Abstract
A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA.
Highlights
Introduction and Literature ReviewMost real-world problems in industries and commerce are studied as an optimization problem involving a single objective
Some numerical examples are given to illustrate the application of the proposed multiobjective particle swarm optimization (MOPSO) algorithm in real-world environments and to evaluate and compare its performances with the ones obtained by a multiobjective genetic algorithm (MOGA) method
Dev) of the objective values of the 30 generated problems shows that the MOPSO has the better performance in terms of the objective values in comparison with the MOGA
Summary
Most real-world problems in industries and commerce are studied as an optimization problem involving a single objective. Yaghin et al [29] first addressed an inventory-marketing system to determine the production lot size, marketing expenditure, and selling prices in which the model was formulated as a fuzzy nonlinear multiobjective program They converted the model to a classical single-objective one by a fuzzy goal programming method where an efficient solution procedure using PSO was provided to solve the resulting nonlinear problem. The goal is to find the optimum inventory levels of the items in each period such that the total inventory cost and the total required warehouse space are minimized simultaneously Since it is not easy for the managers to allocate the crisp values to the weights of the objectives in a decision making process, considering these weights as fuzzy numbers will be taken as an advantage. Due to some unforeseen matters, such as production limitation, the
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
