Flow shop rescheduling under different types of disruption

Katragjini, Ketrina; Vallada, Eva; Ruíz, Rubén

doi:10.1080/00207543.2012.666856

Cited by 108 publications

(57 citation statements)

References 52 publications

(68 reference statements)

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“…Aytug et al (2005) survey types of uncertainties faced in the execution of schedules and possible reactive/predictive solution approaches. Katragjini, Vallada, and Ruiz (2013) give a short review of rescheduling research and consider a rescheduling problem in a flow shop scheduling environment where they consider the objectives of makespan and the number of jobs with altered starting times in the new schedule. They consider minimising the weighted sum of two objectives and propose heuristic solution approaches.…”

Section: Introductionmentioning

confidence: 99%

Rescheduling with controllable processing times for number of disrupted jobs and manufacturing cost objectives

Gürel

Cincioğlu

2014

International Journal of Production Research

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We consider a machine rescheduling problem that arises when a disruption such as machine breakdown occurs to a given schedule. Machine unavailability due to a breakdown requires repairing the schedule as the original schedule becomes infeasible. When repairing a disrupted schedule a desirable goal is to complete each disrupted job on time, i.e. not later than the planned completion time in the original schedule. We consider the case where processing times of jobs are controllable and compressing the processing time of a job requires extra processing cost. Usually, there exists a nonlinear relation between the processing time and manufacturing cost. We solve a bicriteria rescheduling problem that trades off the number of on-time jobs and manufacturing cost objectives. We give a mixed-integer second-order cone programming formulation for the problem. We develop a heuristic search algorithm to generate efficient solutions for the problem. Heuristic algorithm searches solution space by moving and swapping jobs among machines. We develop cost change estimates for job moves and swaps so that the heuristic implements only promising moves and hence generates a set of efficient solutions in reasonably short CPU times.

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Section: Introductionmentioning

confidence: 99%

Rescheduling with controllable processing times for number of disrupted jobs and manufacturing cost objectives

Gürel

Cincioğlu

2014

International Journal of Production Research

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“…Rahmani et al investigated the rescheduling problem in flexible flowshop problems, in which three types of disruptions are considered, i.e., new job arrival, machine breakdown, and job processing time variation [24]. Katragjini et al utilized an iterated greedy (IG) algorithm to solve rescheduling problems with consideration of simultaneous disruptions in flowshop scheduling problems [25]. These works demonstrate that evolutionary or meta-heuristic algorithms are promising for solving rescheduling problems.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Fruit Fly Optimization Algorithm for the Realistic Hybrid Flowshop Rescheduling Problem in Steelmaking Systems

Pan

Mao

2016

IEEE Trans. Automat. Sci. Eng.

100

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In this study, we propose a hybrid fruit fly optimization algorithm (HFOA) to solve the hybrid flowshop rescheduling problem with flexible processing time in steelmaking casting systems. First, machine breakdown and processing variation disruptions are considered simultaneously in the rescheduling problem. Second, each solution is represented by a fruit fly with a well-designed solution representation. Third, two novel decoding heuristics considering the problem characteristics, which can significantly improve the solution quality, are developed. Several routing and scheduling neighborhood structures are proposed to balance the exploration and exploitation abilities. Finally, we propose an effective HFOA with well-designed smell and vision search procedures. In addition, an iterated greedy (IG) local search is embedded in the proposed algorithm to further enhance its exploitation ability. The proposed algorithm is tested on sets of instances generated from industrial data. Through comprehensive computational comparisons and statistical analyses, the performance of the proposed HFOA algorithm is favorably compared against several algorithms in terms of both solution quality and efficiency.Note to Practitioners-The steelmaking rescheduling process is critical to the effective operation of iron and steel production. This study models the steelmaking rescheduling problem with flexible processing time as a complex hybrid flowshop in which two types of disruptions, machine breakdown and processing variation, are considered concurrently. A weighted sum of the five objectives, including minimization of the average sojourn time, earliness Manuscript penalty, tardiness penalty, cast-break penalty, and system instability penalty, is considered in the proposed algorithm. We develop an effective hybrid fruit fly optimization algorithm (HFOA) that applies two vectors to represent individuals and presents routing and scheduling neighborhood structures. An IG-based local search procedure is embedded to enhance the exploitation ability of the proposed algorithm. Two decoding heuristics considering the problem characteristics are developed. The effectiveness of the proposed HFOA is demonstrated through comparisons to other well-known and recently developed meta-heuristics. This work can be extended to practical problems by considering other types of disruptions. In addition, the proposed HFOA can also be generalized, and to other hybrid flowshop rescheduling problems. Index Terms-Fruit fly optimization algorithm, heuristic, hybrid flowshop, iterated greedy, rescheduling.1545-5955 where he became an Associate Professor in 2008. He has authored more than 30 refereed papers. His current research interests include intelligent optimization and scheduling.Quan-Ke Pan (M'15) received the B.Sc. and Ph.D.

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“…These factors can be divided into four categories, according to their origin: the inherent disturbance factors; disturbance factors during production; disturbance factors caused by changes to an enterprise's external environment; and discrete disturbance factors [17,18]. Table I illustrates these disturbance factors.…”

Section: Description and Classification Of Disturbance Factors Of Jobmentioning

confidence: 99%

Rescheduling Methods for Manufacturing Firms Applying Make-to-Order Strategy

Song¹,

Gu²,

Jin³

et al. 2015

Int. j. simul. model.

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To combat the problem of frequent production plan change in an environment where manufacturing systems are constantly varying, an improved hybrid rescheduling strategy is proposed. Disturbance factors are classified according to their origin and influence, and their disturbance degree is determined using a fuzzy-neural network. On the basis of this, an adaptive rescheduling process for manufacturing systems has been constructed and an improved hybrid rescheduling strategy is proposed. Finally, the effectiveness of this improved strategy is illustrated by a case study of practical manufacturing. The results show that this strategy can effectively reduce rescheduling times and maintain the stability of manufacturing systems.

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Flow shop rescheduling under different types of disruption

Cited by 108 publications

References 52 publications

Rescheduling with controllable processing times for number of disrupted jobs and manufacturing cost objectives

Rescheduling with controllable processing times for number of disrupted jobs and manufacturing cost objectives

A Hybrid Fruit Fly Optimization Algorithm for the Realistic Hybrid Flowshop Rescheduling Problem in Steelmaking Systems

Rescheduling Methods for Manufacturing Firms Applying Make-to-Order Strategy

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