An inventory control system having multiple items in stock is developed in this paper to optimize total cost of inventory and space requirement. Inventory modeling for both the raw material storage and work in process (WIP) is designed considering independent demand rate of items and no volume discount. To make the model environmentally aware, the equivalent carbon emission cost is also incorporated as a cost function in the formulation. The purpose of this study is to minimize the cost of inventories and minimize the storage space needed. The inventory models are shown here as a multi-objective programming problem with a few nonlinear constraints which has been solved by proposing a meta-heuristic algorithm called multi-objective particle swarm optimization (MOPSO). A further meta-heuristic algorithm called multi-objective bat algorithm (MOBA) is used to determine the efficacy of the result obtained from MOPSO. Taguchi method is followed to tune necessary response variables and compare both algorithm's output. At the end, several test problems are generated to evaluate the performances of both algorithms in terms of six performance metrics and analyze them statistically and graphically.

In the present competitive world, facility location is an important aspect of the supply chain (sc) optimization. It involves selecting specific locations for facility construction and allocation of the distribution channel among different SC levels. In fact, it is a strategic issue which directly affects many operational/tactical decisions. Besides the accessibility, which results in customer satisfaction, the present paper optimizes the establishment costs of a number of distribution channels by considering their proximity to the stock market of the goods they distribute, and proposes mathematical models for two objective functions using the set covering problem. Then, two objective functions are proposed into one through the ε-constraint method and solved by the metaheuristic Genetic Algorithm (GA). To test the resulted model, a smaller scale problem is solved. Results from running the algorithm with different ε-values show that, on average, a 10% increase in ε, which increases the value of the second objective function - distance covered by customers will cause a 2% decrease in the value of the first objective function including the costs of establishing distribution centers). The repeatability and solution convergence of the two-objective model presented by the GA are other results obtained in this study.