Scheduling Single-Machine Problem Oriented by Just-In-Time Principles - A Case Study

Developments in advanced autonomous production resources have increased the interest in the Single-Machine Scheduling Problem (SMSP). Until now, researchers used SMSP with little to no practical application in industry, but with the introduction of multi-purpose machines, able of executing an entire task, such as 3D Printers, replacing extensive production chains, single-machine problems are becoming a central point of interest in real-world scheduling. In this paper we study how simple, easy to implement, Just-in-Time (JIT) based, constructive heuristics, can be used to optimize customer and enterprise oriented performance measures. Customer oriented performance measures are mainly related to the accomplishment of due dates while enterprise-oriented ones typically consider other time-oriented measures.The authors wish to acknowledge the support of the Fundação para a Ciência e Tecnologia (FCT), Portugal, through the grant “Projeto Estratégico – UI 252 – 2011–2012” reference PEst-OE/EME/UI0252/2011 and FCOMP-01-0124FEDER-PEst-OE/EEI/UI0760/2014info:eu-repo/semantics/publishedVersio

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

New solution methods for single machine bicriteria scheduling problem: Minimization of average flowtime and number of tardy jobs

Cataloged from PDF version of article.We consider the bicriteria scheduling problem of minimizing the number of tardy jobs and average flowtime on a single machine. This problem, which is known to be NP-hard, is important in practice, as the former criterion conveys the customer’s position, and the latter reflects the manufacturer’s perspective in the supply chain. We propose four new heuristics to solve this multiobjective scheduling problem. Two of these heuristics are constructive algorithms based on beam search methodology. The other two are metaheuristic approaches using a genetic algorithm and tabu-search. Our computational experiments indicate that the proposed beam search heuristics find efficient schedules optimally in most cases and perform better than the existing heuristics in the literature. 2009 Elsevier B.V. All rights reserved

Group Scheduling in a Cellular Manufacturing Shop to Minimise Total Tardiness and nT: a Comparative Genetic Algorithm and Mathematical Modelling Approach

In this paper, family and job scheduling in a cellular manufacturing shop is addressed where jobs have individual due dates. The objectives are to minimise total tardiness and the number of tardy jobs. Family splitting among cells is allowed but job splitting is not. Two optimisation methods are employed in order to solve this problem, namely mathematical modelling (MM) and genetic algorithm (GA). The results showed that GA found the optimal solution for most of the problems with high frequency. Furthermore, the proposed GA is efficient compared to the MM especially for larger problems in terms of execution times. Other critical aspects of the problem such as family preemption only, impact of family splitting on common due date scenarios and dual objective scenarios are also solved. In short, the proposed comparative approach provides critical insights for the group scheduling problem in a cellular manufacturing shop with distinctive cases

Multi-Period Cell Loading and Job Sequencing in a Cellular Manufacturing System

In this paper, a multi-period cell loading problem is addressed, where the objectives are to minimise the number of tardy jobs (nT) in a multi-period planning horizon and optimise the scheduling of tardy jobs. Three cell loading and job scheduling strategies are proposed and tested with two newly developed mixed integer programming models. Additionally, three types of due dates (tight, medium and loose) and three different demand levels were considered. Finally, two tardy job assignment methods were proposed to observe the impact on nT. Case problems were solved based on minimising nT, Tmax and total tardiness (TT) objectives and cost sensitivity analysis was performed. Results indicated that, the first strategy, (early start allowance and tardy job assignment after each period) performed better in terms of nT. For the secondary objectives, tradeoffs were observed among different strategies depending on the type of due date, demand level and tardy job assignment method

Hybrid Genetic Bees Algorithm applied to Single Machine Scheduling with Earliness and Tardiness Penalties

This paper presents a hybrid Genetic-Bees Algorithm based optimised solution for the single machine scheduling problem. The enhancement of the Bees Algorithm (BA) is conducted using the Genetic Algorithm's (GA's) operators during the global search stage. The proposed enhancement aims to increase the global search capability of the BA gradually with new additions. Although the BA has very successful implementations on various type of optimisation problems, it has found that the algorithm suffers from weak global search ability which increases the computational complexities on NP-hard type optimisation problems e.g. combinatorial/permutational type optimisation problems. This weakness occurs due to using a simple global random search operation during the search process. To reinforce the global search process in the BA, the proposed enhancement is utilised to increase exploration capability by expanding the number of fittest solutions through the genetical variations of promising solutions. The hybridisation process is realised by including two strategies into the basic BA, named as â\u80\u9creinforced global searchâ\u80\u9d and â\u80\u9cjumping functionâ\u80\u9d strategies. The reinforced global search strategy is the first stage of the hybridisation process and contains the mutation operator of the GA. The second strategy, jumping function strategy, consists of four GA operators as single point crossover, multipoint crossover, mutation and randomisation. To demonstrate the strength of the proposed solution, several experiments were carried out on 280 well-known single machine benchmark instances, and the results are presented by comparing to other well-known heuristic algorithms. According to the experiments, the proposed enhancements provides better capability to basic BA to jump from local minima, and GBA performed better compared to BA in terms of convergence and the quality of results. The convergence time reduced about 60% with about 30% better results for highly constrained jobs

