A control approach to scheduling flexibly configurable jobs with dynamic structural-logical constraints

Abstract

This study theorizes how the optimal control paradigm can be applied to modeling and algorithmic solutions in manufacturing systems with dynamically changing hybrid structural-logical-terminal constraints. Our study conceptualizes and operationalizes a unique class of problems with both flexible machines and flexible jobs that can be frequently encountered in manufacturing systems with individualized products when process and schedule are created simultaneously. We offer a model to schedule jobs in manufacturing systems when the structural-logical terminal constraints are changing dynamically, and an algorithm to obtain a tractable solution analytically within the proven axiomatic of the optimal program control and mathematical optimization. We develop a dynamic decomposition methodology for modeling and control of schedules in highly flexible production systems combining the advantages of continuous and discrete optimization. The findings suggest that our approach can be of value for approaching problems with a simultaneous process design (i.e., task composition) and operation sequencing (i.e., service composition). Utilizing the outcomes of this research could also support the consideration of dynamics in the operations control. The operations execution can be accurately modeled in continuous time as state variables the updates of which allow for data-driven control of machine availability and capacity disturbances. Besides, the method developed theorizes further generalized insights into decomposition methods for scheduling and is supported by an analytical analysis and an algorithmic realization.

Lean resilience: AURA (Active Usage of Resilience Assets) framework for post-COVID-19 supply chain management

“…The research community has recognized an urgent and crucial need to investigate the impacts of digital technology on supply chain resilience and ripple effect (Ivanov et al , 2019; Blackhurst et al , 2021; Dubey et al , 2021). Technologies such as Industry 4.0, additive manufacturing, sensors, cobots, trace and tracking systems and blockchain render highly flexible supply network and manufacturing systems (Rossit et al , 2018; Ralston and Blackhurst, 2020; Dolgui et al , 2020c; Zouari et al , 2020; Ivanov et al , 2021) entailing inherent adaptability inside the supply chains. In other words, the conventional understanding of supply chain adaptation to disruption as an external and event-driven action (or a course of actions) to “return-to-normal” is replaced by adaptation as a normal existence form of supply chain operations.…”

Adoption of innovative strategies to mitigate supply chain disruption: COVID-19 pandemic

“…Sustainable supply chain performance required various kinds of resources, processes, and operational excellence during pandemic disruption situations in the Pakistani context. As mentioned by Ivanov et al ( 2021 ), the supply chain operations of food and beverages, apparel, IT, agricultural, electronics industries are directly and significantly affected by COVID-19. Thus, adopting green practices, smart technologies, and implementation of supply chain crisis mitigation strategies are ideal concepts that require specific adoption by supply chain manufacturing firms without disrupting their supplies during a lockdown.…”

“…Moreover, Ivanov et al ( 2021 ) suggested a strategic framework to dealing with supply chain and logistics challenges and supply losses during strong, medium, and mild disasters. Ivanov ( 2020b ) introduced a viable supply chain model that guided the supply chain firms to redesign and restructure their SCs during long or short term crises such COVID-19 epidemic to sustain its sustainable supply chain performance and survive in a fluctuating environment.…”

Generalised Performance Estimation in Novel Hybrid MPC Architectures: Modeling the CONWIP Flow-Shop System

“…The WIP sizing problem has already been addressed in the scientific literature, however it has only been solved in closed form for problems with restrictive assumption about processing time and production line configuration (i.e., mostly flow-shop) Hopp and Roof [26], Reyes Levalle et al [27]. Other researchers, instead, tackled the WIP sizing problem with the application of the traditional control theory in a dynamic way [28][29][30][31]. Among these, Vespoli et al [32] proposed a first implementation of the aforementioned semi-heterarchical architecture for Spearman et al [25]'s controlled work-in-progress (CONWIP) flow-shop production line, taking advantage of the availability of closed form analytical models.…”