A DE-LS Metaheuristic Algorithm for Hybrid Flow-Shop Scheduling Problem considering Multiple Requirements of Customers

Abstract

In this paper, we address a hybrid flow-shop scheduling problem with the objective of minimizing the makespan and the cost of delay. The concerned problem considers the diversity of the customers’ requirements, which influences the procedures of the productions and increases the complexity of the problem. The features of the problem are inspired by the real-world situations, and the problem is formulated as a mixed-integer programming model in the paper. In order to tackle the concerned problem, a hybrid metaheuristic algorithm with Differential Evolution (DE) and Local Search (LS) (denoted by DE-LS) has been proposed in the paper. The differential evolution is a state-of-the-art metaheuristic algorithm which can solve complex optimization problem in an efficient way and has been applied in many fields, especially in flow-shop scheduling problem. Moreover, the study not only combines the DE and LS, but also modifies the mutation process and provides the novel initialization process and correction strategy of the approach. The proposed DE-LS has been compared with four variants of algorithms in order to justify the improvements of the proposed algorithm. Experimental results show that the superiority and robustness of the proposed algorithm have been verified.

Makespan Optimisation in Cloudlet Scheduling with Improved DQN Algorithm in Cloud Computing

“…Sun and Qi [15] proposed a hybrid tasks scheduler based on Local search and differential evolution (DE) to enhance the makespan and the cost metrics. e authors in the paper [16] presented a parallel optimized relay selection protocol to minimise latency, collision, and energy for wake-up radioenabled WSNs.…”

A Parallel Permutation Flow-Shop Scheduling Model by Using a Two-Step Evolutionary Algorithm to Minimize Intermediate Storage with Tolerable Maximum Completion Time

“…Here are a few of the most recent ones. Sun and Qi [5] conducted research in the parallel FSP where there are multiple requirements for every job. This was about a customized job due to a customer's request.…”

“…multi-objective multi-verse optimizer (IMOMVO) [22] make-span iterated greedy algorithm [3] total weighted completion time artificial bee colony algorithm [10] make-span, a measure of service level and total energy consumption MILP [16] total completion time. cloud theory-based simulated annealing (CSA) [23] total actual flow time MILP [24] total weighted tardiness constructive heuristics and branch-and-bound algorithm (B&B) [4] total tardiness GA [5] make-span and the cost of delay differential evolution (DE) and local search (LS) [12] make-span MILP and iterated greedy algorithm [25] total completion time IEGA [9] total weighted tardiness, total operation time, and total cost of the company's reputational damage MILP, MOPSO, NSGA II [7] maximum completion time of jobs constructive heuristics the distributed permutation FSP. Naderi & Ruiz [8] compared 14 heuristic-based dispatching methods to solve problems in distributed permutation FSP.…”

“…Thus, FSP has many derivatives, such as classical model, permutation, non-permutation, parallel, two-machine, hybrid, etc. Research on FSP was conducted with specific constraints and the processes ran in specific circumstances, such as deteriorating jobs [3], sequence-dependent setup times [4], multiple requirements from customers [5], limited waiting times [4], no-idle machines [6], and blocking limitations [7].…”

“…The majority of FSP research intended to minimize the maximum completion time [8]. Other objectives include tardiness [9], penalties [5], energy consumption [10], and so on. Ironically, intermediate storage as an objective was rarely investigated, even though it is a crucial part of the flow-shop system.…”