This paper proposes a goal-programming modeling approach to address three-dimensional concurrent engineering (3D-CE) problems involving product, process and supply chain design. The model enables straightforward representation of the interrelations among multiple objectives and analysis of tradeoffs among those that exhibit conflicts. The model is demonstrated through a discussion of integrality versus modularity in product and supply chain designs that is motivated by events that took place in the automotive industry over the last decade. A numerical example is used to illustrate the model and the paper concludes with possible extensions and guidelines for implementation. #
I n this research, we apply robust optimization (RO) to the problem of locating facilities in a network facing uncertain demand over multiple periods. We consider a multi-period fixed-charge network location problem for which we find (1) the number of facilities, their location and capacities, (2) the production in each period, and (3) allocation of demand to facilities. Using the RO approach we formulate the problem to include alternate levels of uncertainty over the periods. We consider two models of demand uncertainty: demand within a bounded and symmetric multi-dimensional box, and demand within a multi-dimensional ellipsoid. We evaluate the potential benefits of applying the RO approach in our setting using an extensive numerical study. We show that the alternate models of uncertainty lead to very different solution network topologies, with the model with box uncertainty set opening fewer, larger facilities. Through sample path testing, we show that both the box and ellipsoidal uncertainty cases can provide small but significant improvements over the solution to the problem when demand is deterministic and set at its nominal value. For changes in several environmental parameters, we explore the effects on the solution performance.
Global climate change requires stakeholders to consider energy elements in their decision-making. Electricity costs, in particular, constitute a significant portion of operational costs in most manufacturing systems. The electricity bills can be lowered if electricity-consuming operations are correctly scheduled. We consider a manufacturing operations control problem with known time-varying electricity prices in a finite planning horizon. Each operation is unique and has its own concave electricity consumption function. Pre-emptions of operations are allowed, yet postponing an operation incurs a cumulative penalty for each time period. In addition, each pre-emption is considered a new operation. The electricity cost in each time period is exogenous and there exists a capacity constraint on the total electricity amount consumed in each period due to infrastructure and provider's limitations. There is a fixed start-up cost incurred for switching on the machine and a fixed reservation cost incurred for keeping the machine 'On'. The system also includes a rechargeable battery. The customer has to determine when to process each operation within the time horizon so as to minimise total electricity consumption and operations postponement penalty costs. A dynamic programming solution is proposed and the complexity of the models is analysed. After examining several special cases of the model, the optimum times to charge and discharge the rechargeable battery are determined. A polynomial time algorithm for a special case of a single operation with uniform capacity is proposed.
PurposeThe purpose of this research is to (1) analyse the effect of customised on-demand 3DP on surgical flow time, its variability and clinical outcomes (2) provide a framework for hospitals to decide whether to invest in 3DP or to outsource.Design/methodology/approachThe research design included interviews, workshops and field visits. Design science approach was used to analyse the impact of the 3D printing (3DP) interventions on specific outcomes and to develop frameworks for hospitals to invest in 3DP, which were validated through further interviews with stakeholders.FindingsEvidence from this research shows that deploying customised on-demand 3DP can reduce surgical flow time and its variability while improving clinical outcomes. Such outcomes are obtained due to rapid development of the anatomical model and surgical guides along with precise cutting during surgery.Research limitations/implicationsWe outline multiple opportunities for research on supply chain design and performance assessment for surgical 3DP. Further empirical research is needed to validate the results.Practical implicationsThe decision to implement 3DP in hospitals or to engage service providers will require careful analysis of complexity, demand, lead-time criticality and a hospital's own objectives. Hospitals can follow different paths in adopting 3DP for surgeries depending on their context.Originality/valueThe operations and supply chain management community has researched on-demand distributed manufacturing for multiple industries. To the best of our knowledge, this is the first paper on customised on-demand 3DP for surgeries.
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