Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models

Özcan, Uğur; Toklu, Bilal

doi:10.1016/j.cor.2008.06.009

Cited by 130 publications

(59 citation statements)

References 38 publications

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“…The work reported in this paper introduced a framework and methodology for decision-making in the manufacturing envi- (Low et al 2006), manufacturing process selection and process planning (Shah 2012;Sormaz and Khoshnevis 2003) or assembly line balancing (Özcan and Toklu 2009;Jolai et al 2009). Regarding operational decisions, they are concerned with deciding order quantities (Demirtas and Üstün 2008), machine/resource allocation (Ertay and Ruan 2005;Taha and Rostam 2012) or material handling (Hao and Shen 2008).…”

Section: Discussionmentioning

confidence: 99%

“…At the strategic level in the manufacturing context, this process can be applied to the selection of a facility location (Farahani and Asgari 2007;Malakooti 2011), choosing suppliers (Amid et al 2009;Ho et al 2010;Pang and Bai 2013), purchasing pieces of production equipment (Abdi 2009;Wernz and Deshmukh 2012) or designing manufacturing systems (Chan et al 2000;Li and Huang 2009). Similarly, tactical decisions involve decisions on scheduling (Low et al 2006), manufacturing process selection and process planning (Shah 2012;Sormaz and Khoshnevis 2003) or assembly line balancing (Özcan and Toklu 2009;Jolai et al 2009). Regarding operational decisions, they are concerned with deciding order quantities (Demirtas and Üstün 2008), machine/resource allocation (Ertay and Ruan 2005;Taha and Rostam 2012) or material handling (Hao and Shen 2008).…”

Section: Introductionmentioning

confidence: 99%

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Decision-making in the manufacturing environment using a value-risk graph

et al. 2014

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Next, performance and risk measures are derived from the corresponding objective and risk models to evaluate the scenario under study. After that, upper and lower bounds, and target value is defined for each measure in order to determine goals and constraints for the given scenario. Because of the multidimensionality nature of performance, the identified objectives and risks, and so, their corresponding measures are usually numerous and heterogeneous in nature. These measures are therefore consolidated to obtain a global performance indicator of value and global indicator of risk while keeping in views the inter-criteria influences. Finally, the global indicators are employed to develop minimum acceptable value and maximum acceptable risk for the scenario under study and plotted on the VR-Graph to demarcate zones of "highly desirable", "feasible", "and risky" as well as the "unacceptable" one. The global scores of the indicators: (value-risk) pair of the actual scenario is then plotted on the defined VR-Graph to facilitate decision-making by rendering the scenarios' performance more visible and clearer. The proposed decision-making tool is illustrated with an example from manufacturing setup in the process context but it can be extended to product or systems evaluation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decision-making in the manufacturing environment using a value-risk graph

et al. 2014

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“…The problem is to assign a set of tasks to workstation with some measure of performance to be optimized under the following restrictions: (i) each task is assigned to one and only one workstation, (ii) the precedence relationship among the tasks cannot be violated, and (iii) the sum of the task times of any workstation should not exceed the cycle time [11]. Since the task times allotted to workstations may be unequal, parts are produced at different speeds on the line.…”

Section: Review Of Assembly Line Balancingmentioning

confidence: 99%

Biologically inspired genetic algorithm to minimize idle time of the assembly line balancing

Razali

Geraghty

2011

2011 Third World Congress on Nature and Biologically Inspired Computing

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Abstract-Assembly line balancing (ALB) is a well-known combinatorial optimization problem in production and operations management area. Due to the NP-hard nature of the ALB problem, many attempts have been made to solve the problem efficiently. In this study, biologically inspired evolutionary computing tool which is genetic algorithm (GA) is adopted to solve the ALB problem with the objective of minimizing the idle time in the workstation. The key issue in solving ALB is how to generate a feasible task sequence which does not violate the precedence constraints. This task sequencing is a vital work to be solved prior assigning tasks to workstation. In order to generate only feasible solution, a repairing strategy based topological sort is integrated in the GA procedure. The ALB test problems benchmarked from the literature are used in the study and the computational results show that the proposed approach is capable to obtain feasible solution with minimum idle time for a simple model assembly line.

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“…The workstation is a set of tasks that are to be assigned to some resources and the cycle time is defined as the time between completions of successive products. Kara et al (2009), Özcan and Toklu (2009), Scholl and Becker (2006), Lapierre et al(2006), Kim et al(2009), Levitin et al(2006), Andre´s et al(2008) and Toksarı et al(2008) addressed the ALB problem with different objective functions and the cycle time is considered to be fixed in most of these papers. Although in a real situation, the cycle time could be increased due to tardiness and lag caused by the precedence workstation, Gupta and Gupta (1990) and Browne and Yechialli (1990) made the first endeavor in this field by considering a dependent function on starting time for processing of elements in workstation.…”

Section: Introductionmentioning

confidence: 99%

Simple assembly line balancing problem under task deterioration

Noushabadi¹,

Bahalke²,

Dolatkhahi³

et al. 2011

IJIEC

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This paper introduces the effect of task deterioration in simple assembly line balancing problem. In many realistic assembly lines, a deterioration task is considered when a task is started earlier than the assigned time since the station time is constant and the earliness of the task does not reduce the cycle time. This phenomenon is known as deteriorating tasks. Therefore, we seek an optimal assignment and schedule of tasks in workstations, in order to minimize the number of stations for a given cycle time, which is known as SALBP-1. For this purpose, a mathematical model is proposed. Since the pure SALBP-1 is proved to be NP-hard and considering task deterioration complicates problem further, we propose a genetic algorithm for solving such problem. Several well-known test problems are solved to study the performance of the proposed approach.

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Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models

Cited by 130 publications

References 38 publications

Decision-making in the manufacturing environment using a value-risk graph

Decision-making in the manufacturing environment using a value-risk graph

Biologically inspired genetic algorithm to minimize idle time of the assembly line balancing

Simple assembly line balancing problem under task deterioration

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