China
Africa
Explore chapters and articles related to this topic
Restoration Schemes in the Survivability of Optical Networks
Published in Partha Pratim Sahu, Advances in Optical Networks and Components, 2020
The problem 1 is NP-complete and a disjoint path problem [2,9]. Here, the main aim is to estimate mutually node-disjoint paths between multiple s–d pairs in a network. When an artificial node is added to the network, then this node is connected to all the nodes in the s–d pairs and denotes the two-hop paths through this node between these s–d pairs as primary paths. As all primary paths share the common fault, no capacity is shared between alternate paths. The centralized approach uses a heuristic approach based on distributed computation for solving this problem. A distributed heuristic approach is discussed here. As mentioned earlier, the problems 1 and 2 use the same basic algorithms. Problem 2 includes an additional step for finding the best locations to use the new capacity to estimate restoration routes in case of unrouted demands with minimum additional capacity. The approach involves distributed pre-estimation of restoration routes and permits progressively better capacity utilization. The source node of each demand searches for its restoration route independently. Following are the four basic issues: How does the origin node of a demand search its route?How does the source estimate whether the link has sufficient spare capacity, if the source node of a demand uses a link on the restoration route of that demand?How can deadlocks be disallowed when multiple demands are simultaneously contending for link capacities during their searches (for restoration routes)?How can the capacity utilization be optimized?
Characteristics of Business Processes
Published in Vivek Kale, Enterprise Process Management Systems, 2018
Capacity utilization measures the degree to which resources are effectively utilized by a process. Capacity utilization indicates the extent to which resources, which represent invested capital, are utilized to generate outputs (e.g., flow units and, ultimately, profits).
Smart semiconductor manufacturing for pricing, demand planning, capacity portfolio and cost for sustainable supply chain management
Published in International Journal of Logistics Research and Applications, 2022
Chien-Fu Chien, Hsuan-An Kuo, Yun-Siang Lin
Risk management aims to deal with risks arising between planning instability demand planning and capacity planning. The fundamental goal for risk management is to decide on a proper capacity utilisation level to achieve demand fulfilment while maintaining production flexibility. On the one hand, a capacity surplus increases additional costs. For mid-term planning, exceeding capacity causes unnecessary capital expenditure costs. For short-term planning, energy consumption from low utilisation leads to more waste during the production program. On the other hand, capacity shortage causes unfulfilled demands and potential loss of market share, due to surge demands or conservative capacity expansion strategy. Furthermore, while maximising capacity utilisation, fully loaded work in process may affect the throughput and the production cycle time.
A comparative analysis between different resource allocation and operating strategy implementation mechanisms using a system dynamics approach
Published in International Journal of Production Research, 2020
We used both numbers and graphs as the outputs of simulations to examine and compare the dynamic features of the four models. Table 4 summarises the optimisation results and the corresponding system performance. In Table 4, the optimal joint decisions (i.e. SAT, k / SEobj, ADEobj) are obtained from simulation. The capacity utilisation is measured by the average of production divided by the maximum production capacity over time. The profit ratio is calculated by the average of the obtained profit divided by the maximum profit that can be obtained theoretically, calculated under the existence of two circumstances: full production capacity utilisation and maximum sales efficiency (namely, no delays of products needing to be fulfilled). We used the profit ratio instead of the absolute value of profit because we wanted to compare system performance across the four models, and this relative value of profit ratio made it possible to compare them along the same scale. The mean and standard deviation of S in the sales subsystem and UF in the production subsystem are descriptive statistics that can be obtained from simulation.
A routine-based framework implementing workload control to address recurring disturbances
Published in Production Planning & Control, 2018
Sayyed Shoaib-ul-Hasan, Marco Macchi, Alessandro Pozzetti, Ruth Carrasco-Gallego
To evaluate the impact of the experimental factors on performance, four measures are used: (i) demand fulfillment rate and (ii) assembly lead time (corresponding to the goal of customer service); (iii) capacity utilisation; and (iv) throughput (corresponding to the goal of productivity).Demand fulfillment rate is the probability of satisfying the products demand in the same period (i.e. week) of their arrival. It is calculated based on the actual number of orders produced within their period of arrival.Assembly lead time is calculated by measuring the average life-span (i.e. time from order acceptance till completion) of orders completed during each period.Capacity utilisation is calculated for each period by comparing the total actual working time with the total available working time during that period.The throughput is the total number of items produced by the system in each period.