School of Mathematics and Statistics - Theses
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ItemEconomic and Social Aspects of Heterogeneous Assembly Lines( 2023-10)Assembly lines with heterogeneous resources are widely present in manufacturing industries. The procedures to build a broad range of products employ various skilled workers or robots equipped with diverse tools. In order to remain competitive, it is crucial for a company to efficiently meet the market demand and reduce expenses at the same time. Since resources such as facilities, workforce wages, machines, and tools are quite costly, this requirement gives rise to the need to design economically viable lines. Conversely, we must take a myriad of technological and physical factors into account as well. For instance, assembly lines might appear in straight or U-shaped layouts, have continuous/(a)synchronous paces, operate with two-sided or multi-manned stations, manufacture multiple products in a mixed-model fashion, and employ a specialised workforce with different capabilities. Furthermore, as demonstrated by the COVID-19 pandemic scenario, possessing some resiliency for quick adaptations is a desirable feature. In addition, taking good care of employees fosters job satisfaction and reduces ergonomic risks, avoiding unnecessary turnover. Finally, it is also manageable to positively contribute towards societal issues, such as integrating workers with disabilities among ``conventional'' or robotic ones. Therefore, this thesis proposes three problems to investigate the complexities of economic and social aspects found in assembly lines with resource and workforce heterogeneity: the Resource-Constrained Assembly Line Balancing Problem (RCALBP), the Assembly Line Worker Assignment and Rebalancing Problem (ALWARP), and the Multi-manned Assembly Line Worker Integration and Balancing Problem (MALWIBP) are herein studied and discussed. We develop Mixed-Integer Linear Programming (MILP) formulations for all three problems. They either aim at minimising the cycle time given limited resources or wages and facilities costs at the desired production rate. Moreover, a state-of-the-art survey on Benders Decomposition (BD) approaches applied to the Assembly Line Balancing Problem (ALBP) is also presented. In the RCALBP and ALWARP, we explore resource and workforce heterogeneity appearing in real-world industrial applications. The former minimises cycle time given a limited number of stations and resources, whilst the latter aims at preserving jobs while minimising labour costs. We consider dedicated and alternative resource types for tasks, take scenarios with falling demands into account, and impose regularity metrics on workload reductions. These problems are mathematically modelled within a MILP framework, and benchmark instances are solved in commercial solvers along with case studies. As both RCALBP and ALWARP instances have very restrictive constraints, we can optimally solve large cases with commercial solvers by making problem-specific adjustments in the parameter tuning, as well as incorporating strong valid inequalities, variable reduction, and lower bounding techniques into the formulation. However, when the problem grows too complex, we may have to resort to heuristic approaches to obtain near-optimal solutions in a reduced computational time. The MALWIBP examines the balancing of assembly lines with multi-manned stations running on a heterogeneous workforce. This union creates a highly combinatorial problem: we must further link the already coupled decisions on assigning tasks to heterogeneous workers and workers to stations with task scheduling assessments. Thus, two heuristic solution procedures are developed, which tackle the problem with a hierarchical decomposition approach, showing that multi-operated stations can reduce the assembly line's length even in the presence of a heterogeneous workforce. Lastly, inspired by the decomposition methods realm, a well-established exact strategy for solving large optimisation problems is surveyed. More specifically, a comprehensive literature review on applying classical and logic-based BD approaches to ALBP variations is inspected. As several literature contributions have recently employed BD algorithms to tackle ALBPs with practical extensions, this survey attempts to consolidate the current body of knowledge by providing a detailed literature review on each application's particular aspects and ideas. We summarise existing gaps to offer insights into the BD efficiency for combinatorial problems such as ALBPs from a managerial perspective and indicate a shift in the research trend. In concluding remarks, the contributions of the developed works are summarised and future research avenues are suggested for all problems.