This study is concerned about how the quality of perishable products can be IMPROVED by shortening the time interval between production and distribution. Since special types of food, such as dairy products decay fast, the integration of production and distribution scheduling (IPDS) is investigated. This article deals with a variation of IPDS that contains a short shelf life product; hence, there is no inventory of the product in the process. Once a specific amount of products are produced, they must be transported with the least transportation time directly to various customer positions within its limited lifespan to minimize delivery and tardy costs required to complete producing and distributing the product to satisfy the demand of customers to the limited deadline. After developing a mixed-integer nonlinear programming model of the problem, because it is NP-hard, an IMPROVED PARTICLE SWARM OPTIMIZATION ((IPSO)) is proposed. (IPSO) performance is compared with a commercial OPTIMIZATION software for small-sized and large-sized problems. The model has developed as an Integer- nonlinear model and it solved by using an IMPROVED PARTICLE SWARM OPTIMIZATION which is IMPROVED by 1-opt and 2-opt operator and is coded in Matlab program. The model has been investigated through the real data gathered from Pegah company and the outcomes has been compared to the previous order system. Computational experiments show the e ffi ciency and effectiveness of the proposed (IPSO) in terms of both the quality of the solution and the time of achieving the best solution.

Economic dispatch at minimum production cost is one of the most important subjects in the power system operation, which is a complicated nonlinear constrained OPTIMIZATION problem. To solve the dynamic economic dispatch problem, it is assumed that a thermal unit commitment has been previously determined. Since dynamic economic load dispatch was introduced, several methods have been used to solve this problem. However, all of those methods may not be able to provide an optimal solution and usually getting stuck at a local optimal.In this paper, an IMPROVED PARTICLE SWARM Optimizer ((IPSO)) has been proposed to solve dynamic economic load dispatch problem. This algorithm is applied to a complex dynamic economic dispatch problem for 6-unit power systems with a 24-h load demand at each 1-h time intervals. Comparing (IPSO) results with other methods' results that reported in literature shows the ability of (IPSO) to reach better solutions.

The purpose of load frequency control is to reduce transient oscillation frequencies than its nominal value and achieve zero steady-state error for it. A common technique used in real applications is to use the proportional integral controller (PI). But this controller has a longer settling time and a lot of Extra mutation in output response of system so it required that the parameters be adjusted as appropriate. In this paper, we aim to design a system based on PI controllers using IMPROVED PARTICLE SWARM OPTIMIZATION algorithm for load frequency control. Multi-population approach and local search to improve the OPTIMIZATION algorithms is used and displayed. That this approach will lead to accelerating the achievement of results, preventing entrapment in a local minimum, and get better system output compared with similar methods.