Balancing and Sequencing of Mixed Model Assembly Lines

Assembly lines are cost efficient production systems that mass produce identical products. Due to customer demand, manufacturers use mixed model assembly lines to produce customized products that are not identical. To stay efficient, management decisions for the line such as number of workers and assembly task assignment to stations need to be optimized to increase throughput and decrease cost. In each station, the work to be done depends on the exact product configuration, and is not consistent across all products. In this dissertation, a mixed model line balancing integer program (IP) that considers parallel workers, zoning, task assignment, and ergonomic constraints with the objective of minimizing the number of workers is proposed. Upon observing the limitation of the IP, a Constraint Programming (CP) model that is based on CPLEX CP Optimizer is developed to solve larger assembly line balancing problems. Data from an automotive OEM are used to assess the performance of both the MIP and CP models. Using the OEM data, we show that the CP model outperforms the IP model for bigger problems. A sensitivity analysis is done to assess the cost of enforcing some of the constraint on the computation complexity and the amount of violations to these constraints once they are disabled. Results show that some of the constraints are helpful in reducing the computation time. Specifically, the assignment constraints in which decision variables are fixed or bounded result in a smaller search space. Finally, since the line balance for mixed model is based on task duration averages, we propose a mixed model sequencing model that minimize the number of overload situation that might occur due to variability in tasks times by providing an optimal production sequence. We consider the skip-policy to manage overload situations and allow interactions between stations via workers swimming. An IP model formulation is proposed and a GRASP solution heuristic is developed to solve the problem. Data from the literature are used to assess the performance of the developed heuristic and to show the benefit of swimming in reducing work overload situations

Automated medical scheduling : fairness and quality

Dans cette thèse, nous étudions les façons de tenir compte de la qualité et de l’équité dans les algorithmes de confection automatique d’horaires de travail. Nous découpons ce problème en deux parties. La modélisation d’un problème d’horaires permet de créer des horaires plus rapidement qu’un humain peut le faire manuellement, puisqu’un ordinateur peut évaluer plusieurs horaires simultanément et donc prendre des décisions en moins de temps. La première partie du problème étudié consiste à améliorer la qualité des horaires en encodant des contraintes et des préférences à l’aide de modèles mathématiques. De plus, puisque la création est plus rapide à l’aide d’un ordinateur, il est plus facile pour un ordinateur de trouver l’horaire ayant la meilleure qualité lorsque les règles et préférences sont clairement définies. Toutefois, déterminer les règles et préférences d’un groupe de personne n’est pas une tâche facile. Ces individus ont souvent de la difficulté à exprimer formellement leurs besoins et leurs préférences. Par conséquent, la création d’un bon modèle mathématique peut prendre beaucoup de temps, et cela même pour un expert en création d’horaires de travail. C’est pourquoi la deuxième partie de cette thèse concerne la réduction du temps de modélisation à l’aide d’algorithmes capable d’apprendre un modèle mathématique à partir de solutions données comme par exemple, dans notre cas, des horaires de travail.In this thesis, we study the ways to take quality and fairness into account in the algorithms of automatic creation of work schedules. We separate this problem into two subproblems. The modeling of a scheduling problem allows a faster creation of schedules than what a human can produce manually. A computer can generate and evaluate multiple schedules at a time and therefore make decisions in less time. This first part of the studied problem consists in improving the quality of medical schedules by encoding constraints and preferences using mathematical models. Moreover, since the creation is faster, it is easier for a computer to find the schedule with the highest quality when the rules and the preferences are clearly defined. However, determining the rules and preferences of a group of people is not an easy task. Those individuals often have difficulties formally expressing their requirements and preferences. Therefore, the creation a good mathematical model might take a long time, even for a scheduling expert. This is why the second part of this thesis concerns the reduction of modeling time using algorithms able to learn mathematical models from given solutions, in our case schedules

Scheduling and staffing of multiskilling of workforce in the context of off-side construction

