Background and Objective: This study examines takes into account three key timing factors that have an impact on the effectiveness of human-machine interfaces (HVI). A threshold-based mechanism is created to for both cooperative driving advanced vehicle control system (AVCS) scenarios. For AVCS driving, developed model on-board machine interface time, human transmission time. Methods: threshold...

In this paper we extend the work presented in the papers [1–3] where we considered optimal control of a linear, discrete time system subject to input constraints and stochastic disturbances. Here we basically look at the same problem but we additionally consider state constraints. We discuss several approaches for incorporating state constraints in a stochastic optimal control problem. We consi...

A multicriteria linear programming problem with integer variables and parameterized optimality principle ”from lexicographic to Slater” is considered. A situation in which initial coefficients of penalty cost functions are not fixed but may be potentially a subject to variations is studied. For any efficient solution, appropriate measures of the quality are introduced which incorporate informat...

Motivated by applications in guaranteed delivery in computational advertising, we consider the general problem of fair allocation in a bipartite supply-demand setting. Our formulation captures the notion of deviation from fairness by a convex penalty function. While this formulation admits a convex programming solution, we strive for more timeand space-efficient algorithms. For the case of L1 p...

In this paper, we propose novel adaptation rules for the selforganizing map to solve the prize-collecting traveling salesman problem (PC-TSP). The goal of the PC-TSP is to find a cost-efficient tour to collect prizes by visiting a subset of a given set of locations. In contrast with the classical traveling salesman problem, where all given locations must be visited, locations in the PC-TSP may ...

Abstract—In most classical scheduling models, a job is assumed to deliver to a customer at the instant of a job processing completion. In numerous practical situations, however, multiple delivery dates exist, where the time interval between any two consecutive delivery dates is constant. This type of fixed delivery strategy results in substantial cost savings for transportation and handling. Th...

Pareto optimization solves a constrained optimization task by reformulating the task as a bi-objective problem. Pareto optimization has been shown quite effective in applications; however, it has little theoretical support. This work theoretically compares Pareto optimization with a penalty approach, which is a common method transforming a constrained optimization into an unconstrained optimiza...

This paper presents a simple Evolution Strategy and three simple selection criteria to solve engineering optimization problems. This approach avoids the use of a penalty function to deal with constraints. Its main advantage is that it does not require the definition of extra parameters, other than those used by the evolution strategy. A self-adaptation mechanism allows the algorithm to maintain...

Information relaxation and duality in Markov decision processes have been studied recently to derive upper bounds on the maximal expected reward (or lower bounds on the minimal expected cost). The idea is to relax the non-anticipativity constraint on the controls and impose a penalty to punish such a violation. In this paper we generalize this dual approach to controlled Markov diffusions. We d...

The problem of determining a schedule of jobs with unit time lengths on a single machine that minimizes the total weighted earliness and tardiness penalties with respect to arbitrary rational due dates is formulated as an integer programming problem We show that if the penalties meet a certain criterion called the Dominance Condition then there exists an extremal optimal solution to the LP rela...