scholarly journals A Decomposition Strategy for Optimal Design of a Soda Company Distribution System

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
J. A. Marmolejo ◽  
I. Soria ◽  
H. A. Perez
Keyword(s):  
Distribution System ◽  
Large Scale ◽  
Programming Model ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Computational Time ◽  
Fixed Costs ◽  
Distribution Centers ◽  
Cross Docking

This work presents a distribution problem of products of a soda bottling company. Commodities are produced at several plants with limited capacity and the demand of distribution centers is satisfied by shipping via cross-docking warehouses. The decomposition strategy is proposed to determine which warehouse needs to be opened to consolidate the demand and by which warehouse each distribution center is served exclusively. The objective is minimizing fixed costs and total transportation costs. The model presented is a mixed-integer programming model with binary variables for which we propose a decomposition strategy based on Benders algorithm. Numerical results show that the proposed strategy can provide the optimal solution of several instances. A large-scale case study based on a realistic company situation is analyzed. Solutions obtained by the proposed method are compared with the solution of full scale problem in order to determine the quality bound and computational time.

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 Location Routing Problem with Consideration of CO2 Emissions Cost: A Case Study

2020 ◽  
Vol 21 (2) ◽  
pp. 225-234
Author(s):  
Ananda Noor Sholichah ◽  
Y Yuniaristanto ◽  
I Wayan Suletra
Keyword(s):  
Co2 Emissions ◽  
Time Windows ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Distribution Centers ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Study Results

Location and routing are the main critical problems investigated in a logistic. Location-Routing Problem (LRP) involves determining the location of facilities and vehicle routes to supply customer's demands. Determination of depots as distribution centers is one of the problems in LRP.  In LRP, carbon emissions need to be considered because these problems cause global warming and climate change. In this paper, a new mathematical model for LRP considering CO2 emissions minimization is proposed. This study developed a new  Mixed Integer Linear Programming (MILP)  model for LRP with time windows and considered the environmental impacts.  Finally, a case study was conducted in the province of Central Java, Indonesia. In this case study, there are three depot candidates. The study results indicated that using this method in existing conditions and constraints provides a more optimal solution than the company's actual route. A sensitivity analysis was also carried out in this case study.

scholarly journals Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

2019 ◽  
Vol 11 (11) ◽  
pp. 3127 ◽  
Author(s):  
Tarik Chargui ◽  
Abdelghani Bekrar ◽  
Mohamed Reghioui ◽  
Damien Trentesaux
Keyword(s):  
Energy Consumption ◽  
Programming Model ◽  
Mixed Integer ◽  
Computational Time ◽  
Neighborhood Search ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Sustainable Logistics ◽  
Cross Docking ◽  
Physical Internet

In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.

scholarly journals Supply chain optimization with variable demand by considering financial criteria and scenarios

2017 ◽  
Vol 26 (44) ◽  
pp. 21 ◽  
Author(s):  
John Willmer Escobar
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Net Present Value ◽  
Programming Model ◽  
Supply Chain Design ◽  
Mixed Integer ◽  
Supply Network ◽  
Distribution Centers ◽  
Supply Chain Optimization ◽  
Starting Point

This paper contemplates the supply chain design problem of a large-scale company by considering the maximization of the Net Present Value. In particular, the variability of the demand for each type of product at each customer zone has been estimated. As starting point, this paper considers an established supply chain for which the main problem is to determine the decisions regarding expansion of distribution centers. The problem is solved by using a mixed-integer linear programming model, which optimizes the different demand scenarios. The proposed methodology uses a scheme of optimization based on the generation of multiple demand scenarios of the supply network. The model is based on a real case taken from a multinational food company, which supplies to the Colombian and some international markets. The obtained results were compared with the equivalent present costs minimization scheme of the supply network, and showed the importance and efficiency of the proposed approach as an alternative for the supply chain design with stochastic parameters.

