scholarly journals A FUZZY BINARY BI OBJECTIVE TRANSPORTATION MODEL: IRANIAN STEEL SUPPLY NETWORK

Transport ◽  
2018 ◽  
Vol 33 (3) ◽  
pp. 810-820 ◽  
Author(s):  
Hannan AMOOZAD MAHDIRAJI ◽  
Moein BEHESHTI ◽  
Seyed Hossein RAZAVI HAJIAGHA ◽  
Edmundas Kazimieras ZAVADSKAS
Keyword(s):  
Supply Chain ◽  
Programming Model ◽  
Fuzzy Programming ◽  
Transportation Costs ◽  
Mixed Integer ◽  
Supply Network ◽  
Realistic Situation ◽  
Demand Rate ◽  
Network Transportation ◽  
Uncertain Time

Prominent influence of transportation costs on supply chain overall profit indicates the importance and emer-gence of transportation optimization models. Regarding this issue and in view of realistic situation consisting of non-de-terministic information, in this research optimizing inbound and outbound transportation costs of a multi echelon supply chain has been considered. To deal with uncertain time deliveries and pricing strategies adopted by different members of supply chain, in conjunction with unpredictable demand rate, fuzzy logic and specifically Trapezoidal Fuzzy Numbers (TrFNs) are included. After designing a fuzzy binary multi objective model based upon structural assumptions, the solving approach is proposed and the model is employed on Iranian steel supply network to illustrate the potential and advantages of our scheduled model. The bi-objective mixed integer fuzzy programming model presents and encompasses many realis-tic circumstances making the model applicable in network transportation cases.

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 Local Blood Supply Chain Optimization: A Case Study

2020 ◽  
Vol 18 (4) ◽  
Author(s):  
Reza Babazadeh ◽  
Ali Sabbaghnia ◽  
Fatemeh Shafipour
Keyword(s):  
Supply Chain ◽  
Blood Supply ◽  
Blood Donation ◽  
Programming Model ◽  
Human Life ◽  
Mixed Integer ◽  
Model Parameters ◽  
Supply Chain Optimization ◽  
Blood Supply Chain ◽  
Proposed Model

: Blood and its products play an undeniable role in human life. In recent years, although both academics and practitioners have investigated blood-related problems, further enhancement is still warranted. In this study, a mixed-integer linear programming model was proposed for local blood supply chain management. A supply network, including temporary and fixed blood donation facilities, blood banks, and blood processing centers, was designed regarding the deteriorating nature of blood. The proposed model was applied in a real case in Urmia, Iran. The numerical results and sensitivity analysis of the key model parameters ensured the applicability of the proposed model.

scholarly journals Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Guangcan Xu ◽  
Maozeng Xu ◽  
Yong Wang ◽  
Yong Liu ◽  
Qiguang Lv
Keyword(s):  
Energy Supply ◽  
Time Window ◽  
Programming Model ◽  
Urban Transportation ◽  
Mixed Integer ◽  
Supply Network ◽  
Transportation Energy ◽  
The Sustainable Development ◽  
Petrol Station ◽  
Chongqing City

Energy supply is an important system that affects the overall efficiency of urban transportation. To improve the system operational efficiency and reduce costs, we formulate and solve a collaborative multidepot petrol station replenishment problem with multicompartments and time window assignment by establishing a mixed-integer linear programming model. The hybrid heuristic algorithm composed of genetic algorithm and particle swarm optimization is used as a solution, and then the Shapley value method is applied to analyze the profit allocation of each petrol depot under different coalitions. The optimal membership sequence of the cooperation is determined according to the strict monotone path. To analyze and verify the effectiveness of the proposed method, a regional petrol supply network in Chongqing city in China is investigated. Through cooperation between petrol depots in the supply network, the utilization of customer clustering, time window coordination, and distribution truck sharing can significantly reduce the total operation costs and improve the efficiency of urban transportation energy supply. This approach can provide theoretical support for relevant government departments and enterprises to make optimal decisions. The implementation of the joint distribution of energy can promote the sustainable development of urban transportation.

