A fuzzy-based prediction approach for blood delivery using machine learning and genetic algorithm

Multiple diseases require a blood transfusion on daily basis. The process of a blood transfusion is successful when the type and amount of blood is available and when the blood is transported at the right time from the blood bank to the operating room. Blood distribution has a large portion of the cost in hospital logistics. The blood bank can serve various hospitals; however, amount of blood is limited due to donor shortage. The transportation must handle several requirements such as timely delivery, vibration avoidance, temperature maintenance, to keep the blood usable. In this paper, we discuss in first section the issues with blood delivery and constraint. The second section present routing and scheduling system based on artificial intelligence to deliver blood from the blood-banks to hospitals based on single blood bank and multiple blood banks with respect of the vehicle capacity used to deliver the blood and creating the shortest path. The third section consist on solution for predicting the blood needs for each hospital based on transfusion history using machine learning and fuzzy logic. The last section we compare the results of well-known solution with our solution in several cases such as shortage and sudden changes.

Energy efficient routing and task scheduling for autonomous transfer vehicles in intra logistics

Autonomous Transfer Vehicles (ATVs) are becoming increasingly prevalent in intra logistics. Industry 4.0 is bringing us closer to the efficient routing and scheduling of autonomous multi robot systems which perform transportation tasks. In this study, an energy efficient routing and scheduling system is proposed to minimize the total energy that the vehicles spend. Not only travelled distance but also the load of the vehicle is considered between two points. The routes of vehicles are obtained by using the proposed Hybrid Simulated Annealing Algorithm. An algorithm for the initial solution is also proposed for determining of the minimum number of vehicles for pickup and delivery requests. The performance of the algorithm is compared with the best solutions of the test problems in the literature. Besides, the proposed energy efficient routing and task scheduling model is compared with the classical distance model for routing and scheduling with backhauls. An analysis of trade-offs between energy and distance is proposed for intra logistics.

Long-Haul Vehicle Routing and Scheduling with Biomathematical Fatigue Constraints

Hours of service (HOS) regulations are among the conventional safety constraints that are compiled by long-haul truck drivers. These regulations have been considered in models and algorithms of vehicle routing problems to assign safe schedules to drivers. However, the HOS regulations neglect a few crucial fatigue risk factors and, at times, fail to generate fatigue-reducing schedules. In this study, a set of biomathematical fatigue constraints (BFCs) derived from biomathematical models are considered for a long-haul vehicle routing and scheduling problem. A BFC scheduling algorithm and a BFC-HOS scheduling algorithm have been developed and then embedded within a tabu search heuristic to solve the combined vehicle routing and scheduling problem. All the solution methods have been tested on modified Solomon instances and a real-life instance, and the computational results confirm the advantages of employing a sophisticated and fatigue-reducing scheduling procedure when planning long-haul transportation.

Route Optimization Tool (RoOT) for distribution of vaccines and health products

Ensuring the delivery and availability of health products, including temperature-sensitive vaccines, is vital to saving lives in low- and middle-income countries (LMICs). In many LMICs routes are hand drawn by logisticians and are adjusted based on vehicle availability and product quantities. Easy-to-use real-time supply chain tools are needed to create or adjust routes for available vehicles and road conditions. Having more efficient and optimized distribution is especially critical for COVID-19 vaccine distribution. Route Optimization Tool (RoOT) works best for planning routes for 50 health facilities or less, in two minutes. We develop RoOT using a variant of a Vehicle Routing and Scheduling Algorithm (VeRSA) that is coded in Python but reads and writes Excel files to make data input and using outputs easier. RoOT can be used for routine operations or in emergency situations, such as delivery of new COVID-19 vaccine. The tool has a user-centric design with easy dropdown menus and the ability to optimize on time, risk, or combination of both. RoOT is an open-source tool for optimal routing of health products. It provides optimized routes faster than most commercial software and is tailored to meet the needs of government stakeholders We trained supply chain logisticians in Mozambique on using RoOT, and their feedback validates that RoOT is a practical tool to improve planning and efficient distribution of health products, especially vaccines. We also illustrate how RoOT can be adapted for an emergency situation by using a test scenario of a cyclone. Currently, RoOT does not allow multi-day routes, and is designed for trips that can be completed within twenty-four hours. Areas for future development include multi-day routing and integration with mapping software to facilitate distance calculations and visualization of routes.

A Bi-Objective Scheduling Problem in a Home Care Business

In this work we study a routing and scheduling problem for a home care business. The problem is composed of two conflicting objectives, therefore we study it as a bi-objective one. We obtain the Pareto frontier for small size instances using the AUGMECON2 method and, for bigger cases, we developed an heuristic algorithm. We also obtained some preliminary results that show the algorithm has good behaviour.