We propose a new framework for multi-criteria optimization in constraint-based temporal reasoning. Motivated by a real-world domain, we augment one of the most expressive current formalisms, the Disjunctive Temporal Problem with Preferences (DTPP), in two crucial ways. First, we model optimality criteria as being attributed to subsets of soft constraints, in contrast to the direct mapping to in...

One of the key elements in production planning hierarchy is master production scheduling. The aim of this study is to evaluate and compare thirteen alternative MPS development methods, including multi-objective optimization as well as twelve heuristics, in different operating conditions for multi-product single-level capacity-constrained production systems. We extract six critical criteria from...

The multi-objective optimization for a multi-product multi-period four-echelon supply chain network consisting of manufacturing plants, distribution centers (DCs) and retailers each with uncertain services and uncertain customer nodes are aimed in this paper. The two objectives are minimization of the total supply chain cost and maximization of the average number of products dispatched to custo...

This paper presents a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) for the optimal operation of a complex multipurpose and multi-reservoir system. Firstly, MOEA/D decomposes a multi-objective optimization problem into a number of scalar optimization sub-problems and optimizes them simultaneously. It uses information of its several neighboring sub-problems for optimizin...

in a robust parameter design (rpd) problem, the experimenter is interested to determine the values of con-trol factors such that responses will be robust or insensitive to variability of the noise factors. response sur-face methodology (rsm) is one of the effective methods that can be employed for this purpose. since quality of products or processes is usually evaluated through several quality ...

In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult featur...

Ant Colony Optimization (ACO) algorithms are basically developed for discrete optimization and hence their application to continuous optimization problems require the transformation of a continuous search space to a discrete one by discretization of the continuous decision variables. Thus, the allowable continuous range of decision variables is usually discretized into a discrete set of allowab...