Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria using Interactive Genetic Algorithms

This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives; and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multi-objective optimization, genetic algorithms and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multi-objective approaches show promising results in fitness convergence, design diversity and user satisfaction metrics

Solving Combinatorial Optimization Problems Using Genetic Algorithms and Ant Colony Optimization

This dissertation presents metaheuristic approaches in the areas of genetic algorithms and ant colony optimization to combinatorial optimization problems. Ant colony optimization for the split delivery vehicle routing problem An Ant Colony Optimization (ACO) based approach is presented to solve the Split Delivery Vehicle Routing Problem (SDVRP). SDVRP is a relaxation of the Capacitated Vehicle Routing Problem (CVRP) wherein a customer can be visited by more than one vehicle. The proposed ACO based algorithm is tested on benchmark problems previously published in the literature. The results indicate that the ACO based approach is competitive in both solution quality and solution time. In some instances, the ACO method achieves the best known results to date for the benchmark problems. Hybrid genetic algorithm for the split delivery vehicle routing problem (SDVRP) The Vehicle Routing Problem (VRP) is a combinatory optimization problem in the field of transportation and logistics. There are various variants of VRP which have been developed of the years; one of which is the Split Delivery Vehicle Routing Problem (SDVRP). The SDVRP allows customers to be assigned to multiple routes. A hybrid genetic algorithm comprising a combination of ant colony optimization, genetic algorithm, and heuristics is proposed and tested on benchmark SDVRP test problems. Genetic algorithm approach to solve the hospital physician scheduling problem Emergency departments have repeating 24-hour cycles of non-stationary Poisson arrivals and high levels of service time variation. The problem is to find a shift schedule that considers queuing effects and minimizes average patient waiting time and maximizes physicians’ shift preference subject to constraints on shift start times, shift durations and total physician hours available per day. An approach that utilizes a genetic algorithm and discrete event simulation to solve the physician scheduling problem in a hospital is proposed. The approach is tested on real world datasets for physician schedules

Solving a Bi-objective Nurse Rerostering Problem by Using a Utopic Pareto Genetic Heuristic

Nurse rerostering arises when at least one nurse announces that she will be unable to undertake the tasks previously assigned to her. The problem amounts to building a new roster that satisfies the hard constraints already met by the current one and, as much as possible, fulfils two groups of soft constraints which define the two objectives to be attained. A bi-objective genetic heuristic was designed on the basis of a population of individuals characterised by pairs of chromosomes, whose fitness complies with the Pareto ranking of the respective decoded solution. It includes an elitist policy, as well as a new utopic strategy, introduced for purposes of diversification. The computational experiments produced promising results for the practical application of this approach to real life instances arising from a public hospital in Lisbon

Patient and Nurse Considerations in Home Health Routing with Remote Monitoring Devices

We build on the current consistent vehicle routing problem literature by formulating a novel multi- objective mathematical model of the home health scheduling and routing problem that includes the option of assigning some patient visits to remote monitoring devices, with the objectives of mini- mizing total cost, achieving nurse consistency and creating balanced nurse workloads. A heuristic solution approach that approximates the efficient frontier of this multiobjective problem is pre- sented and validated, and the results of using this methodology to solve several realistic instances are included. We also analyze the assignment of patients to devices and present some managerial insights into making these assignments in practice

A Literature Review of Cuckoo Search Algorithm

Optimization techniques play key role in real world problems. In many situations where decisions are taken based on random search they are used. But choosing optimal Optimization algorithm is a major challenge to the user. This paper presents a review on Cuckoo Search Algorithm which can replace many traditionally used techniques. Cuckoo search uses Levi flight strategy based on Egg laying Radius in deriving the solution specific to problem. CS optimization algorithm increases the efficiency, accuracy, and convergence rate. Different categories of the cuckoo search and several applications of the cuckoo search are reviewed. Keywords: Cuckoo Search Optimization, Applications , Levy Flight DOI: 10.7176/JEP/11-8-01 Publication date:March 31st 202

