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 Evaluation of a facility location for a food assistance supply chain. The case of Bienestarina in Colombia

2020 ◽  
Vol 39 (3) ◽  
pp. 50-61
Author(s):  
Feizar Javier Rueda-Velasco ◽  
Wilson Adarme-Jaimes ◽  
Angélica Garzón-Luna ◽  
Jhonatan Marroquín-Ávila ◽  
Gabriel Parada-Caro
Keyword(s):  
Supply Chain ◽  
Facility Location ◽  
Programming Model ◽  
Food Assistance ◽  
Social Program ◽  
Policy Implications ◽  
Mixed Integer ◽  
Current Configuration ◽  
Location Allocation ◽  
The Government

The evaluation of the strategic supply chain configuration is considered one of the strategic logistics decisions, especially in food assistance supply chains focused on generating better nutritional conditions in vulnerable populations. In Colombia, there is a social program called Bienestarina, which aims to promote food and nutritional security in a vulnerable population. Although the government supports the program for improving nutritional support, there are currently inconsistencies in freight flows, lack of coverage in some areas, and delivery delays. Therefore, this work aims to evaluate the current configuration of the supply chain and propose improvements related to the facility location. Such advances would enable the increase in the efficacy of the network and the reduction of malnutrition in the country. For this purpose, a mixed-integer mathematical programming model is presented, which considers the weighted distance criterion for different demand scenarios and supports the location-allocation decision in a social assistance supply chain. The current network configuration was compared with the optimal proposed structure. The comparisons show highly potential improvements in freight flow allocation, suggests several variations in the existing warehouses emplacement, and generates public policy implications to reduce the logistic cost in the system, prioritizing in turn the demand covering.

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.

A multi-objective approach to design strategic supply chains and develop responsiveness- efficiency frontiers

2018 ◽  
Vol 29 (1) ◽  
pp. 365-386 ◽  
Author(s):  
Raed AlHusain ◽  
Reza Khorramshahgol
Keyword(s):  
Supply Chain ◽  
Integer Programming ◽  
Programming Model ◽  
Efficient Frontier ◽  
Decision Makers ◽  
Binary Integer Programming ◽  
Content Type ◽  
Strategic Fit ◽  
Cost Efficient ◽  
Alternative Solutions

Purpose The purpose of this paper is twofold. Initially, a multi-objective binary integer programming model is proposed for designing an appropriate supply chain that takes into consideration both responsiveness and efficiency. Then, a responsiveness-cost efficient frontier is generated for the supply chain design that can help organizations find the right balance between responsiveness and efficiency, and hence achieve a strategic fit between organizational strategy and supply chain capabilities. Design/methodology/approach The proposed SC design model used both cross-functional and logistical SC drivers to build a binary integer programming model. To this end, various alternative solutions that correspond to different SC design portfolios were generated and a responsiveness-cost efficient frontier was constructed. Findings Various alternative solutions that correspond to different SC designs were generated and a responsiveness-cost efficient frontier was constructed to help the decision makers to design SC portfolios to achieve a strategic fit between organizational strategy and SC capabilities. Practical implications The proposed methodology enables the decision makers to incorporate both qualitative and quantitative judgements in SC design. The methodology is easy to use and it can be readily implemented by a software. Originality/value The proposed methodology allows for subjective value judgements of the decision makers to be considered in SC design and the efficiency-responsiveness frontier generated by the methodology provides a trade-off to be used when choosing between speed and cost efficiency in SC design.

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.

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 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.

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