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Showing 5 results for Tavakkoli-Moghaddam
Noori Darvish, Tavakkoli-Moghaddam, Javadian,
Volume 3, Issue 1 (4-2012)
We consider an open shop scheduling problem. At first, a bi-objective possibilistic mixed-integer programming formulation is developed. The inherent uncertainty in processing times and due dates as fuzzy parameters, machine-dependent setup times and removal times are the special features of this model. The considered bi-objectives are to minimize the weighted mean tardiness and weighted mean completion times. After converting the original formulation into a single-objective crisp one by using an interactive approach and obtaining the Pareto-optimal solutions for small-sized instances, an efficient multi-objective particle swarm optimization (MOPSO) is proposed in order to achieve a good approximate Pareto-optimal set for medium and large-sized examples. This algorithm exploits new selection regimes of the literature for the global best and personal best. Furthermore, a modified decoding scheme is designed to reduce the search area in the solution space, and a local search algorithm is proposed to generate initial particle positions. Finally, the efficiency of the proposed MOPSO (PMOPSO) is shown by comparing with the common MOPSO (CMOPSO) by the use of the design of experiments (DOE) based on three comparison metrics.
Volume 3, Issue 2 (9-2012)
We present a new mathematical model for a
permutation flowshop scheduling problem with sequence-dependent setup times
considering minimization of two objectives, namely makespan and weighted mean
total earliness/tardiness. Only small-sized problems with up to 20 jobs can be
solved by the proposed integer programming approach. Thus, an effective
multi-objective immune system (MOIS) is specially proposed to solve the given
problem. Finally, the computational results are reported showing that the
proposed MOIS is effective in finding solutions of large-sized problems.
Volume 4, Issue 1 (5-2013)
We relax some assumptions of the traditional scheduling problem and suggest an adapted meta-heuristic algorithm to optimize efficient utilization of resources and quick response to demands simultaneously. We intend to bridge the existing gap between theory and real industrial scheduling assumptions (e.g., hot metal rolling industry, chemical and pharmaceutical industries). We adapt and evaluate a well-known algorithm based on electromagnetic science. The motivation behind our proposed meta-heuristic approach has arisen from the attraction-repulsion mechanism of electromagnetic theories in physics. In this basic idea, we desire to bring our search closer to a region with a superior objective function while going away from the region with the inferior objective function in order to move the solution gradually towards optimality. The algorithm is carefully evaluated for its performance against two existing algorithms using multi-objective performance measures and statistical tools. The results show that our proposed solution method outperforms the others.
Ms. Maryam Akbari-Jafarabadi, Prof. Reza Tavakkoli-Moghaddam, Mr. Mehdi Mahmoodjanloo, Mr. Yaser Rahimi,
Volume 6, Issue 2 (9-2015)
In general, any system may be at risk in a case of losing the critical facilities by natural disasters or terrorist attacks. This paper focuses on identifying the critical facilities and planning to reduce the effect of this event. A three-level model is suggested in the form of a defender-attacker-defender. It is assumed that the facilities are hierarchical and capable of nesting. Also, the attacker budget for the interdiction and defender budget for fortification is limited. At the first level, a defender locates facilities in order to enhance the system capability with the lowest possible cost and full covering customer demand before any interdiction. The worst-case scenario losses are modeled in the second-level. At the third level, a defender is responsible for satisfying the demand of all customers while minimizing the total transportation and outsourcing costs. We use two different approaches to solve this model. In the first approach, the third level of the presented model is coded in Gams software, its second level is solved by an explicit enumeration method, and the first level is solved by tabu search (TS). In the second approach the first level is solved by the bat algorithm (BA). Finally, the conclusion is provided.
Mr. Mirmohammad Musavi, Dr. Reza Tavakkoli-Moghaddam, Ms. Farnaz Rayat,
Volume 8, Issue 1 (4-2017)
We present a bi-objective model for a green truck scheduling and routing problem at a cross-docking system. This model determines three key decisions at the cross dock: (1) defining a sequence and schedule of inbound trucks at the receiving door, (2) specifying a sequence and a schedule of outbound trucks at the shipping door, and (3) determining the routes of the outbound truck while serving customers. The first objective function is related to responsiveness of the network that minimizes time window violations and the second objective function minimizes total fuel consumption of trucks in order to consider the environmental factor of the network. Also, a learning effect is considered in loading and unloading process times. To solve the bi-objective model, an archived multi-objective simulated annealing (AMOSA) is used and modified. Finally, a number of test problems are solved and the efficiency of the proposed AMOSA is compared with the e-constraint method.