Pub Date : 2018-12-01DOI: 10.1109/POMS.2018.8629450
R. Patidar, B. Venkatesh, Saurabh Pratap, Yash Daultani
The agri-food supply chain network design plays a significant role in the economy of the country. The existing traditional agri-food supply chain (AFSC) network in India is a vast supply chain which has been operating haphazardly in an unstructured manner. The system is basically controlled by various intermediaries; this is leading unsustainable and inefficient supply chain in terms of time and money. The most affecting factor in the agri-food supply chain goes in credit of the unorganized manner of transportation from the farmers to the nearest food hub (market) wherein, they sell their products. This process has been a major bottleneck in terms of profitability to the farmers wherein the transportation costs are the major contributor to the overall costs. In traditional Indian AFSC, geographically dispersed individual farmers bring their product into the market for selling. In this phenomenon, they have to pay higher transportation cost. Therefore, we propose the vehicle routing for the collection of products from farmers to the hub. A single period vehicle routing model is developed to form an optimum travel route incurring minimum costs and allowing a pool of products (from the farmers) supply to reach the destination hub. Genetic algorithm (GA) and particle swarm optimization (PSO) is used to solve the proposed mathematical model and validated on a practical case scenario of the central part of India.
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Pub Date : 2018-12-01DOI: 10.1109/POMS.2018.8629447
Abdur Rahman, S. Sarker, Md. Tawhidul Islam
Furniture manufacturing is getting more and more competitive due to shorter product life cycles and ever-increasing customer demand. As a result, furniture manufacturers often need to re-engineer their production lines to satiate the preferences of different consumers. One of the key challenges here is the expense to re-engineer the production lines and to analyze the development of a new product, or a customization on an old product. In today’s progressive manufacturing system, simulation of a production line can be invaluable in making re-engineering decisions. With the help of simulation, it is possible to accurately model engineering processes before making drastic changes. In this paper, a simulation model of the cutting line of a furniture manufacturing industry is developed and presented. The resultant model facilitated in identifying two configurations of machine sets. The first one maximized the machine utilization and the second one minimized the waiting time of parts in the queue. The variations of these performance parameters for different batch sizes are also illustrated.
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Pub Date : 2018-12-01DOI: 10.1109/POMS.2018.8629467
Sundaravalli Narayanaswami
Planning and scheduling transportation operations is considered computationally hard and time demanding. We consider a complex network of interconnected links across a set of locations and a set of vehicle movements on these links to fulfil some traffic demands. In such a set-up, basically two types of computational solutions are sought: one is a decision making, if a link (and/or a vehicle) is to be included in a route and second, is computation of a specific time duration during which a link and a vehicle are allocated to fulfil a traffic demand. Such planning efforts are undertaken at a strategic level. At the operational level, the planned schedules are executed. For any unforeseen reasons, if a planned schedule gets disrupted, while en-route, the entire planning efforts turn futile. The situation turns an emergency, as the available time to restore the original schedule is very small and it also knocks down other transport schedules that follow. Using an available approach to reschedule disrupted transportation services, we developed a learning architecture that is capable of learning and creating a knowledge base from the solved instances of disruption resolution. When a new disruption happens, the learner deduces the applicability of any solved instances in the knowledge base to quickly resolve the disruption. If not, the disruption is resolved completely and the resolution is added to the existing knowledge base. The combined abilities of the agents based architecture include monitoring, identification, deduction, decision making, and resolution execution, by learning, storing solution seeds and facilitating quick resolutions. The architecture and the application were developed using JADE Giovanni et al. (2007) toolkit on Ubuntu OS; we verified the deployability of the system on a Windows machine. The agent architecture and empirical results are presented in this paper. Our proposed architecture is faster than traditional methods and novel as an evidence of agent capabilities in solving real-life complex problems and safety-critical domains.
计划和调度运输操作被认为是计算困难和时间要求。我们考虑一个复杂的网络，这个网络由一组地点的相互连接的链路和这些链路上的一组车辆运动来满足一些交通需求。在这种设置中，基本上需要寻求两种类型的计算解决方案:一种是决策制定，如果一条链路(和/或一辆车)要包括在一条路线中;第二种是计算一个特定的时间持续时间，在此期间，一条链路和一辆车被分配以满足交通需求。这种规划工作是在战略一级进行的。在操作级别，执行计划的进度表。由于任何不可预见的原因，如果计划的时间表在途中被打乱，整个计划的努力就会白费。由于恢复原始计划的可用时间非常少，并且还会影响随后的其他运输计划，因此情况变得紧急。使用一种可用的方法来重新安排中断的运输服务，我们开发了一个学习体系结构，它能够从解决的中断解决实例中学习和创建知识库。当一个新的中断发生时，学习者推断知识库中任何解决实例的适用性，以快速解决中断。如果没有，中断将被完全解决，并将该解决方案添加到现有知识库中。基于代理的体系结构的综合能力包括通过学习、存储解决方案种子和促进快速解决方案来监视、识别、推理、决策和解决方案执行。该架构和应用程序是使用JADE Giovanni et al.(2007)工具包在Ubuntu OS上开发的;我们验证了系统在Windows机器上的可部署性。本文给出了智能体的体系结构和实证结果。我们提出的体系结构比传统方法更快，并且作为解决现实生活中复杂问题和安全关键领域的代理能力的证据是新颖的。
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