Interactive Scheduling Decision Support System a Case Study for Fertilizer Production on Supply Chain

Abstract

This paper presents the architecture of an interactive scheduling decision support system (ISDSS) allowing users to find the optimal solution for fertilizer production on parallel heterogeneous processors. The proposed approach takes into account different production process constraints such as launch time, delivery date, preventive maintenance and the impact of scheduling on supply chain management. The ISDSS implemented is run by a relational database used to customize the structural data and the problem parameters. A user interface is available for ISDSS users to define the scheduling problem and design the solution based on tables and graphs for detecting possible issues. The ISDSS architecture was implemented on java using independent modules.

Decision support system in supply chain: A systematic literature review

“…DSS is present as a tool that can help solve problems that tend to be customized and unique. In the supply chain, until now the use of DSS is still very focused on the manufacturing sector, based on the results of this research, it was found that more than 50% of the articles focus on making DSS in the supply chain in the manufacturing sector (Azzamouri et al, 2019;Eydi & Fazil, 2019;Boonsothonsatit, 2017;Moynihan & Wan, 2015;Scott et al, 2015;Dong & Srinivasan, 2013;Greco, et al 2011). The manufacturing sector is yet at the center of the attention of researchers in the world compared with other sectors because the standard system that has been built so that factors related to the assumption of supply chain DSS can already be predicted.…”

“…This extensive use of the approaches shows the strength and versatile of this approach, however another limitation that often arises is the weakness in determining weight and the formulation of hierarchical structures that are still quite subjective and rigid, therefore when the complexity of the problem increases, the flexibility of adjustments for changing conditions in the field is hard to accomplish which takes time to adjust. Other approaches to DSS for supply chains such as artificial intelligence (Silva & Rupasinghe, 2017) and big data (Vera-Baquero et al, 2014), spatial approaches (Guerlain, et al 2019) and the webbased (Azzamouri, et al 2019) have still not been widely used. The trend still is on track in the opposite direction with the current trend, when the development of big data, data mining and web-based systems have been widely used in various fields, whereas studies in the DSS supply chain field are still very limited.…”

Interactive Scheduling Decision Support System for ore extraction at a phosphate mine

“…This led us to design a test to exclude scenarios leading to extract SQs unfit to meet global MQ demand. This rejection test involves a set of blending problems using the same composition constraints that are defined over several planning horizons (1, 3, 6, 9 and 12 months) and relying on standard LP blending formulation [2]. This exercise shows that many scenarios that seem interesting in the short term (next 3 months) actually fail to meet demand in the medium term (9 to 12 months).…”