more text on paper and bibtex

Author: jbytecode

paper/paper.bib

@@ -18,4 +18,16 @@ @article{JuMP
     journal = {Mathematical Programming Computation},
     year = {2023},
     doi = {10.1007/s12532-023-00239-3}
+}
+
+@article{HiGHS,
+  title={Parallelizing the dual revised simplex method},
+  author={Huangfu, Qi and Hall, JA Julian},
+  journal={Mathematical Programming Computation},
+  volume={10},
+  number={1},
+  pages={119--142},
+  year={2018},
+  doi={10.1007/s12532-017-0130-5},
+  publisher={Springer}
 }

paper/paper.md

@@ -20,14 +20,16 @@ bibliography: paper.bib
 
 # Summary
 
-`OperationsResearchModels.jl` is a Julia package [@julia]...
+`OperationsResearchModels.jl` is a Julia package [@julia] that offers comprehensive implementations for numerous topics typically covered in an Operations Research curriculum. Its primary objective during development was to serve academic and pedagogical purposes, providing a clear and accessible platform for learning and applying OR concepts. While not optimized for high-performance computing, the package leverages JuMP for its underlying mathematical modeling, which inherently provides a reasonable level of computational efficiency. This design allows the package to deliver a suite of functions that solve classical operations research problems with remarkable ease and consistency, simplifying the process for students and researchers alike.
 
 # Statement of Need
 
 JuMP [@JuMP] provides an excellent interface and macros for uniformly accessing optimizer functionality. Any mathematical optimization problem can be assembled with three core components: the objective function (`@objective`), variable definitions (`@variable`), and constraints (`@constraints`). The researcher's role is to formulate the original problem as a mathematical optimization problem and then translate it using JuMP's macros.
 
 OperationsResearchModels.jl streamlines the model translation stage by automatically constructing mathematical problems from problem-specific input data. Its extensive functionality encompasses a significant portion of the Operations Research domain. This includes, but is not limited to: the linear transportation problem, the assignment problem, the classical knapsack problem, various network models (Shortest Path, Maximum Flow, Minimum Spanning Tree), project management techniques (CPM and PERT), the uncapacitated p-median problem for location selection, Johnson's Rule for flow-shop scheduling, a genetic algorithm for scheduling problems intractable by Johnson's Rule, a zero-sum game solver, and a Simplex solver for real-valued decision variables.
 
+Although the majority of computations are performed by the HiGHS optimizer [@HiGHS] through JuMP, OperationsResearchModels.jl incorporates dedicated, hand-coded Simplex routines. These routines serve a valuable pedagogical purpose in Operations Research curricula, enabling users to observe and verify the detailed, step-by-step calculations involved in solving linear programming problems.
+
 # An Example
 
 The example below defines a linear transportation problem with a given cost matrix