A survey of parallel hybrid applications to the permutation flow shop scheduling problem and similar problems

Parallel algorithms have focused an increased interest due to advantages in computation time and quality of solutions when applied to industrial engineering problems. This communication is a survey and classification of works in the field of hybrid algorithms implemented in parallel and applied to combinatorial optimization problems similar to the to the permutation flowshop problem with the objective of minimizing the makespan, Fm|prmu|Cmax according to the Graham notation, the travelling salesman problem (TSP), the quadratic assignment problem (QAP) and, in general, those whose solution can be expressed as a permutation

Using simulation to provide alternative solutions to the flowshop sequencing problem

In this paper we present SS-GNEH, a simulation-based algorithm for the Permutation Flowshop Sequencing Problem (PFSP). Given a PFSP instance, the SSGNEH algorithm incorporates a randomness criterion to the classical NEH heuristic and starts an iterative process in order to obtain a set of alternative solutions, each of which outperforms the NEH algorithm. Thus, a random but oriented local search of the space of solutions is performed, and a list of "good alternative solutions" is obtained. We can then consider several desired properties per solution other than maximum time employed, such as balanced idle times among machines, number of completed jobs at a given target time, etc. This allows the decision-maker to consider multiple solution characteristics other than just those defined by the aprioristic objective function. Therefore, our methodology provides flexibility during the sequence selection process, which may help to improve the scheduling process. Several tests have been performed to discuss the effectiveness of this approach. The results obtained so far are promising enough to encourage further developments on the algorithm and its applications in real-life scenariosPostprint (published version

Flow shop scheduling decisions through Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS)

[EN] The flow-shop scheduling problem (FSP) has been widely studied in the literature and having a very active research area. Over the last few decades, a number of heuristic/meta-heuristic solution techniques have been developed. Some of these techniques offer excellent effectiveness and efficiency at the expense of substantial implementation efforts and being extremely complicated. This paper brings out the application of a Multi-Criteria Decision Making (MCDM) method known as techniques for order preference by similarity to an ideal solution (TOPSIS) using different weighting schemes in flow-shop environment. The objective function is identification of a job sequence which in turn would have minimum makespan (total job completion time). The application of the proposed method to flow shop scheduling is presented and explained with a numerical example. The results of the proposed TOPSIS based technique of FSP are also compared on the basis of some benchmark problems and found compatible with the results obtained from other standard procedures.Gupta, A.; Kumar, S. (2016). Flow shop scheduling decisions through Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS). International Journal of Production Management and Engineering. 4(2):43-52. doi:10.4995/ijpme.2016.4102.SWORD43524

A parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics

This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex con guration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of cases

Scheduling flow lines with buffers by ant colony digraph

This work starts from modeling the scheduling of n jobs on m machines/stages as flowshop with buffers in manufacturing. A mixed-integer linear programing model is presented, showing that buffers of size n - 2 allow permuting sequences of jobs between stages. This model is addressed in the literature as non-permutation flowshop scheduling (NPFS) and is described in this article by a disjunctive graph (digraph) with the purpose of designing specialized heuristic and metaheuristics algorithms for the NPFS problem. Ant colony optimization (ACO) with the biologically inspired mechanisms of learned desirability and pheromone rule is shown to produce natively eligible schedules, as opposed to most metaheuristics approaches, which improve permutation solutions found by other heuristics. The proposed ACO has been critically compared and assessed by computation experiments over existing native approaches. Most makespan upper bounds of the established benchmark problems from Taillard (1993) and Demirkol, Mehta, and Uzsoy (1998) with up to 500 jobs on 20 machines have been improved by the proposed ACO

A Bee Colony Optimization Approach for Mixed Blocking Constraints Flow Shop Scheduling Problems

The flow shop scheduling problems with mixed blocking constraints with minimization of makespan are investigated. The Taguchi orthogonal arrays and path relinking along with some efficient local search methods are used to develop a metaheuristic algorithm based on bee colony optimization. In order to compare the performance of the proposed algorithm, two well-known test problems are considered. Computational results show that the presented algorithm has comparative performance with well-known algorithms of the literature, especially for the large sized problems

A Hybrid Estimation of Distribution Algorithm for Simulation-Based Scheduling in a Stochastic Permutation Flowshop

