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Soleimani and Kannan (2015) developed a deterministic multi-echelon, multi-product, multi-period model for a closed-loop SC network and presented a new hybrid particle swarm optimization and genetic algorithm to solve various kinds of problems.
On the other hand, this research considers improving closed-loop supply chain network optimization processes through dealing with mathematical programming tools; developing a deterministic multi-product, multi-echelon, multi-period model; and finally presenting an appropriate methodology to solve various sizes of instances.
This multi-period model determines the following decisions at each period: 1.
A multi-period model framwork is also established on a more realistic multi-period situation.
We perform additional experiments to quantify the benefit of constructing a multi-period model rather than a static one.
The proposed model is a three-echelon, multi-commodity, and multi-period model for the tactical and strategic decision making.
We then compare the outputs of the multi-period model with those of its single-period counterpart.
However, many tax issues such as depreciation schedules or tax loss carryforwards can only be analyzed by the use of a multi-period model.
Neiro et al. (2006) presented a stochastic multi-period model with two Lagrangian decomposition strategies to represent a petroleum refinery under uncertainty.
Numerical results are presented that compares the solution of the multi-period model with that of a conventional annual-average approach.
A multi-objective, multi-period model for optimizing the design and operating strategy of district energy systems is proposed by authors [1].
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