Topic
Single-machine scheduling
About: Single-machine scheduling is a research topic. Over the lifetime, 2473 publications have been published within this topic receiving 56288 citations.
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TL;DR: In this article, the authors considered the single machine scheduling problem of jobs with controllable processing times and compression costs and the objective was to minimize the total weighted job completion time plus the cost of compression.
Abstract: This paper considers the single machine scheduling problem of jobs with controllable processing times and compression costs and the objective to minimise the total weighted job completion time plus the cost of compression The problem is known to be intractable, and therefore it was decided to be tackled by population-based heuristics namely differential evolution (DE), particle swarm optimisation (PSO), genetic algorithms (GAs), and evolution strategies (ES) Population-based heuristics have found wide application in most areas of production research including scheduling theory It is therefore surprising that this problem has not yet received any attention from the corresponding heuristic algorithms community This work aims at contributing to fill this gap An appropriate problem representation scheme is developed together with a multi-objective procedure to quantify the trade-off between the total weighted job completion time and the cost of compression The four heuristics are evaluated and compared
17 citations
TL;DR: This paper designs one hybrid heuristic algorithm, combining the advantages of qubit representation in quantum computing and Non-dominated Sorting Genetic Algorithm (NSGA-II), and demonstrates that the proposed algorithm is effective for machine disruption management.
17 citations
TL;DR: This paper considers a single-machine scheduling problem with a position-based learning effect and fuzzy processing times, simultaneously, and proposes a polynomial time algorithm for the problem where the objective is to minimize the total completion time.
Abstract: In scheduling problems with learning effects, most researches assume that processing times are deterministic This paper considers a single-machine scheduling problem with a position-based learning effect and fuzzy processing times, simultaneously The position-based learning effect of a job is assumed to be a function of its position The processing times are considered to be triangular fuzzy numbers A polynomial time algorithm is proposed for the problem where the objective is to minimize the total completion time The solution procedure is based on applying the shortest processing time rule to triangular fuzzy processing times Computational results show that our model gives better results than the model ignoring the uncertainty
17 citations
TL;DR: In this paper, the authors studied single machine scheduling problems with two synergetic agents, each has a set of non-preemptive jobs and a regular objective function depending on the completion times of its jobs only.
17 citations
TL;DR: In this paper, a set of new jobs have to be scheduled after a schedule of old jobs has been set, which results in a disruption cost that has to be minimized together with the sum of setup times of the overall schedule.
17 citations