A bi-objective Optimization Model for a Dynamic Cell Formation Integrated with Machine and Cell Layouts in a Fuzzy Environment

Abstract

In this paper, a bi-objective optimization model is developed to integrate the cell formation and inter/intra-cell layouts in continuous space by considering fuzzy conditions to minimize the total cost of parts relocations as well as cells reconfigurations. The intra-and intercell movements for both parts and machines using batch sizes for transferring parts are related to the distance traveled through a rectilinear distance in a fuzzy environment. To solve the proposed problem as a bi-objective mixed-integer non-linear programming model is NP-hard, four meta-heuristic algorithms based on a multi-objective optimization structure are tackled to address the problem. In this regard, not only Genetic Algorithm (GA), Keshtel Algorithm (KA) and Red Deer Algorithm (RDA) are employed to solve the problem, but also a novel hybrid meta-heuristic algorithm based on the benefits of aforementioned algorithms is developed. Finally, by considering some efficient assessment metrics of Pareto-based algorithms, the results indicate that the proposed hybrid algorithm not only is more appropriate than the exact solver but it also outperforms the performance of individual ones particularly in medium-and large-sized problems.

Optimization of the Cooling System of Electric Vehicle Batteries

“…As shown, the charging mode of the Electric Vehicle is considered scenario dependent. The maximum and minimum capacity of the electric vehicle battery are met in relation to (2). Eq.…”

“…The second component is a set of metal blades that help keep excess heat away from the plate or disc [1]. The last part, and in fact the most vital part of the coolers, is a fan that works by eliminating and dissipating heat around the metal blades and expelling the heat (Like the process you see behind the case of a home computer) [2][3][4][5]. This process has been a major component of these devices for almost half a century and has not changed.…”

Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment

“…Since all stochastic optimizers such as GA, KA, and RDA use a continuous search space, an encoding plan to transform it into a discrete area to confirm that the algorithm can address the constraints of our model is highly needed [21][22][23]. A general view of the encoding scheme is depicted in Figure 1.…”

“…e levels and tuned values for each parameter are given in Table 2. It should be noted that all candidate level values are taken from similar studies in the literature [18,21,26].…”

A Multi-objective MILP Model Proposed to Optimize a Supply Chain Network for Microalgae-based Biofuels and Co-products: A Case Study in Iran

“…Various types of multi-objective optimization problems are associated with the use of many settlement methods such as the global criterion method, the weighted-sum method and the ε-constraint method. The ε-constraint and the weighted sum methods have been commonly used in the literature to solve multiobjective optimization problems (Abraham & Jain, 2005;Beiki, Seyedhosseini, Mihardjo, & Seyedaliakbar, 2021;Branke, Deb, Miettinen, & Slowiński, 2008;Ghaderi, Moini, & Pishvaee, 2018;Golmohammadi, Honarvar, Hosseini-Nasab, & Tavakkoli-Moghaddam, 2020). In many similar studies, analytical hierarchy process (AHP) and data envelopment analysis (DEA) have been used in decision-making processes with several competing options and criteria (Salomon, 2018).…”