Journal of Industrial Engineering and Management Studies

Abstract All Social orders depend on and advantage from the significant and important parcel of worldwide trade that it’s backed by consolidation-based transportation over brief, medium, long and interconversion separations. By consolidating the cargo of different shippers into the same stacking units for their full or fractional journeys, consolidation looks for to extend operational and financial efficiency. This paper's focus is on consolidation-based transport and the tactical planning difficulties carriers confront when creating a set of scheduled services that viably and profitably match resource allocation with expected shipping requests over a medium- to long-term timeframe. The main contribution of this research is to provide a new integrated MOFCCP model for supply chain (SC) planning that simultaneously calculates the total tardiness, minimizes the total costs including fixed and variable travelling, purchasing and waiting cost and minimizes the total risk of travel routes. This study addresses the crucial supply chain challenges of multi modal transportation routes. Global SCs encounter major difficulties when it comes to SCM due to uncertainty. In this paper, a supply chain network (SCN) is designed using a novel multi-objective optimization model that accounts for multi modal transportation routes uncertainty. Fuzzy goal programming (FGP) is used to assist businesses in making decisions and the trade-off between the costs and benefit of alternative options because of multiple competing objectives. The primary goal of designing the suggested SCN is to minimize the overall risk of multi modal transportation costs. In order to manage the uncertainty, the novel multi-objective mathematical model is subjected to fuzzy chance constrained programming (FCCP), and a case study in steel company is carried out to investigate.

Abstract Scheduling is one of the key parameters to maintain competitive advantage of organizations, and can directly affect productivity, reduce production time and increase the profitability of an organization. Job shop scheduling problem (JSSP) seeks to find the optimal sequence of performing various jobs related to group of machines. The purpose of this paper is to provide a multi objective to optimize makespan, energy consumption and machine erosion in flexible JSSP. The problem of this paper is to assign each operation to a machine and to order the operations on the machines, such that the maximal completion time (makespan) of all operations is minimized. The obtained model belongs to NP-Hard class of optimization problems. In terms of overcoming NP-hardness of the proposed model and solve the complicated problem, a non-dominated sorting genetic algorithm (NSGAII) is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm (NRGA) is developed to validate the results obtained and test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms. In this study, to evaluate the performance of these algorithms, they were statistically analyzed using T-test. Ultimately, results of the selected model were ranked by applying the technique for order of preference by similarity to ideal solution (TOPSIS).