https://m2pi.ca/authors/uke/Ulviye Karya EllibeşWowchemy (https://wowchemy.com)en-us© 2025 Pacific Institute for the Mathematical Scienceshttps://m2pi.ca/authors/uke/avatar_hua93b07be45d80e929e5dcadf8ce47971_142334_270x270_fill_q90_lanczos_center.jpghttps://m2pi.ca/authors/uke/https://m2pi.ca/project/2026/qavis/Tue, 24 Mar 2026 00:00:00 +0000https://m2pi.ca/project/2026/qavis/<p> <figure > <div class="d-flex justify-content-center"> <div class="w-100" ><img src="IOTOLogo.png" alt="" loading="lazy" data-zoomable /></div> </div></figure> </p> <h3 id="overview">Overview</h3> <p>Qavis builds a hybrid quantum-AI optimization platform currently serving logistics (vehicle routing, delivery scheduling). We are expanding into manufacturing, where Job-Shop Scheduling (JSSP) is a core challenge: N jobs must be processed across M machines, each operation requiring a specific machine for a specific duration, with precedence and no-overlap constraints, minimizing makespan. We need a complete mathematical formulation of JSSP as a binary quadratic optimization problem suitable for quantum and hybrid solvers.</p> <h2 id="problem-statement">Problem Statement</h2> <p>The key research questions are:</p> <ol> <li>How should decision variables, objective, and constraints be encoded as quadratic penalty terms, and which variable encoding (time-indexed, position-based, order-based) yields the best structure?</li> <li>How should penalty coefficients be chosen — can we derive theoretical bounds and sensitivity guidelines?</li> <li>How can large instances be decomposed into smaller sub-problems while preserving solution quality? The project is purely mathematical - no quantum hardware or production software is required.</li> </ol> <p>Deliverables are a formal formulation document, penalty analysis, decomposition strategy, and an M2PI final report. Qavis provides benchmark datasets, domain context, and optional IBM simulator access for interested team members.</p>