Javad Navaei

• PhD
• University of Windsor

Capacity planning is one of the major factors in saving capitals and avoiding unnecessary costs in any manufacturing system particularly large OEMs. However, many manufacturing systems still suffer from huge costs incurred due to a lack of applying a robust capacity planning optimization model. Most of the developed models in literature do not...

In this paper, a novel image processing-based approach is proposed to optimize vehicle headlamps aiming. Currently, most aiming devices rely on numerical derivative oriented methods to find the essential focal features, also called reference points, to perform the aiming process. However, these approaches are not robust, and minor changes in isocur...

A novel integer programing (IP) model is developed to assign various operations of product variants to candidate machines and select the best place for each machine among the candidate locations. Product variants are considered to have operation sequence flexibility which are optimized for each product variant. The objective is to minimize the tota...

This research capitalises on commonalities between members of a product family to increase the speed, consistency and efficiency of constructing a master operations sequence and optimal operations sequences for new variants. Two novel mixed integer programming (MIP) models are developed for generating master operations sequence based on available o...

In some flow shop environments, set-up times between product variants have an important role and are considered more significant than processing times. Therefore, optimal sequence of various product variants is based on improving machine utilisation. A flow shop environment which addresses the set-up criterion where bypassing is not permitted is co...

Operations flow based similarity is an important criterion for grouping variants. Similarity coefficient for product variants with networked sequence of operations has not been considered in the literature. Previously proposed similarity coefficients, which are based on operation/assembly sequence, focused on variants with serial operations sequenc...

A new method for calculating the commonality between product variants in terms of machine usage is proposed. Grouping products has been studied by various authors under different titles such as product family formation, cell formation, part-machine groups formation. However, having more than one alternative machine that can be selected for performi...

This paper addresses the two-stage assembly flow-shop problem (TSAFP) with multiple non-identical assembly machines in second stage with the objective function of makespan minimization. This problem is a generalization of previously proposed problems in TSAFP. Mathematical mixed-integer linear programming model of this problem is defined, and for i...

This paper addresses the No-Wait Two-Stage Assembly Flow-shop Scheduling Problem (NWTSAFSP) with the objective of makespan minimization. The problem is a generalization of previously proposed general problem in the Two-Stage Assembly Flow Shop Scheduling Problem (TSAFSP). The TSAFSP is NP-hard, thus the NWTSAFSP is NP-hard too, and three meta-heuri...

In this paper, we address the two stage assembly flow-shop problem with multiple non-identical assembly machines in stage two to minimise weighted sum of makespan and mean completion time. Also, sequence dependent setup times are considered for the first stage. This problem is a generalisation of previously proposed two stage assembly flow-shop pro...

This paper addresses a scenario-based stochastic mixed-integer linear programming model for a production-distribution network design problem under uncertainty. The main uncertain parameters are transportation costs, the penalty costs of non-utilized capacity, manufacturing costs, and retailer demands. We consider facilities locations as strategic a...

In this paper the two stage assembly flow shop problem (TSAFSP) with bi-objective of number of tardy and makespan minimization is addressed. The problem is known to be NP-hard and is thus solved with two metaheuristics: Simulated Annealing (SA) and Variable Neighborhood Search (VNS) and the Pareto-optimal solutions approach is taken to find optimal...