non-regular performance measures, which penalises jobs that are early as well as late. In this paper we develop a lower bound on MSD for a given partial schedule and present a branch and bound algorithm to solve the problem. Optimal solutions for problem instances up to 35 jobs have been obtained for different values of due dates and the results of computational testing are presented. Based on our experiments we observe that when the due date exceeds a certain value the second machine becomes undesirable. We also propose a heuristic to provide quick solutions for problems of larger size.
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TL;DR: In this article, a polynomial time heuristic algorithm is proposed for the two-machine flow shop scheduling problem to minimize makespan, where setup times are treated as separate from processing times.
23 citations
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..., Srirangacharyulu and Srinivasan [30]....
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TL;DR: In this paper, an algorithm is proposed to find an upper bound on the makespan in case the upper bound is not given or unknown, and a proposed algorithm (PAL) with five versions L (1, 5, 10, 15, and 20) and a genetic algorithm (GA) are utilized for solving the problem.
Abstract: The m-machine no-wait flowshop scheduling problem, with the objective of minimizing total completion time subject to the constraint that makespan has to be less than or equal to a certain value, is addressed in this paper. First, an algorithm is proposed to find an upper bound on the makespan in case the upper bound is not given or unknown. Given the upper bound on makespan, a proposed algorithm (PAL) with five versions L (1, 5, 10, 15, and 20) and a genetic algorithm (GA) are utilized for solving the problem. Furthermore, a dominance relation is established for the case of four machines. The five versions of PAL and GA are evaluated on randomly generated problems with different number of jobs and number of machines. Computational experiments show that the errors of PA1
0, PA15, and PA20 are much smaller than that of GA while the CPU times of PA10, PA15, and PA20 are significantly smaller than that of GA. Therefore, the algorithms PA10, PA15, and PA20 are superior to the GA algorithm.
22 citations
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..., Srirangacharyulu and Srinivasan [41]....
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...There are some other performance measures such as minimizing mean squared deviation of job completion times, e.g., Srirangacharyulu and Srinivasan [41]....
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TL;DR: This paper addresses the m-machine no-wait flowshop scheduling problem and finds that the heuristic HH1 significantly outperforms the other heuristics.
21 citations
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..., Srirangacharyulu and Srinivasan [36]....
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...There are some other performance measures which are job due date related, for example, a performance measure related to early and tardy penalties, e.g., Valente and Schaller [34], job lateness, e.g., Soroush [35], or squared deviation of job completion times, e.g., Srirangacharyulu and Srinivasan [36]....
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TL;DR: Experimental results show that ABC outperforms its opponents in view of solution quality as swarm intelligence based metaheuristic algorithm.
Abstract: This paper investigates unrelated parallel machine scheduling problems where the objectives are to minimize total weighted sum of earliness/tardiness costs. Three different metaheuristic algorithms are compared with others to determine what kind (swarm intelligence based, evolutionary or single solution) of metaheuristics is effective to solve these problems. In this study, artificial bee colony (ABC), genetic algorithm and simulated annealing algorithm are chosen as swarm intelligence based algorithm, evolutionary algorithm and single solution algorithm. All proposed algorithms are created without modification in order to determine effectiveness of these metaheuristics. Experimental results show that ABC outperforms its opponents in view of solution quality as swarm intelligence based metaheuristic algorithm.
11 citations
TL;DR: This paper is the first to study the objective of minimising the maximum inter-completion time, i.e. the maximum time difference between any two consecutive completion times of jobs.
Abstract: Motivated by scheduling practices that require a response to unplanned high-priority jobs as soon as possible without preempting any in-processing jobs, this paper considers a deterministic identic...
4 citations
Cites background from "Minimising mean squared deviation o..."
...Srirangacharyulu and Srinivasan (2011) studied the two machines in parallel with the objective of minimising the mean squared deviation of job completion times, assuming the due dates to be relatively small....
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