Makespan minimization flowshop with position dependent job processing times—computational complexity and solution algorithms

Rudek, Agnieszka; Rudek, Radosław

doi:10.1016/j.cor.2013.02.024

Cited by 19 publications

(3 citation statements)

References 42 publications

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“…The results obtained from the use of heuristics depend on the system configuration: single machine problem (14.3%), parallel machine problem (25.7%), and flowshop problem (40.3%). Dispatching rules are the most frequently employed e.g., earliest due date (EDD), shortest processing time (SPT), weighted shortest processing time (WSPT), as well as the Framinan and Leisten (FL) and Nawaz, Enscore, Ham (NEH)-based heuristics [101,102]. Note that most of the authors tested the performance of the heuristics against each other.…”

Section: Heuristicsmentioning

confidence: 99%

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

2022

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In traditional scheduling problems, job processing times are considered constant and known in advance. This assumption is, however, a simplification when it comes to hand-intensive real-life production contexts because workers usually induce variability in the job processing times due to several factors such as learning, monotony, fatigue, psychological factors, etc. These effects can decrease or increase the actual processing time when workers execute a job. The academic literature has reported several modeling and resolution approaches to deal with the phenomenon in a variety of configurations. However, there is no comprehensive review of these research outputs to the best of our knowledge. In this paper, we follow a systematic approach to review relevant contributions addressing the scheduling problem with learning and deterioration effects. Modeling approaches for learning and deterioration effects, objective functions, and solution methods employed in the literature are the main topics for the taxonomy proposed in this review. A total of 455 papers from 1999 to 2021 are included and analyzed. Different areas of interest are presented, and some opportunities for future research are identified.

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Section: Heuristicsmentioning

confidence: 99%

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

2022

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“…A. Rudek and R. Rudek [4] proved the ordinary NP-hardness for two-machine flow shop makespan problem when job processing times are described by nondecreasing position dependent functions (aging effect) on at least one machine and indicated the strong NP-hardness if job processing times are varying on both machines. Aydilek and Allahverdi [5] presented a polynomial time heuristic algorithm for the twomachine flow shop makespan problem with release dates.…”

Section: Introductionmentioning

confidence: 99%

A Local Search Algorithm for the Flow Shop Scheduling Problem with Release Dates

Ren

Guo

Lin

et al. 2015

Discrete Dynamics in Nature and Society

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This paper discusses the flow shop scheduling problem to minimize the makespan with release dates. By resequencing the jobs, a modified heuristic algorithm is obtained for handling large-sized problems. Moreover, based on some properties, a local search scheme is provided to improve the heuristic to gain high-quality solution for moderate-sized problems. A sequence-independent lower bound is presented to evaluate the performance of the algorithms. A series of simulation results demonstrate the effectiveness of the proposed algorithms.

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“…Although recently heuristic and meta-heuristic algorithms are frequently used in fields close to ATP such as lot sizing (Li et al, 2014;Motta Toledo et al, 2013), production planning (Figueira et al, 2013;Almeder & Hartl, 2013;Seker et al, 2013;Lamghari & Dimitrakopoulos, 2012;Wang & Zheng, 2013;Kazemi Zanjani et al, 2013;Han et al, 2012) or scheduling (Rudek & Rudek, 2013;Behnamian & Fatemi Ghomi, 2013;Lamghari et al, 2013;Eskandari & Hosseinzadeh, 2013Wauters et al, 2012Merdan et al, 2012), just a few studies in the ATP literature have taken advantage of them (Cheng & Cheng, 2011;C-Ritzo et al, 2011;Jeong et al, 2002 In this section, in order to solve medium and large size problems we present a two-phase heuristic algorithm. The first phase is designed to generate an initial solution, which includes allocating the most proper WIP to every order to reduce the total weighted remaining processing time and make an initial feasible schedule.…”

Section: Heuristic Algorithm For Inventory Allocation and Order Schedmentioning

confidence: 99%

Designing an advanced available-to-promise mechanism compatible with the make-to-forecast production systems through integrating inventory allocation and job shop scheduling with due dates and weighted earliness/tardiness cost

Rabbani¹,

Monshi²,

Manavizadeh³

et al. 2016

10.5267/j.ijiec

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In the competitive business world, applying a reliable and powerful mechanism to support decision makers in manufacturing companies and helping them save time by considering varieties of effective factors is an inevitable issue. Advanced Available-to-Promise is a perfect tool to design and perform such a mechanism. In this study, this mechanism which is compatible with the Make-to-Forecast production systems is presented. The ability to distinguish between batch mode and real-time mode advanced available-to-promise is one of the unique superiorities of the proposed model. We also try to strengthen this mechanism by integrating the inventory allocation and job shop scheduling by considering due dates and weighted earliness/tardiness cost that leads to more precise decisions. A mixed integer programming (MIP) model and a heuristic algorithm according to its disability to solve large size problems are presented. The designed experiments and the obtained results have proved the efficiency of the proposed heuristic method.

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Makespan minimization flowshop with position dependent job processing times—computational complexity and solution algorithms

Cited by 19 publications

References 42 publications

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

A Local Search Algorithm for the Flow Shop Scheduling Problem with Release Dates

Designing an advanced available-to-promise mechanism compatible with the make-to-forecast production systems through integrating inventory allocation and job shop scheduling with due dates and weighted earliness/tardiness cost

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