Decomposition techniques with mixed integer programming and heuristics for home healthcare planning

We tackle home healthcare planning scenarios in the UK using decomposition methods that incorporate mixed integer programming solvers and heuristics. Home healthcare planning is a difficult problem that integrates aspects from scheduling and routing. Solving real-world size instances of these problems still presents a significant challenge to modern exact optimization solvers. Nevertheless, we propose decomposition techniques to harness the power of such solvers while still offering a practical approach to produce high-quality solutions to real-world problem instances. We first decompose the problem into several smaller sub-problems. Next, mixed integer programming and/or heuristics are used to tackle the sub-problems. Finally, the sub-problem solutions are combined into a single valid solution for the whole problem. The different decomposition methods differ in the way in which subproblems are generated and the way in which conflicting assignments are tackled (i.e. avoided or repaired). We present the results obtained by the proposed decomposition methods and compare them to solutions obtained with other methods. In addition, we conduct a study that reveals how the different steps in the proposed method contribute to those results. The main contribution of this paper is a better understanding of effective ways to combine mixed integer programming within effective decomposition methods to solve real-world instances of home healthcare planning problems in practical computation time

Impact of Socioeconomic Environment on Home Social Care Service Demand and Dependant Users

An aging population and rising life expectancy lead to an increased demand for social services to care for dependent users, among other factors. In Barcelona, home social care (HSC) services are a key agent in meeting this demand. However, demand is not evenly distributed among neighborhoods, and we hypothesized that this can be explained by the user's social environment. In this work, we describe the user's environment at a macroscopic level by the socioeconomic features of the neighborhood. This research aimed to gain a deeper understanding of the dependent user's socioeconomic environment and service needs. We applied descriptive analytics techniques to explore possible patterns linking HSC demand and other features. These methods include principal components analysis (PCA) and hierarchical clustering. The main analysis was made from the obtained boxplots, after these techniques were applied. We found that economic and disability factors, through users' mean net rent and degree of disability features, are related to the demand for home social care services. This relation is even clearer for the home-based social care services. These findings can be useful to distribute the services among areas by considering more features than the volume of users/population. Moreover, it can become helpful in future steps to develop a management tool to optimize HSC scheduling and staff assignment to improve the cost and quality of service. For future research, we believe that additional and more precise characteristics could provide deeper insights into HSC service demand

Resource constrained routing and scheduling: Review and research prospects

In the service industry, it is crucial to efficiently allocate scarce resources to perform tasks and meet particular service requirements. What considerably complicates matters is when these resources, for example skilled technicians, nurses, and home carers have to visit different customer locations. This paper provides a comprehensive survey on resource constrained routing and scheduling that unveils the problem characteristics with respect to resource qualifications, service requirements and problem objectives. It also identifies the most effective exact and heuristic algorithms for this class of problems. The paper closes with several research prospects

Homecare staff scheduling with three-step algorithm

This paper introduces a three-step algorithm, an efficient framework for solving a homecare staff scheduling problem (HSSP) service schedule, a multi-objective problem requiring a combination of the VRP and the staff scheduling problem. The proposed scheduling technique takes account of the design of optimal daily service routes and the dispatch of caregivers to visit patients under time and capacity constraints. The framework consists of three major stages: Step 1) Route scheduling creates effective routes for homecare caregivers to service patients at different task locations with the shortest path. Step 2) Resource selection seeks to match qualified staff to each route with the minimum cost and preferences under possible time, qualification requirement constraints, and modes of transportation. Step 3) Local improvement enhances the output solution generated by the resource selection by swapping tasks based on the cost function. Our empirical study reveals that the proposed scheduling technique can explore the improved service plan for an adapted case study with the minimum service cost and highest efficiency for arranging service tasks compared to the manual procedure

Crew Scheduling for Netherlands Railways: "destination: customer"

: In this paper we describe the use of a set covering model with additional constraints for scheduling train drivers and conductors for the Dutch railway operator NS Reizigers. The schedules were generated according to new rules originating from the project "Destination: Customer" ("Bestemming: Klant" in Dutch). This project is carried out by NS Reizigers in order to increase the quality and the punctuality of its train services. With respect to the scheduling of drivers and conductors, this project involves the generation of efficient and acceptable duties with a high robustness against the transfer of delays of trains. A key issue for the acceptability of the duties is the included amount of variation per duty. The applied set covering model is solved by dynamic column generation techniques, Lagrangean relaxation and powerful heuristics. The model and the solution techniques are part of the TURNI system, which is currently used by NS Reizigers for carrying out several analyses concerning the required capacities of the depots. The latter are strongly influenced by the new rules.crew scheduling;dynamic column generation;lagrange relaxation;railways;set covering model

Evaluating Network Analysis and Agent Based Modeling for Investigating the Stability of Commercial Air Carrier Schedules

For a number of years, the United States Federal Government has been formulating the Next Generation Air Transportation System plans for National Airspace System improvement. These improvements attempt to address air transportation holistically, but often address individual improvements in one arena such as ground or in-flight equipment. In fact, air transportation system designers have had only limited success using traditional Operations Research and parametric modeling approaches in their analyses of innovative operations. They need a systemic methodology for modeling of safety-critical infrastructure that is comprehensive, objective, and sufficiently concrete, yet simple enough to be deployed with reasonable investment. The methodology must also be amenable to quantitative analysis so issues of system safety and stability can be rigorously addressed

Vehicle sharing and workforce scheduling to perform service tasks at customer sites

Most of the research done in the Vehicle Routing Problem (VRP) assumes that each driver is assigned to one and only one vehicle. However, in recent years, research in the VRP has increased its scope to further accommodate more restrictions and real-life features. In this line, vehicle sharing has grown in importance inside large companies with the aim of reducing vehicle emissions. The aim of this thesis is to study different situations where sharing vehicles brings an improvement. Our main study focuses on developing a framework that is capable of assigning different workers to a common vehicle, allowing them to share their journey. We introduce a mathematical programming model that combines the vehicle routing and the scheduling problem with time constraints that allows workers to share vehicles to perform their activities. To deal with bigger instances of the problem an algorithm capable of solving large scenarios needs to be implemented. A multi-phase algorithm is introduced, Phase 1 allows us to solve the non-sharing scheduling/routing problem whose aim is to find the best schedule for workers. Phase 2 will merge the allocated workers into common vehicles when possible, while Phase 3 is the improvement procedure of the algorithm. The algorithm is tested in three different settings; using workers as drivers, hiring dedicated drivers, and allowing workers to walk between jobs when possible. Results show that sharing vehicles is practicable under specific conditions, and it is able to reduce both the number of vehicles and the total distance, without affecting the performance of workers schedule