Iranian Journal of Operations Research

Forward and Backward Uncertainty Quantification in Optimization

Bijan Mohammadi

This contribution gathers some of the ingredients presented during the Iranian Operational Research community gathering in Babolsar in 2019.It is a collection of several previous publications on how to set up an uncertainty quantification (UQ) cascade with ingredients of growing computational complexity for both forward and reverse uncertainty propagation.

A Bi-objective Model for Cellular Manufacturing System Considering Worker Skills, Part Priorities,and Equipment Levels

Mohammad Mahdi Paydar

Here, a new mathematical model for cellular manufacturing systems considering three important features of part priority, levels of machine’s technology, and the operator’s skill is developed. Simultaneous consideration of these features provides a more realistic analysis of the problems in cellular manufacturing systems. A model with multiple design features including cell formation, human resources flexibility with different skills, machines flexibility, operational sequence, processing time, and the capacity of machine and manpower is proposed in this article. Ourfocus is on the design of cells to implementtwo dissimilar goals. The first goal is the reduction of inter-cellular movements of parts and workers. The second goal is the creation of efficient cellsby making cell’s quality level identical for produced products so that the production of all the different parts have good quality. Two approaches of augmented ε-constraint and non-dominated sorting genetic algorithm II (NSGA-II) are used to solve this model. By comparison of these two approaches, we realizethat the multi-objective evolutionary optimization algorithm creates a Pareto-optimal front in a reasonable amount of time forlarge-scale problems

Optimization of A Thermal Coupled Flow Problem of Semiconductor Melts

Günter Bärwolff

In this paper we describe the formal Lagrange-technique to optimize the production process of solid state crystals from a mixture crystal melt. After the construction of the adjoint equation system of the Boussinesq equation of the crystal melt the forward and backward problems (KKT-system) are discretized by a conservative finite volume method.

A New Algorithm for Constructing the Pareto Front of Bi-objective Optimization Problems

Mehrdad Ghaznavi

Here, scalarization techniques for multi-objective optimization problems are addressed. A new scalarization approach, called unified Pascoletti-Serafini approach, is utilized and a new algorithm to construct the Pareto front of a given bi-objective optimization problem is formulated. It is shown that we can restrict the parameters of the scalarized problem. The computed efficient points provide a nearly equidistant approximation of the whole Pareto front. The performance of the proposed algorithm is illustrated by various test problems and its effectiveness with respect to some existing methods is shown.

Approximating Bayes Estimates by Means of the Tierney Kadane, Importance Sampling and Metropolis-Hastings within Gibbs Methods in the Poisson-Exponential Distribution: A Comparative Study

Seyyed Mohamad Taghi Kamel Mirmostafaee

Here, we work on the problem of point estimation of the parameters of the Poisson-exponential distribution through the Bayesian and maximum likelihood methods based on complete samples. The point Bayes estimates under the symmetric squared error loss (SEL) function are approximated using three methods, namely the Tierney Kadane approximation method, the importance sampling method and the Metropolis-Hastings within Gibbs algorithm. The interval estimators are also obtained. The performance of the point and interval estimators are compared with each other by means of a Monte Carlo simulation. Several conclusions are given at the end.

A Novel Hybrid Modified Binary Particle Swarm Optimization Algorithm for the Uncertain p-Median Location Problem

Fahimeh Baroughi

Here, we investigate the classical p-median location problem on a network in which the vertex weights and the distances between vertices are uncertain. We propose a programming model for the uncertain p-median location problem with tail value at risk objective. Then, we show that it is NP-hard. Therefore, a novel hybrid modified binary particle swarm optimization algorithm is presented to obtain the approximate optimal solution of the proposed model. The algorithm contains the tail value at risk simulation and the expected value simulation. Finally, by computational experiments, the algorithm is illustrated to be efficient.