scholarly journals Charging Network Planning for Electric Bus Cities: A Case Study of Shenzhen, China

2019 ◽  
Vol 11 (17) ◽  
pp. 4713 ◽  
Author(s):  
Yuping Lin ◽  
Kai Zhang ◽  
Zuo-Jun Max Shen ◽  
Lixin Miao
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Cost Minimization ◽  
Transportation Network ◽  
Power Grid ◽  
Mixed Integer ◽  
Charging Infrastructure ◽  
Second Order Cone ◽  
Charging Stations

In 2017, Shenzhen replaced all its buses with battery e-buses (electric buses) and has become the first all-e-bus city in the world. Systematic planning of the supporting charging infrastructure for the electrified bus transportation system is required. Considering the number of city e-buses and the land scarcity, large-scale bus charging stations were preferred and adopted by the city. Compared with other EVs (electric vehicles), e-buses have operational tasks and different charging behavior. Since large-scale electricity-consuming stations will result in an intense burden on the power grid, it is necessary to consider both the transportation network and the power grid when planning the charging infrastructure. A cost-minimization model to jointly determine the deployment of bus charging stations and a grid connection scheme was put forward, which is essentially a three-fold assignment model. The problem was formulated as a mixed-integer second-order cone programming model, and a “No R” algorithm was proposed to improve the computational speed further. Computational studies, including a case study of Shenzhen, were implemented and the impacts of EV technology advancements on the cost and the infrastructure layout were also investigated.

scholarly journals Design of a Distribution Network Using Primal-Dual Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
J. A. Marmolejo ◽  
R. Rodríguez ◽  
O. Cruz-Mejia ◽  
J. Saucedo
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Distribution Network ◽  
Mixed Integer ◽  
Direct Solution ◽  
Distribution Centers ◽  
Dual Decomposition ◽  
Primal Dual ◽  
Cross Decomposition ◽  
Primal And Dual

A method to solve the design of a distribution network for bottled drinks company is introduced. The distribution network proposed includes three stages: manufacturing centers, consolidation centers using cross-docking, and distribution centers. The problem is formulated using a mixed-integer programming model in the deterministic and single period contexts. Because the problem considers several elements in each stage, a direct solution is very complicated. For medium-to-large instances the problem falls into large scale. Based on that, a primal-dual decomposition known as cross decomposition is proposed in this paper. This approach allows exploring simultaneously the primal and dual subproblems of the original problem. A comparison of the direct solution with a mixed-integer lineal programming solver versus the cross decomposition is shown for several randomly generated instances. Results show the good performance of the method proposed.

scholarly journals Hybrid model to design a distribution network in contract farming

DYNA ◽  
2019 ◽  
Vol 86 (208) ◽  
pp. 102-109 ◽  
Author(s):  
Rafael Granillo-Macias ◽  
Isidro Jesus Gonzalez Hernandez ◽  
Jose Luis Martinez-Flores ◽  
Santiago Omar Caballero-Morales ◽  
Elias Olivarez-Benitez
Keyword(s):  
Hybrid Model ◽  
Programming Model ◽  
Distribution Network ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Distribution Problem ◽  
Contract Farming ◽  
The Impact ◽  
Distribution Cost

This paper suggests a hybrid model to solve a distribution problem incorporating the impact of uncertainty in the solution. The model combines the deterministic approach and the simulation including stochastic variables such as harvest yield, loss risk and penalties/benefits to design a distribution network with the minimal cost. Through a case study that includes farmers, hubs and malt producers in the supplying chain of barley in Mexico, nine possible scenarios were analyzed to plan and distribute the harvested grain based on contract farming. This approach gets an optimal solution through an iterative process simulating the suggested solution by a mixed-integer linear programming model under uncertain conditions. The results show the convenience of maintaining the operation between four and five hubs depending on the possible scenario; besides, the variation of the levels of the barley producers’ capacities are key elements in the planning to minimize the distribution cost throughout the suggested chain

scholarly journals Multi Objective Optimization of Coordinated Scheduling of Cranes and Vehicles at Container Terminals

