Calibrating cross-training to meet demand mix variation and employee absence

We address the problem of determining the cross-training that a work team needs in order to cope with demand mix variation and absences. We consider the case in which all workers can be trained on all tasks, the workforce is a resource that determines the capacity and a complete forecasting of demand is not available. The demand mix variation that the organization wants to be able to cope with is fixed by establishing a maximum time to devote to each product. We contend that this approach is straightforward, has managerial practicality and can be applied to a broad range of practical scenarios. It is required that the demand mix variation be met, even if there are a certain level of absences. To numerically solve the mathematical problem, a constraint-based selection procedure is developed, which we term CODEMI. We provide illustrated examples demonstrating solution quality for the approximation, and we report on an illustrative set of computational cases. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.Peer ReviewedPostprint (published version

A game theory model based on Gale-Shapley for dual-resource constrained (DRC) flexible job shop scheduling

Most job shops in practice are constrained by both machine and labor availability. Worker assignment in these so-called Dual Resource Constrained (DRC) job shops is typically solved in the literature via the use of meta-heuristics, i.e. “when” and “where” rules, or heuristic assignment rules. While the former does not necessarily lead to optimal results, the latter suffers from high computational time and complexity, especially when there is a large number of workstations. This paper uses game theory to propose a new worker assignment rule for DRC job shops. The Gale-Shapley model (also known as the stable marriage problem) forms a ‘couple’ made up of a worker and machine following a periodic review strategy. Simulation is used to evaluate and compare the proposed model to “when” and “where” rules previously proposed in the literature. Simulation experiments under different conditions demonstrate that the Gale-Shapley model provides better results for worker assignments in complex DRC systems, particularly when the workers have different efficiency levels. The implications of the findings for research and practice are outlined

Review evolution of cellular manufacturing system’s approaches: Human resource planning method

This paper presents a review of human resource planning methods, related techniques, and their effects on cellular manufacturing systems (CMS). In-depth analysis has been conducted through a review of 43 dominant research papers available in the literature. The advantages, limitations, and drawbacks of material transferring methods have been discussed as well. The domains of the examined studies include some of the important problems in staff planning, such as worker assigning, hiring and firing, optimum number of workers, skilled workers, cross-functional ex-perts, worker satisfaction and outsourcing. The results of this study can fill research gaps and clarify many related questions in CMS problems

Worker Assignment in Dual Resource Constrained Assembly Job Shops with Worker Heterogeneity:An Assessment by Simulation

Most shops in practice are constrained by more than one resource. Consequently, a large body of literature on dual resource constrained shops has emerged. This research typically focuses on worker assignment rules, with attention being on when and where to move workers. In contrast, the decision concerning who to reallocate to a station has received limited attention. The limited prior work assumes workers are assigned to a new station as soon as they become available or seeks to minimise the risk of worker idleness. Using simulation, we question this assumption and show that it can be beneficial to introduce additional worker idleness to ensure workers only work at their most efficient station(s). In general, it is less likely that there are several workers available for one station than it is for there to be multiple stations available for one worker. Consequently, the Who Rule is used less frequently than the Where rule and has less of an impact on performance. Finally, considering the criticality of work orders as part of the Where Rule is important in assembly shops; but if labour is heterogeneous then the focus should be on efficiency. The findings have important implications for research and practice

Workload control in dual resource constrained high-variety shops:an assessment by simulation

Workload Control (WLC) seeks to align capacity with demand, where capacity is typically assumed to be restricted by a single constraint – machine capacity. In practice however, shops are often restricted by dual resource constraints: labor and machines. This study therefore uses simulation to investigate the performance of WLC in Dual Resource Constrained (DRC) highvariety shops with fully interchangeable labor. By considering several environmental factors and different labor assignment and dispatching rules, it is demonstrated that the order release function of WLC maintains its positive impact on performance in a DRC shop under different staffing levels. The positive effect of considering labor availability at release, as proposed in previous research, could not however be confirmed. Thus, the original release method can be applied if labor is fully interchangeable. In terms of labor assignment, we show that a distinct assignment pattern that differs between upstream and downstream stations improves performance if the routing is directed. Meanwhile, dispatching plays a less important role but creates important interaction effects with the assignment rule. Finally, the results suggest that increasing the service rate is a better response to the reduction in capacity that results from labor absenteeism than lowering the input frequency of wor

