Industries tend to manufacture higher quality products with a low cost due to the competitive environmental loop and dynamic customers’ demand. The integration of intelligent manufacturing planning to agricultural operations and products will permit a spike in efficiency of yield, especially in the regions of agriculturally based economies. Though, to gain a vivid exploitation, the old-fashioned agriculture products manufacturing planning and scheduling techniques needs to be revised, especially the methodology in job shop planning must be augmented with efficient operational sequential scheduling. Cellular Manufacturing Systems (CMS) inclined to possess a higher complexity than the traditional manufacturing systems. Three major problems are coping in CMS domain: cell formation, product family selection, and product scheduling. This work deals with the problem of product scheduling in the CMS environment. A mixed integer linear programming mathematical model is introduced for the conflicting performance measures i.e. minimization of work in process (WIP) and maximization of average machine cell utilization. Since the current problem is considered as NP-hard problem, so a modified particle swarm optimization (MPSO) algorithm is proposed to find the optimum scheduling under the given constraint model of conflicting objectives. In the proposed MPSO, basic PSO is integrated with NEH heuristic to achieve better optimal sequence in less computation time. The obtained results are compared with other hybrid PSO algorithms with seed solutions from Gupta and Palmer heuristics. Furthermore, results are also compared against meta-heuristics such as genetic algorithm (GA), standard PSO, and artificial bee colony (ABC) algorithm. It shows that the proposed MPSO algorithm performs better than the existing compared algorithms. A real time case scenario of agriculture-based manufacturing industry is solved to validate the proposed planning algorithm.

由于竞争环境循环和动态客户需求，行业倾向于以低成本制造高质量的产品。将智能制造计划整合到农业运营和产品中，将使产量效率飙升，尤其是在以农业为基础的经济体地区。但是，为了获得生动的利用，需要修改老式的农产品生产计划和调度技术，特别是必须通过有效的操作顺序调度来扩充车间计划中的方法。蜂窝制造系统（CMS）倾向于拥有比传统制造系统更高的复杂性。CMS域中的三个主要问题是应对：细胞形成，产品族选择和产品计划。这项工作解决了CMS环境中的产品计划问题。引入了混合整数线性规划数学模型，用于相互矛盾的性能度量，即，最小化在制品（WIP）和最大平均机房利用率。由于当前问题被认为是NP难题，因此提出了一种改进的粒子群优化（MPSO）算法，以在给定的目标冲突约束模型下找到最优调度。在所提出的MPSO中，基本PSO与NEH启发式技术相集成，从而以更少的计算时间实现了更好的最佳序列。将获得的结果与其他混合PSO算法（具有Gupta和Palmer heuristics的种子解决方案）进行比较。此外，还将结果与元启发式算法（例如遗传算法（GA），标准PSO和人工蜂群（ABC）算法。结果表明，提出的MPSO算法比现有的比较算​​法具有更好的性能。解决了一个基于农业的制造业的实时案例，以验证所提出的计划算法。