PMID- 36247034
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20221019
IS  - 2730-6852 (Electronic)
IS  - 2730-6852 (Linking)
VI  - 2
IP  - 1
DP  - 2022
TI  - Identifying future partner agencies: helping Brazos Valley Food Bank in the fight 
      against food insecurity.
PG  - 37
LID - 10.1007/s43762-022-00064-9 [doi]
LID - 37
AB  - Brazos Valley Food Bank (BVFB) is a non-profit organization in the Bryan-College 
      Station area of Texas. It distributes food supplies through partner agencies and 
      special programs to eradicate hunger in Brazos Valley. However, a big gap exists 
      between the meals distributed by BVFB and the size of the food-insecure 
      population. This research is motivated by BVFB's desire to reach more people by 
      recruiting more sustainable partner agencies. We used Geographic Information 
      Systems (GIS) to map food desert areas lacking access to nutritious food. We 
      combined expert knowledge with multi-criteria decision-making (MCDM) to address 
      the challenges and time consumption of manually identifying sustainable partner 
      agencies for local food delivery. We identified evaluation criteria for all 
      agencies based on BVFB managers' preferences using a qualitative approach, and 
      then applied three quantitative decision-making models: the Weighted Sum Model 
      (WSM), the Technique for Order Preference by Similarity to Ideal Solution 
      (TOPSIS), and the Multi-criteria Optimization and Compromise Solution (VIKOR) 
      models to obtain ranking results. We compared the quantitative models' rankings 
      to BVFB managers' manual choices and discussed the impacts of our research. The 
      key innovation of the research is to develop a mixed method by combining expert 
      knowledge with mathematical decision models and GIS to support spatial decision 
      making in food distribution. Although our results were specific to BVFB, these 
      procedures can be applied to food banks in general. Future studies include 
      finetuning our models to measure and address human biases, wider applications and 
      more data collections.
CI  - (c) The Author(s) 2022.
FAU - Saari, Sanni
AU  - Saari S
AD  - College Station High School, 4002 Victoria Avenue, College Station, TX 77845 USA.
FAU - Li, Ying
AU  - Li Y
AUID- ORCID: 0000-0002-3423-4310
AD  - Department of Information and Operations Management, Mays Business School, Texas 
      A&M University, College Station, TX 77843 USA. GRID: grid.264756.4. ISNI: 0000 
      0004 4687 2082
FAU - Avila, Shannon
AU  - Avila S
AD  - Brazos Valley Food Bank, 1501 Independence Avenue, Bryan, TX 77803 USA.
FAU - Knight, Ebony
AU  - Knight E
AD  - Brazos Valley Food Bank, 1501 Independence Avenue, Bryan, TX 77803 USA.
LA  - eng
PT  - Journal Article
DEP - 20221009
PL  - Singapore
TA  - Comput Urban Sci
JT  - Computational urban science
JID - 9918284258606676
PMC - PMC9547752
OTO - NOTNLM
OT  - Food bank
OT  - Food desert
OT  - Food insecurity
OT  - Geographic information systems
OT  - Multi-criteria decision making
COIS- Competing interestsThe authors have no financial or proprietary interests in any 
      material discussed in this article. The authors have no competing interests 
      related to this research.
EDAT- 2022/10/18 06:00
MHDA- 2022/10/18 06:01
PMCR- 2022/10/09
CRDT- 2022/10/17 04:42
PHST- 2022/05/30 00:00 [received]
PHST- 2022/09/26 00:00 [accepted]
PHST- 2022/10/17 04:42 [entrez]
PHST- 2022/10/18 06:00 [pubmed]
PHST- 2022/10/18 06:01 [medline]
PHST- 2022/10/09 00:00 [pmc-release]
AID - 64 [pii]
AID - 10.1007/s43762-022-00064-9 [doi]
PST - ppublish
SO  - Comput Urban Sci. 2022;2(1):37. doi: 10.1007/s43762-022-00064-9. Epub 2022 Oct 9.