Consumer choice in competitive location models: Formulations and heuristics

A new direction of research in Competitive Location theory incorporates theories of Consumer Choice Behavior in its models. Following this direction, this paper studies the importance of consumer behavior with respect to distance or transportation costs in the optimality of locations obtained by traditional Competitive Location models. To do this, it considers different ways of defining a key parameter in the basic Maximum Capture model (MAXCAP). This parameter will reflect various ways of taking into account distance based on several Consumer Choice Behavior theories. The optimal locations and the deviation in demand captured when the optimal locations of the other models are used instead of the true ones, are computed for each model. A metaheuristic based on GRASP and Tabu search procedure is presented to solve all the models. Computational experience and an application to 55-node network are also presented.Distance, competitive location models, consumer choice behavior, GRASP, tabu

An exploration of facility location metrics in international supply chain

International audienceCompanies could gain competitive advantage through the supply chain network. Especially facility location represent possible source of cost and service performance improvement. The goal of this article is to explore and expose what could be the facility key performance indicators. A literature review is conducted to examine research relating to supply chain network distribution performance measurement, facility location and KPIs on global and local level. An exploration of the supply chain performance literature reveals global and local KPIs that could used for the facility location measurement. A list of key performance metrics related to facility location is presented

An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification

In recent years, a variety of proposed methods based on deep convolutional neural networks (CNNs) have improved the state of the art for large-scale person re-identification (ReID). While a large number of optimizations and network improvements have been proposed, there has been relatively little evaluation of the influence of training data and baseline network architecture. In particular, it is usually assumed either that networks are trained on labeled data from the deployment location (scene-dependent), or else adapted with unlabeled data, both of which complicate system deployment. In this paper, we investigate the feasibility of achieving scene-independent person ReID by forming a large composite dataset for training. We present an in-depth comparison of several CNN baseline architectures for both scene-dependent and scene-independent ReID, across a range of training dataset sizes. We show that scene-independent ReID can produce leading-edge results, competitive with unsupervised domain adaption techniques. Finally, we introduce a new dataset for comparing within-camera and across-camera person ReID.Comment: To be published in 2018 15th Conference on Computer and Robot Vision (CRV

Two stages of uniform delivered pricing and a monopolistic network in competitive electricity markets

In this contribution we assess the impact of spatial and non-spatial pricing techniques on market outcomes in deregulated electricity supply. Our analytical framework is a combination of the theory of spatial pricing and the theory of vertically related markets. A model of the traditional regional monopolies with vertical integration serves as a point of reference. The deregulated setting is characterized by a monopolistic transmission-network (upstream) and competitive production (downstream). The monoplistic network remains vertically integrated to one of the competitive producers, and serves at the same time as an essential input-facility to all producers, including the downstream-newcomers. The treatment of transport as a distinct market stage with endogenously determinded transmission- or access-rates sets this study apart from common analysis on spatial oligopolies. Specifically, we design two microeconomic models to compare two alternative pricing-arrangements: Uniform delivered pricing downstream and spatial pricing upstream on the one hand versus uniform delivered pricing downstream as well as upstream on the other hand. These options are both being practiced in different countries after deregulation and are subject to an ongoing political debate with little reference being made to theoretical foundations. The findings are threefold: Firstly, the strategic pricing behaviour on both, the monopolistic and the competitive stage is made visible. We show that in either arrangements there is no incentive on the side of the unregulated network-monopolist for complete vertical foreclosure, i.e. to set the network prices in such a way that all competition is excluded from his traditional market area. Secondly, we find that the preferences of consumers and of both types of firms vis-a-vis the spatial or non-spatial pricing policies deviate from those intuitively assumed by a number of authors. Thirdly, and most importantly, it can be shown that the total neglect of spatial components in network-pricing is accompanied by short run-welfare losses. Thus, if the simplification of network-pricing schemes by the abolishment of location- or distance-specific components induces intensified competition and - as the popular argument goes - enhanced productivity in suit, these gains will have to be weighed against the negative welfare effects caused by the disregard of spatial aspects. Too little attention is being paid to the latter side of the named trade-off.