Ingredients of Locational Analysis (J. Krarup & P. Pruzan). The p-Median Problem and Generalizations (P. Mirchandani). The Uncapacitated Facility Location Problem (G. Cornuejols, et al.). Multiperiod Capacitated Location Models (S. Jacobsen). Decomposition Methods for Facility Location Problems (T. Magnanti & R. Wong). Covering Problems (A. Kolen & A. Tamir). p-Center Problems (G. Handler). Duality: Covering and Constraining p-Center Problems on Trees (B. Tansel, et al.). Locations with Spatial Interactions: The Quadratic Assignment Problem (R. Burkard). Locations with Spatial Interactions: Competitive Locations and Games (S. Hakimi). Equilibrium Analysis for Voting and Competitive Location Problems (P. Hansen, et al.). Location of Mobile Units in a Stochastic Environment (O. Berman, et al.). Index.

https://link.springer.com/content/pdf/10.1057%2Fjors.1991.208.pdf

Discrete location theory

Stochastic Models, an Algorithmic Approach , by Henk C. Tijms (Chichester: Wiley, 1994), 375 pages, paperback.

This paper concerns a multilocation newsboy problem with normal demand at each location and identical linear holding and penalty cost functions at each location. Consolidation of demand from several facilities is considered, and an expression is derived for the resulting expected holding and penalty costs as a function of the demand parameters for each location (means, variances, and correlation coefficients). The expression is used to demonstrate that (i) the expected holding and penalty costs in a decentralized system exceed those in a centralized system; (ii) the magnitude of the saving depends on the correlation of demands; and (iii) if demands are identical and uncorrelated, the costs increase as the square root of the number of consolidated demands. (FACILITIES/EQUIPMENT PLANNING; INVENTORY/PRODUCTIONOPERATING CHARACTERISTICS; INVENTORY/PRODUCTION-STOCHASTIC MODELS)

https://www.jstor.org/stable/pdfplus/2630280

EFFECTS OF CENTRALIZATION ON EXPECTED COSTS IN A MULTI-LOCATION NEWSBOY PROBLEMt

We study the problem of designing a two-echelon spare parts inventory system consisting of a central plant and a number of service centers each serving a set of customers with stochastic demand. Processing and storage capacities at both levels of facilities are limited. The manufacturing process is modeled as a queuing system at the plant. The goal is to optimize the base-stock levels at both echelons, the location of service centers, and the allocation of customers to centers simultaneously, subject to service constraints. A mixed integer nonlinear programming model (MINLP) is formulated to minimize the total expected cost of the system. The problem is NP-hard and a Lagrangian heuristic is proposed. We present computational results and discuss the trade-off between cost and service.

A two-echelon inventory-location problem with service considerations

Resource pooling strategies have been widely used in industry to match supply with demand. However, effective implementation of these strategies can be challenging. Firms need to integrate the heterogeneous service level requirements of different customers into the pooling model and allocate the resources (inventory or capacity) appropriately in the most effective manner. The traditional analysis of inventory pooling, for instance, considers the performance metric in a centralized system and does not address the associated issue of inventory allocation. Using Blackwell’s Approachability Theorem, we derive a set of necessary and sufficient conditions to relate the fill rate requirement of each customer to the resources needed in the system. This provides a new approach to studying the value of resource pooling in a system with differentiated service requirements. Furthermore, we show that with “allocation flexibility,” the amount of safety stock needed in a system with independent and identically distribut...

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5498&context=lkcsb_research

Resource Pooling and Allocation Policies to Deliver Differentiated Service

We consider the problem of allocating demand that originates from multiple sources among multiple inventory locations. Demand from each source arrives dynamically according to an independent Poisson process. The cost of fulfilling each order depends on both the source of the order and its fulfillment location. Inventory at all locations is replenished from a shared production facility with a finite production capacity and stochastic production times. Consequently, supply lead times are load dependent and affected by congestion at the production facility. Our objective is to determine an optimal demand allocation and optimal inventory levels at each location so that the sum of transportation, inventory, and backorder costs is minimized. We formulate the problem as a nonlinear optimization problem and characterize the structure of the optimal allocation policy. We show that the optimal demand allocations are always discrete, with demand from each source always fulfilled entirely from a single inventory location. We use this discreteness property to reformulate the problems as a mixed-integer linear program and provide an exact solution procedure. We show that this discreteness property extends to systems with other forms of supply processes. However, we also show that supply systems exist for which the property does not hold. Using numerical results, we examine the impact of different parameters and provide some managerial insights.

Dongsheng Xu

Papers

Demand Allocation in Systems with Multiple Inventory Locations and Multiple Demand Sources