An extension to possibilistic linear programming

Julien, Benoı̂t

doi:10.1016/0165-0114(94)90333-6

Cited by 75 publications

(27 citation statements)

References 23 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

“…The author uses three approaches based on the works by Zimmermann (1976). 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.…”

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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“…Construct crisp LP problem (AP1) Maximize Subject to (6) and let be optimal solution of the problem P1…”

Section: Algorithmmentioning

confidence: 99%

“…Existing methods can be divided into two groups in general, depending on the fuzziness of decision parameters and decision variables. In the first group, the researchers assumed the decision parameters are fuzzy numbers while the decision variables are crisp ones; see Buckley (1988), Lai and Hwang (1992), Julien (1994), Shaocheng (1994), Liu (2001). This means that in an uncertain environment, a crisp decision is made to meet some decision criteria.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy variable linear programming with fuzzy technical coefficients

Ahmad

Sardar

2012

Pak.j.stat.oper.res.

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Fuzzy linear programming is an application of fuzzy set theory in linear decision making problems and most of these problems are related to linear programming (LP) with fuzzy variables. In this paper, an approximate but convenient method for solving these problems with fuzzy non-negative technical coefficient and without using the ranking functions, is proposed. With the help of numerical examples, the method is illustrated.

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An extension to possibilistic linear programming

Cited by 75 publications

References 23 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

Fuzzy variable linear programming with fuzzy technical coefficients

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