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: The single machine scheduling problem with a common due date and non-identical ready times for the jobs is examined and the application of constructive heuristics that exploit specific characteristics of the problem to improve their performance is investigated.
Abstract: The single machine scheduling problem with a common due date and non-identical ready times for the jobs is examined in this work. Performance is measured by the minimization of the weighted sum of earliness and tardiness penalties of the jobs. Since this problem is NP-hard, the application of constructive heuristics that exploit specific characteristics of the problem to improve their performance is investigated. The proposed approaches are examined through a computational comparative study on a set of 280 benchmark test problems with up to 1000 jobs.
13 citations
TL;DR: This paper considers a single-machine scheduling with linear deterioration rate of processing speed and multiple rate-modifying activities simultaneously and proposes an optimal schedule, which can solve the problem in O(n\log n) time where n is the number of jobs.
Abstract: This paper considers a single-machine scheduling with linear deterioration rate of processing speed and multiple rate-modifying activities simultaneously. A rate-modifying activity can change the processing rate of machine under consideration, which means after each rate-modifying activity the speed of the machine is fully recovered. The integration of these two concept is motivated by human operators and semi-automatic systems that experience performance degradation over time and require rate-modifying activities for recovery. The objective is to minimize the makespan. We need to decide the sequence of jobs and when to schedule the rate-modifying activities. An optimal schedule is proposed, which can solve the problem in $$O(n\log n)$$O(nlogn) time where $$n$$n is the number of jobs.
13 citations
TL;DR: In this paper, a multi-objective scheduling of m job families on a single machine by multiobjective genetic algorithm (MOGA) is presented, where each job has three main distinct features including arrival time, time of processing and due date.
Abstract: This paper presents multi-objective scheduling of m job families on a single machine by multi-objective genetic algorithm (MOGA). We follow optimisation in three objectives: improving the tardiness, increasing the machine utilisation and decreasing the cycle time. MOGA is the combination of genetic algorithm with multi-criteria decision making. Moreover, N jobs are placed for m job families. Each job has three main distinct features including arrival time, time of processing and due date. Also, we consider setup time for each job and sequence-dependent setup time for changing jobs in different families. In order to determine the superiority of MOGA solution, we compared it with shortest processing time and earliest due date solutions. The improvement of MOGA over other approaches is shown by different cases.
13 citations
05 Dec 2010
TL;DR: It turns out that GGA only slightly outperforms the previous genetic GAs but it is faster when a lot processing environment is considered, and the RKGA behaves similar to the best performing GAs described in the literature with respect to solution quality and computing time.
Abstract: This research is motivated by a scheduling problem found in 300-mm semiconductor wafer fabrication facilities (wafer fabs). Front opening unified pods (FOUPs) are used to transfer wafers in wafer fabs. The number of FOUPs is kept limited because of the potential overload of the automated material handling system (AMHS). Different orders are grouped into one FOUP because orders of an individual customer very often fill only a portion of a FOUP. We study the case of lot processing and single item processing. The total weighted completion time objective is considered. In this paper, we propose a grouping genetic algorithm (GGA) to form the content of the FOUPs and sequence them. The GGA is hybridized with local search. Furthermore, we also study a random key genetic algorithm (RKGA) to sequence the orders and assign the orders to FOUPs by a heuristic. We compare the performance of the two GAs based on randomly generated problem instances with simple heuristics and other GAs from the literature. It turns out that GGA only slightly outperforms the previous genetic GAs but it is faster when a lot processing environment is considered. The RKGA behaves similar to the best performing GAs described in the literature with respect to solution quality and computing time.
13 citations
TL;DR: It is proved that the single-machine common due-window assignment problem with generalized earliness/tardiness penalties and a rate-modifying activity can be solved in time.
Abstract: This article investigates the single-machine common due-window assignment problem with generalized earliness/tardiness penalties and a rate-modifying activity. Job processing times include two case...
13 citations