scholarly journals A MILP facility location model with distance value adjustments for demand fulfillment using Google Maps

2021 ◽  
Author(s):  
Kevin Palomino ◽  
◽  
David Garcia ◽  
Carmen Berdugo ◽  
◽  
...  
Keyword(s):  
Facility Location ◽  
Location Model ◽  
Branch And Cut ◽  
Mixed Integer ◽  
Distribution Centers ◽  
Google Maps ◽  
Allocation Model ◽  
Demand Fulfillment ◽  
Web Mapping ◽  
Positive Effects

In this manuscript, a facility location model was designed to support logistics operations, considering service distance limitations for demand fulfillment and a list of candidate locations within a supply chain. Consequently, an allocation model was designed using Mixed-Integer Linear Programming (MILP), in which a finite number of demand nodes could be satisfied by a set of supply nodes, considering not only the costs related to these locations but also restrictions aimed at improving the level of service based on distance. Besides, an integrated solution scheme was proposed that includes a macro in VBA language that calculates the distance between nodes using the web mapping service developed by Google Maps and solving the model through a branch and cut algorithm. Subsequently, a case study was executed where the supply operation of an important Colombian retail company is analyzed. The results reflected positive effects not only on costs but also on the prioritization of average distance traveled and on the satisfaction of store demand by distribution centers. Thus, the conditions in which the implementation of this model provides strategic benefits were verified, functioning as a tool to support decision making.

scholarly journals Combined Use of Mathematical Optimization and Design of Experiments for the Maximization of Profit in a Four-Echelon Supply Chain

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-25 ◽  
Author(s):  
Daniel Arturo Olivares Vera ◽  
Elias Olivares-Benitez ◽  
Eleazar Puente Rivera ◽  
Mónica López-Campos ◽  
Pablo A. Miranda
Keyword(s):  
Supply Chain ◽  
Design Of Experiments ◽  
Mathematical Optimization ◽  
Fractional Factorial ◽  
Mixed Integer ◽  
Supply Chain Network ◽  
Distribution Centers ◽  
Cost Component ◽  
Allocation Model

This paper develops a location-allocation model to optimize a four-echelon supply chain network, addressing manufacturing and distribution centers location, supplier selection and flow allocation for raw materials from suppliers to manufacturers, and finished products for end customers, while searching for system profit maximization. A fractional-factorial design of experiments is performed to analyze the effects of capacity, quality, delivery time, and interest rate on profit and system performance. The model is formulated as a mixed-integer linear programming problem and solved by using well-known commercial software. The usage of factorial experiments combined with mathematical optimization is a novel approach to address supply chain network design problems. The application of the proposed model to a case study shows that this combination of techniques yields satisfying results in terms of both its behavior and the obtained managerial insights. An ANOVA analysis is executed to quantify the effects of each factor and their interactions. In the analyzed case study, the transportation cost is the most relevant cost component, and the most relevant opportunity for profit improvement is found in the factor of quality. The proposed combination of methods can be adapted to different problems and industries.

scholarly journals Optimal Urban Logistics Facility Location with Consideration of Truck-Related Greenhouse Gas Emissions: A Case Study of Shenzhen City

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Mi Gan ◽  
Dandan Li ◽  
Mingfei Wang ◽  
Guangyuan Zhang ◽  
Shuai Yang ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Facility Location ◽  
Building Materials ◽  
Ghg Emissions ◽  
Multivariate Regression Analysis ◽  
Location Model ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Large Set ◽  
Estimation Function

The logistics facility location is always involved with great deals of investment. Its construction and operation also bring out a huge amount of the greenhouse gas (GHG) emission due to the consumption of building materials, energy, the running of trucks, and other logistics equipment. Particularly, trucking activities in the urban logistics networks (ULN) are a major source of GHG. This paper aims to formulate an eco-facility location model to minimize both the total cost of ULN construction and operation and the GHG emissions of truck trips. Based on the mathematical relations of GHG emissions rates and several macroscopic factors, which we obtained by multivariate regression analysis on a large set of empirical trucking data in our previous research, the data-driven emissions rates estimation function is acquired. Then, we link the estimation function of each trip purpose by various kinds of logistics facilities through a qualitative analysis. The eco-facility location problem is modeled by integrating the pure facility location model and the GHG emissions function. The problem is first converted to a biobjective mixed-integer program, and the Particle Swarm Optimization algorithm is applied to solve the model. Through experiments with real case, the effectiveness of the models and algorithms is verified. The eco-facility location model for ULN tends to obtain the environment-friendly location decision. Our analytical results also verify the hypothesis that locations of facility do impact the relevant truck-related GHG emissions, especially to transfer transport, as well as inbound and outbound freight.

