Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing systems. We propose a new approach based on limited discrepancy search to solve the problem. Our method is tested with reference to a proposed lower bound as well as the best-known solutions in literature. Computational results show that the developed approach is efficient in particular for large-size problems

Deterministic Assembly Scheduling Problems: A Review and Classification of Concurrent-Type Scheduling Models and Solution Procedures

Many activities in industry and services require the scheduling of tasks that can be concurrently executed, the most clear example being perhaps the assembly of products carried out in manufacturing. Although numerous scientific contributions have been produced on this area over the last decades, the wide extension of the problems covered and the lack of a unified approach have lead to a situation where the state of the art in the field is unclear, which in turn hinders new research and makes translating the scientific knowledge into practice difficult. In this paper we propose a unified notation for assembly scheduling models that encompass all concurrent-type scheduling problems. Using this notation, the existing contributions are reviewed and classified into a single framework, so a comprehensive, unified picture of the field is obtained. In addition, a number of conclusions regarding the state of the art in the topic are presented, as well as some opportunities for future research.Ministerio de Ciencia e Innovación español DPI2016-80750-

A hybrid flow shop model for an ice cream production scheduling problem

In this paper we address the scheduling problem that comes from an ice cream manufacturing company. This production system can be modelled as a three stage nowait hybrid flow shop with batch dependent setup costs. To contribute reducing the gap between theory and practice we have considered the real constraints and the criteria used by planners. The problem considered has been formulated as a mixed integer programming. Further, two competitive heuristic procedures have been developed and one of them will be proposed to schedule in the ice cream factoryPeer Reviewe

ADAPTIVE, MULTI-OBJECTIVE JOB SHOP SCHEDULING USING GENETIC ALGORITHMS

This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time

A study on flexible flow shop and job shop scheduling using meta-heuristic approaches

Scheduling aims at allocation of resources to perform a group of tasks over a period of time in such a manner that some performance goals such as flow time, tardiness, lateness, and makespan can be minimized. Today, manufacturers face the challenges in terms of shorter product life cycles, customized products and changing demand pattern of customers. Due to intense competition in the market place, effective scheduling has now become an important issue for the growth and survival of manufacturing firms. To sustain in the current competitive environment, it is essential for the manufacturing firms to improve the schedule based on simultaneous optimization of performance measures such as makespan, flow time and tardiness. Since all the scheduling criteria are important from business operation point of view, it is vital to optimize all the objectives simultaneously instead of a single objective. It is also essentially important for the manufacturing firms to improve the performance of production scheduling systems that can address internal uncertainties such as machine breakdown, tool failure and change in processing times. The schedules must meet the deadline committed to customers because failure to do so may result in a significant loss of goodwill. Often, it is necessary to reschedule an existing plan due to uncertainty event like machine breakdowns. The problem of finding robust schedules (schedule performance does not deteriorate in disruption situation) or flexible schedules (schedules expected to perform well after some degree of modification when uncertain condition is encountered) is of utmost importance for real world applications as they operate in dynamic environments

Programación de la producción en un sistema flow shop híbrido sin esperas y tiempos de preparación dependientes de la secuencia.

Se estudia la programación de la producción en sistemas flow shop híbrido con tiempos de preparación dependientes de la secuencia de piezas a fabricar. Las piezas pueden pertenecer a diferentes familias y las máquinas requerirán un tiempo de preparación cada vez que se deba cambiar de familia. Se han desarrollado procedimientos heurísticos para el caso monocriterio en el que el objetivo buscado en la programación de la producción es la minimización del retraso medio, equivalente a minimizar la suma de retrasos de las piezas, y para el caso bicriterio en el que se tendrá en cuenta tanto la minimización de una función objetivo formada por la suma ponderada del retraso medio más la suma de los tiempos medios de proceso. Además se han adaptado los métodos implementados para trabajar bajo la restricción nowait

Heuristics and metaheuristics for heavily constrained hybrid flowshop problems

Due to the current trends in business as the necessity to have a large catalogue of products, orders that increase in frequency but not in size, globalisation and a market that is increasingly competitive, the production sector faces an ever harder economical environment. All this raises the need for production scheduling with maximum efficiency and effectiveness. The first scientific publications on production scheduling appeared more than half a century ago. However, many authors have recognised a gap between the literature and the industrial problems. Most of the research concentrates on optimisation problems that are actually a very simplified version of reality. This allows for the use of sophisticated approaches and guarantees in many cases that optimal solutions are obtained. Yet, the exclusion of real-world restrictions harms the applicability of those methods. What the industry needs are systems for optimised production scheduling that adjust exactly to the conditions in the production plant and that generates good solutions in very little time. This is exactly the objective in this thesis, that is, to treat more realistic scheduling problems and to help closing the gap between the literature and practice. The considered scheduling problem is called the hybrid flowshop problem, which consists in a set of jobs that flow through a number of production stages. At each of the stages, one of the machines that belong to the stage is visited. A series of restriction is considered that include the possibility to skip stages, non-eligible machines, precedence constraints, positive and negative time lags and sequence dependent setup times. In the literature, such a large number of restrictions has not been considered simultaneously before. Briefly, in this thesis a very realistic production scheduling problem is studied. Various optimisation methods are presented for the described scheduling problem. A mixed integer programming model is proposed, in order to obtaiUrlings ., T. (2010). Heuristics and metaheuristics for heavily constrained hybrid flowshop problems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8439Palanci

