RFP: Cafeteria Services for the National Mine Health and Safety Academy in Beaver, West Virginia
LABOR, DEPARTMENT OF
Notice type
Combined Synopsis/Solicitation
Solicitation #
1605C426R00010
NAICS
722310
PSC
H973
Set-aside
No Set aside used
Posted
June 18, 2026
Response due
July 6, 2026
Place of performance
Beaver, WV
What this opportunity is
The U.S. Department of Labor is seeking proposals for cafeteria services at the National Mine Health and Safety Academy in Beaver, West Virginia, under NAICS 722310. This is an unrestricted solicitation, meaning any business can compete, but priority will be given to the West Virginia Division of Rehabilitation Services as per the Randolph-Sheppard Act. Interested vendors should note that this is a combined synopsis/solicitation, requiring careful attention to the RFP for proposal submission and evaluation criteria.
Analysis by Mindy, grounded in the SAM.gov notice.
Description
The U.S. Department of Labor (DOL), Office of the Senior Procurement Executive (OSPE), on behalf of the Mine Safety and Health Administration (MSHA), has a need for Cafeteria Services for the National Mine Health and Safety Academy (NMHSA) located at 1301 Airport Road in Beaver, West Virginia, in accordance with this Request for Proposal (RFP).
This RFP will result in a single award.
The Period of Performance shall be for one (1) Base Period of 12 months and four (4) 12-month Option Periods.
This acquisition is unrestricted.
Tradeoff source selection procedures will be utilized.
Please read the RFP in its entirety, including instructions for submitting a proposal and the evaluation criteria the Government will use to make an award.
*NOTE: This acquisition will be conducted on an unrestricted basis while giving priority to the West Virginia Division of Rehabilitation Services in accordance with the Randolph-Sheppard Act.
Source: SAM.gov, as posted. Verify the current solicitation before responding.
Pursue this opportunity with Mindy
See who holds it now, who else is bidding, and draft your response — grounded in real government data, not generic AI.