Offline Patient Admission, Room and Surgery Scheduling Problems

Due to its fast service and high utilization, day surgery is becoming more and more important in the medical system. As a result, an effective day surgery scheduling can reasonably release the supply and demand pressure. This paper aims to investigate the day surgery scheduling problem with patient preferences and limited operation room for the sake of increasing operation efficiency and further decreasing surgery costs. A multiple objective stochastic programming model is constructed to seek a satisfactory surgical scheduling for both patients and hospitals under different scenarios. Multi-objective genetic algorithm is designed to solve the model and different scales of scenarios are utilized to test the effectiveness of the algorithm and modeling process. Results show that the proposed model and algorithm can provide a feasible solution for maximizing individual preference of surgeons with surgery date and operation room utilization as well. Patient preference is proposed to be incorporated into day surgery scheduling, and the variability of surgery duration considered to seek a satisfactory surgery scheduling scheme for both patients and hospitals is more in line with the actual hospital situation.

Using the new method and roulette wheel, the traditional Ant Colony Algorithm is improved in the aspect of searching process and pheromone modification. The defects of low searching efficiency and easy to fall into local minimum in traditional Ant Colony Algorithm are remedied. the validity of the improved algorithm has been verified using a testing function.Moreover,a satisfactory optimum solution for a Power Distribution Network Planning has been obtained. And the effective design method for permanent-magnet synchronous Ant colony algorithm is a simulation based on the species evolution to solve complex optimization problem for heuristic. The idea of ant colony algorithm are simulated ants foraging behavior,That is, the use of a large number of ants in the search space in the random search,And use of information has always been to strengthen the search route, and guide other artificial ants search,At the same time, the introduction of the volatile pheromone mechanism(1). Ant colony algorithm proposed by the Italian scholars Dorigo in the twentieth century nineties. The algorithm in the traveling salesman problem (TSP), quadratic assignment problem (QAP) and shop scheduling problem (JSP) solution to achieve good results. Now ant colony algorithm has been optimized in the motor design, network distribution, function optimization and integrated circuit wiring in areas such as applied. Forestry waste from electricity distribution network from the power station involved in each location, size to meet future demand for electricity in rural areas since, at the same time for each subject since the power station capacity, radial network structure, as well as reliability requirements, such as binding. Because of lot of variables and constraints involved, spontaneous power distribution network planning is a very complex combinatorial optimization problem. In this paper, these issues of improved ant colony algorithm and proposes a multi-modal adaptive ant colony pheromone search mechanisms, and their use for the electricity distribution network optimization, and achieved good results. I. THE MATHEMATICAL MODEL OF ACA The ant optimization algorithm is mainly composed of the switching rules and the renewaling information element rule. As an example,We present the ACO algorithm applied to the TSP(Traveling Salesman Problem) for illustrating the principle of ant colony algorithm. there is the assumption N cities, traveling salesman problem are looking for an optimal travel path of the shortest route. The TSP models the situation of a travelling sales man who is required to pass through a number of cities(2). The goal of the travelling sales man is to traverse these cities(visiting each city exactly once)so that the total travelling distance is minimal. Feasible solution of the traveling salesman problem is a non-repeat sequences of all the citis. Assumeing that only m ants Add to the given n cities: dij(i,j=1,2,…,n) where dij is distance between city i with city j. bi(t) where bi(t) is the number of ants is located in city i when it's t. m= ∑ = n

The schedule for job shop system involving the complex correlated constraints is a complex combinatorial optimization problem, for which currently there is no a methodology claiming to have capability to find the optimum solution. Current research concentrate on the search of acceptable feasible solutions. This research proposes an embedded multi-phase methodology to find the acceptable feasible solutions in a higher efficiency. The thinking of the methodology is to decompose the complex optimization problem into two sub problems of the operation sequence and the machine allocation to lower the complexity of the scheduling system and improve the searching efficiency. The two sub problems are solved orderly respectively, and the results of the first sub problem are embedded into the second sub problem as the original values of design variables. Thus these two sub optimization problems are integrated into a searching loop to ensure the feasibility of solution and improve the searching efficiency in the complex correlated system.

Genetic Algorithms (GAs) have been applied in many complex combinatorial optimization problems and have been proven to yield reasonably good solutions due to their ability of searching in continuous spaces and avoiding local optima. However, one issue in GA application that needs to be carefully explored is to examine sensitivity of critical parameters that may affect the quality of solutions. The key critical GA parameters affecting solution quality include the number of genetic operators, the number of encoded decision variables, the parameter for selective pressure, and the parameter for non-uniform mutation. The effect of these parameters on solution quality is particularly significant for complex problems of combinatorial nature. In this paper the authors test the sensitivity of critical GA parameters in optimizing 3-dimensional highway alignments which has been proven to be a complex combinatorial optimization problem for which an exact solution is not possible warranting the application of heuristics procedures, such as GAs. If GAs are applied properly, similar optimal solutions should be expected at each replication. The authors perform several example studies in order to arrive at a general set of conclusions regarding the sensitivity of critical GA parameters on solution quality. The first study shows that the optimal solutions obtained for a range of scenarios consisting of different combinations of the critical parameters are quite close. The second study shows that different optimal solutions are obtained when the number of encoded decision variables is changed.

