Predicting optimal facility location without customer locations

Deriving meaningful insights from location data helps businesses make better decisions. One critical decision made by a business is choosing a location for its new facility. Optimal location queries ask for a location to build a new facility that optimizes an objective function. Most of the existing works on optimal location queries propose solutions to return best location when the set of existing facilities and the set of customers are given. However, most businesses do not know the locations of their customers. In this paper, we introduce a new problem setting for optimal location queries by removing the assumption that the customer locations are known. We propose an optimal location predictor which accepts partial information about customer locations and returns a location for the new facility. The predictor generates synthetic customer locations by using given partial information and it runs optimal location queries with generated location data. Experiments with real data show that the predictor can find the optimal location when sufficient information is provided. © 2017 Copyright held by the owner/author(s)

Studying Solutions of the p-Median Problem for the Location of Public Bike Stations

The use of bicycles as a means of transport is becoming more and more popular today, especially in urban areas, to avoid the disadvantages of individual car traffic. In fact, city managers react to this trend and actively promote the use of bicycles by providing a network of bicycles for public use and stations where they can be stored. Establishing such a network involves the task of finding best locations for stations, which is, however, not a trivial task. In this work, we examine models to determine the best location of bike stations so that citizens will travel the shortest distance possible to one of them. Based on real data from the city of Malaga, we formulate our problem as a p-median problem and solve it with a variable neighborhood search algorithm that was automatically configured with irace. We compare the locations proposed by the algorithm with the real ones used currently by the city council. We also study where new locations should be placed if the network grows.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research was partially funded by the University of Málaga, Andalucı́a Tech, the Spanish MINECO and FEDER projects: TIN2014- 57341-R, TIN2016-81766-REDT, and TIN2017-88213-R. C. Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO

Generation and optimisation of real-world static and dynamic location-allocation problems with application to the telecommunications industry.

The location-allocation (LA) problem concerns the location of facilities and the allocation of demand, to minimise or maximise a particular function such as cost, profit or a measure of distance. Many formulations of LA problems have been presented in the literature to capture and study the unique aspects of real-world problems. However, some real-world aspects, such as resilience, are still lacking in the literature. Resilience ensures uninterrupted supply of demand and enhances the quality of service. Due to changes in population shift, market size, and the economic and labour markets - which often cause demand to be stochastic - a reasonable LA problem formulation should consider some aspect of future uncertainties. Almost all LA problem formulations in the literature that capture some aspect of future uncertainties fall in the domain of dynamic optimisation problems, where new facilities are located every time the environment changes. However, considering the substantial cost associated with locating a new facility, it becomes infeasible to locate facilities each time the environment changes. In this study, we propose and investigate variations of LA problem formulations. Firstly, we develop and study new LA formulations, which extend the location of facilities and the allocation of demand to add a layer of resilience. We apply the population-based incremental learning algorithm for the first time in the literature to solve the new novel LA formulations. Secondly, we propose and study a new dynamic formulation of the LA problem where facilities are opened once at the start of a defined period and are expected to be satisfactory in servicing customers' demands irrespective of changes in customer distribution. The problem is based on the idea that customers will change locations over a defined period and that these changes have to be taken into account when establishing facilities to service changing customers' distributions. Thirdly, we employ a simulation-based optimisation approach to tackle the new dynamic formulation. Owing to the high computational costs associated with simulation-based optimisation, we investigate the concept of Racing, an approach used in model selection, to reduce the high computational cost by employing the minimum number of simulations for solution selection

Generating Representative ISP Technologies From First-Principles

Understanding and modeling the factors that underlie the growth and evolution of network topologies are basic questions that impact capacity planning, forecasting, and protocol research. Early topology generation work focused on generating network-wide connectivity maps, either at the AS-level or the router-level, typically with an eye towards reproducing abstract properties of observed topologies. But recently, advocates of an alternative "first-principles" approach question the feasibility of realizing representative topologies with simple generative models that do not explicitly incorporate real-world constraints, such as the relative costs of router configurations, into the model. Our work synthesizes these two lines by designing a topology generation mechanism that incorporates first-principles constraints. Our goal is more modest than that of constructing an Internet-wide topology: we aim to generate representative topologies for single ISPs. However, our methods also go well beyond previous work, as we annotate these topologies with representative capacity and latency information. Taking only demand for network services over a given region as input, we propose a natural cost model for building and interconnecting PoPs and formulate the resulting optimization problem faced by an ISP. We devise hill-climbing heuristics for this problem and demonstrate that the solutions we obtain are quantitatively similar to those in measured router-level ISP topologies, with respect to both topological properties and fault-tolerance

