Scheduling with release dates on a single machine to minimize total weighted completion time

Belouadah, Hocine; Posner, Martin A.; Potts, Chris N.

doi:10.1016/0166-218x(92)90255-9

Cited by 102 publications

(104 citation statements)

References 8 publications

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“…The earlier works on this problem were due to [18][19][20]. Belouadah et al [21] dealt with a similar problem with the objective of minimizing the total weighted completion time. Later on, Alves and Almeida [22] developed various dominance rules to solve the problem.…”

Section: Surveys Of the Earliness/tardiness Single Machine Schedulingmentioning

confidence: 99%

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

Chen

Chang

et al. 2011

Entropy

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An Estimation of Distribution Algorithm (EDA), which depends on explicitly sampling mechanisms based on probabilistic models with information extracted from the parental solutions to generate new solutions, has constituted one of the major research areas in the field of evolutionary computation. The fact that no genetic operators are used in EDAs is a major characteristic differentiating EDAs from other genetic algorithms (GAs). This advantage, however, could lead to premature convergence of EDAs as the probabilistic models are no longer generating diversified solutions. In our previous research [1], we have presented the evidences that EDAs suffer from the drawback of premature convergency, thus several important guidelines are provided for the design of effective EDAs. In this paper, we validated one guideline for incorporating other meta-heuristics into the EDAs. An algorithm named "EA/G-GA" is proposed by selecting a well-known EDA, EA/G, to work with GAs. The proposed algorithm was tested on the NP-Hard single machine scheduling problems with the total weighted earliness/tardiness cost in a just-in-time environment. The experimental results indicated that the EA/G-GA outperforms the compared algorithms statistically significantly across different stopping criteria and demonstrated the robustness of the proposed algorithm. Consequently, this paper is of interest and importance in the field of EDAs.

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Section: Surveys Of the Earliness/tardiness Single Machine Schedulingmentioning

confidence: 99%

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

Chen

Chang

et al. 2011

Entropy

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“…Since the total completion time minimization is equivalent to total (mean) flow time, total (mean) waiting time and total (mean) lateness minimization (Conway, Maxwell and Miller, 1967), these latter criteria are over-regular as well. This problem has been extensively studied in single machine case with unequal release dates (Ahmadi and Bagchi, 1990;Liu and MacCarthy, 1991;Belouadah, Posner and Potts, 1992;Bianco and Ricciardelli, 1982;Chandra, 1979;Deogun, 1983;Dessouky and Deogun, 1981;Hariri and Potts, 1983). Among the optimal algorithms, the one proposed by Chu (1992è) capable of solving problems with up to 100 jobs seems to be the most effective.…”

Section: {0mentioning

confidence: 99%

A new class of scheduling criteria and their optimization

Chen¹

1996

RAIRO-Oper. Res.

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“…For example, in scheduling it is common to build an instance I from an original instance I by splitting tasks into unary processing time jobs. In certain cases, the obtained instance I dominates I and may possibly be solved easily (see for example [41]). Solving this new instance could then produce a lower bound of the value of an optimal solution of I .…”

Section: Definition 14 Consider Two Instances I and I Of The Same Prmentioning

confidence: 99%

Dominance rules in combinatorial optimization problems

Jouglet

Carlier

2011

European Journal of Operational Research

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The aim of this paper is to study the concept of a "dominance rule" in the context of combinatorial optimization. A dominance rule is established in order to reduce the solution space of a problem by adding new constraints to it, either in a procedure that aims to reduce the domains of variables, or directly in building interesting solutions. Dominance rules have been extensively used over the last fifty years. Surprisingly, to our knowledge, no detailed description of them can be found in the literature other than a few short formal descriptions in the context of enumerative methods. We are therefore proposing an investigation into what dominance rules are. We first provide a definition of a dominance rule with its different nuances. Next, we analyze how dominance rules are generally formulated and what are the consequences of such formulations. Finally, we enumerate the common characteristics of dominance rules encountered in the literature and in the usual process of solving combinatorial optimization problems.

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Scheduling with release dates on a single machine to minimize total weighted completion time

Cited by 102 publications

References 8 publications

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

A new class of scheduling criteria and their optimization

Dominance rules in combinatorial optimization problems

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