| dc.description |
<div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 137.24px; top: 375.068px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.926405);"><span style="background-color: rgb(255, 239, 198);">This study explores an economic production quantity (EPQ) model designed with the assumptions of discrete delivery orders </span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 75.6px; top: 395.068px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.889779);"><span style="background-color: rgb(255, 239, 198);">and storage capacity c</span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 209.033px; top: 395.068px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.931868);"><span style="background-color: rgb(255, 239, 198);">onstraints for a multi</span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 335.083px; top: 395.068px; font-size: 15px; font-family: sans-serif;"><span style="background-color: rgb(255, 239, 198);">-</span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 340.083px; top: 395.068px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.917181);"><span style="background-color: rgb(255, 239, 198);">item production inventory system. The main purpose of this study is to determine the optimal </span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 75.6px; top: 415.068px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.871364);"><span style="background-color: rgb(255, 239, 198);">order quantity, the optimal number of deliveries and the optimal delivery quantity. First, the developed model as part of thi</span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 843.967px; top: 415.068px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.847022);"><span style="background-color: rgb(255, 239, 198);">s study </span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 892.967px; top: 415.068px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.903);"><span style="background-color: rgb(255, 239, 198);">is </span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 75.6px; top: 435.068px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.91875);"><span style="background-color: rgb(255, 239, 198);">analyzed using Genetic Algorithm (GA). Numerical analysis results </span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 485.483px; top: 435.068px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.842032);"><span style="background-color: rgb(255, 239, 198);">are</span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 507.283px; top: 435.068px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.904581);"><span style="background-color: rgb(255, 239, 198);">compared with those of previous studies and it was found that it is </span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 75.6px; top: 455.118px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.896492);"><span style="background-color: rgb(255, 239, 198);">possible to have better results with an increasing number of iteration. The same model </span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 593.117px; top: 455.118px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.929147);"><span style="background-color: rgb(255, 239, 198);">is</span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 606.517px; top: 455.118px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.894001);"><span style="background-color: rgb(255, 239, 198);">then analyzed using Particle</span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 777.767px; top: 455.118px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.930121);"><span style="background-color: rgb(255, 239, 198);">Swarm Optimization </span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 75.6px; top: 475.118px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.907021);"><span style="background-color: rgb(255, 239, 198);">(PSO) algorithm. A comparison of the optimization methods showed that PSO gives better results over the GA under the same num</span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 867.367px; top: 475.118px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.919942);"><span style="background-color: rgb(255, 239, 198);">ber of </span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 75.6px; top: 495.118px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.911748);"><span style="background-color: rgb(255, 239, 198);">iterations and using the same population. The effects of important model parameters such as number of it</span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 713.917px; top: 495.118px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.908341);"><span style="background-color: rgb(255, 239, 198);">erations, population, crossover, </span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 75.6px; top: 515.118px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.91777);"><span style="background-color: rgb(255, 239, 198);">mutation rate on the optimal solution </span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 301.633px; top: 515.118px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.842032);"><span style="background-color: rgb(255, 239, 198);">are</span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 323.683px; top: 515.118px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.898758);"><span style="background-color: rgb(255, 239, 198);">analyzed. The results showed that PSO performs better than the GA with respect to the total cost </span></div><div style="padding: 0px; margin: 0px; position: absolute; white-space: pre; cursor: text; transform-origin: 0% 0%; left: 75.6px; top: 535.118px; font-size: 15px; font-family: sans-serif; transform: scaleX(0.909223);"><span style="background-color: rgb(255, 239, 198);">and the total runtime as the solution of the problem in question.</span></div> |
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