A single-machine scheduling problem with multiple unavailability constraints: A mathematical model and an enhanced variable neighborhood search approach

AbstractThis research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS) is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective

Heuristic Algorithms to Minimize Total Weighted Tardiness on the Single Machine and Identical Parallel Machines with Sequence Dependent Setup and Future Ready Time

This study generates heuristic algorithms to minimize the total weighted tardiness on the single machine and identical parallel machines with sequence dependent setup and future ready time. Due to the complexity of the considered problem, we propose two new Apparent Tardiness Cost based (ATC-based) rules. The performances of these two rules are evaluated on the single machine and identical parallel machines. Besides of these two rules, we also propose a look-ahead identical parallel machines heuristic (LAIPM). When a machine becomes idle, it selects a job to process from available jobs and near future jobs. The proposed method, LAIPM, is evaluated with other look-ahead methods on the identical parallel machines

Hybrid genetic algorithm for assembly flow-shop scheduling problem with sequence-dependent setup and transportation times

Ovaj rad prikazuje hibridni genetski algoritam za planiranje poslova montaže na tekućoj traci s vremenima za montiranje i transport ovisnima o redoslijedu odvijanja poslova. Objektivna funkcija upotrijebljena u ovom istraživanju sastoji se od smanjenja zbroja ukupno procijenjenih zakašnjenja na kvadrat, vremena potrebnog za izradu (makespan), ukupno procijenjenih ranije obavljenih poslova na kvadrat i broja zakašnjelih poslova. Da bi se potvrdio predloženi model, korišten je program Lingo 8.0. Usporedba rezultata dobivenih pomoću Lingo 8.0 i hibridnog genetskog algoritma pokazuje da kod većih problema (ako je n >10, gdje je n broj poslova) Lingo ne daje odgovarajuću efikasnost i ne može se usporediti s predloženim hibridnim genetskim algoritmom u odnosu na vrijeme izračuna i devijaciju od minimalne objektivne funkcije. Rezultati ispitivanja daju se za veliki broj slučajeva.This paper presents a hybrid genetic algorithm for assembly flow-shop scheduling problem with sequence-dependent setup and transportation times. The used objective function in this research consists of minimizing of the sum of total weighted squared tardiness, makespan, total weighted squared earliness and number of tardy job. Since the problem is NP-hard, we solved this problem by hybrid genetic algorithm. To validate the proposed model, the Lingo 8.0 software was used. Comparison between the results of the Lingo 8.0 and hybrid genetic algorithm shows that in larger problems (if n >10, where n is the number of jobs) the results obtained by Lingo do not have adequate efficiency and cannot be compared with the proposed hybrid genetic algorithm in terms of computational time and deviation from the minimum objective function. Test results are provided for a wide range of problem instances

NEW HEURISTICS FOR MINIMISING TOTAL COMPLETION TIME AND THE NUMBER OF TARDY JOBS CRITERIA ON A SINGLE MACHINE WITH RELEASE TIME

<p>ENGLISH ABSTRACT: This paper considers the bi-criteria scheduling problem of simultaneously minimising the total completion time and the number of tardy jobs with release dates on a single machine. Since the problem had been classified as NP-Hard, two heuristics (HR9 and HR10) were proposed for solving this problem. Performance evaluations of the proposed heuristics and selected solution methods (HR7 and BB) from the literature were carried out on 1,100 randomly generated problems ranging from 3 to 500 jobs. Experiment results show that HR7 outperformed HR10 when the number of jobs (n) is less than 30, while HR10 outperformed HR7 for n≥ 30.</p><p>AFRIKAANSE OPSOMMING: In hierdie artikel word die bi-kriteria-skeduleringsprobleem bestudeer waar die totale voltooiingstyd en die aantal take wat laat is op ‘n enkele masjien geminimiseer moet word. Verskeie heuristieke word voorgestel en getoets om sodoende die beste benadering te identifiseer.</p&gt