Using Four Metaheuristic Algorithms to Reduce Supplier Disruption Risk in a Mathematical Inventory Model for Supplying Spare Parts

Abstract

Due to the unexpected breakdowns that can happen in various components of a production system, failure to reach production targets and interruptions in the process of production are not surprising. Since this issue remains for manufactured products, this halting results in the loss of profitability or demand. In this study, to address a number of challenges associated with the management of crucial spare parts inventory, a mathematical model is suggested for the determination of the optimal quantity of orders, in the case of an unpredicted supplier failure. Hence, a production system that has various types of equipment with crucial components is assumed, in which the crucial components are substituted with spare parts in the event of a breakdown. This study’s inventory model was developed for crucial spare parts based on the Markov chain process model for the case of supplier disruption. Moreover, for optimum ordering policies, re-ordering points, and cost values of the system, four metaheuristic algorithms were utilized that include Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Moth–Flame Optimization (MFO) Algorithm, and Differential Evolution (DE) Algorithm. Based on the results, reliable suppliers cannot meet all of the demands; therefore, we should sometimes count on unreliable suppliers to reduce unmet demand.

Expectations of manufacturing companies towards suppliers in terms of implementing improvement activities

“…the implementation of improvement solutions, including those that stimulate the growth of product innovation. Baghizadeh, Ebadi, Zimon, & Jum'a, 2022). Considering the integration of the supplier's efficiency and its alignment with manufacturing companies' objectives related to quality, innovation, cost reduction, sustainable development and more, this aspect emerges as a crucial factor influencing competitive advantage in the contemporary market.…”

Multi-Objective Disassembly Sequence Planning in Uncertain Industrial Settings: An Enhanced Water Wave Optimization Algorithm

“…Solving DSP problems typically demands exact solving techniques and heuristic algorithms. However, as the complexity of product component structures continues to rise, leading to exponential growth in computation time, this poses significant challenges for both exact-solving techniques and heuristic algorithms [10][11][12][13].…”

“…Furthermore, even the simplest allocation problems are NP-hard, and DSP is no exception [11][12][13]. As the task scale expands, computational resource requirements exhibit exponential growth.…”

“…Case study A: undominated solution set. ,3, 1,16,4,20,19,5,7,10,12,18,6,17,21,14,11,9,8, 15 562.9, 586.9, 611.9 115 17 7 13, 1, 16, 2, 3, 4, 19, 20, 10, 5, 7, 12, 18, 6, 17, 21, 14, 15, 11, 9, 8 565.2, 589.4, 614.7 114 18 8 13, 1, 16, 2, 3, 4, 5, 7, 19, 20, 10, 6, 12, 18, 17, 21, 14, 11, 9, 8, 15 566.4, 590.7, 616.2 111 19 9 13, 1, 2, 16, 3, 4, 5, 7, 10, 20, 6, 12, 18, 17, 21, 14, 15, 11, 9, 8, 19 569.9, 594.5, 620.5 107 21 10 13, 19, 1, 16, 2, 3, 4, 10, 5, 7, 12, 17, 18, 21, 14, 15, 11, 9, 8, 6, 20 573.3, 598.2, 624.6 105 22…”

Extended Stochastic Coati Optimizer

“…In studies regarding electric vehicle (EV) systems, metaheuristics have been implemented to optimize the routing problem of the plugin hybrid electric vehicles (PHEV) [1], fast charging stations [2], distributed photovoltaic systems [3], and so on. In studies regarding manufacturing systems, metaheuristics have been implemented to optimize the coupling between integrated batch machines and direct shipping trucks [4], reducing risk in the spare part inventory system [5], and so on. In studies regarding power systems, metaheuristics have been implemented to optimize the installation and allocation of the distributed generation units [6], radial distribution system [7], reactive power flow in the large-scale power system [8], and so on.…”