E-Fulfillment and Multi-Channel Distribution – A Review

This review addresses the specific supply chain management issues of Internet fulfillment in a multi-channel environment. It provides a systematic overview of managerial planning tasks and reviews corresponding quantitative models. In this way, we aim to enhance the understanding of multi-channel e-fulfillment and to identify gaps between relevant managerial issues and academic literature, thereby indicating directions for future research. One of the recurrent patterns in today’s e-commerce operations is the combination of ‘bricks-and-clicks’, the integration of e-fulfillment into a portfolio of multiple alternative distribution channels. From a supply chain management perspective, multi-channel distribution provides opportunities for serving different customer segments, creating synergies, and exploiting economies of scale. However, in order to successfully exploit these opportunities companies need to master novel challenges. In particular, the design of a multi-channel distribution system requires a constant trade-off between process integration and separation across multiple channels. In addition, sales and operations decisions are ever more tightly intertwined as delivery and after-sales services are becoming key components of the product offering.Distribution;E-fulfillment;Literature Review;Online Retailing

End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location

The facility location problems (FLPs) are a typical class of NP-hard combinatorial optimization problems, which are widely seen in the supply chain and logistics. Many mathematical and heuristic algorithms have been developed for optimizing the FLP. In addition to the transportation cost, there are usually multiple conflicting objectives in realistic applications. It is therefore desirable to design algorithms that find a set of Pareto solutions efficiently without enormous search cost. In this paper, we consider the multi-objective facility location problem (MO-FLP) that simultaneously minimizes the overall cost and maximizes the system reliability. We develop a learning-based approach to predicting the distribution probability of the entire Pareto set for a given problem. To this end, the MO-FLP is modeled as a bipartite graph optimization problem and two graph neural networks are constructed to learn the implicit graph representation on nodes and edges. The network outputs are then converted into the probability distribution of the Pareto set, from which a set of non-dominated solutions can be sampled non-autoregressively. Experimental results on MO-FLP instances of different scales show that the proposed approach achieves a comparable performance to a widely used multi-objective evolutionary algorithm in terms of the solution quality while significantly reducing the computational cost for search.Comment: 14 pages, 3 figure

Systematic approaches for retail service location decisions

Text includes handwritten formulas. No pages 18, 92This thesis investigates systems applications to community facility planning by focusing on the use of models in locating retail facilities. This approach was taken because a number of major concepts employed in retail location are directly transferable to most types of urban services where consumers may choose to utilize a number of different locations. A general decision making process for locating retail services is described. Review of the types of information needed by a retail location planner finds that the central issue he faces is estimating the sales volume of a proposed site. The effect of other competing locations, consumer preferences and accessibility make this task difficult without some type of systematic approach. This could be called the classical "problem" of retail location. An extensive search was made of the work of others related to this problem. A number of approaches were found which attempted to represent the interrelated elements of consumers, access, and retailers which constitute a retail system. No dominant theory has been developed in the area; instead, a number of individual lines of inquiry were found with similarities between. Several selected location models are then reviewed in application to specific problems. The major criticism provided focusses on the degree of difficulty model authors have in representing consumer-retailer behavior and the type of information required to support the modelling. It was found that no one type of model can be regarded as superior since each may have been developed for different planning applications which vary in type of retail service and geographic area represented. There are other steps in retail location decision making where further applications of systems approaches may be valuable. These include population and income forecasting for a small area and economic evaluation of location alternatives once gross sales have been estimated. Further development of these areas in conjunction with the retail models described is suggested. Finally, a number of concepts found in various approaches to retail location may have direct benefit in the successful application of planning standards commonly used by architects and urban designers. Insight gained through certain theoretical approaches to retail location imply that increased care should be taken in the derivation and application of meaningful planning standards

Let’s shuffle: Facility Optimal Location for Stations within Bicycle Sharing Systems in the City of Buenos Aires after the pandemic

People’s habits have changed after the pandemic and cycling around the city of Buenos Aires is no exception. This thesis leverages literature on Capacitated Facility Location Problems (CFLP) to build an optimal bike-sharing network to minimize the total system’s cost. The objective is to decide which stations should be left open to meet projected demand in the worst-possible cases, ensuring that users do not have to walk more than a predefined distance to the facility that is closest to them. Results suggest that there is an excess of stations in the downtown area and idle capacity that could be relocated in peripheral areas, reflected by a positive load factor increase of 2x after the optimization is done. The solution shows that up to 70% of total costs could be saved after using our optimization model, by closing down facilities while meeting demand. While total cost is estimated as the budget that needs to be invested to ramp up the system from scratch, it is a useful metric that shows us how the network could be optimized taking away stations from overcrowded areas without losing any of the current demand. All of these bike-sharing facilities could be relocated to areas that have a low-density of bikes, improving access to the cycling system in the city of Buenos Aires