Optimization and Performance Evaluation of an Integrated Production-Distribution Plan in Supply Chains

2009 ◽  
Author(s):  
Behnam Fahimnia ◽  
Lee Luong ◽  
Romeo Marian
Keyword(s):  
Supply Chain ◽  
Performance Evaluation ◽  
End Users ◽  
Mixed Integer ◽  
Multiple Time ◽  
Supply Network ◽  
Distribution Centers ◽  
Production Distribution ◽  
And Performance ◽  
The Right

Supply Chain Management is the process of integrating and utilizing suppliers, manufacturers, distribution centers, and retailers; so that products are produced and delivered to the end-users at the right quantities and at the right time, while minimizing costs and satisfying customer requirements. From this definition, a supply chain includes three sub-systems: procurement, production, and distribution. The overall performance of a supply-chain is influenced significantly by the decisions taken in its production-distribution plan. A production-distribution plan excludes the procurement activities and integrates the decisions in production, transport and warehousing as well as inventory management. Hence, one key issue in the performance evaluation of a supply network is the modeling and optimization of production-distribution plan considering its actual complexity. This paper develops a mixed integer formulation for a two-echelon supply network that expands the previously reported production-distribution models through the integration of Aggregate Production Plan and Distribution Plan as well as considering the real-world variables and constraints. A Genetic Algorithm is designed for the optimization of the developed model. The methodology will be then implemented to solve a real-life problem incorporating multiple time periods, multiple products, multiple manufacturing plants, multiple warehouses and multiple end-users. To demonstrate the capability of the approach, the validation and performance evaluation of this model will be finally studied for the presented case study.

Model of Supply Chain Disruption Risk Defense System

2014 ◽  
Vol 511-512 ◽  
pp. 1239-1243
Author(s):  
Han Qing Li ◽  
Yi Hong Ru
Keyword(s):  
Supply Chain ◽  
Integer Programming ◽  
Location Problem ◽  
Programming Model ◽  
Facility Location Problem ◽  
Mixed Integer ◽  
Defense System ◽  
Supply Chain Risk ◽  
Supply Chain Disruption ◽  
Disruption Risk

This study is comprised of three main problems. Firstly, a supply chain risk defense problem (SCRDP) is proposed. Then, the study considers a facility location problem in the presence of random facility disruptions where the facilities can be defensed with additional investments. It is formulated as a mixed integer programming model. Finally, a case shows a location solution which designs how to distribute the hardened and non-hardened facilities.

scholarly journals The Nutritious Supply Chain: Optimizing Humanitarian Food Assistance

2021 ◽  
pp. ijoo.2019.0047
Author(s):  
Koen Peters ◽  
Sérgio Silva ◽  
Rui Gonçalves ◽  
Mirjana Kavelj ◽  
Hein Fleuren ◽  
...  
Keyword(s):  
Supply Chain ◽  
Programming Model ◽  
Food Assistance ◽  
Decision Makers ◽  
Mixed Integer ◽  
Sourcing Strategies ◽  
Humanitarian Agency ◽  
Operational Excellence ◽  
Functional Areas

The World Food Programme (WFP) is the largest humanitarian agency fighting hunger worldwide, reaching approximately 90 million people with food assistance across 80 countries each year. To deal with the operational complexities inherent in its mandate, WFP has been developing tools to assist its decision makers with integrating supply chain decisions across departments and functional areas. This paper describes a mixed integer linear programming model that simultaneously optimizes the food basket to be delivered, the sourcing plan, the delivery plan, and the transfer modality of a long-term recovery operation for each month in a predefined time horizon. By connecting traditional supply chain elements to nutritional objectives, we are able to make significant breakthroughs in the operational excellence of WFP’s most complex operations. We show three examples of how the optimization model is used to support operations: (1) to reduce the operational costs in Iraq by 12% without compromising the nutritional value supplied, (2) to manage the scaling-up of the Yemen operation from three to six million beneficiaries, and (3) to identify sourcing strategies during the El Niño drought of 2016.