Dynamic allocation of operators in a hybrid human-machine 4.0 context

La transformation numérique et le mouvement « industrie 4.0 » reposent sur des concepts tels que l'intégration et l'interconnexion des systèmes utilisant des données en temps réel. Dans le secteur manufacturier, un nouveau paradigme d'allocation dynamique des ressources humaines devient alors possible. Plutôt qu'une allocation statique des opérateurs aux machines, nous proposons d'affecter directement les opérateurs aux différentes tâches qui nécessitent encore une intervention humaine dans une usine majoritairement automatisée. Nous montrons les avantages de ce nouveau paradigme avec des expériences réalisées à l'aide d'un modèle de simulation à événements discrets. Un modèle d'optimisation qui utilise des données industrielles en temps réel et produit une allocation optimale des tâches est également développé. Nous montrons que l'allocation dynamique des ressources humaines est plus performante qu'une allocation statique. L'allocation dynamique permet une augmentation de 30% de la quantité de pièces produites durant une semaine de production. De plus, le modèle d'optimisation utilisé dans le cadre de l'approche d'allocation dynamique mène à des plans de production horaire qui réduisent les retards de production causés par les opérateurs de 76 % par rapport à l'approche d'allocation statique. Le design d'un système pour l'implantation de ce projet de nature 4.0 utilisant des données en temps réel dans le secteur manufacturier est proposé.The Industry 4.0 movement is based on concepts such as the integration and interconnexion of systems using real-time data. In the manufacturing sector, a new dynamic allocation paradigm of human resources then becomes possible. Instead of a static allocation of operators to machines, we propose to allocate the operators directly to the different tasks that still require human intervention in a mostly automated factory. We show the benefits of this new paradigm with experiments performed on a discrete-event simulation model based on an industrial partner's system. An optimization model that uses real-time industrial data and produces an optimal task allocation plan that can be used in real time is also developed. We show that the dynamic allocation of human resources outperforms a static allocation, even with standard operator training levels. With discrete-event simulation, we show that dynamic allocation leads to a 30% increase in the quantity of parts produced. Additionally, the optimization model used under the dynamic allocation approach produces hourly production plans that decrease production delays caused by human operators by up to 76% compared to the static allocation approach. An implementation system for this 4.0 project using real-time data in the manufacturing sector is furthermore proposed

Dynamic Scheduling for Maintenance Tasks Allocation supported by Genetic Algorithms

Since the first factories were created, man has always tried to maximize its production and, consequently, his profits. However, the market demands have changed and nowadays is not so easy to get the maximum yield of it. The production lines are becoming more flexible and dynamic and the amount of information going through the factory is growing more and more. This leads to a scenario where errors in the production scheduling may occur often. Several approaches have been used over the time to plan and schedule the shop-floor’s production. However, some of them do not consider some factors present in real environments, such as the fact that the machines are not available all the time and need maintenance sometimes. This increases the complexity of the system and makes it harder to allocate the tasks competently. So, more dynamic approaches should be used to explore the large search spaces more efficiently. In this work is proposed an architecture and respective implementation to get a schedule including both production and maintenance tasks, which are often ignored on the related works. It considers the maintenance shifts available. The proposed architecture was implemented using genetic algorithms, which already proved to be good solving combinatorial problems such as the Job-Shop Scheduling problem. The architecture considers the precedence order between the tasks of a same product and the maintenance shifts available on the factory. The architecture was tested on a simulated environment to check the algorithm behavior. However, it was used a real data set of production tasks and working stations

Dual resource constrained em sistemas de produção industrial: estudo de simulação

Os sistemas de produção industrial são limitados por diversas categorias de recursos: pessoas, máquinas, ferramentas, sistemas de transporte, entre outras. Na terminologia anglosaxónica é comum usar o termo Dual Resource Constrained (DRC), para designar sistemas limitados pela disponibilidade destes tipos de recursos, usualmente pessoas e máquinas. O desempenho de sistemas do tipo DRC é determinado por diversos fatores, nomeadamente pela forma de como os recursos humanos (trabalhadores) são alocados às máquinas (i.e., regras de alocação dos trabalhadores) e pela sequência pela qual os trabalhos são processados nessas máquinas (i.e., regras de despacho). Este trabalho utiliza um ambiente produtivo virtual, com recurso ao software de simulação discreta Arena, para avaliar um conjunto de regras de alocação de trabalhadores e de regras de despacho na operação de sistemas DRC. No estudo são considerados vários cenários experimentais, analisando o impacto destas regras em diferentes configurações dos sistemas de produção. De uma forma geral, os resultados obtidos permitiram concluir que as regras testadas para alocação dos trabalhadores às máquinas têm um forte impacto na performance do sistemas produção testados, nomeadamente no que se refere à definição para onde os trabalhadores se devem deslocar (where rules). O estudo procura ainda apontar possíveis caminhos de trabalho futuro para a melhoria do desempenho deste tipo de sistemas de produção.Industrial production systems are usually constrained by two major resource categories: workers, machines, tools, transportation systems and others. In the Anglo-Saxon terminology, it is usual to use the term Dual Resource Constrained (DRC), to define systems constrained by machines and workers. Their performance is determined by numerous causes: How the workers are allocated to the machines and the order in which the jobs should be processed are some examples. This project uses a virtual production environment, by accessing a discrete simulation software called Arena. It is used to evaluate a set of rules that determine the way workers should move and the order of the jobs being processed in a DRC system. In this paper, are several different scenarios with several rules being analyzed. The results of this project allowed to conclude that between the several tested rules, the ones that command the way workers should move (where rules) had a major impact in the system's performances. This study also tried to point possible future ways to improve the performance of these kinds of systems

Recent developments in Dual Resource Constrained (DRC) system research

Real world manufacturing systems are usually constrained by both machine and human resources. Human operators are often the constraining resource and transfer between workstations to process jobs when required. This kind of system is known as a Dual Resource Constrained (DRC) system and presents additional technical challenges which must be considered during planning and scheduling. These technical challenges can be categorised into the five main dimensions of job release mechanisms, job dispatching, worker flexibility, worker assignment and transfer costs. This paper aims to provide an overview of recent developments in DRC research concerned with each of these areas and also discusses some possible approaches to solving the resource scheduling problem in a DRC system. The focus is on materials published after 1995 and up to 2009. Previous reviews on DRC systems are commented on and followed by a review of recent works associated with each of the five dimensions of DRC system research. Advancements made and new methodologies proposed are discussed and future research directions are identified.(D) Human resources Cross training Workforce scheduling Dispatching Dual Resource Constrained (DRC) systems