Optimization of the Material Distribution Path in the Manufacturing Workshop for Recovery of Material Handling Equipment

doi:10.3901/JME.2025.14.397

Abstract

Abstract: In the production process of manufacturing workshops, the recovery of material handling equipment is a crucial aspect of workshop logistics management, often overlooked in practical production and related research. An optimization method for workshop material delivery routing is proposed, comprehensively considering both material delivery demands and the recovery tasks of material handling equipment, with a focus on the material distribution process involving towed automated guided vehicles(AGV) combined with material handling equipmen. Firstly, considering the capacity constraints of towed AGVs for material handling equipment, a material delivery routing planning model is established aiming to minimize AGV assignment costs, path transportation costs, and time penalty costs in the workshop material delivery process. A heuristic two-stage distributed optimization algorithm is designed to solve this model. In the first stage, for static material delivery demands calculated based on the production plan, an improved non-dominated sorting genetic algorithm II is employed to generate a sequence of multi-objective material demand delivery tasks. In the second stage, for material handling equipment recovery tasks dynamically arising during the production process, a quadrant optimization strategy is adopted to insert these tasks into the existing delivery sequence, optimizing overall costs and efficiency. Finally, a digital workshop of an armature manufacturing enterprise is used as a case study to verify the effectiveness and feasibility of the proposed optimization method, providing reference and support for logistics management in manufacturing workshops.

Key words: material distribution in workshop, recovery of material handling equipment, two-stage distributed optimization algorithm, non-dominated sorting genetic algorithm-II, quadrant optimization

CLC Number:

TH165

GE Maogen, WAN Li, LING Lin, LIU Mingzhou, ZHANG Xi, HU Jing. Optimization of the Material Distribution Path in the Manufacturing Workshop for Recovery of Material Handling Equipment[J]. Journal of Mechanical Engineering, 2025, 61(14): 397-409.

