Joint Optimization of Process Flow and Scheduling in Service-Oriented Manufacturing Systems

Abstract

Customer-oriented management of manufacturing systems is crucial in service-oriented production and product service systems. This paper develops the selection of dispatching rules in combination with alternative process flow designs and demand mix, for a maintenance, repair and overhaul center (MRO) of turbo shaft engines, both for complete engines and engine modules. After an initial systematic screening of priority dispatching rules, the design of experiments and discrete-event simulation allows a quantitative analysis of the better rules for the alternative process flows with internal and service metrics. Next, the design of experiments with analysis of variance and the Taguchi approach enables a search for the optimal combination of process flow and dispatching rules. The consideration of extra costs for overdue work orders into the costing breakdown provides a quantitative evaluation of the optimum range of load for the facility. This facilitates the discussion of the significant trade-offs of cost, service, and flexibility in the production system and the operational management alternatives for decision-making.

Digital Transformation: Moderating Supply Chain Concentration and Competitive Advantage in the Service-Oriented Manufacturing Industry

“…One of the concepts of service-based manufacturing is that companies should be relationship-oriented, thus building long-term and strong ties with their customers [91]. This long-term strategic relationship based on services requires manufacturers to cooperate with their customers more broadly and strategically at multiple structures and levels [92]. According to the social expectedness theory perspective, the cooperative relationships established between manufacturing firms and upstream and downstream firms in the supply chain can reduce production costs and increase production stability; therefore, such relationships can be understood as the relationship-network-based social capital that manufacturing firms actively acquire [12].…”

A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context

“…Step 2: The normalized decision matrix R is calculated using Equation (9). n ij is the norm used by TOPSIS via Equation (10). Additionally, r ij is defined as the element of this matrix.…”

“…Several applications can be traced back to scheduling problems: the regulation of user access to a service, the assignment of operations at workstations during the transformation process of a product, the timing of activities to be carried out within a project complex, the assignment of classrooms to a set of classes, regulation of vehicle accesses at an intersection through traffic light control, the assignment of tracks to railway trains, and the use of tracks and/or gates by planes arriving or departing from an airport. Scheduling problems can be represented through appropriate models [5][6][7][8][9][10]. The main scheduling models are (1) single machine, (2) parallel machines, (3) job shop, (4) flexible job shop, (5) flow shop, (6) flexible flow shop, and (7) open shop.…”

Special Issue of the Manufacturing Engineering Society (MES)

“…No less important, as a whole, are the works on: manufacturing systems: machines, equipment and tooling [39,40,41,42], metrology and quality in manufacturing [43,44,45], product lifecycle management (PLM) technologies [46] and, risks, within the topic “manufacturing engineering and society” [47,48].…”