Particle swarm optimization and differential evolution for multi-objective multiple machine scheduling

Production scheduling is one of the most important issues in the planning and operation of manufacturing systems. Customers increasingly expect to receive the right product at the right price at the right time. Various problems experienced in manufacturing, for example low machine utilization and excessive work-in-process, can be attributed directly to inadequate scheduling. In this dissertation a production scheduling algorithm is developed for Optimatix, a South African-based company specializing in supply chain optimization. To address the complex requirements of the customer, the problem was modeled as a flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and production down time. The algorithm development process focused on investigating the application of both particle swarm optimization (PSO) and differential evolution (DE) to production scheduling environments characterized by multiple machines and multiple objectives. Alternative problem representations, algorithm variations and multi-objective optimization strategies were evaluated to obtain an algorithm which performs well against both existing rule-based algorithms and an existing complex flexible job shop scheduling solution strategy. Finally, the generality of the priority-based algorithm was evaluated by applying it to the scheduling of production and maintenance activities at Centurion Ice Cream and Sweets. The production environment was modeled as a multi-objective uniform parallel machine shop problem with sequence-dependent set-up times and unavailability intervals. A self-adaptive modified vector evaluated DE algorithm was developed and compared to classical PSO and DE vector evaluated algorithms. Promising results were obtained with respect to the suitability of the algorithms for solving a range of multi-objective multiple machine scheduling problems. CopyrightDissertation (MEng)--University of Pretoria, 2009.Industrial and Systems Engineeringunrestricte

Programación de la producción para la sección de formado y lijado en una empresa de fabricación de cepillos profesionales para peluquerías

La programación de la producción se entiende como el proceso que tiene una empresa para la toma de decisiones respecto a la fabricación de sus productos. En este proyecto se estudiará el problema de programación de la producción para la Etapa 2: formado y lijado, la cual se identificó como cuello de botella en la empresa Irca Ltda. Como principal objetivo, se desarrollará un aplicativo en Microsoft Excel ®. El cual se apoyará en un algoritmo genético que logre reducir la sumatoria de las diferencias entre el tiempo de terminación de un trabajo y la fecha de entrega del mismo; entendida como la minimización de la tardanza total. Se tomaron restricciones propias del sistema, las cuales están relacionadas con máquinas en paralelo, elegibilidad de máquina, permutación e interrupción de las órdenes de producción. Este algoritmo genético aplica un enfoque de variabilidad logrando hallar una solución factible y de calidad para el problema. Este enfoque comienza utilizando reglas de despacho como SPT, LPT, EDD y LIFO para el caso de la población inicial, para la selección de padres una probabilidad asociada al de menor tardanza, para el cruce se utiliza aleatoriedad en las particiones a intercambiar y en la mutación se presenta la probabilidad que los tipos de cabo se agrupen para reducción de tiempos de alistamiento. Respecto a los resultados obtenidos es posible evidenciar como el algoritmo propuesto mejora la tardanza en un rango entre el 40-90%, además de mejor solución en comparación con la programación actual de la compañía, al igual que con las reglas de despacho evaluadas.Scheduling is understood as the decision process that a company has to make regarding the manufacture of its products. This project will study the production scheduling problem for Stage 2: formed and sanding, which was identified as a bottleneck in the company Irca Ltda. As an objective, an application will be developed in Microsoft Excel ®. Which will be based on a genetic algorithm that reduces the sum of the differences between the time of completion of a work and the date of delivery of the same, Understood as the minimization of total tardiness. The constraints of this system were applied, this are related to parallel machines, machine eligibility, permutation and interruption of production orders. This genetic algorithm applies a variability approach to find a feasible and quality solution for the problem. This approach starts using dispatch rules such as SPT, LPT, EDD and LIFO for the initial population case, for the selection of parents a probability associated to the lesser delay, for the crossing is used randomness in the partitions to be exchanged and in the Mutation we present the probability that the types of cable are grouped for reduction of enlistment times. Regarding the results obtained, it is possible to show how the proposed algorithm improves the Total Tardiness in a range between 40-90%, as well as a better solution in comparison with the current schedule of the company, as well as with the dispatch rules evaluated.Ingeniero (a) IndustrialPregrad