scholarly journals Risk-Averse Co-Decision for Lower-Carbon Product Family Configuration and Resilient Supplier Selection

2021 ◽  
Vol 14 (1) ◽  
pp. 384
Author(s):  
Dengzhuo Liu ◽  
Zhongkai Li ◽  
Chao He ◽  
Shuai Wang
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Programming Model ◽  
Ghg Emissions ◽  
Product Family ◽  
Mixed Integer ◽  
Risk Averse ◽  
Worst Case ◽  
Risk Neutral ◽  
Disruption Risks

Due to global pandemics, political unrest and natural disasters, the stability of the supply chain is facing the challenge of more uncertain events. Although many scholars have conducted research on improving the resilience of the supply chain, the research on integrating product family configuration and supplier selection (PCSS) under disruption risks is limited. In this paper, the centralized supply chain network, which contains only one major manufacturer and several suppliers, is considered, and one resilience strategy (i.e., the fortified supplier) is used to enhance the resilience level of the selected supply base. Then, an improved stochastic bi-objective mixed integer programming model is proposed to support co-decision for PCSS under disruption risks. Furthermore, considering the above risk-neutral model as a benchmark, a risk-averse mixed integer program with Conditional Value-at-Risk (CVaR) is formulated to achieve maximum potential worst-case profit and minimum expected total greenhouse gases (GHG) emissions. Then, NSGA-II is applied to solve the proposed stochastic bi-objective mixed integer programming model. Taking the electronic dictionary as a case study, the risk-neutral solutions and risk-averse solutions that optimize, respectively, average and worst-case objectives of co-decision are also compared under two different ranges of disruption probability. The sensitivity analysis on the confidence level indicates that fortifying suppliers and controlling market share in co-decision for PCSS can effectively reduce the risk of low-profit/high-cost while minimizing the expected GHG emissions. Meanwhile, the effects of low-probability risk are more likely to be ignored in the risk-neutral solution, and it is necessary to adopt a risk-averse solution to reduce potential worst-case losses.

scholarly journals Five-Echelon Multiobjective Health Services Supply Chain Modeling under Disruption

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Farnaz Javadi Gargari ◽  
Mahjoube Sayad ◽  
Seyed Ali Posht Mashhadi ◽  
Abdolhossein Sadrnia ◽  
Arman Nedjati ◽  
...  
Keyword(s):  
Supply Chain ◽  
Optimization Model ◽  
Optimization Problem ◽  
Programming Model ◽  
Transportation Cost ◽  
Real Data ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Hub Location ◽  
Goal Programming Model

Medicine unreliability problem is taken into consideration as one of the most important issues in health supply chain management. This research is associated with the development of a multiobjective optimization problem for the selection of suppliers and distributors. To achieve the purposes, the optimal quota allocation is determined with respect to disruption of suppliers in a five-echelon supply chain network and consideration of the distributor centers as a hub location-allocation mode. The objective of the optimization model is involved in simultaneous minimization of transactions costs dealing with suppliers, expected purchasing costs from suppliers, expected percentages of delayed and returned products in each distributor, as well as transportation cost in each echelon and fixed cost for distributor centers, and finally maximization of the expected scores for suppliers and high priority of product customers. The optimization problem is formulated as a mixed-integer nonlinear programming model. The proposed optimization model is utilized to investigate a numerical case study for asthma-specific medicines. The analyzing procedure is conducted based on the collected real data from Cobel Darou pharmaceutical company in 2019. Furthermore, a fuzzy multichoice goal programming model is considered to solve the proposed optimization model by R optimization solver. The numerical results confirmed the authenticity of the model.

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