Aim: There is an increase of interest in multiskilling research from the academy, industry and governmental authorities. Multiskilling of a workforce refers to enhancing flexibility of production by enabling labor to be reallocated in response to change in production priorities during the production horizon. Production priorities can change for several reasons; however, this study considers changes due to alterations in bottleneck configurations. The aim of this research is to investigate the extent to which operational benefits associated with different multiskilled resource management policies pertaining to bottleneck configurations can be achieved in off-site construction. To achieve this aim, first the multiskilling of a workforce in an off-site construction context should be understood as it is a complex matter in both conception and application. Second, an appropriate scheduling method should be developed to allocate an existing workforce to the right tasks, based on their skill level and set, during the production makespan. Third, a staffing platform should be developed to facilitate recruiting and hiring of a multiskilled workforce with an appropriate skill level and set. Methodology: In the Chapter 2 a two-stage paper-screening methodology was used to collect relevant papers in the literature review section. A flow-shop-based optimization methodology is used in the Chapter 3 to schedule multiskilled crew during the production makespan to achieve the production objective. A quadratic resource allocation model was developed to allocate a workforce to different tasks with consideration of the scheduling cost. A piece-wise linearization method is deployed to linearize quadratic constraints and decrease solution time. The Chapter 4 adopts a hybrid method including optimization and multi-criteria decision-making techniques to advise the best multiskilling strategy by comparing the performance of existing multiskilling staffing configurations based upon a range of existing qualitative and quantitative criteria. PROMETHEE is recognized as a suitable multicriteria decision-making approach to incorporate qualitative criteria. A flow shop scheduling method is used to obtain an optimized performance from alternatives pertaining to quantitative criteria. The Chapter 5 of this thesis presents a decision-support tool to optimize a multiskilled staffing strategy. The methodology in this chapter differs from that in Chapter 3 in that the developed staffing optimization platform explores every possible multiskilling strategy to find the optimal staffing configuration. Findings: In Chapter 2, the literature review results in the development of a construction multiskilling framework. This framework investigates multiskilling literature in conception and application. Multiskilling framework includes four main categories of multiskilling context, collateral effects, Mainstream research and strategy. A developed scheduling platform in Chapter 3 indicates that an optimal multiskilled labor allocation can lead to significantly different outcomes in terms of cost and time, based upon whether the location of the bottleneck is fixed or variable. The findings in Chapter 4 indicate that chaining and hiring a multiskilled workforce which is able to contribute to four different tasks, are the best multiskilling staffing strategies among existing ones. Sensitivity analysis pertaining to different criteria weight illustrates that the results of this investigation are stable in a wide range of alterations in the weight allocation. In Chapter 5 the decision-support tool illustrates that the optimal multiskilling strategy is highly context specific and should be customized in relation to production circumstances and data, especially the magnitude of bottlenecks. A slight alteration in the production characteristics can lead to significant changes in the optimal cross-training policy. Subjective multiskilling of a workforce could lead to counterproductive results such as a significant cost overrun. Numerical experiments indicate that if there is no extra capacity to allocate more workers to a bottleneck workstation, multiskilling of the workforce in the workstation immediately preceding the bottleneck workstation can lead to enhancement in the productivity. Contribution: The main contribution of the Chapter 2 is to identify theoretical gaps in the cross-training research and pave the way for comprehensive studies to produce more realistic multiskilling knowledge that considers both technical and managerial details. Research findings in Chapter 3, contribute to the scheduling literature by presenting an optimization platform for multi-skilled resource allocation and relocation during the makespan pertaining to the project objective. Research findings in Chapter 4 contribute to staffing literature by presenting a hybrid methodology which can encompass qualitative criteria as well. Research findings in Chapter 5 contribute to staffing literature by presenting a novel optimization platform to optimize configuration of multiskilled labor pertaining to their skill set. Chapter 3, 4 and 5 make another important contribution to the body of knowledge which is quantifying how performance measures and labor skill sets interact with each other. The decision-support tool, which is incorporated in Chapter 5, can help off-site construction industry practitioners, without a relevant academic background, to staff and schedule a workforce to achieve their production objective

Best matching processes in distributed systems

The growing complexity and dynamic behavior of modern manufacturing and service industries along with competitive and globalized markets have gradually transformed traditional centralized systems into distributed networks of e- (electronic) Systems. Emerging examples include e-Factories, virtual enterprises, smart farms, automated warehouses, and intelligent transportation systems. These (and similar) distributed systems, regardless of context and application, have a property in common: They all involve certain types of interactions (collaborative, competitive, or both) among their distributed individuals—from clusters of passive sensors and machines to complex networks of computers, intelligent robots, humans, and enterprises. Having this common property, such systems may encounter common challenges in terms of suboptimal interactions and thus poor performance, caused by potential mismatch between individuals. For example, mismatched subassembly parts, vehicles—routes, suppliers—retailers, employees—departments, and products—automated guided vehicles—storage locations may lead to low-quality products, congested roads, unstable supply networks, conflicts, and low service level, respectively. This research refers to this problem as best matching, and investigates it as a major design principle of CCT, the Collaborative Control Theory. The original contribution of this research is to elaborate on the fundamentals of best matching in distributed and collaborative systems, by providing general frameworks for (1) Systematic analysis, inclusive taxonomy, analogical and structural comparison between different matching processes; (2) Specification and formulation of problems, and development of algorithms and protocols for best matching; (3) Validation of the models, algorithms, and protocols through extensive numerical experiments and case studies. The first goal is addressed by investigating matching problems in distributed production, manufacturing, supply, and service systems based on a recently developed reference model, the PRISM Taxonomy of Best Matching. Following the second goal, the identified problems are then formulated as mixed-integer programs. Due to the computational complexity of matching problems, various optimization algorithms are developed for solving different problem instances, including modified genetic algorithms, tabu search, and neighbourhood search heuristics. The dynamic and collaborative/competitive behaviors of matching processes in distributed settings are also formulated and examined through various collaboration, best matching, and task administration protocols. In line with the third goal, four case studies are conducted on various manufacturing, supply, and service systems to highlight the impact of best matching on their operational performance, including service level, utilization, stability, and cost-effectiveness, and validate the computational merits of the developed solution methodologies