Arijit Mondal

software-defined vehicles (SDVs) employ zonal architectures whose zones exchange periodic real-time traffic over a high-speed Time-Sensitive Ethernet (IEEE 802.1Q) backbone. Existing research has produced many deterministic routing schemes for such backbone Ethernet networks, yet has largely ignored the complementary problem of payload-level frame minimization: multiplexing dozens of small zonal messages—whether CAN, LIN, or any other in-zone protocol—into the fewest possible Ethernet frames so that bandwidth, switch buffers and latency budgets are not squandered. This letter concentrates on that neglected frame-minimization facet and presents an optimization flow dedicated to reducing the number of Ethernet frames. We first formulate an exact Satisfiability Modulo Theories (SMTs) model that finds the minimal set of multiplexed frames required across an entire hyper-period. Because SMT becomes intractable on large vehicle topologies, we then introduce a matrix-based heuristic aggregation (MaHA) algorithm that reproduces the SMT’s frame-minimization decisions to within a few percent while executing in milliseconds. Experiments on synthetic SDV workloads show that naive one zonal message per Ethernet frame policies waste significant available payload capacity; our SMT eliminates this waste completely, and the heuristic achieves closely similar savings with up to three orders of magnitude less run-time, making it a practical drop-in solution for next-generation SDV networks.

Modern cyber–physical systems (CPSs) and IoT-enabled smart factories rely on human–robot collaboration (HRC) to combine human intuition and robotic precision in real time. Balancing such HRC assembly lines, where each task may execute in human-only, robot-only, or collaborative modes, poses a combinatorial challenge that defies scalable mixed integer linear programming (MILP) and oversimplified heuristics. In this letter, we present IBSHRC, a proof-of-concept Iterative Beam Search framework designed for single-product, straight-line CPS assembly systems. IBSHRC leverages mode-aware initialization, binary-search cycle-time refinement, and efficient pruning to navigate vast scheduling spaces at the network edge. On benchmark instances up to 100 tasks, our method delivers near-optimal cycle times with up to $300\times $ speed-ups over MILP (subsecond runtimes), demonstrating its promise for real-time, IoT-driven industrial scheduling.

Cyber-Physical Systems controlling assembly line operations are central to manufacturing processes. Assembly line systems have diversified over time, depending on multiple factors, including the products being manufactured, the workstations and resources used, factory layouts, and so on. This diversity in assembly line configurations has added layers of complexity to the Assembly Line Balancing Problem (ALBP). While many powerful meta-heuristic techniques exist, their performance can vary significantly depending on the specific characteristics of the ALBP instance, such as the structure of the precedence graph, the distribution of task times, and the number of workstations. Recognizing the need for a more versatile solution, this article introduces a generic local search strategy called Flexible Meta-Heuristic (FMH), which includes a set of adjustable tuning parameters for adapting to specific scenarios. FMH combines and extends the strengths of Hill Climbing (HC), Simulated Annealing (SA), and Genetic Algorithm (GA) to provide effective solutions across a wide range of problems. Through extensive experiments using standard benchmarks and randomly generated datasets, FMH demonstrates high accuracy, deviating by at most 0.9% from best-known benchmark values. Additionally, FMH is significantly less resource-intensive, solving problems with up to 150 tasks in minutes where exact solvers can take hours, making it more scalable and applicable to large industrial scenarios. Our findings suggest that the algorithm’s flexibility and strategic hyper-parameter tuning contribute significantly to its effectiveness in solving diverse ALBPs.

The rapid adoption of Electric Vehicles (EVs), driven by stringent environmental regulations and rising fuel costs, is reshaping the landscape of Vehicle Routing Problems (VRP). This shift has led to the Electric Vehicle Routing Problem (EVRP), which incorporates EV-specific operational constraints such as limited driving range, energy consumption, recharging strategies, and detour-related charging costs. The challenge becomes even more critical in modern mixed fleets , where Electric and Internal Combustion Engine Vehicles (ICEVs) coexist and must be co-routed efficiently. A widely adopted two-step strategy first uses Capacitated VRP (CVRP) algorithms to generate energy-oblivious routes, then makes EV routes energy-feasible via charging station insertion. While VRP and CVRP are extensively studied, methods for efficiently ensuring energy feasibility for EVs on fixed routes remain limited. This article introduces the Fixed Route Vehicle Charging Problem with Discrete Partial Charging (FRVCP-DPC) , extending FRVCP by allowing partial recharging up to predefined discrete levels. We develop a scalable optimal Dynamic Programming algorithm, Best Energy Feasible Route Generator (BEFRG) , to select detour points, charging stations, and charge levels that minimize total route time while maintaining energy feasibility. To evaluate BEFRG in dynamic traffic conditions, we introduce EFRGen , a traffic-aware EVRP simulator built on Simulation of Urban Mobility (SUMO) and OpenStreetMap (OSM). Experiments on the Montoya benchmark—spanning 120 instances with up to 320 demand points and 38 charging stations—show that BEFRG computes optimal solutions for all cases within one minute.