A beam search heuristic for scheduling a single machine with release dates and sequence dependent setup times to minimize the makespan

Vélez-Gallego, Mario C.; Maya, Jairo; Montoya‐Torres, Jairo R.

doi:10.1016/j.cor.2016.04.009

Cited by 33 publications

(17 citation statements)

References 25 publications

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“…In addition, sequence-dependent processing times between jobs have not been taken into account until recently (Vallada & Ruiz, 2011;Sereshti & Bijari, 2013). Furthermore, research papers related to the problem of parallel machine scheduling with setup times/costs have mainly focused on makespan minimization (Vélez-Gallego et al 2016;Xanthopoulos et al, 2016). When addressing due-date related objective functions, the majority of research articles have approached the minimization of tardiness.…”

Section: Scheduling Problemmentioning

confidence: 99%

“…The major concern of scheduling is how to provide a perfect match or near perfect match between machines and jobs and subsequently determine the processing sequence of the jobs on each machine to achieve certain classic objectives such as minimize makespan (Hung et al, 2017). Finding a feasible schedule is sufficient for most manufacturers (Hung et al, 2017) who mostly prefer the use of simple heuristic algorithms, such as dispatching policies (Montoya-Torres et al, 2016).…”

Section: Problem Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

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%

Section: Problem Descriptionmentioning

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

show abstract

“…Since then, many studies focused on developing solution algorithms to solve the single-machine scheduling with release times; Genetic Algorithm (GA; [17]), learning-based Branch-and-bound of [18,19], Simulated Annealing of [20], Ant Colony Optimization of [21], and the hybrid GA developed by [22] are the seminal examples. Some studies extended the basic single-machine scheduling with release times, for example through developing a two-agent singlemachine problem with release time [23], considering expiration time in addition to the release times [24], and including sequence-dependent setup times [25].…”

Section: Introductionmentioning

confidence: 99%

Improved Beam Search for Optimizing No-Wait Flowshops With Release Times

et al. 2020

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Production management of perishable goods is highly complex and requires well-informed decisions in corresponding stages. In such production environments, scheduling problems with time constraints are of high relevance to ensure the timely flow of the work-in-process material and goods. This study introduces the no-wait flowshop scheduling problem with release times (NWFSP-RT) to help advance decision support systems in the food production industry. For this purpose, an original mixed-integer linear programming (MILP) formulation is proposed for minimizing the makespan. Since Beam Search (BS) algorithm has been successfully applied to solve various scheduling problems, a BS algorithm, and an improved variant, the local search-based Beam Search (BSLS) algorithms are developed to solve the NWFSP-RT problem. Extensive numerical analysis is conducted to analyze the performance of the algorithms in solving this highly intractable extension of the scheduling problems. We showed that BSLS effectively avoids early convergence and local optimality while dismissing non-promising search directions within a partial enumeration solution approach. The statistical analysis confirmed that the improved BS algorithm performs better in terms of solution quality. Applications of the developed heuristic are worthwhile research topics to pursue in solving other complex optimization problems. INDEX TERMS Flowshop scheduling, no-wait, release time, Beam Search.

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“…According to the production environment, the single-machine scheduling problem (SMSP) is one of the most common scheduling problems that have been studied in the past few decades. However, in some real-world manufacturing systems, studying the SMSP under the setup consideration between jobs is also required such as the manufacturing systems in painting and metalworking industries [2]. Comprehensive reviews regarding the setup consideration are provided [3][4][5] for all scheduling problem environments such as the single-machine scheduling, parallel machine scheduling, and flow shop scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

“…An ant colony optimization was presented in [16] to minimize job tardiness for an SMSP with sequence-dependent setups. A single-machine scheduling problem with sequence-dependent setup times and release dates had been less studied [2,17,18]; one of these studies is [2] where a beam search and mixed-integer linear model were used to study the SMSP subject to the release date and nonanticipatory dependent setup time to minimize the makespan.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling and Discrete Firefly Algorithm to Optimize Scheduling Problem with Release Date, Sequence-Dependent Setup Time, and Periodic Maintenance

Qamhan

Al-Harkan

et al. 2019

Mathematical Problems in Engineering

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An evolutionary discrete firefly algorithm (EDFA) is presented herein to solve a real-world manufacturing system problem of scheduling a set of jobs on a single machine subject to nonzero release date, sequence-dependent setup time, and periodic maintenance with the objective of minimizing the maximum completion time “makespan.” To evaluate the performance of the proposed EDFA, a new mixed-integer linear programming model is also proposed for small-sized instances. Furthermore, the parameters of the EDFA are regulated using full factorial analysis. Finally, numerical experiments are performed to demonstrate the efficiency and capability of the EDFA in solving the abovementioned problem.

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A beam search heuristic for scheduling a single machine with release dates and sequence dependent setup times to minimize the makespan

Cited by 33 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

Improved Beam Search for Optimizing No-Wait Flowshops With Release Times

Mathematical Modeling and Discrete Firefly Algorithm to Optimize Scheduling Problem with Release Date, Sequence-Dependent Setup Time, and Periodic Maintenance

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