fix minor changes in manusript

Author: jbytecode

paper/paper.bib

@@ -43,7 +43,7 @@ @misc{gurobi-optimods
 
 @misc{Graphs2021,
   author       = {Fairbanks, James and Besan{\c{c}}on, Mathieu and Simon, Sch{\"o}lly and Hoffiman, J{\'u}lio and Eubank, Nick and Karpinski, Stefan},
-  title        = {JuliaGraphs/Graphs.jl: an optimized graphs package for the {J}ulia programming language},
+  title        = {Julia{G}raphs/{G}raphs.jl: an optimized graphs package for the {J}ulia programming language},
   year         = 2021,
   url = {https://github.com/JuliaGraphs/Graphs.jl/}
 }
@@ -63,4 +63,4 @@ @book{kwon2019julia
   year={2019},
   publisher={Changhyun Kwon},
   isbn={9781798205471}
-}
+}

paper/paper.md

@@ -32,7 +32,7 @@ Gurobi OptiMods is an open-source Python package that provides pre-implemented o
 
 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 Cost-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.
+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 cost-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 biased key genetic algorithm for scheduling problems that are intractable to obtain a solution 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.