Large scale stochastic inventory routing problems with split delivery and service level constraints

A stochastic inventory routing problem (SIRP) is typically the combination of stochastic inventory control problems and NP-hard vehicle routing problems, which determines delivery volumes to the customers that the depot serves in each period, and vehicle routes to deliver the volumes. This paper aims to solve a large scale multi-period SIRP with split delivery (SIRPSD) where a customer’s delivery in each period can be split and satisfied by multiple vehicle routes if necessary. This paper considers SIRPSD under the multi-criteria of the total inventory and transportation costs, and the service levels of customers. The total inventory and transportation cost is considered as the objective of the problem to minimize, while the service levels of the warehouses and the customers are satisfied by some imposed constraints and can be adjusted according to practical requests. In order to tackle the SIRPSD with notorious computational complexity, we first propose an approximate model, which significantly reduces the number of decision variables compared to its corresponding exact model. We then develop a hybrid approach that combines the linearization of nonlinear constraints, the decomposition of the model into sub-models with Lagrangian relaxation, and a partial linearization approach for a sub model. A near optimal solution of the model found by the approach is used to construct a near optimal solution of the SIRPSD. Randomly generated instances of the problem with up to 200 customers and 5 periods and about 400 thousands decision variables where half of them are integer are examined by numerical experiments. Our approach can obtain high quality near optimal solutions within a reasonable amount of computation time on an ordinary PC

Tabu search heuristic for inventory routing problem with stochastic demand and time windows

This study proposes the hybridization of tabu search (TS) and variable neighbourhood descent (VND) for solving the Inventory Routing Problems with Stochastic Demand and Time Windows (IRPSDTW). Vendor Managed Inventory (VMI) is among the most used approaches for managing supply chains comprising multiple stakeholders, and implementing VMI require addressing the Inventory Routing Problem (IRP). Considering practical constraints related to demand uncertainty and time constraint, the proposed model combines multi-item replenishment schedules with unknown demand to arrange delivery paths, where the actual demand amount is only known upon arrival at a customer location with a time limit. The proposed method starts from the initial solution that considers the time windows and uses the TS method to solve the problem. As an extension, the VND is conducted to jump the solution from its local optimal. The results show that the proposed method can solve the IRPSDTW, especially for uniformly distributed customer locations

Hybrid genetic algorithm for inventory routing problem with carbon emission consideration

Inventory Routing Problem (IRP) has been continuously developed and improved due to pressure from global warming issue particularly related to greenhouse gases (GHGs) emission. The burning of fossil fuel for transportations such as cars, trucks, ships, trains, and planes primarily emits GHGs. Carbon dioxide (CO2) from burning of fossil fuel to power transportation and industrial process is the largest contributor to global GHGs emission. Therefore, the focus of this study is on solving a multi-period inventory routing problem (MIRP) involving carbon emission consideration based on carbon cap and offset policy. Hybrid genetic algorithm (HGA) based on allocation first and routing second is used to compute a solution for the MIRP in this study. The objective of this study is to solve the proposed MIRP model with HGA then validate the effectiveness of the proposed HGA on data of different sizes. Upon validation, the proposed MIRP model and HGA is applied on real data and parameter sensitivity analysis is performed on the MIRP model. The HGA is found to be able to solve small size and large size instances effectively by providing near optimal solution in relatively short CPU execution time. In addition, the increase in unit carbon price results in the increase of the supply chain’s total cost while the increase in carbon cap results in the decrease of supply chain’s total cost. The results from the analysis gave an indication that the unit carbon price and carbon cap need to be thoroughly designed so that it will not burden the participating companies of carbon emission regulation and environment

Integrating demand uncertainty in inventory routing for recyclable waste collection

Osteoblast cell adhesion to the extracellular matrix is established through two main pathways: one is mediated by the binding between integrin and a minimal adhesion sequence (RGD) on the extracellular protein, the other is based on the interactions between transmembrane proteoglycans and heparin-binding sequences found in many matrix proteins. The aim of this study is the evaluation in an in vivo endosseous implant model of the early osteogenic response of the peri-implant bone to a biomimetic titanium surface functionalized with the retro-inverso 2DHVP peptide, an analogue of Vitronectin heparin binding site. The experimental plan is based on a bilateral study design of Control and 2DHVP implants inserted respectively in the right and left femur distal metaphysis of adult male Wistar rats (n=16) weighing about 300 gr and evaluated after 15 days. Fluorochromic bone vital markers, were given at specific time frame, in order to monitor the dynamic of new bone deposition. The effect inducted by the peptidomimetic coating on the surrounding bone were qualitatively and quantitatively evaluated by means of static and dynamic histomorphometric analyses performed within three concentric and subsequent circular Regions of Interest (ROI) of equivalent thickness (220 μm), ROI1 adjacent to the interface, ROI2, the middle, and ROI3 the farthest. The data indicated that these functionalized implants stimulated a higher bone apposition rate (p<0,01) and larger and rapid osteoblast activation in terms of mineralising surface within ROI1 compared to the Control (p<0,01). These higher osteoblast recruitment and activation leads to a greater bone to implant contact reached for DHVP samples (p<0,5). This represents an initial stimulus of the osteogenic activity that might results in a faster and better osteointegration process

A ROBUST OPTIMIZATION ASSESSMENT OF INVENTORY ROUTING PROBLEMS WITH ROUTE DISRUPTIONS AND GREEN FACTORS

This work assesses robust optimization as a solution method for the combined issue of vehicle routing and inventory management, adding different characteristics. Thus, its behavior may resemble the industry’s current reality. This work also emphasizes the utilization of time windows, considers possible disruptions to the routes that connect the different nodes, and includes a Green Factor assessment. For these purposes, the minimax regret criteria is applied to a set of pre-established instances from the literature, adding the necessary information requirements to evaluate how the method behaves through different parameter combinations, thus changing the number of nodes, costs, and available alternatives and scenarios. The study compares the solutions achieved against the solutions from the classical model to assess the relation between the results from the exercise and the different parameter modifications implemented, achieving a 40% improvement in the total cost algorithm—proportional to the increase in the alternatives and scenarios assessed. The approach proposed allows us to create disruption scenarios for the distribution process, which are connected to the mathematical optimization problem that allows us to determine the best routing process given the uncertainty associated with the disruption events. Our results also allow us to analyze the trades off between the green factors when they are included in the objective function and the results without them

Coordinated delivery in urban retail

In the Coordinated Delivery Problem (CDP), we study the passive and proactive coordination strategies that coordinate the delivery among urban retail stores. We formulate the CDP as mixed integer programs and develop a matheuristic, the effectiveness of which is evaluated via newly generated instances. Our numerical study shows that, when the stores prefer placing orders based on their own inventory policies, the proactive coordination strategy is able to achieve similar logistics and services performances to Vendor Managed Inventory (VMI), while respecting the store order decisions as under Retailer Managed Inventory (RMI), and thus offers an excellent combination of VMI and RMI