Multi-projects scheduling with resource constraints & Priority rules by the use of Simulated Annealing Algorithm

Ovaj rad predstavlja hibridni genetski algoritam za problem programiranja kod montaže na tekućoj traci s vremenima podešavanja i prijenosa ovisnima o dijelu radne operacije, s ciljevima postizanja smanjenja ponderiranog, zbroja ukupnog ponderiranog kašnjenja na kvadrat, vremenskog intervala, ukupne ponderirane prijevremenosti na kvadrat i broja zakašnjenja. Budući da je problem NP-težak, riješili smo ga hibridnim genetskim algoritmom. Za provjeru predloženog modela korišten je softver Lingo 8.0. Usporedba između rezultata ovog softvera i hibridnog genetskog algoritma pokazuje da kod većih problema (ako je n > 10, gdje je n broj poslova) rezultati dobiveni softverom Lingo 8.0 nisu dovoljno učinkoviti i ne mogu se uspoređivati s predloženim hibridnim genetskim algoritmom u odnosu na vrijeme računanja i devijaciju od minimalne ciljne funkcije. Dani se rezultati ispitivanja za veliki broj rješavanih problema.This paper presents a hybrid genetic algorithm for assembly flow-shop scheduling problem with sequence-dependent setup and transportation times, with objectives, namely the minimizing of weighted, sum of total weighted squared tardiness, makespan, and total weighted squared earliness and number of tardy jobs. Since the problem is NP-hard, we solved this problem by hybrid genetic algorithm. To validate the proposed model, the Lingo 8.0 software is 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 good 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

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

A study on the effect of inflation and time value of money on lot sizing in spite of reworking in an inventory control model

U hipotezama modela isplative ekonomske proizvodnje su ne proizvodnja neispravnih dijelova tijekom proizvodnih operacija i ne obraćanje pažnje na inflaciju i promjenu vrijednosti novca. No izvršena proučavanja pokazuju da uzimanje u obzir neispravnih dijelova pri određivanju opsega proizvodnje ili uključivanje pitanja inflacije i promjene vrijednosti novca u vremenu vodi ka promjeni optimalne količine proizvodnje. Stoga je u proizvodnim sustavima koji uključuju proizvodnju neispravnih dijelova potrebno razmotriti ova dva faktora kako bi se odredio opseg proizvodnje. U ovom se članku razmatra učinak promjene vrijednosti novca na model ekonomične proizvodnje unatoč ponavljanju. Zbog složenosti funkcije cijene nije moguće lako pronaći optimalno rješenje te se stoga u članku koristi algoritam zasnovan na kombinaciji dviju istraživačkih metoda – akcelerirajuće i Dico Thomas, kako bi se riješio problem. Na kraju je provedena analiza osjetljivosti na osnovi kamatne stope, stope inflacije i zajedničke stope. Numerički proračun pokazuje da neuzimanje u obzir inflacije i promjene u vrijednosti novca rezultira relativno velikim greškama u cijeni.Non production of defective parts during production operations and non-attention to inflation and time value of money are among the hypotheses of economic production quantity model. But the performed studies show that considering defective parts in production size determination models or including the subject of inflation and time value of money in them leads to the change of optimal quantity of production class. Therefore, in production systems with defective parts production, it is necessary to consider these two factors in order to determine production class size. This article studies the effect of time value of money on economic production quantity model in spite of repetition. Due to the complexity of cost function, it is not possible to find an optimal answer easily; therefore, this article uses an algorithm based on combination of two search methods, accelerating and Dico Thomas, in order to solve the problem. Finally, sensitivity analysis has been done on the basis of interest rate, inflation rate and joint rate. Numerical calculation shows that failure to consider inflation and time value of money causes relatively high error in cost

A Multi-objective Mathematical Model Considering Economic and Social Criteria in Dynamic Cell Formation

Part 1: Knowledge-Based SustainabilityInternational audienceThis paper addresses a Dynamic Cellular Manufacturing Systems (DCMS) problem considering both economic and social criteria, that is, the problem deals with the minimization of the total costs and maximization of social issues. We develop a bi-objective mathematical model of this problem in order to capture the trade-off between these two objectives. The strategic decisions considered in the model define the configuration of cells and part-families in each period. In order to solve our model, we design a new Non-dominated Sorting Genetic Algorithm (NSGA-II) as a meta-heuristic method. Our approach is illustrated on two samples of problems randomly generated