Bi-level multi-objective mathematical model for job-shop scheduling: the application of Theory of Constraints

Kasemset, Chompoonoot; Kachitvichyanukul, Voratas

doi:10.1080/00207540903176705

Cited by 23 publications

(13 citation statements)

References 25 publications

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“…2) The schedule representation step is differently designed comparing with any PSO method for JSPs because the starting time, X ij , should be derived by considering one additional parameter called "Earliest Starting Time" and this parameter can be calculated based on transfer lot concept proposed in Kasemset and Kachitvichyanukul (2010).…”

Section: Pso Based Methods For Solving Bi-levelmentioning

confidence: 99%

“…The bi-level multi-objective job-shop scheduling was first proposed by Kasemset and Kachitvichyanukul (2010 Objective: Minimize…”

Section: The Bi-level Multi-objective Job-shop Scheduling Formulationmentioning

confidence: 99%

“…Those objectives are subjected to the set of constraint of precedence constraints, machine conflict constraints, job ready time constraint, earliest starting time constraints calculated from transfer lot size, non-negativity constraints and binary constraints. The completed mathematical model and numerical examples are presented in detail as in Kasemset and Kachitvichyanukul (2010).…”

Section: The Bi-level Multi-objective Job-shop Scheduling Formulationmentioning

confidence: 99%

“…The decision of the upper level generally influences the decision on the lower level. The bi-level scheduling problem is found in many cases, for example: the case of multi-level production systems by Lin et al (1997) and Semnani and Zamanifar (2010), the case of cellular manufacturing by Logendran et al (1995), and the case of job-shop scheduling by Kasemset and Kachitvichyanukul (2010)…”

Section: Introductionmentioning

confidence: 99%

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Application of Adaptive Particle Swarm Optimization to Bi-level Job-Shop Scheduling Problem

Kasemset

2014

Industrial Engineering and Management Systems

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This study presents an application of adaptive particle swarm optimization (APSO) to solving the bi-level job-shop scheduling problem (JSP). The test problem presented here is 10×10 JSP (ten jobs and ten machines) with tribottleneck machines formulated as a bi-level formulation. APSO is used to solve the test problem and the result is compared with the result solved by basic PSO. The results of the test problem show that the results from APSO are significantly different when compared with the result from basic PSO in terms of the upper level objective value and the iteration number in which the best solution is first identified, but there is no significant difference in the lower objective value. These results confirmed that the quality of solutions from APSO is better than the basic PSO. Moreover, APSO can be used directly on a new problem instance without the exercise to select parameters.

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Section: Pso Based Methods For Solving Bi-levelmentioning

confidence: 99%

“…The bi-level multi-objective job-shop scheduling was first proposed by Kasemset and Kachitvichyanukul (2010 Objective: Minimize…”

Section: The Bi-level Multi-objective Job-shop Scheduling Formulationmentioning

confidence: 99%

Section: The Bi-level Multi-objective Job-shop Scheduling Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Adaptive Particle Swarm Optimization to Bi-level Job-Shop Scheduling Problem

Kasemset

2014

Industrial Engineering and Management Systems

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“…The fundamental structure of the five focusing steps however remains the same. Mabin and Davies (2003), Pass and Ronen (2003), Umble, Umble and Murakami (2006), Coman and Ronen (2007), Geri and Ahituv (2008), Gupta and Snyder (2009), Kasemset and Kachitvichyanukul (2010) and Spector (2011) in their research largely discuss the five focussing steps as part of the underlying literature reviews as they were originally presented by Goldratt (1990a, 58-63). Authors that have discussed and analysed the five focussing steps in more detail are amongst others Coman and Ronen (1995), Ronen and Starr (1990) and Floyd and Ronen (1989), and all of them are alluding to seven steps rather than five steps only.…”

Section: Introductionmentioning

confidence: 99%

Introducing in-between decision points to TOC’s five focusing steps

Pretorius

2013

International Journal of Production Research

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This paper builds on the original five focusing steps as defined by Goldratt (1992) as one of the constructs of the Theory of Constraints (TOC). The shortcomings identified are the sequential nature suggested by the five seemingly sequential steps, the lack of clarity relating to decisions that allows moving to next or previous steps, the assumed inapplicability of the five steps to dealing with market-and non-physical constraints (such as policy and behavioural constraints), and the lack of clarity surrounding an ideal constraint location. Deductive reasoning is applied to existing literature to arrive at conceptual solutions to the identified problems. The paper transforms the five focusing steps into a decision map which still includes all five steps, but allowing appropriate decision points to guide application of this process. It expands the applicability of the five focusing steps to both market and non-physical constraints, as well as presenting a logical argument why the best possible constraint location is 'the size of the market chosen to be served well'. Finally further clarity is provided why exploiting and subordinating to the constraint is necessary before constraint elevation should take place.Keywords: theory of constraints (TOC), five focusing steps, decision points, continuous improvement IntroductionThe Theory of Constraints is a philosophy of focused continuous improvement based on systems thinking and system principles (Goldratt 1990a, 8-9, 58-63). The system principle, from which this philosophy lends its name, is the principle that all systems are constrained, and if the constraint is managed well, will provide a leverage point from which huge gains can be achieved (Senge 1990, 63-65). Goldratt (in Chapter 1 of Cox and Schleier, 2010) argues that TOC can be summarised with this one word: focus. One of the well known elements of the Theory of Constraints, is the five focusing steps, which allows for the focus element of the philosophy. The five focusing steps were put forward by Goldratt (1990a, 58-63) as a logical sequence of steps in order to capitalise on the constraint principle. It further allows that once a constraint is broken, one should continue seeking the next constraint, which even though the next constraint will not be as restricting as the previous constraint, will nevertheless still constrain the organisation.Even though the five focusing steps make logical sense, and are always described as five sequential steps in the literature, in reality a number of decision points are to be found within the five focusing steps. Understanding these decision points will allow proper application of the five focusing steps since these decision points direct the focus to the essence of what the five focusing steps strive to achieve, namely a continuous process of doing what is necessary, and refraining from doing what is not necessary, in order to maximise system performance. This new verbalisation aims to improve the understanding and practical application of the five focusing steps. The ...

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The dynamic 4S auto maintenance shop scheduling in a multi-constraint machine environment based on the theory of constraints

Qin

Liu

Huang

et al. 2015

Int J Adv Manuf Technol

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Bi-level multi-objective mathematical model for job-shop scheduling: the application of Theory of Constraints

Cited by 23 publications

References 25 publications

Application of Adaptive Particle Swarm Optimization to Bi-level Job-Shop Scheduling Problem

Application of Adaptive Particle Swarm Optimization to Bi-level Job-Shop Scheduling Problem

Introducing in-between decision points to TOC’s five focusing steps

The dynamic 4S auto maintenance shop scheduling in a multi-constraint machine environment based on the theory of constraints

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