An artificial bee colony algorithm approach for unrelated parallel machine scheduling with processing set restrictions, job sequence-dependent setup times, and due date

Caniyilmaz, Erdal; Benli, Betül; Ilkay, Mehmet S.

doi:10.1007/s00170-014-6614-9

Cited by 37 publications

(19 citation statements)

References 25 publications

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“…Logendran et al (2007) and Pfund (2008) proposed the application of various heuristic dispatching rules to deal with the total weighted tardiness problem with the dynamic arrivals of production orders, while Mönch (2009, 2011) proposed a variable neighborhood search procedure and some of its variants to solve the parallel machine problem with total weighted tardiness, precedence constraints and order release times. Some multi-objective approaches evaluating tardiness and flow time or makespan are also available in the studies by Gupta and Sivakumar (2005) Chyu and Chang (2010), Torabi et al (2013), Caniyilmaz et al (2015).…”

Section: Scheduling Problemmentioning

confidence: 99%

Improving effectiveness of parallel machine scheduling with earliness and tardiness costs: A case study

Muñoz-Villamizar¹,

Santos²,

Montoya‐Torres³

et al. 2019

10.5267/j.ijiec

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This paper assesses the effectiveness in scheduling independent jobs with earliness/tardiness costs and variable setup times applying the Overall Equipment Effectiveness (OEE). The OEE is a common metric for measuring the manufacturing productivity. We defined a mixed-integer linear programming formulation of the parallel machine scheduling problem with four different objective functions in order to compare different scheduling configurations. Real data, from a plastic container manufacturing company located in the Basque Country (Spain), were used to validate this approach. A sensitivity analysis was performed with different production capacities and earliness/tardiness costs in order to evaluate the trade-offs between economic performance (i.e., costs) and the partial rates of OEE (i.e., quality, performance and availability). The objective of this study is to propose a guideline to help management make decisions regarding the measurement and improvement of scheduling effectiveness through contemplating earliness, tardiness and variable setup times.

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Section: Scheduling Problemmentioning

confidence: 99%

Improving effectiveness of parallel machine scheduling with earliness and tardiness costs: A case study

Muñoz-Villamizar¹,

Santos²,

Montoya‐Torres³

et al. 2019

10.5267/j.ijiec

View full text Add to dashboard Cite

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“…Han et al [36] solved blocking shop scheduling problem using hybrid ABC (HABC) algorithm. Caniyilmaz et al [37] solved parallel machine scheduling using ABC algorithm. The HABC algorithm flowchart is represented in Figure 1.…”

Section: Problem Statementmentioning

confidence: 99%

Solving SDST Flow shop Scheduling Problems using Hybrid Artificial Bee Colony Algorithms

Kumar*,

Singh,

Kumar

2019

IJRTE

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Sequence dependent setup time scheduling plays a vital role in real manufacturing industries. Setup time depends on the job sequence. Heuristic or dispatching rules are suitable for producing good initial seed sequence. For this reason, heuristic or dispatching rules based artificial bee colony algorithm is used to minimize the makespan and total flowtime in SDST shop problems. Computation result confirms that heuristic based method produced better result when compared to the dispatching based method. The recital of the algorithms is estimated by conducting Statistical test.

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“…Stop. scheduling [36][37][38]. ABC algorithm is a promising swarm intelligence algorithm for optimization problems, which simulates the foraging behavior of honeybees.…”

Section: Exact Algorithmmentioning

confidence: 99%

Robust Parallel Machine Scheduling Problem with Uncertainties and Sequence-Dependent Setup Time

Qin

2016

Scientific Programming

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A parallel machine scheduling problem in plastic production is studied in this paper. In this problem, the processing time and arrival time are uncertain but lie in their respective intervals. In addition, each job must be processed together with a mold while jobs which belong to one family can share the same mold. Therefore, time changing mold is required for two consecutive jobs that belong to different families, which is known as sequence-dependent setup time. This paper aims to identify a robust schedule by min–max regret criterion. It is proved that the scenario incurring maximal regret for each feasible solution lies in finite extreme scenarios. A mixed integer linear programming formulation and an exact algorithm are proposed to solve the problem. Moreover, a modified artificial bee colony algorithm is developed to solve large-scale problems. The performance of the presented algorithm is evaluated through extensive computational experiments and the results show that the proposed algorithm surpasses the exact method in terms of objective value and computational time.

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An artificial bee colony algorithm approach for unrelated parallel machine scheduling with processing set restrictions, job sequence-dependent setup times, and due date

Cited by 37 publications

References 25 publications

Improving effectiveness of parallel machine scheduling with earliness and tardiness costs: A case study

Improving effectiveness of parallel machine scheduling with earliness and tardiness costs: A case study

Solving SDST Flow shop Scheduling Problems using Hybrid Artificial Bee Colony Algorithms

Robust Parallel Machine Scheduling Problem with Uncertainties and Sequence-Dependent Setup Time

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