A fuzzy possibilistic bi-objective hub covering problem considering production facilities, time horizons and transporter vehicles

Ghodratnama, Ali; Tavakkoli–Moghaddam, Reza; Azaron, Amir

doi:10.1007/s00170-012-4318-6

Cited by 22 publications

(17 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Constraints (17) and (18) guarantee that delivery of extra products to the located distribution centers is not allowed. Constraints (19) and (20) ensure that delivery of extra products to each customer is not allowed. Constraints (21) and (22) guarantee that all the ordered products and customers' demands must be satis ed during the horizon planning.…”

Section: The Proposed Modelmentioning

confidence: 99%

“…In this respect, Ghodratnama et al [19] presented a fuzzy possibilistic bi-objective mathematical programming model with the aim of minimizing the total costs. Mohammadi et al [20] developed a new multiobjective multi-mode transportation model in order to minimize current investment costs and maximum transportation time based on stochastic parameters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty

2018

View full text Add to dashboard Cite

Selecting the most suitable optimal point among Pareto optimal points could help experts make an appropriate decision in an uncertain and complex situation. In this paper, an evaluation and ranking approach is proposed based on a hesitant fuzzy set environment to assess the Pareto optimal points obtained through the proposed biobjective multi-echelon supply chain model by locating distribution centers. In this respect, the proposed model has been utilized for perishable products based on fuzzy customers' demand. To address this issue, the possibilistic chance-constrained programming approach has been utilized based on the trapezoidal fuzzy membership function. Moreover, the proposed hesitant fuzzy ranking approach is constructed based on group decision analysis and the last aggregation approach. Thereby, the last aggregation approach by aggregating the experts' opinions in the last step could prevent the data loss. However, a case study about the perishable dairy products is considered to indicate the applicability of the proposed bi-objective multi-echelon supply chain model by locating distribution centers. Finally, a comparative analysis is provided between the obtained results and the current practice to show the feasibility and e ciency of the proposed approach.

show abstract

Section: The Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty

2018

View full text Add to dashboard Cite

show abstract

“…In a recent study, Fazel Zarandi et al [11] proposed a Q-coverage multipleallocation hub set-covering problem and required the distances between hubs to be greater than a lower bound. Ghodratnama et al [12] presented a fuzzy biobjective model for a hub covering location problem in which the rst objective function aimed at minimizing network costs and the second objective function was concerned with minimizing the shipping time of products, considering di erent transportation vehicles and time periods. Korani and Sahraeian [13] developed a single-allocation hierarchical hub covering model in which all origin destination pairs were required to have a travel time lower than a determined value.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints (7) and (8) require the numbers of primary and back-up hubs to be respectively equal to P and Q. Constraint (9) ensures the primary and back-up hubs not to be located in the same nodes. Constraint (10) represents that between any O-D pair (i; j), there is just one primary route through primary hubs k and m. In the same way, Constraint (11) shows that between any O-D pair (i; j), there is just one back-up route through back-up hub n. Constraints (12) and (13) ensure that non-hub nodes are not connected directly in the network and prohibit the ow between origin i to destination j from being routed through non-hub nodes. Constraint (14) is like constraints (12) and (13), with exception that this constraint concerns backup hubs and back-up routes instead of primary ones.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-objective Fuzzy Goal Programming P-hub Location and Protection Model with Back-up Hubs Considering Hubs Establishment Fixed Costs

2016

View full text Add to dashboard Cite

Abstract. The critical and undeniable role of hubs in telecommunication and networking brings about some precautions to be taken to protect networks against any disruption. In this paper, a multi-objective model in a group of hub location problems, referred to as protection models for survivable network design, is developed. In fact, this model is a hub location problem that aims to maximize the potential ow between the origin and destination node pairs in the network, which has the minimum potential ow among all O-D pairs, including multiple assignment and back-up routes. In addition, it concerns the xed cost of installation of the hub facilities. A fuzzy goal programming method is applied to solve the model. Di erent scenarios are implemented using Turkish data set and also numerical experiments are presented to illustrate the advantages of the proposed model in some aspects, compared to previous models. The results can give useful insights into telecommunication network design.

show abstract

“…Their model aimed to determine the optimal location of the hub facilities among some candidate sites so that the total transportation cost is minimized. Ghodratnama et al (2013) proposed a fuzzy possibilistic bi-objective nonlinear mixed-integer programming formulation for the hub-covering problem. They considered the major parameters of their mathematical formulation to be fuzzy in order to model the uncertainties involved.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Bi-objective optimization of multi-server intermodal hub-location-allocation problem in congested systems: modeling and solution

et al. 2018

View full text Add to dashboard Cite

A new multi-objective intermodal hub-location-allocation problem is modeled in this paper in which both the origin and the destination hub facilities are modeled as an M/M/m queuing system. The problem is being formulated as a constrained bi-objective optimization model to minimize the total costs as well as minimizing the total system time. A small-size problem is solved on the GAMS software to validate the accuracy of the proposed model. As the problem becomes strictly NP-hard, an MOIWO algorithm with an efficient chromosome structure and a fuzzy dominance method is proposed to solve large-scale problems. Since there is no benchmark available in the literature, an NSGA-II and an NRGA are developed to validate the results obtained. The parameters of all algorithms are tuned using the Taguchi method and their performances are statistically compared in terms of some multi-objective metrics. Finally, the entropy-TOPSIS method is applied to show that MOIWO is the best in terms of simultaneous use of all the metrics.

show abstract

A fuzzy possibilistic bi-objective hub covering problem considering production facilities, time horizons and transporter vehicles

Cited by 22 publications

References 34 publications

A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty

A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty

A Multi-objective Fuzzy Goal Programming P-hub Location and Protection Model with Back-up Hubs Considering Hubs Establishment Fixed Costs

Bi-objective optimization of multi-server intermodal hub-location-allocation problem in congested systems: modeling and solution

Contact Info

Product

Resources

About