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The most important strategic issue for several industries is where to find facilities so as to discover a transportation path for optimizing the objectives at the same time. This paper acquaints a streamlining model with incorporate the facility location problem, solid transportation problem, and inventory management under multi-objective environment. The aims of the stated formulation are multi-fold: (i) to seek the optimum locations for potential facilities in Euclidean plane; (ii) to find the amount of distributed commodities; and (iii) to reduce the overall transportation cost, transportation time, and inventory cost along with the carbon emission cost. Here, variable carbon emission cost is taken into consideration because of the variable locations of facilities and the amount of distributed products. After that, a new hybrid approach is introduced dependent on an alternating locate-allocate heuristic and the intuitionistic fuzzy programming to get the Pareto-optimal solution of the proposed formulation. In fact, the performances of our findings are discussed with two numerical examples. Sensitivity analysis is executed to check the resiliency of the parameters. Ultimately, managerial insights, conclusions and avenues of future studies are offered at the end of this study.

The transportation problem (TP) is a prominent kind of linear programming problem (LPP) in which it is necessary to transfer goods from several sources to several destinations while minimizing the overall cost of transportation is its objective. Because of its many practical uses, it's a popular tool in operation research. The utilization of neutrosophic sets (NS) to analyze and resolve diverse decision-making challenges has quickly gained popularity. As a consequence, the neutrosophic theory is progressively being the subject of several current research investigations. In this study, we assess the TP in a neutrosophic setting, where the neutrosophic transportation problem (NTP) is expressed as a table called the neutrosophic transportation table, with triangular fuzzy neutrosophic numbers as its core elements. We make use of a score function to convert the triangular fuzzy neutrosophic values to their equivalent crisp numbers, followed by a stepwise methodology of the proposed approach to obtain the optimal solution. The outcomes are then compared with the previously acquired solutions to demonstrate the method's effectiveness.

Transportation Problem (TP) is a very important problem which has been vastly studied in Operations Research domain. There are some classical methods to find the initial basic feasible solution (IBFS) which minimize the total shipping cost of (TP) such as north-west corner method (NWCM), minimum cost method (MCM) and Vogel's approximation method (VAM) which the best one of them. In this paper, we suggest a new amendment to (VAM) to find (IBFS) of (TP), which is an iterative method and the results will be near the optimal solution and in some cases equal to the optimal solution. In the numerical experiences we compare the results of the new approach with other classical methods to verify the efficiency of the new method. The proposed method is very effective and well-suited for use in solving these problems of various sizes.

In this research work, a soft computing optimization operating approach is developed for a multi-objective aspirational level fractional transportation problem. In the proposed technique, a mathematical model is formulated for the multi-objective aspirational level fractional transportation problem (MOFTP) based on the highest value of one and all objectives of the model. We also used the symmetry concept over our model to identify the best optimum solution based on symmetrical data. We constructed the membership grades for the set of fetched parameters having symmetry. In this work, we also used the concept of ranking function in our mathematical model to obtain the optimum solution of the fuzzy multi-objective fractional transportation. In this proposed algorithm, the aspiration levels are also associated with the objective function of MOFTP. We are also proposing a new approach for the optimization of fractional problems in which the objectives are being optimized by using the numerator function and denominator function simultaneously. Further, a methodology is also developed to find the average cost for each fractional objective of the model. After that, we will find the ranking function for each parameter by using the defuzzification method. By this methodology, we will be able to convert the MOFTP into a bi-objective transportation problem. The provided technique is elaborated with the help of numerical computations to prove the beauty and power of the proposed technique.

During past few decades, fuzzy decision is an important attention in the areas of science, engineering, economic system, business, etc. To solve day-to-day problem, researchers use fuzzy data in transportation problem for presenting the uncontrollable factors; and most of multi-objective transportation problems are solved using goal programming. However, when the problem contains interval-valued data, then the obtained solution was provided by goal programming may not satisfy by all decision-makers. In such condition, we consider a fixed-charge solid transportation problem in multi-objective environment where all the data are intuitionistic fuzzy numbers with membership and non-membership function. The intuitionistic fuzzy transportation problem transforms into interval-valued problem using ( α , β ) -cut, and thereafter, it reduces into a deterministic problem using accuracy function. Also the optimum value of alternative corresponds to the optimum value of accuracy function. A numerical example is included to illustrate the usefulness of our proposed model. Finally, conclusions and future works with the study are described.

