We continue the analysis of Cephoids. A Cephoid is a Minkowski sum of finitely many standardized simplices ("deGua simplices''). Pareto faces of a Cephoid are maximal polyhedra facing "outward''. A Pareto face consists of an algebraic sum of subsimplices of the deGua Simplices involved, the...

This study introduces a stochastic mixed-integer nonlinear programming (MINLP) model designed to optimize budget allocation for digital marketing campaigns under uncertainty. The model maximizes Expected Net Reach (ENR) by integrating the Sainsbury Normal Method (SNM) to correct for audience...

Open shop scheduling problems (OSSP) are highly significant in engineering and industry, involving critical scheduling challenges. The job type determines the duration required for material transfer between machines and remains unaffected by distance, eliminating any waiting time for transfers....

We present a novel variational inequality model (VIM) to capture the complex real decision-making process in multi-tiered supply chain networks (MSCN) without strictly limiting the features of related functions. The VIM is formulated with the equilibrium conditions on links as the optimization...

The presented paper considers the pricing and lot-sizing decisions for a manufacturer who produces and sells a single product in different selling channels i.e physical stock, website, mobile, etc. The objective is to find the production plan and prices of each channel to maximize the total...

We consider the solution of a recurrent sub–problem within both constrained and unconstrained Nonlinear Programming: namely the minimization of a quadratic function subject to linear constraints. This problem appears in a number of LBM frameworks, and to some extent it reveals a close analogy...

Portfolio optimization is an activity for balancing return and risk. In this paper, we used mean-variance (M-V) portfolio models with buy-in threshold and cardinality constraints. This model can be formulated as a mixed integer nonlinear programming (MINLP) problem. To solve this constrained...

Solving non-linear parabolic partial differential equations (PDEs) in high dimension becomes an interest for its curse of dimensionality problem. Recently, a deep learning method associated with a backward stochastic differential equation (deep-BSDE) to solve the PDEs draws intensive discussions...