Multiobjective Multi-Product Production Planning Problem Using Intuitionistic and Neutrosophic Fuzzy Programming

Khan, Mohammad Faisal; Haq, Ahteshamul; Ahmed, Aquil; Ali, Irfan

doi:10.1109/access.2021.3063725

Cited by 21 publications

(6 citation statements)

References 35 publications

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“…Rani and Mishra [26] proposed a neutrosophic programming model for the compromise selection of a recycling partner for electronics equipment sustainable waste. Khan et al [17] solved the industrial production problem to maximize the net profit under resource and system constraints using a neutrosophic programming approach. Ahmad [1] suggested a neutrosophic optimization method for a compromise solution to intuitionistic multi-objective pricing and managerial problems.…”

Section: Solution Methodologymentioning

confidence: 99%

Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming

Ullah,

Shabbir,

Alomair

et al. 2024

Heliyon

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Section: Solution Methodologymentioning

confidence: 99%

Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming

Ullah,

Shabbir,

Alomair

et al. 2024

Heliyon

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“…If the market possibilities are known, the first two techniques provide viable options. By contrast, fuzzy optimization provides feasible and robust solutions, that is, solutions that can work in various situations [23][24][25]. × × Zhu et al [18] According to a literature review, downstream oil and gas supply chain problems contain significant uncertainties.…”

Section: Literature Reviewmentioning

confidence: 99%

Multimodal Fuzzy Downstream Petroleum Supply Chain: A Novel Pentagonal Fuzzy Optimization

Freen¹,

Kousar²,

Kausar³

et al. 2023

Computers, Materials &Amp; Continua

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The petroleum industry has a complex, inflexible and challenging supply chain (SC) that impacts both the national economy as well as people's daily lives with a range of services, including transportation, heating, electricity, lubricants, as well as chemicals and petrochemicals. In the petroleum industry, supply chain management presents several challenges, especially in the logistics sector, that are not found in other industries. In addition, logistical challenges contribute significantly to the cost of oil. Uncertainty regarding customer demand and supply significantly affects SC networks. Hence, SC flexibility can be maintained by addressing uncertainty. On the other hand, in the real world, decision-making challenges are often ambiguous or vague. In some cases, measurements are incorrect owing to measurement errors, instrument faults, etc., which lead to a pentagonal fuzzy number (PFN) which is the extension of a fuzzy number. Therefore, it is necessary to develop quantitative models to optimize logistics operations and supply chain networks. This study proposed a linear programming model under an uncertain environment. The model minimizes the cost along the refineries, depots, multimode transport and demand nodes. Further developed pentagonal fuzzy optimization, an alternative approach is developed to solve the downstream supply chain using the mixed-integer linear programming (MILP) model to obtain a feasible solution to the fuzzy transportation cost problem. In this model, the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number. Furthermore, defuzzification is performed using an accuracy function. To validate the model and technique and feasibility solution, an illustrative example of the oil and gas SC is considered, providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.

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“…In [17], the researchers deal with the problem as a multi-objective problem, their aim is to find an ideal team who can get the job done while keeping project management and personnel costs to a minimum. Since multi-objective is an area of mathematics used in multiple criterion decision-making, it deals with optimization issues involving two or more objective functions that must be optimized at the same time [18,19]. This assumption makes the problem even more difficult, as we have multi-objective that must be dealt with.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation燩roblem

Nageh¹,

Elshamy²,

Hassan³

et al. 2022

Computers, Materials &Amp; Continua

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Team Formation (TF) is considered one of the most significant problems in computer science and optimization. TF is defined as forming the best team of experts in a social network to complete a task with least cost. Many real-world problems, such as task assignment, vehicle routing, nurse scheduling, resource allocation, and airline crew scheduling, are based on the TF problem. TF has been shown to be a Nondeterministic Polynomial time (NP) problem, and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms. This paper proposes two improved swarm-based algorithms for solving team formation problem. The first algorithm, entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm (HBOSA), uses a single crossover operator to improve the performance of a standard heap-based optimizer (HBO) algorithm. It also employs the simulated annealing (SA) approach to improve model convergence and avoid local minima trapping. The second algorithm is the Chaotic Heap-based Optimizer Algorithm (CHBO). CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space. During HBO's optimization process, a logistic chaotic map is used. The performance of the two proposed algorithms (HBOSA) and (CHBO) is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills. Furthermore, the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer (HBO), Developed Simulated Annealing (DSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Genetic Algorithm (GA). Finally, the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database (IMDB). The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance, with fast convergence to the global minimum.

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Multiobjective Multi-Product Production Planning Problem Using Intuitionistic and Neutrosophic Fuzzy Programming

Cited by 21 publications

References 35 publications

Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming

Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming

Multimodal Fuzzy Downstream Petroleum Supply Chain: A Novel Pentagonal Fuzzy Optimization

Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation燩roblem

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