A Neighborhood Search for Sequence-dependent Setup Time in Flow Shop Fabrics Making of Textile Industry

Abstract This paper proposes a neighborhood search to solve scheduling for fabrics making in a textile industry. The production process consists of three production stages from spinning, weaving, and dyeing. All stages have one processor. Setup time between two consecutive jobs with different color is considered. This paper also proposes attribute’s decomposition of a single job to classify available jobs to be processed and to consider setup time between two consecutive jobs. Neighborhood search (NS) algorithm is proposed in which the permutation of set of jobs with same attribute and the permutation among set of jobs is conducted. Solution obtained from neighborhood search, which might be trapped in local solution, then is compared with other known optimal methods

Weighted tardiness minimization for unrelated machines with sequence-dependent and resource-constrained setups

Motivated by the need of quick job (re-)scheduling, we examine an elaborate scheduling environment under the objective of total weighted tardiness minimization. The examined problem variant moves well beyond existing literature, as it considers unrelated machines, sequence-dependent and machine-dependent setup times and a renewable resource constraint on the number of simultaneous setups. For this variant, we provide a relaxed MILP to calculate lower bounds, thus estimating a worst-case optimality gap. As a fast exact approach appears not plausible for instances of practical importance, we extend known (meta-)heuristics to deal with the problem at hand, coupling them with a Constraint Programming (CP) component - vital to guarantee the non-violation of the problem's constraints - which optimally allocates resources with respect to tardiness minimization. The validity and versatility of employing different (meta-)heuristics exploiting a relaxed MILP as a quality measure is revealed by our extensive experimental study, which shows that the methods deployed have complementary strengths depending on the instance parameters. Since the problem description has been obtained from a textile manufacturer where jobs of diverse size arrive continuously under tight deadlines, we also discuss the practical impact of our approach in terms of both tardiness decrease and broader managerial insights

Exact and heuristic approaches for lot splitting and scheduling on identical parallel machines

In this paper, we address a lot splitting and scheduling problem existent in a textile factory. The factory we study produces a set of products that are made of, or assembled from, a list of components. During production, each component can be split into one or several lots of different sizes and each lot will be produced independently on one of a group of identical parallel machines. We formulate the problem into a mixed integer programming model and develop a heuristic method to solve the model. The heuristic method is based on a network flow model with the objective to minimise the weighted sum of the total tardiness of products and the deviations occurred during production of each product. The deviation of a product is measured by the deviation of product completion time (the last component lot completion time) and completion time of the rest of components lots for the same product. We present computational results and performance measures of the network flow heuristic for a set of randomly generated instances based on real world data.(undefined

A review of discrete-time optimization models for tactical production planning

This is an Accepted Manuscript of an article published in International Journal of Production Research on 27 Mar 2014, available online: http://doi.org/10.1080/00207543.2014.899721[EN] This study presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production-planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, set-up times, parallel machines, overtime capacities and network-type multisite configuration stand out in terms of extensions; (4) the most widely used modelling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modelling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.This study has been funded by the Universitat Politècnica de València projects: ‘Material Requirement Planning Fourth Generation (MRPIV)’ (Ref. PAID-05-12) and ‘Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics’ (PAID-06-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Peidro Payá, D. (2014). A review of discrete-time optimization models for tactical production planning. International Journal of Production Research. 52(17):5171-5205. doi:10.1080/00207543.2014.899721S51715205521

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

A statistical comparison of metaheuristics for unrelated parallel machine scheduling problems with setup times

Manufacturing scheduling aims to optimize one or more performance measures by allocating a set of resources to a set of jobs or tasks over a given period of time. It is an area that considers a very important decision-making process for manufacturing and production systems. In this paper, the unrelated parallel machine scheduling problem with machine-dependent and job-sequence-dependent setup times is addressed. This problem involves the scheduling of tasks on unrelated machines with setup times in order to minimize the makespan. The genetic algorithm is used to solve small and large instances of this problem when processing and setup times are balanced (Balanced problems), when processing times are dominant (Dominant P problems), and when setup times are dominant (Dominant S problems). For small instances, most of the values achieved the optimal makespan value, and, when compared to the metaheuristic ant colony optimization (ACOII) algorithm referred to in the literature, it was found that there were no significant differences between the two methods. However, in terms of large instances, there were significant differences between the optimal makespan obtained by the two methods, revealing overall better performance by the genetic algorithm for Dominant S and Dominant P problems.FCT—Fundação para a Ciência e Tecnologia through the R&D Units Project Scope UIDB/00319/2020 and EXPL/EME-SIS/1224/2021 and PhD grant UI/BD/150936/2021

