Influence of Level of Detail (LOD) in Hydraulic Model Geometry with Demand Allocation by Voronoi Polygon Method on Chosen Parameters

Deciding on the level of model complexity is one of the first decisions that has to be made before engaging in the creation of a functional hydraulic model of a municipal water supply system. There are a number of influencing factors such as time needed to simulate the network, time needed to create such model and ease of use. The aim of this work is to prove that level of detail has influence on parameters such as pressure at measurement points, spread of flow speed and flow volume at different parts of mains. The Voronoi polygon method is one of the basic demand allocation methods, which, in the event of a different number of junctions depending on model complexity, has to generate a varying number of polygons of different sizes used to aggregate the demands.

Single-Period Capacity and Demand Allocation Decision Making under Uncertainty

The newsvendor model deals with a single-period capacity allocation problem under uncertainty. The real world examples include perishable products (e.g., fish, vegetable), holiday-related products (e.g., Easter, Christmas, Halloween), seasonal products (e.g., fashion), and promotional products. This section addresses three newsvendor models: traditional newsvendor, inverse newsvendor, and sequential newsvendor models. The main decision under the traditional newsvendor setting is capacity allocation (i.e., how much to order), whereas the main decision under the inverse newsvendor setting is demand allocation (i.e., how many customers to be served) under the fixed capacity. This section demonstrates how to compare profit maximization approach to customer-oriented approach under the traditional newsvendor. The inverse newsvendor applies to revenue management for the hospitality industry. The sequential newsvendor model determines the optimal sequence when the number of customers to be served (determined by the inverse newsvendor model) is given. Normal distribution is considered for analytical solution and numerical studies. In addition, a discrete distribution is considered for numerical studies.

Price-Directed Cost Sharing and Demand Allocation Among Service Providers with Multiple Demand Sources and Multiple Facilities

Problem definition: We consider capacity sharing through demand allocation among firms with multiple demand sources and multiple service facilities. Firms decide on the allocation of demand from different sources to different facilities to minimize delay costs and service-fulfillment costs possibly subject to service-level requirements. If firms decide to operate collectively as a coalition, they must also decide on a scheme for sharing the total cost. Academic/practical relevance: We study capacity sharing through demand allocation in service systems in the presence of service-fulfillment costs. Our problem is motivated by service collaboration in healthcare involving public–private partnerships. Methodology: We formulate the problem as a cooperative game and identify a cost allocation that is in the core. Results: The cost-allocation scheme we identify is price-directed, and the cost of each firm consists of three components: (1) the delay cost incurred within the firm; (2) a cost paid for the capacity used by the firm at facilities owned by other firms; and (3) a payment received for fulfilling demand of other firms at facilities owned by the firm. Interestingly, we show that the cost-allocation scheme is equivalent to a market equilibrium—that is, it can be implemented in a decentralized fashion. We extend our analysis to settings where the capacity of each facility is endogenously determined and to settings where a service-priority policy is deployed. Our results are robust to a variety of generalizations: partial sharing, partial transfer, facilities modeled as general queueing systems, and convex delay costs. Managerial implications: Our findings provide guidelines for and insights into how to carry out demand allocation and cost sharing among different firms in the presence of service-fulfillment costs to foster service collaboration. In particular, the equilibrium market prices can be viewed as the prices/subsidies for service collaboration in a public–private partnership.