Metaheuristics for solving a multimodal home-healthcare scheduling problem

Abstract We present a general framework for solving a real-world multimodal home-healthcare scheduling (MHS) problem from a major Austrian home-healthcare provider. The goal of MHS is to assign home-care staff to customers and determine efficient multimodal tours while considering staff and customer satisfaction. Our approach is designed to be as problem-independent as possible, such that the resulting methods can be easily adapted to MHS setups of other home-healthcare providers. We chose a two-stage approach: in the first stage, we generate initial solutions either via constraint programming techniques or by a random procedure. During the second stage, the initial solutions are (iteratively) improved by applying one of four metaheuristics: variable neighborhood search, a memetic algorithm, scatter search and a simulated annealing hyper-heuristic. An extensive computational comparison shows that the approach is capable of solving real-world instances in reasonable time and produces valid solutions within only a few seconds

An investigation of heuristic decomposition to tackle workforce scheduling and routing with time-dependent activities constraints

This paper presents an investigation into the application of heuristic decomposition and mixed-integer programming to tackle workforce scheduling and routing problems (WSRP) that involve timedependent activities constraints. These constraints refer to time-wise dependencies between activities. The decomposition method investigated here is called repeated decomposition with con ict repair (RDCR) and it consists of repeatedly applying a phase of problem decomposition and sub-problem solving, followed by a phase dedicated to con ict repair. In order to deal with the time-dependent activities constraints, the problem decomposition puts all activities associated to the same location and their dependent activities in the same sub-problem. This is to guarantee the satisfaction of time-dependent activities constraints as each sub-problem is solved exactly with an exact solver. Once the assignments are made, the time windows of dependent activities are fixed even if those activities are subject to the repair phase. The paper presents an experimental study to assess the performance of the decomposition method when compared to a tailored greedy heuristic. Results show that the proposed RDCR is an effective approach to harness the power of mixed integer programming solvers to tackle the diffcult and highly constrained WSRP in practical computational time. Also, an analysis is conducted in order to understand how the performance of the different solution methods (the decomposition, the tailored heuristic and the MIP solver) is accected by the size of the problem instances and other features of the problem. The paper concludes by making some recommendations on the type of method that could be more suitable for different problem sizes

Efficient Heuristics for Solving Precedence Constrained Scheduling Problems

This paper discusses the occurrence of dependency relationships within NP hard personnel scheduling problems. These dependencies, commonly referred to as precedence constraints, arise in a number of industries including but not limited to: maintenance scheduling, home health care, and unmanned aerial vehicle scheduling. Precedence relationships, as demonstrated in this research, can significantly impact the quality of solution that can be obtained. In such a competitive market it is imperative that new and innovative ways of finding high quality solutions in short computational times are discovered. This paper presents novel datasets, containing 100-1000 jobs to allocate, that are used to benchmark two heuristic algorithms; an intelligent decision heuristic and a greedy heuristic. Each heuristic is coupled with a multi start metaheuristic to provide a set of benchmark results

Solving the service technician routing and scheduling problem with time windows

n this paper a greedy randomized heuristic is used to solve a service technician routing and scheduling problem with time windows. Time window constraints occur in many sectors such as telecommunications, maintenance, call centres, warehouses and healthcare, and is a way of service providers differentiating themselves to maintain customer satisfaction and ultimately retain market share. The greedy randomized heuristic is coupled with a simulated annealing with restart metaheuristic and tested on 36 problem instances. The greedy randomized heuristic is compared against a parallel adaptive large neighbourhood search heuristic, presenting new best known results in 18% of the dataset

A Constraint Programming Approach to Electric Vehicle Routing with Time Windows

The Electric Vehicle Routing Problem with Time Windows (EVRPTW) extends traditional vehicle routing to address the recent development of electric vehicles (EVs). In addition to traditional VRP problem components, the problem includes consideration of vehicle battery levels, limited vehicle range due to battery capacity, and the presence of vehicle recharging stations. The problem is related to others in emissions-conscious routing such as the Green Vehicle Routing Problem (GVRP). We propose the first constraint programming (CP) approaches for modeling and solving the EVRPTW and compare them to an existing mixed-integer linear program (MILP). Our initial CP model follows the alternative resource approach previously applied to routing problems, while our second CP model utilizes a single resource transformation. Experimental results on various objectives demonstrate the superiority of the single resource transformation over the alternative resource model, for all problem classes, and over MILP, for the majority of medium-to-large problem classes. We also present a hybrid MILP-CP approach that outperforms the other techniques for distance minimization problems over long scheduling horizons, a class that CP struggles with on its own