an interactive heuristic approach can offer a practical solution to the problem of linear integer programming (lip) by combining an optimization technique with the decision maker’s (dm) judgment and technical supervision. this is made possible using the concept of bicriterion linear programming (blp) problem in an integer environment. this model proposes two bicriterion linear programs for iden...

In the literature, the application of multi-objective dynamic scheduling problem and simple priority rules are widely studied. Although these rules are not efficient enough due to simplicity and lack of general insight, composite dispatching rules have a very suitable performance because they result from experiments. In this paper, a dynamic flexible flow line problem with sequence-dependent se...

Institutional investors usually employ mean-variance analysis to determine optimal portfolio weights. Almost immediately upon implementation, however, the portfolio's weights become sub-optimal as changes in asset prices cause the portfolio to drift away from the optimal targets. In an idealized world without transaction costs investors would rebalance continually to the optimal weights. In the...

Markov decision processes (MDPs) are a general framework used in artificial intelligence (AI) to model decision theoretic planning problems. Solving real world MDPs has been a major and challenging research topic in the AI literature, since classical dynamic programming algorithms converge slowly. We discuss two approaches in expediting dynamic programming. The first approach combines heuristic...

This chapter is concerned with the application of approximate dynamic programming techniques (ADP) to solve for the value function, and hence the optimal control policy, in discrete-time nonlinear optimal control problems having continuous state and action spaces. ADP is a reinforcement learning approach (Sutton & Barto, 1998) based on adaptive critics (Barto et al., 1983), (Widrow et al., 1973...

We propose a new approach to solving optimization problems based on an adaptive critic design called Action-Dependent Heuristic Dynamic Programming (ADHDP) [1]. We consider the well-known Traveling Salesman Problem (TSP) as a helpful benchmark to highlight main principles of our approach. TSP formulation and many methods to solve it including the classical sequential nearest city algorithm and ...