Solving fuzzy transportation problems based on extension principle

Liu, Shiang-Tai; Kao, Chiang

doi:10.1016/s0377-2217(02)00731-2

Cited by 212 publications

(92 citation statements)

References 13 publications

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“…FL also contributed to modelling the vendor selection strategies (Kumar et al, 2004;Amid et al, 2006). Similar investigations use FL for modelling uncertainty in transportation (e.g., for the time and quantity) between several echelons in SCs (Chanas et al, 1993;Julien, 1994;Liu & Kao, 2004;Liang, 2006). Moreover, other fields of application of FL in SCs are the planning of the correct production-distribution system and the investigation of the effects of different strategies for procurement-production-distribution (Sakawa et al, 2001;Liang, 2007;Selim et al, 2008;Aliev et al, 2007;Chen & Chang, 2006;Torabi & Hassini, 2008).…”

Section: Managing Uncertainty In the Scmentioning

confidence: 99%

The role of uncertainty in supply chains under dynamic modeling

Fera¹,

Fruggiero²,

Lambiase³

et al. 2017

10.5267/j.ijiec

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The uncertainty in the supply chains (SCs) for manufacturing and services firms is going to be, over the coming decades, more important for the companies that are called to compete in a new globalized economy. Risky situations for manufacturing are considered in trying to individuate the optimal positioning of the order penetration point (OPP). It aims at defining the best level of information of the client's order going back through the several supply chain (SC) phases, i.e. engineering, procurement, production and distribution. This work aims at defining a system dynamics model to assess competitiveness coming from the positioning of the order in different SC locations. A Taguchi analysis has been implemented to create a decision map for identifying possible strategic decisions under different scenarios and with alternatives for order location in the SC levels. Centralized and decentralized strategies for SC integration are discussed. In the model proposed, the location of OPP is influenced by the demand variation, production time, stock-outs and stock amount. Results of this research are as follows: (i) customer-oriented strategies are preferable under high volatility of demand, (ii) production-focused strategies are suggested when the probability of stock-outs is high, (iii) no specific location is preferable if a centralized control architecture is implemented, (iv) centralization requires cooperation among partners to achieve the SC optimum point, (v) the producer must not prefer the OPP location at the Retailer level when the general strategy is focused on a decentralized approach.

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Section: Managing Uncertainty In the Scmentioning

confidence: 99%

The role of uncertainty in supply chains under dynamic modeling

Fera¹,

Fruggiero²,

Lambiase³

et al. 2017

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…Chanas (1983) and Julien (1994), who contemplate: the capacities of ports, the fulfilling demand, the capacities of the loading and unloading operations, and the constraints associated with traffic control. Liu and Kao (2004) develop a method to obtain the membership function of the total transport cost by considering this as a fuzzy objective value where the shipment costs, supply and demand are fuzzy numbers. The method was based on the extension principle defined by Zadeh (1978) to transform the fuzzy transport problem into a pair of mathematical programming models.…”

Section: Literature Reviewmentioning

confidence: 99%

Applying Fuzzy Linear Programming to Supply Chain Planning with Demand, Process and Supply Uncertainty

Peidro¹,

Mula²

2009

Supply Chain the Way to Flat Organisation

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“…However, due to the structure of the transportation problem, in some cases their method requires the refinement of the problem parameters to be able to derive the bounds of the objective value. There are also studies discussing the fuzzy transportation problem [14]. Chanas …”

Section: Introductionmentioning

confidence: 99%

Multi – Objective Two Stage Fuzzy Transportation Problem with Hexagonal Fuzzy Numbers Using Fuzzy Geometric Programming

Christi¹,

Devi²

2017

IJERA

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Fuzzy geometric programming approach is used to determine the optimal solution of a multi-objective two stage fuzzy transportation problem in which supplies, demands are hexagonal fuzzy numbers and fuzzy membership of the objective function is defined. This paper aims to find out the best compromise solution among the set of feasible solutions for the multi-objective two stage transportation problem. To illustrate the proposed method, example is used.

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Solving fuzzy transportation problems based on extension principle

Cited by 212 publications

References 13 publications

The role of uncertainty in supply chains under dynamic modeling

The role of uncertainty in supply chains under dynamic modeling

Applying Fuzzy Linear Programming to Supply Chain Planning with Demand, Process and Supply Uncertainty

Multi – Objective Two Stage Fuzzy Transportation Problem with Hexagonal Fuzzy Numbers Using Fuzzy Geometric Programming

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