Multi-Node Distribution Center Location Model under Manufacturing and Remanufacturing System

2010 ◽  
Vol 39 ◽  
pp. 140-145 ◽  
Author(s):  
Ya Peng Zhao
Keyword(s):  
Distribution Center ◽  
Location Model ◽  
Mixed Integer ◽  
Distribution Centers ◽  
Dea Model ◽  
Logistics Distribution ◽  
Center Location ◽  
Node Distribution ◽  
Effective Decision ◽  
Distribution Center Location

This study establishes the logistics distribution center location model under manufacturing and remanufacturing system and modifies alternative expenses and time- restricting condition s of logistics distribution center location model according to the specific feature of model under manufacturing and remanufacturing system. It’s necessary to consider all the factors during the course of establishing the logistics distribution center location model. And it is also necessary to consider effectiveness of distribution center in model and ask for the experts’ opinion and advice. A location model by combining DEA model and mixed integer programming is presented. In this model, in terms of the results of the evaluation of the efficiency of the distribution center by the DEA model, this result is regarded as a constraint of the model. Finally, An example demonstrates that the model can solve the problem of distribution centers location and provide an effective decision tool for decision-maker.

scholarly journals A stochastic location and allocation model for critical items to response large-scale emergencies: A case of Turkey

2016 ◽  
Vol 7 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Erkan Celik ◽  
Nezir Aydin ◽  
Alev Taskin Gumus
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Mixed Integer ◽  
Distribution Centers ◽  
Allocation Model ◽  
Proposed Model ◽  
Stochastic Mixed Integer Programming ◽  
Stochastic Location ◽  
Relief Organizations

This paper aims to decide on the number of facilities and their locations, procurement for pre and post-disaster, and allocation to mitigate the effects of large-scale emergencies. A two-stage stochastic mixed integer programming model is proposed that combines facility location- prepositioning, decisions on pre-stocking levels for emergency supplies, and allocation of located distribution centers (DCs) to affected locations and distribution of those supplies to several demand locations after large-scale emergencies with uncertainty in demand. Also, the use of the model is demonstrated through a case study for prepositioning of supplies in probable large-scale emergencies in the eastern and southeastern Anatolian sides of Turkey. The results provide a framework for relief organizations to determine the location and number of DCs in different settings, by using the proposed model considering the main parameters, as; capacity of facilities, probability of being affected for each demand points, severity of events, maximum distance between a demand point and distribution center. 

scholarly journals META-HEURISTIC APPROACH FOR HIGH-DEMAND FACILITY LOCATIONS CONSIDERING TRAFFIC CONGESTION AND GREENHOUSE GAS EMISSION

2016 ◽  
Vol 24 (4) ◽  
pp. 233-244 ◽  
Author(s):  
Taesung HWANG ◽  
Minho LEE ◽  
Chungwon LEE ◽  
Seungmo KANG
Keyword(s):  
Greenhouse Gas ◽  
Facility Location ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Greenhouse Gas Emission ◽  
Memetic Algorithms ◽  
Distribution Centers ◽  
Ghg Emission ◽  
High Demand ◽  
Facility Locations

Large facilities in urban areas, such as storage facilities, distribution centers, schools, department stores, or public service centers, typically generate high volumes of accessing traffic, causing congestion and becoming major sources of greenhouse gas (GHG) emission. In conventional facility-location models, only facility construction costs and fixed transportation costs connecting customers and facilities are included, without consideration of traffic congestion and the subsequent GHG emission costs. This study proposes methods to find high-demand facility locations with incorporation of the traffic congestion and GHG emission costs incurred by both existing roadway traffic and facility users into the total cost. Tabu search and memetic algorithms were developed and tested with a conventional genetic algorithm in a variety of networks to solve the proposed mathematical model. A case study to determine the total number and locations of community service centers under multiple scenarios in Incheon City is then presented. The results demonstrate that the proposed approach can significantly reduce both the transportation and GHG emission costs compared to the conventional facility-location model. This effort will be useful for decision makers and transportation planners in the analysis of network-wise impacts of traffic congestion and vehicle emission when deciding the locations of high demand facilities in urban areas.

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