Ordonnancement de tâches sous contraintes sur des métiers à tisser

Dans une usine de production de textile, il y a des métiers à tisser. Ces métiers à tisser peuvent être configurés de différentes façons. Des tâches doivent être exécutées sur ces métiers à tisser et le temps d’exécution d’une tâche est fonction du métier sur lequel elle est effectuée. De plus, chaque tâche est seulement compatible avec les métiers à tisser étant configurés de certaines façons. Un temps de mise en course peut permettre de configurer ou préparer un métier à tisser pour l’exécution d’une tâche. Le temps de mise en course est dépendant de la tâche qui précède et de celle qui suit. Nous souhaitons alors créer un horaire pour minimiser les temps de fabrication et les retards. Toutefois, certaines contraintes doivent être respectées. Lorsque des préparations surviennent sur des métiers différents en même temps, le nombre d’employés doit être suffisant. Un métier ne peut faire qu’une seule action à la fois. L’ordonnancement d’une seule machine est un problème NP-Difficile. Dans ce projet, il faut ordonnancer environ 800 tâches sur 90 machines dans un horizon de deux semaines, tout en respectant les contraintes de personnel. Des évènements stochastiques doivent être pris en compte pour obtenir un meilleur horaire. Le bris d’un fil n’étant pas un évènement rare, l’occurrence des bris est donnée sous la forme d’une loi de Poisson. Nous proposons alors une approche de résolution utilisant une heuristique de branchement basée sur le problème du commis voyageur. Cette approche permet d’obtenir de bonnes solutions pour le problème d’ordonnancement exploré. Les solutions trouvées sont 5 à 30% meilleures en termes de fonction objectif qu’une heuristique semblable à celle utilisée par l’équipe de planification de notre partenaire industriel. Nous présentons aussi un algorithme pour garantir la robustesse d’un horaire. Notre algorithme permet de générer des horaires plus réalistes et qui résistent bien aux évènements imprévus. La combinaison de ces deux pratiques mène à l’intégration et l’utilisation du produit final par notre partenaire industriel.In a textile factory, there are looms. Workers can configure the looms to weave different pieces of textiles. A loom can only weave a piece of textiles if the piece of textiles is compatible with its loom configuration. To change its configuration, a loom requires a setup. The setups are performed manually by workers. There are also sequence-dependent setups to prepare a loom for the upcoming piece of textiles. We wish to minimize the setups duration and the lateness. A solution must satisfy some constraints. The problem is subject to cumulative resources. The quantity of workers simultaneously configuring machines can’t exceed the total number of employees. A loom can only weave a piece of textiles at a time. Scheduling tasks on a single loom is an NP-Hard problem. In this project, we must schedule tasks an average of 800 tasks on 90 looms with a two-week horizon. Stochastic events might occur and must be accounted for. We must design an algorithm to create robust schedules under uncertainty. As a thread breaking during the weaving process isn’t a rare occurrence, a better schedule could greatly impact the performances of a company when applying the schedule to a real situation. We formulate that the number of breaks per task follows a Poisson distribution. First, we propose a branching heuristic based on the traveling salesperson problem in order to leverage computation times. The solutions found are 5 to 30% better according to their objective function than the ones of a greedy heuristic similar to what our industrial partner uses. We also present a filtering algorithm to guarantee robustness of solutions in respect to a confidence level. This algorithm improves robustness and creates more realist schedules. The algorithm is also efficient in computation time by achieving bound consistency in linear time. Combining both these techniques leads to the integration of our research in the decision system of our industrial partner

Process Control Parameters Evaluation Using Discrete Event Simulation for Business Process Optimization

The quest for manufacturing process improvement and higher levels of customer satisfaction mandates that organizations must be equipped with advanced tools and techniques in order to respond towards ever changing internal and external customer demands by maintaining the optimal process performance, lower cost and higher profit levels. A manufacturing process can be defined as a collection of activities designed to produce a specific output for a particular customer or market. To achieve internal and external objectives, significant process parameters must be identified and evaluated to optimize the process performance. This even becomes more important to deal with fierce competition and ever changing customer demands. This paper illustrates an integrated approach using design of experiments techniques and discrete event simulation (Simul8) to understand and optimize the system dynamic based on operational control parameter evaluation and their boundary conditions. Further, the proposed model is validated using a real world manufacturing process case study to optimize the manufacturing process performance. Discrete event simulation tool is used to mimic the real world scenario, which provides a flexible and powerful way to comprehensively understand the manufacturing process variations and allows controlled 'What-If´ analysis based on design of experiments approach. Finally, this paper discusses the potential applications of the proposed methodology in the cable industry in order to optimize the cable manufacturing process by regulating the operational control parameters such as dealing with various product configurations with different equipment settings, different product flows and work in process (WIP) space limitations