The permutation flowshop scheduling problem (PFSP) is NP-complete and tends to be more complicated when considering stochastic uncertainties in the real-world manufacturing environments. In this paper, a two-stage simulation-based hybrid estimation of distribution algorithm (TSSB-HEDA) is presented to schedule the permutation flowshop under stochastic processing times. To deal with processing time uncertainty, TSSB-HEDA evaluates candidate solutions using a novel two-stage simulation model (TSSM). This model first adopts the regression-based meta-modelling technique to determine a number of promising candidate solutions with less computation cost, and then uses a more accurate but time-consuming simulator to evaluate the performance of these selected ones. In addition, to avoid getting trapped into premature convergence, TSSB-HEDA employs both the probabilistic model of EDA and genetic operators of genetic algorithm (GA) to generate the offspring individuals. Enlightened by the weight training process of neural networks, a self-adaptive learning mechanism (SALM) is employed to dynamically adjust the ratio of offspring individuals generated by the probabilistic model. Computational experiments on Taillard’s benchmarks show that TSSB-HEDA is competitive in terms of both solution quality and computational performance

Scheduling de robôs móveis autônomos em ambiente de manufatura flexível com uso de meta-heurísticas de busca em vizinhanças

TCC (graduação) - Universidade Federal de Santa Catarina, Campus Joinville, Engenharia de Transportes e Logística.A manufatura moderna com flexibilidade de processos introduziu os robôs móveis autônomos (AMR – Autonomous Mobile Robot) em operações intralogísticas, como manufatura, armazenamento, cross-docks, terminais e hospitais. A utilização de tecnologias com o propósito de utilizar de maneira eficiente os seus recursos, trará uma vantagem competitiva com relação ao mercado global. Visando alcançar seus objetivos as empresas tem adotado a mudança para um sistema de manufatura flexível (FMS - flexible manufacturing system), junto aos robôs móveis autônomos, tal mudança permite o sistema adaptar-se às frequentes mudanças nas demandas ao longo dos anos. Considerando isso, este trabalho apresenta o problema de sequenciamento de tarefas de dois robôs móveis autônomos, que tem, como objetivo, abastecer quatro alimentadores de máquinas em uma linha de produção localizada em um ambiente de manufatura flexível. O problema consiste em programar os AMRs de forma que as máquinas não interrompam o funcionamento devido à falta de peças. O modelo proposto tem como objetivo minimizar o makespan – tempo total para processar todas as tarefas – em uma linha de produção com um horizonte de planejamento pré-estabelecido. O estudo leva em consideração as características das máquinas, do robô e define uma janela de tempo restrita para a realização das tarefas. A característica do problema é NP-Hard, portanto, são apresentados dois métodos computacionais baseados em meta-heurísticas de busca em vizinhanças. Para os dados considerados, a meta-heurística baseada em VND tende a encontrar uma solução de boa qualidade para o problema de scheduling, enquanto que a meta-heurística baseada em VNS tende a encontrar uma solução de boa qualidade com mais dificuldade, resultando em uma diferença média de 1,25%, entre os métodos de solução propostos, com relação ao cenário base estudadoModern manufacturing with process flexibility has introduced Autonomous Mobile Robots (AMR) into intralogistics operations such as manufacturing, warehousing, cross-docks, terminals and hospitals. The use of technologies in order to efficiently use its resources will bring a competitive advantage in relation to the global market. Aiming to achieve their goals, companies have adopted the change to a flexible manufacturing system (FMS - flexible manufacturing system), along with autonomous mobile robots, such a change allows the system to adapt to frequent changes in demands over the years. Considering this, this work presents the task sequencing problem of two autonomous mobile robots, which aims to supply four machine feeders in a production line located in a flexible manufacturing environment. The problem is to program the AMRs so that the machines do not stop working due to lack of parts. The proposed model aims to minimize the makespan – total time to process all tasks – in a production line with a pre-established planning horizon. The study takes into account the characteristics of the machines, the robot and defines a restricted time window for carrying out the tasks. The characteristic of the problem is NP-Hard, therefore, two computational methods based on neighborhood search metaheuristics are presented. For the considered data, the VND-based metaheuristic tends to find a good quality solution to the scheduling problem, while the VNS-based metaheuristic tends to find a good quality solution with more difficulty, resulting in a average difference of 1,25% between the proposed solution methods, in relation to the studied base scenario