This paper presents a study on the multi-objective green four-dimensional transportation problem (MOG4DTP) with product blending. Due to uncontrollable circumstances and globalization, it is not always practical to exactly determine the parameters of the MO4DGTP. In such situations, decision experts sometimes have to deal with data that can be described by a membership degree (MD) and a non-membership degree (NMD), such that their total does not fall within the range 0 , 1 . Such a situation cannot be addressed by fuzzy set theory or intuitionistic fuzzy set (IFS) theory. However, there are cases where the sum of the cubes of the MD and the NMD of the data lies within the range 0 , 1 , even though their sum is greater than 1. Fermatean fuzzy sets (FFSs) can deal with such ambiguous data. Thus, we consider parameters such as transportation cost, time, availability, demand, conveyance capacity and carbon emission as triangular Fermatean fuzzy numbers (TrFFNs). Also, since greenhouse gas emission is the most controversial issue in present times, we have considered carbon emission as one of the objectives of our problem. Both these considerations make our problem more realistic. Additionally, we propose a ranking index for TrFFNs and, by utilizing its linearity, transform the Fermatean fuzzy model into its corresponding deterministic form. Further, we obtain the Pareto-optimal solution of this model by four methods, namely, fuzzy TOPSIS, ϵ -constraint method, augmented Tchebycheff method (ATM) and weighted Tchebycheff metrics programming (WTMP) method. We describe a real-world industrial transportation problem (TP) and compare the solutions obtained using different techniques in order to show the value and applicability of the suggested model. The proposed algorithm’s performance is validated through comparisons with state-of-the-art multi-objective algorithms, ensuring credibility and demonstrating its effectiveness in solving complex optimization problems. Further, a comprehensive sensitivity analysis is conducted to assess the robustness of the proposed algorithm, ensuring its reliability across varying parameter settings and problem instances. Lastly, we present key conclusions along with the limitations of the proposed approach, and suggest directions for future research building upon this work.

Goal programming (GP) is one of the widely used and effective method of solving real-world multi-objective decision-making problems. The term “multi-objective transportation problem” (MOTP) covers a specific class of vector maximum (minimum) linear programming problem that typically has multiple, competing, and incompatible objective functions. In this article, GP and weighted goal programming (WGP) are summarized in MOTP. Furthermore, its advantages over GP are illustrated by a theorem. Additionally, by utilizing WGP, a solution procedure is articulated for MOTP. Finally, an application on screen-panels of mobile devices is presented to explore the applicability. The paper ends with a conclusion and including an outlook to future studies, whereby we address internal and external transportation, and view the internal one with a broader understanding of logistics.

Wasserstein barycenters correspond to optimal solutions of transportation problems for several marginals, and as such have a wide range of applications ranging from economics to statistics and computer science. When the marginal probability measures are absolutely continuous (or vanish on small sets) the theory of Wasserstein barycenters is well-developed [see the seminal paper (Agueh and Carlier in SIAM J Math Anal 43(2):904–924, 2011 )]. However, exact continuous computation of Wasserstein barycenters in this setting is tractable in only a small number of specialized cases. Moreover, in many applications data is given as a set of probability measures with finite support. In this paper, we develop theoretical results for Wasserstein barycenters in this discrete setting. Our results rely heavily on polyhedral theory which is possible due to the discrete structure of the marginals. The results closely mirror those in the continuous case with a few exceptions. In this discrete setting we establish that Wasserstein barycenters must also be discrete measures and there is always a barycenter which is provably sparse. Moreover, for each Wasserstein barycenter there exists a non-mass-splitting optimal transport to each of the discrete marginals. Such non-mass-splitting transports do not generally exist between two discrete measures unless special mass balance conditions hold. This makes Wasserstein barycenters in this discrete setting special in this regard. We illustrate the results of our discrete barycenter theory with a proof-of-concept computation for a hypothetical transportation problem with multiple marginals: distributing a fixed set of goods when the demand can take on different distributional shapes characterized by the discrete marginal distributions. A Wasserstein barycenter, in this case, represents an optimal distribution of inventory facilities which minimize the squared distance/transportation cost totaled over all demands.

The main inquisition of this paper is to introduce two methods for solving a multi-objective green 4-dimensional fixed-charge transportation problem (MG4FTP) under neutrosophic environment. The increasing use of transportation vehicles, the condition of roads, vehicle type in daily life to meet our needs that create a lot of problems such as global warming, greenhouse gas (GHG) emissions in the nature. In this paper, we minimize transportation cost, carbon emission and transportation time. In real-life situation, all parameters of transportation problem are not tackled by crisp value, fuzzy numbers and intuitionistic fuzzy numbers, then to accommodate the fact we choice here single valued trapezoidal neutrosophic number (SVTNN) for designing such type of transportation problem. Thereafter we use α , β , γ -cut of SVTNN to convert the parameters in interval form of the proposed model. Two new approaches based on neutrosophic programming (NP) and Pythagorean hesitant fuzzy programming (PHFP) are used to extract a better compromise solution of the proposed problem. A comparison is drawn among the compromise solutions that are derived from the programming, by using the score function of SVTNN. Two numerical examples are included to illustrate the applicability and validity of the proposed problem.