ارايه‌ي مدل و الگوريتم ابتكاري سازنده براي مسيله‌ي مسيريابي موجودي چندمحصولي ـ چنددوره‌يي كمبوددار با پنجره‌ي زماني

A model and Constructive Heuristic Approach for Multi-product Multi-period Inventory Routing Problem with Backlogging and Time Window

امروزه شركت‌ها به ايجاد يكپارچگي منظمي در تمام فرايندهاي توليدي نيازمندند. مديريت زنجيره‌ي تامين‌، فرصت‌هايي براي يكپارچه سازي و مديريت درون شركتي و بين شركتي پيشنهاد مي كند. در اين مطالعه بر آن بخش از زنجيره‌ي تامين‌ كه توامان به حمل و نقل بين تامين‌ كننده و مشتري و مديريت موجودي مشتري، در قالب حل نوعي از مسيله ي مسيريابي موجودي مي‌پردازد، متمركز مي‌شويم. مدل پيشنهادي مسيله با در نظر گرفتن چندمحصولي بودن تقاضاي مشتري و با هدف كمينه‌كردن مجموع هزينه‌هاي حمل و نقل، نگه‌داري موجودي، كمبود و ديركرد موعد تقاضا، در فضاي مدل خطي تركيبي با عدد صحيح ارايه‌ي مي‌شود. با توجه به ماهيت پيچيده‌ي مسيله كه آن را در زمره‌ي مسايل NP-Hard قرار مي‌دهد، يك الگوريتم ابتكاري سازنده براي حل مدل پيشنهاد مي‌شود و نتايج عددي آن با نتايج دو روش حل دقيق و الگوريتم ژنتيك پيشنهادي مقايسه مي‌شود. نتايج عددي حاصله نشان‌گر كارايي الگوريتم پيشنهادي است.

Today, companies need to integrate all production processes from raw material to final consumers. Supply chain management suggests opportunities to achieve integration and management within the companies and between them. Since interests of the loops in the supply chain do not necessarily follow the same function, the Vendor-Managed Inventory (VMI) is an approach that seeks to make interaction and coordination between different loops in supply chain in the area of inventory and demand management. This paper considers a part of the supply chain that involves a transportation between supplier, customer, and inventory customer management simultaneously by VMI approach to deal with the solution of some kinds of inventory routing problems. The proposed mixed integer linear programming model, in terms of multiple product customer demands, aims to minimize the total costs of transportation, inventory storage, lack of demand, and tardy demands. Due to the complexity of the problem which puts it among NP-Hard problems, a constructive heuristic algorithm was proposed to solve the model. Two scenarios, each of which consists of 20 samples, were designed to evaluate the performance of the proposed algorithm. Different scenarios were created to evaluate the flexibility of the objective function in dealing with different conditions. Two sets of problems (of small-medium and large-sizes) were presented to evaluate the proposed algorithm. In small- and medium-sized problems, the results of the proposed algorithm compared with those of linear programming model are solved by Cplex solver in GAMS software. To evaluate the performance of the proposed algorithm in producing high quality solutions at small and medium sizes, two methods were proposed. At first, results of the proposed algorithm are compared with the upper and lower bounds produced by Cplex solver. Second, three performance parameters were defined and the solutions were evaluated by them. To evaluate the proposed algorithm in large-sized scales, a benchmark Genetic Algorithm was used. Numerical results show the performance of the proposed algorithm.