An Energy-Efficient No Idle Permutations Flow Shop Scheduling Problem Using Grey Wolf Optimizer Algorithm

Abstract

Energy consumption has become a significant issue in businesses. It is known that the industrial sector has consumed nearly half of the world's total energy consumption in some cases. This research aims to propose the Grey Wolf Optimizer (GWO) algorithm to minimize energy consumption in the No Idle Permutations Flowshop Problem (NIPFP). The GWO algorithm has four phases: initial population initialization, implementation of the Large Rank Value (LRV), grey wolf exploration, and exploitation. To determine the level of machine energy consumption, this study uses three different speed levels. To investigate this problem, 9 cases were used. The experiments show that it produces a massive amount of energy when a job is processed fast. Energy consumption is lower when machining at a slower speed. The performance of the GWO algorithm has been compared to that of the Cuckoo Search (CS) algorithm in several experiments. In tests, the Grey Wolf Optimizer (GWO) outperforms the Cuckoo Search (CS) algorithm.

A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problem

“…Equation (27) shows that the algebraic description fits this behavior. 𝑋 4 (𝑡 + 1) = 𝑄𝐹 × 𝑋 𝑏𝑒𝑠𝑡 (𝑡) − (𝐺 1 × 𝑋(𝑡) × 𝑟𝑎𝑛𝑑) − 𝐺 2 × 𝐿𝑒𝑣𝑦(𝐷) + 𝑟𝑎𝑛𝑑 × 𝐺 1 (27) where 𝑋 4 (𝑡 + 1) is the result of the fourth search technique (𝑋 4 ) and represents the solution of the subsequent iteration t. QF indicates the quality function computed using the equation used to balance the search strategy (28). 𝐺 1 represents the AO's range of motion during mating passes, calculated using equation (29).…”

“…Several studies have conducted proofs regarding the minimization of energy consumption by employing the No-Idle Permutation Flow Shop Scheduling Problem (NIPFSP) [15], specifically by employing the Iterated Greedy Algorithm [16], the Tabu search (TS) and the Genetic Algorithm (GA) [17], the Iterated reference greedy algorithm [18], the Invasive weed optimization algorithm [19], Memetic algorithm with node and edge histogram [20], collaborative optimization algorithm [21], novel differential evolution algorithm [22], discrete artificial bee colony algorithm [23], a hybrid discrete particle swarm optimization algorithm [24], a hybrid discrete differential evolution algorithm [25], Hybrid Grasshopper Optimization Algorithm [26], the hybrid ant lion optimization flow shop [27]. Some of these studies specifically discuss No-idle, a research Al-Imron et al [28] that aims to minimize energy consumption using the Grey Wolf Optimizer Algorithm. Some studies also analyze Flow Shop Scheduling by using a multi-operator hybrid genetic algorithm [29], multiobjective distributed reentrant permutation flow shop scheduling with sequence-dependent setup time [30], A decision support system for road freight transportation route selection with new fuzzy numbers [31], A systematic literature review on energy-efficient hybrid flow shop scheduling [32], A novel hybrid Archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling [5], and design of decision support system for road freight transportation routing using multilayer zero-one goal programming [33].…”

“…In this study, a comparison will be made between the no-idle flow shop scheduling problem and the previously studied the Grey Wolf Optimizer (GWO) algorithm [28] as a comparison. The Aquila Optimizer (AO) offers advantages such as fast convergence speed, high search efficiency, and a simple structure [34].…”

A No-Idle Flow Shop Scheduling using Fire Hawk Optimizer to Minimize Energy Consumption

“…Equations (7) and (8) are to ensure that each job is processed on all machines at the same machining speed. Equation (9) shows the idle time on each machine. Equations (10), (11), and (12) are used to ensure that no idling occurs between jobs on each machine.…”