•Multi-period facility location problem with capacity adjustment for the facilities.•Customer segments with different sensitivity to delivery lead times.•Proposal of two mixed integer linear ...programming formulations.•Extensive numerical study on randomly generated data with different demand patterns.
We consider a multi-period facility location problem that takes into account changing trends in customer demands and costs. To this end, new facilities can be established at pre-specified potential locations and initially existing facilities can be closed over a planning horizon. Furthermore, facilities operate with modular capacities that can be expanded or contracted over multiple periods. A distinctive feature of our problem is that two customer segments are considered with different sensitivity to delivery lead times. Customers in the first segment require timely demand satisfaction, whereas customers in the second segment tolerate late deliveries. A tardiness penalty cost is incurred to each unit of demand that is satisfied with delay. We propose two alternative mixed-integer linear formulations to redesign the facility network over the planning horizon at minimum cost. Additional inequalities are developed to enhance the original formulations. A computational study is performed with randomly generated instances and using a general-purpose solver. Useful insights are derived from analyzing the impact of several parameters on network redesign decisions and on the overall cost, such as different demand patterns and varying values for the maximum delivery delay tolerated by individual customers.
This study proposes a mixed-integer multi-objective integrated mathematical model solving facility location and order allocation optimisation problems simultaneously in a two-echelon supply chain ...network. The proposed problem is motivated by a factoryless concept and by providing a dynamic decision-making solution under a multi-period time horizon. Within the model, we also determine the optimal replenishment number of production facilities by the multi-objective functions. The multi-objective functions include minimisation of the total cost, rejected and late delivery units and, maximisation of the assessment score of the selected suppliers. The studied dynamic decision model is significant for the cost-efficient management of companies’ supply chain networks. The mixed-integer mathematical model is developed by the LP-metric method and it is solved by the GAMS optimisation software. Due to the NP-hard structure of the problem, for large-scale instances, we utilise the Multi-Objective Particle Swarm Optimisation (MOPSO) and Multi-Objective Vibration Damping Optimisation (MOVDO) heuristic solution approaches. Numerical results show that, for large-scale problems, the MOPSO method performs better in Pareto solutions and decreases run times. However, the MOVDO method performs better regarding the Mean Ideal Distance and the Number of Solutions Cover surface criterion. The developed solution approach by this paper is a generic model which can be applied for any two-level network for simultaneous optimisation of supplier selection, location determination of facilities and their replenishment amounts.
It has been argued that the circular economy (CE) represents an opportunity to achieve a paradigm shift in territory from the current linear model to a low-carbon, zero-waste economy. In this ...context, the implementation of the CE is holistically analysed to measure its impact and contribute to the debate about regional environmental management from the different perspectives of society, public administrations, and the private sector. Through a qualitative case study of a Spanish region, the main barriers of CE, such as the lack of funding for undertaking investments and the supply of recycled products, are identified, and the organisation of a waste-exchange system between companies or awareness campaigns concerning the CE are considered relevant incentives to be included in regional planning and management. This study confirms the economic and social win for CE that will be more effective as more CE activities are implemented at regional level.
The paper examines how new airport infrastructure influences regional tourism. Identification is based on the conversion of a military airbase into a regional commercial airport in the German state ...of Bavaria. The new airport opened in 2007 and promotes travelling to the touristic region of Allgäu in the Bavarian Alps. A synthetic control approach is used to show that the new commercial airport increased tourism in the Allgäu region over the period 2008–16. The positive effect is especially pronounced in the county in which the airport is located. The results suggest that new transportation infrastructure promotes regional economic development.
This paper aims to characterize the relocation of warehouses in the São Paulo Metropolitan Region (SPMR) considering the spatial structure of changes in warehouse locations. We also discuss the ...potential factors that could attract warehouses to their location, especially in peripheral regions. The study encompasses the period between 1992 and 2017 and analyzes the influence of geographical, economics, road infrastructure, municipality service tax rate, cargo theft, and real estate prices on logistics facility location. The results show no evidence of logistics sprawl in SPMR during the studied period, although a small sprawl appeared between 2010 and 2017, moving the barycenter to the Northwestern direction of the SPMR. The main factors that influence the decision to move the warehouses are the low cost of land, lower tax, and a well-served infrastructure with highway intersections. There is a positive spatial correlation between warehouse per capita and cargo theft.
