Integrated inventory management coordinates all party's replenishment policies to provide optimal benefits. Many models have been developed, but none of them have considered capital and warehouse constraints comprehensively. It may cause the model which cannot be applied, since it has exceeded the capacity. This study developed an integrated inventory model that consisted of one vendor, multi-buyer, and one type of item. The main objective was to minimize the joint total expected cost by considering warehouse, capital, and service level constraint. The optimal formula was constructed by using the Lagrange multipliers method.  The results showed that with an increment in holding cost, the vendor tends to reduce lot size to minimize joint total expected cost. It is vice versa to the increment in set up cost. An increment in buyer service level can increase lot size and reduce order frequency. The buyer capacity is essential to determine its capability to apply the optimal replenishment policy.

Determining a transport hub is a strategic decision to build a good distribution flow. In this paper, We suggested a model for choosing hub locations as sources for companies. In previous studies, The determination of hub locations with a vehicle routing problem is not integrated. Therefore, This study built a model to assess the position of the hubs by considering the budget. The business should have a decision on vehicle routing with hubs to reduce total transport costs. In addition, the method of distribution of goods for hubs and non-hubs with third-party logistics was determined by the use of a vehicle routing problem. The optimal weight obtained through the analysis of sensitivity. In the sensitivity analysis, This study found that the best choice in this study was to use a weight of 0.9–1.0. This provides the lowest total cost of transport.

One-door container type of vehicle is the main tool for urban logistics in Indonesia which may take the form of truck, car, or motorcycle container. The operations would be more effective when it is performed through pickup-delivery or forward-reverse at a time. However, there is difficulty to optimize the operation of routing and container loading processes in such a system. This article is proposing an improvement for algorithm for sequential routing- loading process which had been tested in the small datasets but not yet tested in the case of big data set and vehicle routing problem with time windows. The improvement algorithm is tested in big data set with the input of the vehicle routing problem with time windows (VRP-TW) using the solution optimization of the Simulated Annealing process with restart point procedure (SA-R) for the routing optimization and Genetic Algorithm (GA) to optimize the container loading algorithm. The large data sets are hypothetical generated data for 800-2500 single-sized products, 4 types of container capacity, and 100-400 consumer spots. As result, the performance of the proposed algorithm in terms of cost is influenced by the number of spots to be visited by the vehicle and the vehicle capacity. Limitations and further analysis are also described in this article.

The Location Routing Problem with Roaming Delivery Locations (LRPRDL) is a model that represents company activities in delivering products to final customers. Direct delivery to final customers has increased significantly over the growth of e-commerce in the world. E-commerce or business-to-customer companies are urged to increase their last-mile distribution efficiency to survive in the global competition. For that purpose, the LRPRDL  model was proposed to increase the efficiency of the company’s last-mile distribution. The model aims to minimize the sum of open depots and transportation costs by determining the number and location of depots and the shipping routes. The LRPRDL was implemented in an instance with four depot candidates, 15 customers, and six vehicles. The instance was solved to the optimality by using a public solver Gurobi. Furthermore, this research conducted a sensitivity analysis on the open depots and fuel costs, customer demand, and radius. The study indicated that customer’s demand and radius have a significant impact on the purchase decision.