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Seyed Mahdi Homayouni ◽  
Sai Hong Tang
Keyword(s):  
Genetic Algorithm ◽  
Programming Model ◽  
Numerical Test ◽  
Optimal Solution ◽  
Container Terminals ◽  
Automated Guided Vehicles ◽  
Mixed Integer ◽  
Computational Time ◽  
Medium Size ◽  
Coordinated Scheduling

According to previous researches, automated guided vehicles and quay cranes in container terminals have a high potential synergy. In this paper, a mixed integer programming model is formulated to optimize the coordinated scheduling of cranes and vehicles in container terminals. Objectives of the model are to minimize total traveling time of the vehicles and delays in tasks of cranes. A genetic algorithm is developed to solve the problem in reasonable computational time. The most appropriate control parameters for the proposed genetic algorithm are investigated in a medium size numerical test case. It is shown that balanced crossover and mutation rates have the best performance in finding a near optimal solution for the problem. Then, ten small size test cases are solved to evaluate the performance of the proposed optimization methods. The results show the applicability of the genetic algorithm since it can find near optimal solutions, precisely and accurately.

scholarly journals Berth Scheduling Problem Considering Traffic Limitations in the Navigation Channel

2018 ◽  
Vol 10 (12) ◽  
pp. 4795 ◽  
Author(s):  
Ya Xu ◽  
Kelei Xue ◽  
Yuquan Du
Keyword(s):  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Container Terminals ◽  
Mixed Integer ◽  
Computational Time ◽  
Scheduling Problem ◽  
Navigation Channel ◽  
Temporary Closure ◽  
Berth Scheduling

In view of the trend of upsizing ships, the physical limitations of natural waterways, huge expenses, and unsustainable environmental impact of channel widening, this paper aims to provide a cost-efficient but applicable solution to improve the operational performance of container terminals that are enduring inefficiency caused by channel traffic limitations. We propose a novel berth scheduling problem considering the traffic limitations in the navigation channel, which appears in many cases including insufficient channel width, bad weather, poor visibility, channel accidents, maintenance dredging of the navigation channel, large vessels passing through the channel, and so on. To optimally utilize the berth and improve the service quality for customers, we propose a mixed-integer linear programming model to formulate the berth scheduling problem under the one-way ship traffic rule in the navigation channel. Furthermore, we develop a more generalized model which can cope with hybrid traffic in the navigation channel including one-way traffic, two-way traffic, and temporary closure of the navigation channel. For large-scale problems, a hybrid simulated annealing algorithm, which employs a problem-specific heuristic, is presented to reduce the computational time. Computational experiments are performed to evaluate the effectiveness and practicability of the proposed method.

scholarly journals Integrated Production and Distribution Planning for the Iron Ore Concentrate

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Wenbo Liu ◽  
Defeng Sun ◽  
Te Xu
Keyword(s):  
Distribution System ◽  
Iron Ore ◽  
Programming Model ◽  
Mixed Integer ◽  
Planning Problem ◽  
Distribution Centers ◽  
Distribution Planning ◽  
Production And Distribution ◽  
Production And Distribution Planning ◽  
Iron Ore Concentrate

This paper studies the production and distribution planning problem faced by the iron ore mining companies, which aims to minimize the total costs for the whole production and distribution system of the iron ore concentrate. The ores are first mined from multiple ore locations, and then sent to the corresponding dressing plant to produce ore concentrate, which will be sent to distribution centers and finally fulfill the customers' demands. This paper also tackles the difficulty of variable cut-off grade when making mining production planning decisions. A mixed-integer programming model is developed and then solved by a Lagrange relaxation (LR) procedure. Computational results indicate that the proposed solution method is more efficient than the standard solution software CPLEX.

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