This article presents the possibilities in solving the Weighted Multi-Facility Location Problem and its related optimization tasks using a widely available office software—MS Excel with the Solver ...add-in. To verify the proposed technique, a set of benchmark instances with various point topologies (regular, combination of regular and random, and random) was designed. The optimization results are compared with results achieved by a metaheuristic algorithm based on simulated annealing principles. The influence of the hardware configuration on the performance achieved by MS Excel Solver is also examined and discussed from both the execution time and accuracy perspectives. The experiments showed that this widely available office software is practical for solving even relatively complex optimization tasks (Weighted Multi-Facility Location Problem with 100 points and 20 centers, which consists of 40 continuous optimization variables in two-dimensional space) with sufficient quality for many real-world applications. The method used is described in detail and step-by-step using an example.
•A model for locating facilities with location characteristics and rival effects.•Rival effects in this model show the competition and collaboration among facilities.•The model uses data envelopment ...analysis and hyperlink-induced topic search algorithm.•The applicability of our model is displayed with a case study for ranking apartments.
This paper presents a model to the facility location problem which incorporates both location characteristics and rival effects within a business cluster. Location characteristics in the model are predefined selection criteria such as rent, distance, safety and size. Rival effects represent the competition and collaboration relationship among close facility locations in the business cluster where candidate locations are located. The model presented uses data envelopment analysis (DEA) and weighted hyperlink-induced topic search (HITS) algorithm. DEA determines the efficient and inefficient locations as well as benchmarking relationship. The weighted HITS algorithm with the distance parameter considers the rival effects among locations to identify hubs and authorities. The applicability of our proposed model is demonstrated with a case study that rank apartments in the University area.
•We verified that infrastructure negatively affects the levels of household poverty in Brazil.•These effects are strengthened when infrastructure quality and access are greater.•Our results also ...indicate that there are important spatial heterogeneities in household poverty.•Infrastructure policies should take into account the heterogeneities of the infrastructure in the regional scope.
Many scholars have highlighted the role of infrastructure investments in promoting economic growth along with poverty reduction and social inclusion. Developing economies show substantial discrepancies in terms of infrastructure in the rural–urban, regional and income dimensions. These disparities may be reinforcing a social and economic framework marked by a large portion of the population living in poverty. In a scenario characterized by immense regional and income heterogeneities, the present study aims to evaluate the effect of infrastructure investments on household poverty in Brazil. In addition, we verify whether these effects vary according to infrastructure characteristics such as provision, quality and access. The analysis is based on detailed household microdata from the Demographic Census and infrastructure variables at the municipal and state levels, captured from a variety of data sources. An additional contribution of the paper is the novel application of multilevel logistic models to investigate the relationship between infrastructure and poverty at household, municipal and state levels. Our results demonstrate negative effects of the infrastructure provision on poverty. These effects, in turn, are strengthened when infrastructure quality and access are greater, which allows us to infer about the importance of public policies aimed at achieving lower inequalities in access to basic sanitation, Internet, transportation, telephone services and electricity. These policies should also take into account the infrastructure heterogeneities at the regional level, since such heterogeneities have been important in explaining household poverty.
Given a collection of n points in ℝ d , the goal of the (k,z)-clustering problem is to find a subset of k “centers” that minimizes the sum of the z-th powers of the Euclidean distance of each point ...to the closest center. Special cases of the (k,z)-clustering problem include the k-median and k-means problems. Our main result is a unified two-stage importance sampling framework that constructs an ε-coreset for the (k,z)-clustering problem. Compared to the results for (k,z)-clustering in Feldman and Langberg, STOC 2011, our framework saves a ε2 d factor in the coreset size. Compared to the results for (k,z)-clustering in Sohler and Woodruff, FOCS 2018, our framework saves a poly(k) factor in the coreset size and avoids the exp(k/ε) term in the construction time. Specifically, our coreset for k-median (z=1) has size Õ(ε−4 k) which, when compared to the result in Sohler and Woodruff, STOC 2018, saves a k factor in the coreset size. Our algorithmic results rely on a new dimensionality reduction technique that connects two well-known shape fitting problems: subspace approximation and clustering, and may be of independent interest. We also provide a size lower bound of Ω(k· min{2 z/20,d }) for a 0.01-coreset for (k,z)-clustering, which has a linear dependence of size on k and an exponential dependence on z that matches our algorithmic results.
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