Distributed Permutation Flowshop Scheduling Problem Research Articles

The Distributed Assembly Permutation Flowshop Scheduling Problem

Nowadays, improving the management of complex supply chains is a key to become competitive in the twenty-first century global market. Supply chains are composed of multi-plant facilities that must be coordinated and synchronised to cut waste and lead times. This paper proposes a Distributed Assembly Permutation Flowshop Scheduling Problem (DAPFSP) with two stages to model and study complex supply chains. This problem is a generalisation of the Distributed Permutation Flowshop Scheduling Problem (DPFSP). The first stage of the DAPFSP is composed of f identical production factories. Each one is a flowshop that produces jobs to be assembled into final products in a second assembly stage. The objective is to minimise the makespan. We present first a Mixed Integer Linear Programming model (MILP). Three constructive algorithms are proposed. Finally, a Variable Neighbourhood Descent (VND) algorithm has been designed and tested by a comprehensive ANOVA statistical analysis. The results show that the VND algorithm offers good performance to solve this scheduling problem.

Minimising makespan in distributed permutation flowshops using a modified iterated greedy algorithm

The distributed permutation flowshop scheduling problem (DPFSP) is a newly proposed topic in the shop scheduling field, which has important application in globalised and multi-plant environments. This study presents a modified iterated greedy (MIG) algorithm for this problem to minimise the maximum completion time among all the factories. Compared with previous approaches, the proposed algorithm is simpler yet more effective, more efficient, and more robust in solving the DPFSP. To validate the performance of the proposed MIG algorithm, computational experiments and comparisons are conducted on an extended benchmark problem set of Taillard. Despite its simplicity, the computational results show that the proposed MIG algorithm outperforms all existing algorithms, and the best-known solutions for almost half of instances are updated. This study can be offered as a contribution to the growing body of work on both theoretically and practically useful approaches to the DPFSP.

An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem

In this paper, an effective estimation of distribution algorithm (EDA) is proposed to solve the distributed permutation flow-shop scheduling problem (DPFSP). First, the earliest completion factory rule is employed for the permutation based encoding to generate feasible schedules and calculate the schedule objective value. Then, a probability model is built for describing the probability distribution of the solution space, and a mechanism is provided to update the probability model with superior individuals. By sampling the probability model, new individuals can be generated among the promising search region. Moreover, to enhance the local exploitation, some local search operators are designed based on the problem characteristics and utilized for the promising individuals. In addition, the influence of parameter setting of the EDA is investigated based on the Taguchi method of design of experiments, and a suitable parameter setting is suggested. Finally, numerical simulations based on 420 small-sized instances and 720 large-sized instances are carried out. The comparative results with some existing algorithms demonstrate the effectiveness of the proposed EDA in solving the DPFSP. In addition, the new best-known solutions for 17 out of 420 small instances and 589 out of 720 large instances are found.

An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem

Distributed permutation flow shop scheduling problem (DPFSP) is a newly proposed scheduling problem, which is a generalisation of classical permutation flowshop scheduling problem. Studies on algorithms for solving this problem are in the early stage. In this paper, we propose a new tabu algorithm for solving this problem, which exploits a novel tabu strategy. A method that swaps sub-sequences of jobs is presented to generate neighbourhood. Moreover, an enhanced local search method is proposed and also combined into the tabu algorithm. We also use the well-known benchmark of Taillard (extended to the distributed permutation flowshop problems) to test the algorithm. From the intensive experiments we carried out, we conclude that the proposed tabu algorithm outperforms all the existing algorithms including heuristics algorithms (i.e. NEH1, NEH2, VND(a) and VND(b)) and a hybrid genetic algorithm, so the best-known solutions for 472 instances are updated. Moreover, it is worth mentioning that the efficiency of the tabu algorithm is also better than that of the genetic algorithm.

An NEH-based heuristic algorithm for distributed permutation flowshop scheduling problems

Distributed Permutation Flowshop Scheduling Problem (DPFSP) is a newly proposed scheduling problem with a strong engineering background. Whereas there is a body of work on scheduling problems in the past decades, the literature on DPFSPs is scant and in its infancy. Motivated by the good performances of some heuristics in a very recent work, we propose a constructive heuristic algorithm enhanced through a novel dispatching rule to deal with the DPFSP. Given multiple factories in a DPFSP, our dispatching rule will insert a group of jobs to the factories at one time instead of inserting one job at one time like the original rules. The time complexity of the proposed heuristic algorithm is the same as that of the NEH with the original rule. To validate the proposed heuristic, intensive benchmark experiments are carried out on the large problem instances, and the results show that the proposed algorithm outperforms the existing heuristics in terms of tradeoff between solution quality and running time. Key words: Distributed scheduling problem, flowshop, heuristic, branch and bound.

A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem

Distributed Permutation Flowshop Scheduling Problem (DPFSP) is a newly proposed scheduling problem, which is a generalization of classical permutation flow shop scheduling problem. The DPFSP is NP-hard in general. It is in the early stages of studies on algorithms for solving this problem. In this paper, we propose a GA-based algorithm, denoted by GA_LS, for solving this problem with objective to minimize the maximum completion time. In the proposed GA_LS, crossover and mutation operators are designed to make it suitable for the representation of DPFSP solutions, where the set of partial job sequences is employed. Furthermore, GA_LS utilizes an efficient local search method to explore neighboring solutions. The local search method uses three proposed rules that move jobs within a factory or between two factories. Intensive experiments on the benchmark instances, extended from Taillard instances, are carried out. The results indicate that the proposed hybrid genetic algorithm can obtain better solutions th...