Material Requirement Planning with fuzzy constraints and fuzzy coefficients

Mula, Josefa; Mula, Josefa; García-Sabater, José P.

doi:10.1016/j.fss.2006.11.003

Cited by 109 publications

(51 citation statements)

References 33 publications

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“…As a solution methodology, the PLP model is converted to an LP model by using mathematical operations defined for fuzzy numbers. Mula et al 33 investigate a material requirements planning problem in an industrial environment under lack of knowledge in data and existent fuzziness jointly in the processes of production planning. Their model considers fuzzy constraints related to the total costs, the market demand and the available capacity of the productive resources and fuzzy coefficients for the costs due to the backlog of demand and for the required capacity.…”

Section: Related Workmentioning

confidence: 99%

Stochastic Capacity Acquisition and Allocation Model for Bandwidth Brokers under Fuzzy Volume Based Pricing Scheme

Turan¹,

Kasap²,

et al. 2016

IJCIS

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In this paper, bandwidth acquisition and allocation problem of a telecommunications Bandwidth Broker (BB) is analyzed under uncertain end-user capacity requests and pay-per-byte (volume) based pricing policy. Furthermore, related objective function coefficients such as revenue and costs are modeled as fuzzy numbers in order to cope with vague market conditions. By integrating fuzzy mathematical programming and two-stage stochastic programming techniques, deterministic equivalent of single objective profit maximization problem of BB is obtained solved to optimality. In addition, infrastructure related performance measures such as delay and jitter amounts in the network are modelled via stochastic parameters that obey some known probability distributions. Two performance statistics namely fuzzy Expected Value of Perfect Information (EVPI) and fuzzy Value of Stochastic Solution (VSS) are defined to demonstrate the efficiency of proposed methodology compared to deterministic approach. In addition, several secondary performance measures such as expected capacity utilization, expected demand fulfilment ratio and capacity loss are calculated under different problem settings. In conclusion, numerical experiments showed that fuzzy stochastic method provides more profit depending upon problem size in compression with deterministic strategy.

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Section: Related Workmentioning

confidence: 99%

Stochastic Capacity Acquisition and Allocation Model for Bandwidth Brokers under Fuzzy Volume Based Pricing Scheme

Turan¹,

Kasap²,

et al. 2016

IJCIS

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show abstract

“…Yang and Liu [18] applied expected value model, chance-constrained programming model, and dependent-chance programming in a fixed charge solid transportation problem in a fuzzy environment. Mula, Poler, and Garcia Sabater [19] applied possibilistic programming approach to a material requirement planning problem with fuzzy constraints and fuzzy coefficients using the definition of possibility measure of fuzzy number. Chen and Tsai [20] considered time-cost trade-off problem with fuzzy parameters and constructed a two-level mathematical program for it using the concept α-cut of fuzzy number.…”

Section: Literature Reviewmentioning

confidence: 99%

A solid transportation problem with safety factor under different uncertainty environments

Baidya

Bera

Maiti

2013

J. Uncertain. Anal. Appl.

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In this paper, we introduce a new concept 'safety factor' in a transportation problem. When items are transported from plants to destinations through different conveyances, there are some difficulties/risks to transport the items due to bad road, insurgency, land slide, etc. in some routes. Due to these, a desired total safety factor is being introduced, and depending upon the nature of the safety factor, we develop five models. In this paper, a solid transportation problem (STP) with imprecise unit costs is considered. The sources' availabilities, destinations' demands, and capacities of conveyances are also represented by fuzzy numbers like trapezoidal and triangular numbers. The transportation problem has been formulated with and without a safety factor. To reduce the different models into its crisp equivalent, we introduce different methods as chance-constraint programming, an approach using interval approximation of fuzzy numbers and the application of the expected value model. Generalized reduced gradient technique is used to find the optimal solutions for a set of given numerical data. To illustrate the model, a numerical example has been presented and solved using LINGO.12 software. The effect of safety factors on transported amount is illustrated.

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“…However, the premise of a completely deterministic scenario does not match the reality of a manufacturing environment. The literature reports different approaches to consider uncertainty in MRP systems, such as simulation [11], [12], stochastic inventory control [13], fuzzy logic [14], fuzzy mathematical programming [15][16][17][18], fuzzy programming with resources based on the credibility theory [19] and MRP parameterization [20][21][22], among others. Other approaches to consider uncertainty in MRP systems can be found in several reviews [23][24][25].…”

Section: Literature Reviewmentioning

confidence: 99%

A Conceptual Model for Integrating Transport Planning: MRP IV

Díaz‐Madroñero

Peidro

2012

Advances in Production Management Systems. Value Networks: Innovation, Technologies, and Management

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Abstract. In this article, a conceptual model, called MRP IV, is proposed in order to serve as a reference to develop a new production technology that integrates material planning decisions, production resource capacities and supply chain transport for the purpose of avoiding the suboptimization of these plans which, today, are usually generated sequentially and independently. This article aim is twofold: (1) it identifies the advances and deficiencies in the MRP calculations, mainly based on the dynamic multi-level capacitated lot-sizing problem (MLCLSP); and (2) it proposes a conceptual model, defining the inputs, outputs, modeling and solution approaches, to overcome the deficiencies identified in current MRP systems and act as a baseline to propose resolution models and algorithms required to develop MRP IV as a decision-making system.

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Material Requirement Planning with fuzzy constraints and fuzzy coefficients

Cited by 109 publications

References 33 publications

Stochastic Capacity Acquisition and Allocation Model for Bandwidth Brokers under Fuzzy Volume Based Pricing Scheme

Stochastic Capacity Acquisition and Allocation Model for Bandwidth Brokers under Fuzzy Volume Based Pricing Scheme

A solid transportation problem with safety factor under different uncertainty environments

A Conceptual Model for Integrating Transport Planning: MRP IV

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