AN EVOLUTIONARY ALGORITHM FOR PURE FUZZY FLOWSHOP SCHEDULING PROBLEMS

2003 ◽  
Vol 11 (06) ◽  
pp. 655-669 ◽  
Author(s):  
G. CELANO ◽  
A. COSTA ◽  
S. FICHERA
Keyword(s):  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Shop Floor ◽  
Test Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Due Dates ◽  
Flowshop Scheduling ◽  
Real Process ◽  
Flowshop Scheduling Problem

The pure flowshop scheduling problem is here investigated from a perspective considering me uncertainty associated with the execution of shop floor activities. Being the flowshop problem is NP complete, a large number of heuristic algorithms have been proposed in literature to determine an optimal solution. Unfortunately, these algorithms usually assume a simplifying hypothesis: the problem data are assumed as deterministic, i.e. job processing times and the due dates are expressed through a unique value, which does not reflect the real process variability. For this reason, some authors have recently proposed the use of a fuzzy set theory to model the uncertainty in scheduling problems. In this paper, a proper genetic algorithm has been developed for solving the fuzzy flowshop scheduling problem. The optimisation involves two different objectives: the completion time minimisation and the due date fulfilment; both the single and multi-objective configurations have been considered. A new ranking criterion has been proposed and its performance has been tested through a set of test problems. A numerical analysis confirms the efficiency of the proposed optimisation procedure.

Heuristics for an m-Machine Re-Entrant Permutation Flowshop with the Objective of Total Tardiness

2015 ◽  
Vol 752-753 ◽  
pp. 890-895 ◽  
Author(s):  
Seong Woo Choi
Keyword(s):  
Heuristic Algorithms ◽  
Total Tardiness ◽  
Computational Experiments ◽  
Test Problems ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Permutation Flowshop ◽  
Flowshop Scheduling Problem ◽  
Flowshop Problem

We focus on an m-machine re-entrant flowshop scheduling problem with the objective of minimizing total tardiness. In the re-entrant flowshop considered here, routes of all jobs are identical as in ordinary flowshops, but the jobs must be processed multiple times on the machines. We present heuristic algorithms, which are modified from well-known existing algorithms for the general m-machine flowshop problem or newly developed in this paper. For evaluation of the performance of the algorithms, computational experiments are performed on randomly generated test problems and results are reported.

scholarly journals A Discrete Group Search Optimizer for Hybrid Flowshop Scheduling Problem with Random Breakdown

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhe Cui ◽  
Xingsheng Gu
Keyword(s):  
Discrete Group ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Flowshop Scheduling ◽  
Hybrid Flowshop ◽  
Random Breakdown ◽  
Hybrid Flowshop Scheduling ◽  
Discrete Operators ◽  
Flowshop Scheduling Problem ◽  
Group Search

The scheduling problems have been discussed in the literature extensively under the assumption that the machines are permanently available without any breakdown. However, in the real manufacturing environments, the machines could be unavailable inevitably for many reasons. In this paper, the authors introduce the hybrid flowshop scheduling problem with random breakdown (RBHFS) together with a discrete group search optimizer algorithm (DGSO). In particular, two different working cases, preempt-resume case, and preempt-repeat case are considered under random breakdown. The proposed DGSO algorithm adopts the vector representation and several discrete operators, such as insert, swap, differential evolution, destruction, and construction in the producers, scroungers, and rangers phases. In addition, an orthogonal test is applied to configure the adjustable parameters in the DGSO algorithm. The computational results in both cases indicate that the proposed algorithm significantly improves the performances compared with other high performing algorithms in the literature.

Formulation of an IP-Based Model for Reactive Flow-Shop Scheduling Problem Subject to Arrival of New Orders

2015 ◽  
Vol 1115 ◽  
pp. 616-621
Author(s):  
M. Abdesselam ◽  
A.N. Mustafizul Karim ◽  
H.M. Emrul Kays ◽  
Mohamed Abdul Rahman ◽  
R.A. Sarker
Keyword(s):  
Integer Programming ◽  
Flow Shop ◽  
Low Cost ◽  
Flow Shop Scheduling ◽  
Shop Floor ◽  
Reactive Flow ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Due Dates ◽  
Shop Scheduling

In order to survive in a competitive environment, industries are required to adopt strategies that ensure their abilities to provide their customers with a product featured by good quality, low cost and short delivery time. Short term scheduling plays a pivotal role in this context by ensuring the operations to be executed and monitored in an optimal or sub-optimal manner which guarantees the product shipping within the customers’ due dates at lower cost and/or higher utilization of resources. However, the dynamic nature of the shop floor environment causes the predictive schedules to be no longer optimal or even feasible. Frequent disruptions occurring during the execution of the predictive schedule require the operations managers to be reactive to make appropriate decision considering the new situation. Adequate research works based on integer programming are available in literature to cope with static scheduling problems, but there is a dearth in integer programming based approaches for dynamic or reactive situations. The aim of this work is to formulate a model that solves the reactive flow-shop scheduling problem subject to arrival of new orders. Objective function for makespan minimization and the comprehensive equations for predictive and reactive schedules are presented with the necessary elaboration.

scholarly journals A Deterministic Flowshop Scheduling Problem to minimizing the Makespan using PA-ACO

2020 ◽  
Vol 9 (3) ◽  
pp. 1555-1560 ◽  
Keyword(s):  
Optimization Technique ◽  
Optimal Path ◽  
Energy Utilization ◽  
Computational Time ◽  
Pareto Optimal ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Flowshop Scheduling ◽  
Pheromone Trails ◽  
Flowshop Scheduling Problem

Scheduling problems are NP-hard in nature. Flowshop scheduling problems, are consist of sets of machines with number of resources. It matins the continuous flow of task with minimum time. There are various traditional algorithms to maintain the order of resources. Here, in this paper a new stochastic Ant Colony optimization technique based on Pareto optimal (PA-ACO) is implemented for solving the permutation flowshop scheduling problem (PFSP) sets. The proposed technique is employed with a novel local path search technique for initializing and pheromone trails. Pareto optimal mechanism is used to select the best optimal path solution form generated solution sets. A comparative study of the results obtained from simulations shows that the proposed PA-ACO provides minimum makespan and computational time for the Taillard dataset. This work will applied on large scale manufacturing production problem for efficient energy utilization.

scholarly journals Minimizing total completion time for flowshop scheduling problem with uncertain processing times

2020 ◽  
Author(s):  
Muberra Allahverdi ◽  
Ali Allahverdi
Keyword(s):  
Completion Time ◽  
Optimal Solution ◽  
Upper Bounds ◽  
Total Completion Time ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Lower And Upper Bounds ◽  
Processing Times ◽  
Flowshop Scheduling Problem ◽  
Machine Problem

The four-machine flowshop scheduling problem is investigated with the objective of minimizing total completion time. Job processing times are uncertain where only the lower and upper bounds are known. This problem is common in some manufacturing environments. Some mathematical (dominance) relations are established, and an algorithm (with ten scenarios) is proposed. The proposed algorithm converts the four-machine problem to a single machine problem for which an optimal solution is known for the deterministic problem. The difference among the scenarios is related to the weights assigned to the lower and upper bounds of processing times on the machines. The proposed algorithm is further improved by the established mathematical relations and are evaluated based on extensive computational experiments. The computational results indicate that three scenarios of the proposed algorithm perform much better than the others, and the errors of these three scenarios get better as the size of the problem increases. The results are statistically verified by constructing the confidence intervals.

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