References

[1] GABOR N，SALHI S. Heuristic algorithms for single and multiple depot vehicle routing problems with pickups and deliveries[J]. European Journal of Operational Research，2005，162(1):126-141.
[2] SZE S N，SEK S，SZE J F，et al. Vehicle routing problem with simultaneous pickup and delivery[J]. International Journal on Advanced Science Engineering and Information Technology，2020，10(4):1360-1366.
[3] 周蓉，沈维蕾. 装卸一体化车辆路径问题的自适应并行遗传算法[J]. 中国机械工程，2018，29(22):2740-2749. ZHOU Rong，SHEN Weilei. Adaptive parallel genetic algorithm for the loading and unloading integrated vehicle path problem[J]. China Mechanical Engineering，2018，29(22):2740-2749.
[4] 张守京，张仪. 考虑余-废料资源回收的车间物料配送路径规划研究[J]. 工业工程，2020，23(2):83-90. ZHAGN Shoujing，ZHANG Yi. Study on workshop material distribution path planning considering surplus-waste resource recycling[J]. Industrial Engineering，2020，23(2):83-90.
[5] 童傅娇，徐进，张守京. 考虑工位优先级的智能车间双向物料配送路径规划[J]. 机电工程，2021(11):1465-1471. TONG Fujiao，XÜ Jin，ZHANG Shoujing. Intelligent workshop two-way material distribution path planning considering station priority [J]. Mechanical and Electrical Engineering，2021(11):1465-1471.
[6] MENDOZA J E，CASTANIER B，GUÉRET C，et al. A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands[J]. Computers & Operations Research，2010，37(11):1886-1898.
[7] 牟健慧，段培永，高亮，等. 基于混合遗传算法求解分布式流水车间逆调度问题[J]. 机械工程学报2022，58(6):295-308. MOU Jianhui，DUAN Peiyong，GAO Liang，et al. Solving the inverse scheduling problem of distributed flow workshop based on mixed genetic algorithm[J]. Journal of Mechanical Engineering，2022，58(6):295-308.
[8] ZOU W Q，PAN Q K，MENG T，et al. An effective discrete artificial bee colony algorithm for multi-AGVs dispatching problem in a matrix manufacturing workshop[J]. Expert Systems with Applications，2020，161:113675.
[9] 刘旭，楼佩煌，钱晓明，等. 基于改进遗传算法的物料配送多AGV调度优化[J]. 机械设计与制造工程，2015，44(3):6. LIU Xu，LOU Peihuang，QIAN Xiaoming，et al. Multi-AGV scheduling optimization for material distribution based on an improved genetic algorithm[J]. Mechanical Design and Manufacturing Engineering，2015，44(3):6.
[10] WANG Y J，LIU X Q，LENG J Y，et al. Study on scheduling and path planning problems of multi-AGVs based on a heuristic algorithm in intelligent manufacturing workshop[J]. Advances in Production Engineering & Management，2022，17(4):505-513.
[11] 杨雷博，周俊. 改进蚁群算法下的车间物料配送路径规划研究[J]. 制造业自动化，2022，44(11):128-131. YANG Leibo，ZHOU Jun. Research on workshop material distribution path planning under improved ant colony algorithm[J]. Automation in Manufacturing Industry，2022，44(11):128-131.
[12] MIRANDA D M，CONCEIÇÃO S V. The vehicle routing problem with hard time windows and stochastic travel and service time[J]. Expert Systems with Applications，2016，64:104-116.
[13] ZHANG Y，ZHANG G，DU W，et al. An optimization method for shopfloor material handling based on real-time and multi-source manufacturing data[J]. International Journal of Production Economics，2015，165:282-292.
[14] ALNAHHAL M，NOCHE B. Dynamic material flow control in mixed model assembly lines[J]. Computers & Industrial Engineering，2015，85:110-119.
[15] 葛妍娇，郭宇，黄少华，等. 基于智能感知网的物料配送动态优化方法[J]. 计算机工程与应用，2019，55(22): 212-218，230. GE Yanjiao，GUO Yu，HUANG Shaohua，et al. Dynamic optimization method of material distribution based on intelligent perception network[J]. Computer Engineering and Application，2019，55(22):212-218，230.
[16] 凌琳，刘明周，葛茂根，等. 计及漂移瓶颈的时变物料配送路径优化[J]. 机械工程学报，2015，51(23):133-143. LING Lin，LIU Mingzhou，GE Maogen，et al. Optimize the time-varying material distribution path of the drift bottleneck[J]. Journal of Mechanical Engineering，2015，51(23):133-143.
[17] 孙军艳，李晶晶，张媛媛. 数字化车间物料动态配送规划研究[J]. 包装工程，2023，44(1):101-110. SUN Junyan，LI Jingjing，ZHANG Yuanyuan. Research on dynamic material distribution planning in digital workshop[J]. Packaging Engineering，2023，44(1):101-110.
[18] HUANG S W Y. An internet-of-things-based production logistics optimisation method for discrete manufacturing[J]. International Journal of Computer Integrated Manufacturing，2019，32(1):13-16.
[19] LIU L，QU T，THÜRER M，et al. New knowledge-guided multi-objective optimisation for the multi-AGV dispatching problem in dynamic production environments[J]. International Journal of Production Research，2023，61(17):6030-6051.
[20] 刘雪梅，王笑，柳跃雷. 模糊时间窗约束下的装配线物料配送方案优化[J]. 中国机械工程，2020，31(17):2095-2103. LIU Xuemei，WANG Xiao，LIU Yuelei. Optimization of material distribution scheme for assembly line under fuzzy time window constraints[J]. China Mechanical Engineering，2020，31(17):2095-2103
[21] ZHANG Min，WANG Liang. Dynamic scheduling for flexible job shop with insufficient transportation resources via graph neural network and deep reinforcement learning[J]. Computers and Industrial Engineering，2023，186:109718.
[22] NISHIDA K，NISH T. Dynamic optimization of conflict-free routing of automated guided vehicles for just-in-time delivery[J]. IEEE Transactions on Automation Science and Engineering，2022，20(3):2099-2144.
[23] 萧筝，程书培，郑东伟，等. 数字孪生驱动的车间自动导引车路径规划[J]. 计算机集成制造系统，2023，29(6):1905-1915. XIAO Zhang，CHENG Shupei，ZHENG Dongwei，et al. AGV path planning method for workshop driven by digital twin[J]. Computer Integrated Manufacturing System，2023，29(6):1905-1915.