Distributed Permutation Flow-shop Problem Research Articles

On a bi-objective distributed permutation flow shop problem with batch processing machines

On a bi-objective distributed permutation flow shop problem with batch processing machines

Nondominated sorting genetic algorithm-II with Q-learning for the distributed permutation flowshop rescheduling problem

The distributed permutation flowshop problem (DPFSP) has been extensively studied in recent years. However, most of the research has overlooked the disturbance factors in the processing environment, such as the arrival of new jobs. To address this issue, a nondominated sorting genetic algorithm-II (NSGA-II) with Q-learning has been developed. First, an iterated greedy algorithm (IG) is proposed to generate an initial solution for the first stage. Then, the NSGA-II algorithm is designed to optimize dual-objective problems in the second stage, and the Q-learning algorithm is used to adjust the algorithm parameters. Next, two local search strategies based on key factories are adopted, including critical factory-based insert and swap operations. Finally, a comprehensive experiment of the proposed algorithm against other advanced multiobjective algorithms is conducted. The results confirm that the proposed algorithm can solve the distributed permutation flowshop rescheduling problem with high efficiency.

Problem-specific knowledge MOEA/D for energy-efficient scheduling of distributed permutation flow shop in heterogeneous factories

With the development of the global economy and the enhancement of environmental awareness, energy-efficient permutation flow shop scheduling gets more attention. Nevertheless, research on distributed scheduling with heterogeneous factories is scarce. In this paper, a knowledge-driven MOEA/D (KMOEA/D) is proposed to address the energy-efficient scheduling of distributed permutation flow shop problem in heterogeneous factories (DPFSP-HF) with the criteria of minimizing the makespan (Cmax) and total energy consumption (TEC). First, an efficient energy-saving strategy is proposed to reduce the TEC criteria. Second, a constructive heuristic is designed to generate a high-quality solution set. Third, an ingenious genetic operator is utilized to maintain population diversity. Fourth, the knowledge-driven local search operator combined the problem-specific knowledge is constructed according to the properties of DPFSP-HF. Additionally, the Taguchi approach is used to calibrate the parameter configuration of KMOEA/D. We evaluate the effectiveness of each improvement of KMOEA/D and compare it to other well-known multi-objective optimization algorithms on different instances. The results indicate the effectiveness of each improvement of KMOEA/D, and verify that KMOEA/D is an efficient approach to address DPFSP-HF.

An improved iterated greedy algorithm for distributed robotic flowshop scheduling with order constraints

Research in robotic scheduling has gained significant focus, especially for multi-factory manufacturing systems. In addition, production orders should be considered during the scheduling procedure. Therefore, this study considers an extension of the distributed permutation flow shop problem (DPFSP) with order constraints, in which the jobs of the same production order must be assigned to the same factory. Each factory has a single robot responsible for transporting jobs, and the deterioration time constraint is also considered. The objective is to minimize the maximum completion time of all factories. To this end, an improved iterated greedy (IIG) algorithm is investigated to solve the DPFSP with both robotic transportation and order constraints. In the proposed algorithm, each solution is represented by a two-dimensional vector to report the order assignment and the factory assignment. Next, an efficient decoding heuristic is developed to consider the robot routing during the transportation process. Then, an improved destruction and construction approach is embedded in the proposed algorithm to improve the computational complexity. Four problem-specific neighbourhood search operators are designed to enhance the local search abilities. Finally, the simulated annealing (SA) algorithm is embedded as the acceptance criterion to improve the exploration search abilities. The IIG algorithm is compared with several efficient algorithms in the literature, and the experimental results show the competitive performance of the proposed algorithm.

Economic Lot Sizing and Scheduling in Distributed Permutation Flow Shops

This paper addresses a new mixed integer nonlinear and linear mathematical programming economic lot sizing and scheduling problem in distributed permutation flow shop problem with number of identical factories and machines. Different products must be distributed between the factories and then assignment of products to factories and sequencing of the products assigned to each factory has to be derived. The objective is to minimize the sum of setup costs, work-in-process inventory costs and finished products inventory costs per unit of time. Since the proposed model is NP-hard, an efficient Water Cycle Algorithm is proposed to solve the model. To justify proposed WCA, Monarch Butterfly Optimization (MBO), Genetic Algorithm (GA) and combination of GA and simplex are utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure according to Response Surface Methodology is executed.

A scatter search algorithm for the distributed permutation flowshop scheduling problem

The distributed permutation flowshop problem has been recently proposed as a generalization of the regular flowshop setting where more than one factory is available to process jobs. Distributed manufacturing is a common situation for large enterprises that compete in a globalized market. The problem has two dimensions: assigning jobs to factories and scheduling the jobs assigned to each factory. Despite being recently introduced, this interesting scheduling problem has attracted attention and several heuristic and metaheuristic methods have been proposed in the literature. In this paper we present a scatter search (SS) method for this problem to optimize makespan. SS has seldom been explored for flowshop settings. In the proposed algorithm we employ some advanced techniques like a reference set made up of complete and partial solutions along with other features like restarts and local search. A comprehensive computational campaign including 10 existing algorithms, together with statistical analyses, shows that the proposed scatter search algorithm produces better results than existing algorithms by a significant margin. Moreover all 720 known best solutions for this problem are improved.