Minimizing the total completion time in a two-machine flowshop problem with time delays

Msakni, Mohamed Kais; Khallouli, Wael; Al-Salem, Mohamed; Ladhari, Talel

doi:10.1080/0305215x.2015.1099639

Cited by 21 publications

(5 citation statements)

References 24 publications

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“…The [23,24] successfully introduced the iterative greedy (IG) algorithm to address discrete optimization problems. It has been extensively adopted by researchers as a result of its ease of execution and has been acknowledged to yield high-quality solutions [25,26]. In light of the above successful cases, we then employ a population-based IG algorithm, which can avoid falling into local extremum quickly and is capable of increasing the diversity of the solutions [27] in comparison to the original IG, which employs one single solution.…”

Section: A Population-based Iterated Greedy Algorithmmentioning

confidence: 99%

A Robust Single-Machine Scheduling Problem with Two Job Parameter Scenarios

et al. 2022

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In many real-world environments, machine breakdowns or worker performance instabilities cause uncertainty in job processing times, while working environment changes or transportation delays will postpone finished production for customers. The factors that impact the task processing times and/or deadlines vary. In view of the uncertainty, job processing times and/or job due dates cannot be fixed numbers. Inspired by this fact, we introduce a scenario-dependent processing time and due date concept into a single-machine environment. The measurement minimizes the total job tardiness in the worst case. The same problem without the presence of processing time uncertainty has been an NP-hard problem. First, to solve this difficult model, an exact method, including a lower bound and some dominance properties, is proposed. Next, three scenario-dependent heuristic algorithms are proposed. Additionally, a population-based iterated greedy algorithm is proposed in the hope of increasing the diversity of the solutions. The results of all related algorithms are determined and compared using the appropriate statistical tools.

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Section: A Population-based Iterated Greedy Algorithmmentioning

confidence: 99%

A Robust Single-Machine Scheduling Problem with Two Job Parameter Scenarios

et al. 2022

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“…The great advantages of this method are as follows: it is simple to implement and has been confirmed to yield high-quality solutions for flow shop problems [52]. The IG has been extensively employed for the last ten years in the scheduling research, especially in the (permutation or hybrid) flow shop problem, for example, Pan and Ruiz [53], Msakni et al [54], Dubois-Lacoste et al [55], and Pan et al [56]. In this study, we propose an IG algorithm with four different local search methods, i.e., four versions of IG, coded as IGLS1, IGLS2, IGLS3, and IGLS4.…”

Section: Iterated Greedy Algorithmmentioning

confidence: 99%

Minimizing the Makespan for a Two-Stage Three-Machine Assembly Flow Shop Problem with the Sum-of-Processing-Time Based Learning Effect

Lin

2018

Discrete Dynamics in Nature and Society

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Two-stage production process and its applications appear in many production environments. Job processing times are usually assumed to be constant throughout the process. In fact, the learning effect accrued from repetitive work experiences, which leads to the reduction of actual job processing times, indeed exists in many production environments. However, the issue of learning effect is rarely addressed in solving a two-stage assembly scheduling problem. Motivated by this observation, the author studies a two-stage three-machine assembly flow shop problem with a learning effect based on sum of the processing times of already processed jobs to minimize the makespan criterion. Because this problem is proved to be NP-hard, a branch-and-bound method embedded with some developed dominance propositions and a lower bound is employed to search for optimal solutions. A cloud theory-based simulated annealing (CSA) algorithm and an iterated greedy (IG) algorithm with four different local search methods are used to find near-optimal solutions for small and large number of jobs. The performances of adopted algorithms are subsequently compared through computational experiments and nonparametric statistical analyses, including the Kruskal–Wallis test and a multiple comparison procedure.

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“…At the operational level, the scheduling of jobs in manufacturing systems is a form of decisionmaking that plays a crucial role in real industrial contexts where limited resources are allocated to the execution tasks over given time periods, with the goal of optimizing one or more objective functions (Pinedo, 2012). Although the complexity of scheduling models depends on the characteristics of the system under study and the assumptions considered in the model (e.g., Msakni et al, 2016;Kiatmanaroj et al, 2016;Sama et al, 2017), the majority of problems are classified as NP-hard, which means that optimal solutions are hard to obtain in reasonable computational time.…”

Section: Introductionmentioning

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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Minimizing the total completion time in a two-machine flowshop problem with time delays

Cited by 21 publications

References 24 publications

A Robust Single-Machine Scheduling Problem with Two Job Parameter Scenarios

A Robust Single-Machine Scheduling Problem with Two Job Parameter Scenarios

Minimizing the Makespan for a Two-Stage Three-Machine Assembly Flow Shop Problem with the Sum-of-Processing-Time Based Learning Effect

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

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