Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem

Ebrahimnejad, Ali; Enayattabr, M.; Motameni, Homayun; Garg, Harish

doi:10.1007/s40747-021-00278-0

Cited by 34 publications

(14 citation statements)

References 45 publications

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“…• Development of the proposed fuzzy relational NDEA model in the presence of undesirable outputs could be an interesting topic for future research. • The study can also be extended to many practical applications such as fuzzy transportation problem [6,7] and fuzzy shortest path problem [5,26,28,53]. • The proposed approach for fuzzy relational network DEA models in fuzzy environment can be developed for solving them in intuitionistic fuzzy environments [23] and intervalvalued fuzzy environments [17][18][19].…”

Section: Discussionmentioning

confidence: 99%

Fuzzy efficiency evaluation in relational network data envelopment analysis: application in gas refineries

Tabatabaei

Mozaffari

Rostamy-Malkhalifeh

et al. 2022

Complex Intell. Syst.

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In contrast to classical data envelopment analysis (DEA), network DEA has attention to the internal structure of a production system and reveals the relationship between the efficiency of system and efficiencies of the processes. However, the flexibility of weights and the need for crisp input and output data in the evaluation process are two major shortcomings of classical network DEA models. This paper presents a common weights approach for a relational network DEA model in a fuzzy environment to measure the efficiencies of the system and the component processes. The proposed approach first finds upper bounds on input and output weights for a given cut level and then it determines a common set of weights (CSW) for all decision-making units (DMUs). Hence, the fuzzy efficiencies of all processes and systems for all DMUs are obtained based on the resulting CSW. The developed fuzzy relational network DEA and the proposed common weights approach are illustrated with a numerical example. The obtained results confirm that the fuzzy data affects over the efficiency scores and complete ranking of DMUs. The applicability of the proposed network model is illustrated by performance evaluation of gas refineries in Iran.

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Section: Discussionmentioning

confidence: 99%

Fuzzy efficiency evaluation in relational network data envelopment analysis: application in gas refineries

Tabatabaei

Mozaffari

Rostamy-Malkhalifeh

et al. 2022

Complex Intell. Syst.

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“…e proposed algorithm is compared with GA, PSO, and the artificial bee colony algorithms. Ebrahimnejad et al [28] developed an artificial bee colony algorithm under uncertainty for the shortest path method problem. ey considered mixed interval fuzzy numbers for the arc weights.…”

Section: Literature Reviewmentioning

confidence: 99%

EFP‐GA: An Extended Fuzzy Programming Model and a Genetic Algorithm for Management of the Integrated Hub Location and Revenue Model under Uncertainty

2022

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The aviation industry is one of the most widely used applications in transportation. Due to the limited capacity of aircraft, revenue management in this industry is of high significance. On the other hand, the hub location problem has been considered to facilitate the demands assignment to hubs. This paper presents an integrated p-hub location and revenue management problem under uncertain demand to maximize net revenue and minimize total cost, including hub establishment and transportation costs. A fuzzy programming model and a genetic algorithm are developed to solve the proposed model with different sizes. The mining and petroleum industry is used for case studies. Results show that the proposed algorithm can obtain a suitable solution in a reasonable amount of time.

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“…For example, Di Caprio et al (2021) have proposed a routing model in which the weights given to the selected paths are fuzzy and indeterministic, and the proposed model is solved by three algorithms of genetic, particle swarm and ants, and the numerical results are compared. Ebrahimnejad et al (2021) have presented a model of uncertain SP that estimates the Mixed Interval-Valued Fuzzy interval and solve it using MOPSO algorithm, modified artificial bee colony (MABC) algorithm and NSGAII.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling a robust multi-objective locating-routing problem with bounded delivery time using meta-heuristic algorithms

Hashemi

Mahmoodi

Jasemi

et al. 2021

SRT

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Purpose This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located. Design/methodology/approach The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms. Findings According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO. Research limitations/implications In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered. Practical implications The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs. Originality/value This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.

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Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem

Cited by 34 publications

References 45 publications

Fuzzy efficiency evaluation in relational network data envelopment analysis: application in gas refineries

Fuzzy efficiency evaluation in relational network data envelopment analysis: application in gas refineries

EFP‐GA: An Extended Fuzzy Programming Model and a Genetic Algorithm for Management of the Integrated Hub Location and Revenue Model under Uncertainty

Modeling a robust multi-objective locating-routing problem with bounded delivery time using meta-heuristic algorithms

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