Introducing a novel multi-objective optimization model for volunteer assignment in the post-disaster phase: Combining fuzzy inference systems with NSGA-II and NRGA

Each year, disasters (natural or man-made) cause a lot of damage and take many people’s lives. In this situation, many volunteers come to help. While the proper management of volunteers is very effective in controlling the crisis, the lack of proper management of volunteers can create another crisis. Therefore, we introduce a model to deal with the volunteer assignment problem by considering two qualitative objective functions: The first one is minimizing the mean importance of Emergency Department (ED) centers’ unmet needs by volunteers, and the second one is minimizing the mean degree of unsatisfied preferences of selected volunteers. To evaluate the introduced qualitative indexes, two Fuzzy Inference Systems (FISs) are used to encapsulate decision makers’ knowledge as well as the human reasoning process. FISs are embedded in two evolutionary algorithms for solving the proposed model: Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Ranked Genetic Algorithm (NRGA). Also, 30 small-size problems, as well as 30 large-size problems, are randomly generated and solved by both metaheuristic algorithms. Using the obtained data, the performance of NSGA-II and NRGA is measured and compared based on four criteria: CPU Time, Number of Non-dominated Solutions (NNS), Mean Ideal Distance (MID), and Spacing Metric (SM). Statistical tests show that both algorithms have the same performance in small-size problems. However, in large-size problems, NSGA-II is faster, and NRGA produces more optimal solutions. The proposed model is flexible enough to adapt to different scenarios just by updating linguistic rules in FISs. Also, since employed algorithms produce a set of optimal solutions, decision-makers can easily choose the most appropriate solution among the Pareto front based on the circumstancesH2020-EU.1.3. – EXCELLEN

Multiobjective Optimization in Health Care Management. A metaheuristic and simulation approach.

This paper describes a methodology which combines elements of statistics, probability, mathematical programming, simulation, multiobjective optimization and metaheuristics, to analyze management problems in a health care context. We apply this approach to a staffing problem in a primary care center, taking into account both cost and service quality criteria. We illustrate our approach with a case study

Bi-objective Evolutionary Heuristics for Bus Drivers

The Bus Driver Rostering Problem refers to the assignment of drivers to the daily schedules of the company's buses, during a planning period of a given duration. The drivers' schedules must comply with legal and institutional rules, namely the Labour Law, labour agreements and the company's specific regulations. This paper presents a bi-objective model for the problem and two evolutionary heuristics differing as to the strategies adopted to approach the Pareto frontier. The first one, the utopian strategy, extends elitism to include an unfeasible solution in the population, and the second one is an adapted version of the well known SPEA2 (Strength Pareto Evolutionary Algorithm). The heuristics' empirical performance is studied with computational tests on a set of instances generated from vehicle and crew schedules. This research shows that both methodologies are adequate to tackle the instances of the Bus Driver Rostering Problem. In fact, in short computing times, they provide the planning department, with several feasible solutions, rosters that are very difficult to obtain manually and, in addition, identify among them the efficient solutions of the bi-objective model

Disease diagnosis in smart healthcare: Innovation, technologies and applications

To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed

A Memetic Algorithm for a Bi-objective Bus Driver Rostering Problem

The Bus Driver Rostering Problem (DRP) consists of assigning bus drivers to daily duties during a planning period. The problem considers hard constraints imposed by institutional and legal requirements. Solutions should as much as possible satisfy soft constraints that qualify rosters according to either the company's or the drivers' interests. A bi-objective version of the DRP is considered and two models are presented. Due to the high computational complexity of DRP, this paper proposes the Strength Pareto Utopic Memetic Algorithm (SPUMA) a new heuristic algorithm specially devised to tackle the problem. SPUMA genetic component combines utopic elitism with a strength Pareto fitness evaluation and includes an improvement procedure. Computational results show that SPUMA outperforms an adaptation of one of the state-of-the-art most competitive multi-objective evolutionary algorithms, SPEA2