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Abstract: Supply planning of two-level assembly system is considered in this paper in which lead times are stochastic at both levels. Finding optimal order release dates of components is our aim which optimizes problem’s performance metrics. There is a determined due date that if demands are not prepared for delivery in that day, backlogging cost would happen. The other noticeable cost that should be taken in to consideration is holding cost, which may occur if components arrive before their time of assembling. In contrast to most of studies in this field that consider sum of these cost, this paper attempts to make a tradeoff between these objectives’ affects using multi-objective approach. This approach hybridizes an evolutionary optimizer, genetic algorithm, with an electromagnetism-like mechanism (EM). The algorithm’s effectiveness is compared against an adapted non-dominated sorting genetic algorithm II (NSGA-II) which has been presented for this problem. [Copyright &y& Elsevier]

Abstract: This paper proposes an improved multi-objective particle swarm optimizer with proportional distribution and jump improved operation, named PDJI-MOPSO, for dealing with multi-objective problems. PDJI-MOPSO maintains diversity of new found non-dominated solutions via proportional distribution, and combines advantages of wide-ranged exploration and extensive exploitations of PSO in the external repository with the jump improved operation to enhance the solution searching abilities of particles. Introduction of cluster and disturbance allows the proposed method to sift through representative non-dominated solutions from the external repository and prevent solutions from falling into local optimum. Experiments were conducted on eight common multi-objective benchmark problems. The results showed that the proposed method operates better in five performance metrics when solving these benchmark problems compared to three other related works. [Copyright &y& Elsevier]