Scheduling of Flexible Manufacturing Systems using Intelligent heuristic search algorithm (IHSA*)

The complete scheduling of FMS includes two independent processes: sequencing of jobs and scheduling those prioritized jobs. In a flow shop or a Progressive type FMS, scheduling problem involves sequencing of ‘n’ jobs on ‘m’ machines with minimum makespan. Intelligent heuristic search algorithm (IHSA*) is used in this paper, which ensure to find an optimal solution for flow-shop problem involving arbitrary number of machines and jobs provided the job sequence is same on each machine. The initial version of IHSA* is based on the A* algorithm. The final version of IHSA* is the modification of the initial IHSA*. There are three modifications: first modification concerned with the selection of an admissible heuristic function, second modification concerned with the procedure which determine heuristic estimate as the search progresses and the third modification concerned with the searching of multiple optimal solution, if they exist. Both version of the IHSA* are presented in this paper with an example which illustrates the use of both

Flowshop Scheduling Using a Network Approach

In this paper, a network based formulation of a permutation flow shop problem is presented. Two nuances of flow shop problems with different levels of complexity are solved using different approaches to the linear programming formulation. Key flow shop parameters inclosing makespan of the flow shop problems were obtained without recourse to the traditional approach of using Gantt charts. The linear programming models of the flow shop problems considered were solved using LINGO 7.0. The present technique has been shown to be very effective and efficient.http://dx.doi.org/10.4314/njt.v34i2.1

Comparative study for Utilization of machines in the Flow-Shop Scheduling Problems

Scheduling is the procedure of generating the schedule which is a physical document and generally informs the happening of things and demonstrate a plan for the timing of certain activities. The flow shop problem is one of the most widely studied classical scheduling problems and reflects real operation of several industries. The aim of the present work is to evaluate the performance of four methods when it is used to solve flow shop scheduling problems with minimization makespan. The four heuristics methods are Johnson, Palmer, CDS and Gupta methods. In this work, an attempt has been made to solve the flow shop scheduling problem for comparative study for utilization of machines in the flow-shop scheduling problems among pervious methods.  A simulation study has been made to evaluate the performance of the four method under consideration based on two performance measures  makespan and utilization of machine , the results has been proved  that the Palmer and CDS heuristic methods show the minimum value of average of makespan and average utilization of machine  when it compared with other heuristic methods

Flow shop rescheduling under different types of disruption

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 2013, available online:http://www.tandfonline.com/10.1080/00207543.2012.666856Almost all manufacturing facilities need to use production planning and scheduling systems to increase productivity and to reduce production costs. Real-life production operations are subject to a large number of unexpected disruptions that may invalidate the original schedules. In these cases, rescheduling is essential to minimise the impact on the performance of the system. In this work we consider flow shop layouts that have seldom been studied in the rescheduling literature. We generate and employ three types of disruption that interrupt the original schedules simultaneously. We develop rescheduling algorithms to finally accomplish the twofold objective of establishing a standard framework on the one hand, and proposing rescheduling methods that seek a good trade-off between schedule quality and stability on the other.The authors would like to thank the anonymous referees for their careful and detailed comments that helped to improve the paper considerably. This work is partially financed by the Small and Medium Industry of the Generalitat Valenciana (IMPIVA) and by the European Union through the European Regional Development Fund (FEDER) inside the R + D program "Ayudas dirigidas a Institutos tecnologicos de la Red IMPIVA" during the year 2011, with project number IMDEEA/2011/142.Katragjini Prifti, K.; Vallada Regalado, E.; Ruiz García, R. (2013). Flow shop rescheduling under different types of disruption. International Journal of Production Research. 51(3):780-797. https://doi.org/10.1080/00207543.2012.666856S780797513Abumaizar, R. J., & Svestka, J. A. 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Comparative Analysis of Metaheuristic Approaches for Makespan Minimization for No Wait Flow Shop Scheduling Problem

This paper provides comparative analysis of various metaheuristic approaches for m-machine no wait flow shop scheduling (NWFSS) problem with makespan as an optimality criterion. NWFSS problem is NP hard and brute force method unable to find the solutions so approximate solutions are found with metaheuristic algorithms. The objective is to find out the scheduling sequence of jobs to minimize total completion time. In order to meet the objective criterion, existing metaheuristic techniques viz. Tabu Search (TS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are implemented for small and large sized problems and effectiveness of these techniques are measured with statistical metric

Non-Traditional Flow Shop Scheduling Using CSP Scheduling Flow Shop No Tradicional Empleando CSP

Abstract This paper addresses the problem of scheduling in a flow shop manufacturing environment with non-traditional requirements, where some jobs must be scheduled earlier and others later depending on the priority established by the demand characteristics supplied. The problem is formulated mathematically, and given its nonlinearity, we propose a CSP (Constraint Satisfaction Problem) model, which is formulated using constraint programming with the software OPL Studio ® . A set of experiments was performed by varying the weighting of jobs. We also varied the deadlines and waiting times among the machines. Finally, different production schedules were attained according to the type of experiment, thus solving the problem of non-traditional scheduling. Keywords: Scheduling, Operations Programming, Flow-shop Manufacturing Environment, Constraint Programming. Resumen En este documento se aborda el problema del Scheduling en un ambiente de fabricación Flow-Shop con requerimientos no tradicionales, en el cual algunos trabajos deben ser programados en su momento más temprano y otros en su momento más tardío dependiendo de la prioridad establecida por las características de la demanda a suplir. El problema es formulado matemáticamente y dada su no linealidad se propone un modelo CSP (Constraint Satisfaction Problem) para su solución, el cual se formula mediante programación por restricciones utilizando el software OPL studio ® . Se realizaron un conjunto de experimentos, variando la ponderación de los trabajos, así mismo se variaron la fecha de terminación y los tiempos de espera entre máquinas. Finalmente, se obtuvieron diferentes programas de producción acorde al tipo de experimento dando una solución al problema del Scheduling no tradicional

A production model and maintenance planning model for the process industry

In this paper a model is developed to simultaneously plan preventive maintenance and production in a process industry environment, where maintenance planning is extremely important. The model schedules production jobs and preventive maintenance jobs, while minimizing costs associated with production, backorders, corrective maintenance and preventive maintenance. The formulation of the model is flexible, so that it can be adapted to several production situations. The performance of the model is discussed and alternate solution procedures are suggested.Production Models;Maintenance;production

Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing : an empirical study

With the penetration of smart grid into factories, energy-efficient production scheduling has emerged as a promising method for industrial demand response. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, moderate, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling. (C) 2017 Elsevier Ltd. All rights reserved

On the exact solution of the no-wait flow shop problem with due date constraints

Peer ReviewedThis paper deals with the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, the jobs should be completed before their respective due dates; due date constraints are dealt with as hard constraints. The considered performance criterion is makespan. The problem is strongly NP-hard. This paper develops a number of distinct mathematical models for the problem based on different decision variables. Namely, a mixed integer programming model, two quadratic mixed integer programming models, and two constraint programming models are developed. Moreover, a novel graph representation is developed for the problem. This new modeling technique facilitates the investigation of some of the important characteristics of the problem; this results in a number of propositions to rule out a large number of infeasible solutions from the set of all possible permutations. Afterward, the new graph representation and the resulting propositions are incorporated into a new exact algorithm to solve the problem to optimality. To investigate the performance of the mathematical models and to compare them with the developed exact algorithm, a number of test problems are solved and the results are reported. Computational results demonstrate that the developed algorithm is significantly faster than the mathematical models