A Web Logistics Distribution Decision Support Systems for Green Inventory Routing Problem with Simultaneous Pickup and Delivery

• Main Authors: Gao, Shih-Jie, 高識傑
• Other Authors: Liu, Gia-Shie
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/qu846f

碩士 === 龍華科技大學 === 資訊管理系碩士班 === 106 === Most publishing logistics firms in Taiwan decide their distributed routes by considering the inventory related cost and transportation cost independently based on their past intuitive experiences. In fact, by concerning both of them, it is more likely to plan the delivered routes economically and efficiently. Due to the increasing environmental concern by the whole world, this total inventorty routing cost better accounts for Greenhouse gas emission cost regarding global warming. Most literatures of Inventory-Routing problems find the solutions by simulation, rather than solve the real case problem of logistics firms. Furthermore, few of them consider pickup and delivery problems at the same time. Due to environmental awareness, carbon emission cost is also incorporated into the model by considering the interrelationship between the transportation cost and Greenhouse gas emission level. Therefore, this research develops a decision support system to the Green Inventory Routing Problem with Simultaneous Pickup and Delivery (GIRPSPD) and to show the planned routes on GoogleMap. The mathematical model for GIRPSPD is first constructed, then Savings method is applied to obtain the initial feasible solution, finally implement Target insert heuristic method and Target exchange heuristic method to find the optimal solution. The numerical examples will be illustrated by applying this publishing logistics firm’s actual operating data to acquire the optimal delivery routes, and the related economic order quantities, the optimal reorder points and customer service levels of the retail stores located in those proposed routes. Furthermore, the performance of two proposed Target heuristic methods will be compared with Savings method and current routing planning implemented by this specific logistics company. Finally, sensitivity analyses are also conducted based on the parameters including truck loading capacity, inventory carrying cost percentages, unit shortage costs, unit ordering costs, unit ordering costs, and unit transport costs to simulate the optimal distribution system design regarding the total inventoy routing cost and GHG emission level. Consequently, the outcomes of this proposed decision support system to GIRPSPD not only have important theoretical contribution, but also provide pratical application applications for the proposed logistics publishing firm’s distribution system design.