Recent developments in urbanization and e-commerce have pushed businesses to deploy efficient systems to decrease their supply chain cost. Vendor Managed Inventory (VMI) is one of the most widely used strategies to effectively manage supply chains with multiple parties. VMI implementation asks for solving the Inventory Routing Problem (IRP). This study considers a multi-product multi-period inventory routing problem, including a supplier, set of customers, and a fleet of heterogeneous vehicles. Due to the complex nature of the IRP, we developed a Modified Adaptive Genetic Algorithm (MAGA) to solve a variety of instances efficiently. As a benchmark, we considered the results obtained by Cplex software and an efficient heuristic from the literature. Through extensive computational experiments on a set of randomly generated instances, and using different metrics, we show that our approach distinctly outperforms the other two methods.
In this work we compare several new computational approaches to an inventory routing problem, in which a single product is shipped from a warehouse to retailers via an uncapacitated vehicle. We survey exact algorithms for the Traveling Salesman Problem (TSP) and its relaxations in the literature for the routing component. For the inventory control component we survey classical mixed integer linear programming and shortest path formulations for inventory models. We present a numerical study comparing combinations of the two empirically in terms of cost and solution time.
The two-echelon inventory-routing problem (2E-IRP) addresses the coordination of inventory management and freight transportation throughout a two-echelon supply network. The latter consists of geographically widespread customers whose demand over a discrete planning horizon can be met from either their local inventory or intermediate facilities' inventory. Intermediate facilities are located in the city outskirt and are supplied from distant suppliers. The 2E-IRP aims to minimize transportation costs and inventory costs while meeting customers' demand. A route-based formulation is proposed and…
Given the rapid advances in unmanned aerial vehicles, or drones, and increasing need to monitor traffic at a city level, one of the current research gaps is how to systematically deploy drones over multiple periods. We propose a real-time data-driven approach: we formulate the first deterministic arc-inventory routing problem and derive its stochastic dynamic policy. The policy is expected to be of greatest value in scenarios where uncertainty is highest and costliest, such as city traffic monitoring during major events. The Bellman equation for an approximation of the proposed inventory routi…
Tecnologico de Monterrey（蒙特雷科技大学）；Universidade Federal da Bahia（巴伊亚联邦大学）
We consider an NP-hard selective and periodic inventory routing problem (SPIRP) in a waste vegetable oil collection environment. This SPIRP arises in the context of reverse logistics where a biodiesel company has daily requirements of oil to be used as raw material in its production process. These requirements can be fulfilled by using the available inventory, collecting waste vegetable oil or purchasing virgin oil. The problem consists in determining a period (cyclic) planning for the collection and purchasing of oil such that the total collection, inventory and purchasing costs are minimized…
Excessive greenhouse gas emissions from the transportation sector have led companies to move towards a sustainable supply chain network design. In this study we present a new bi-objective non-linear formulation where multiple inventory components are integrated into the location and routing decisions throughout the supply chain network. To efficiently solve the proposed model, we implement an exact method and four evolutionary algorithms for small and large-scale instances. Extensive computational results and sensitivity analysis are performed to validate the efficiency of the proposed approac…
Supply chain management is an integrated approach for planning and controlling materials, information, and finances as they move in a process which begins from suppliers and ends with customers in forward approach. As distribution network planning is strategically done, the related decisions should be optimized. This supply chain planning involves transportation, the location of facilities, and inventory control decisions. This study is a new approach for considering customers' differentiation in an integrated model to location-allocation and inventory control supply chain. The proposed model …
We consider a vehicle routing problem which seeks to minimize cost subject to service level constraints on several groups of deliveries. This problem captures some essential challenges faced by a logistics provider which operates transportation services for a limited number of partners and should respect contractual obligations on service levels. The problem also generalizes several important classes of vehicle routing problems with profits. To solve it, we propose a compact mathematical formulation, a branch-and-price algorithm, and a hybrid genetic algorithm with population management, which…
Home health care problems consist of scheduling visits to home patients by health professionals while following a series of requirements. This paper studies the Home Health Care Routing and Scheduling Problem, which comprises a multi-attribute vehicle routing problem with soft time windows. Additional route inter-dependency constraints apply for patients requesting multiple visits, either by simultaneous visits or visits with precedence. We apply a mathematical programming solver to obtain lower bounds for the problem. We also propose a biased random-key genetic algorithm, and we study the eff…
In today's global business market place, individual firms no longer compete as independent entities with unique brand names but as integral part of supply chain links. Key to success of any business is satisfying customer's demands on time which may result in cost reductions and increase in service level. In supply chain networks decisions are made with uncertainty about product's demands, costs, prices, lead times, quality in a competitive and collaborative environment. If poor decisions are made, they may lead to excess inventories that are costly or to insufficient inventory that cannot mee…
The electric vehicle routing problem (EVRP) has garnered great interest from researchers and industrialists in an attempt to move from fuel-based vehicles to healthier and more efficient electric vehicles (EVs). While it seems that the EVRP should not be much different from traditional vehicle routing problems (VRPs), challenges like limited cruising time, long charging times, and limited availability of charging facilities for electric vehicles makes all the difference. Previous works target logistics and delivery-related solutions wherein a homogeneous fleet of commercial EVs have to return …
KU Leuven（鲁汶大学）；Federal University of Ouro Preto（乌罗普雷托联邦大学）；Pontifícia Universidade Católica do Rio de Janeiro（里约热内卢天主教大学）
We investigate a structural decomposition for the capacitated vehicle routing problem (CVRP) based on vehicle-to-customer "assignment" and visits "sequencing" decision variables. We show that an heuristic search focused on assignment decisions with a systematic optimal choice of sequences (using Concorde TSP solver) during each move evaluation is promising but requires a prohibitive computational effort. We therefore introduce an intermediate search space, based on the dynamic programming procedure of Balas & Simonetti, which finds a good compromise between intensification and computational ef…
University of Nottingham（诺丁汉大学）；University of Nottingham Ningbo China（宁波诺丁汉大学）；The University of Melbourne（墨尔本大学）
Full truckload transportation (FTL) in the form of freight containers represents one of the most important transportation modes in international trade. Due to large volume and scale, in FTL, delivery time is often less critical but cost and service quality are crucial. Therefore, efficiently solving large scale multiple shift FTL problems is becoming more and more important and requires further research. In one of our earlier studies, a set covering model and a three-stage solution method were developed for a multi-shift FTL problem. This paper extends the previous work and presents a signific…
Spatial-temporal-demand clustering for solving large-scale vehicle routing problems with time windows
Logistics and Supply Chain Management, School of Management, Technical University of Munich（物流与供应链管理系，管理学院，慕尼黑技术大学）；Munich Data Science Institute (MDSI), Technical University of Munich（慕尼黑数据科学研究所（MDSI），慕尼黑技术大学）