Network slicing in 5G and beyond networks allows the network to be customized for each application or service by chaining together different virtualized network functions (VNFs) according to service requirements. The increased flexibility offered by network slicing comes at the cost of complexity in management and orchestration, which cannot be solved by traditional reactive human-in-the-loop solutions. This necessitates minimizing human intervention through the use of artificial intelligence techniques (zero touch network management). In particular, the scaling and placement of the chain of VNFs that constitute a network slice is a complex combinatorial optimization problem that is difficult to solve effectively with traditional approaches. Driven by the benefits of deep reinforcement learning (DRL) in solving various combinatorial optimization problems, in this article, we survey various DRL-based approaches to slice scaling and placement, including different ways to model the problem and benefits of various DRL techniques in addressing specific aspects of the problem. Further, we highlight key challenges and open issues in the effective use of DRL for network slice scaling and placement.

An integrated approach for surgery scheduling under uncertainty

Scheduling elective surgeries is a dynamic, sequential decision-making process that must balance the costs of deferring waiting cases and blocking higher-priority cases. Although other surgery scheduling problems have received extensive treatment in the literature, this paper presents the first single-day scheduling problem formulation to capture this aspect of the scheduling process while also incorporating surgical block schedules, block release policies, and waiting lists. Theoretical results for the special case in which all cases have the same duration motivate a range of threshold-based heuristics for the general problem with multiple case durations. Our computational results demonstrate the effectiveness of the proposed heuristics and show how block release dates affect the quality of the scheduling decisions. Based on these results, we propose a new approach to surgery scheduling. In particular, to make more equitable waiting list decisions, operating room (OR) managers should gradually release unused OR time over the course of several days leading up to the day of surgery.

This paper proposes a loss minimum reconfiguration method by Tabu Search for open loop radial distribution system with distributed generators. The problem is to find the optimal normal open sectionalizing switch positions which minimize the total distribution line losses subjected to the line/transformer capacity constraints and voltage constraint. Generally, the problem is mathematically formulated as a complex combinatorial optimization problem or mixed integer programming problem, and is solved by using mathematical programming method, heuristic algorithm, intelligent method, etc. However, satisfactory algorithm for power companies has not yet been attained both in computational burden and solution accuracy. Thus, in this paper, the authors propose a method to solve the above problem by using Tabu Search (TS) method. Reverse power flow caused by distributed generators can be included in the solution algorithm. TS is one of meta-heuristic algorithms, and sometimes evaluated to be better compared with Genetic Algorithm (GA) or Simulated Annealing (SA) from viewpoints of both computational speed and solution accuracy. In order to evaluate the validity and efficiency of the algorithm, several numerical examples are shown in this paper.

The emergence and development of distributed resources (DR) provide a significant technological option for energy-saving and emission-reducing. From the viewpoint of power distribution utility operator, this paper firstly provides a comprehensive analysis of DR to assess the level of environmental benefits, and points out the potential value of DR for reducing air pollution and increasing energy efficiency. Subsequently, a general energy supply optimization model with a wide range of DR, which takes into account environmental impacts (emissions penalties) as external costs to the total costs, is proposed. Considering that the proposed model is a complex combinatorial optimization problem, and that the difficulty is further exacerbated by the uncertainty of power output of intermittent energy resources, heuristic algorithm, which is applied in traditional generation expansion planning, is adopted. Finally, a distribution network is employed to illustrate the proposed model and algorithm, with the simulation results discussed.

This paper reviews the theory and applications of ant algorithms, new methods of discrete optimization based on the simulation of self-organized colony of biologic ants. The colony can be regarded as a multi-agent system where each agent is functioning independently by simple rules. Unlike the nearly primitive behavior of the agents, the behavior of the whole system happens to be amazingly reasonable. The ant algorithms have been extensively studied by European researchers from the mid-1990s. These algorithms have successfully been applied to solving many complex combinatorial optimization problems, such as the traveling salesman problem, the vehicle routing problem, the problem of graph coloring, the quadratic assignment problem, the problem of network-traffic optimization, the job-shop scheduling problem, etc. The ant algorithms are especially efficient for online optimization of processes in distributed nonstationary systems (for example, telecommunication network routing).

The traveling agent problem is a complex combinatorial optimization problem, which solves the problem of planning out an optimal migration path according to the tasks when agents migrate to several hosts in the shortest time. Evolutionary algorithm such as ACO has the well characteristic for heuristic search and robustness. But it has the limitation for the stagnation in the searching. This problem will cause the agent wasting more time in tasking. To avoid it an improved algorithm based on artificial bee colony is introduced. This algorithm uses three kinds of roles: Blaze, Reconnaissance and Follow to routing and finishing the tasks. Agents of the role exchange the information to adopt their own path. The whole group can get the more efficiency path during migration. The experiments show this algorithm has better results in some aspects which needs less times and load for host comparing with the other evolutionary algorithm.KeywordsMobile AgentArtificial bee colonyTAP

The problem of travelling salesmen (TSP) is a well-known task in the field of combinatorial optimisation. However, the problem of the multiple travelling salesman (mTSP), which extends the former, is a more challenging and complex combinatorial optimisation problem. This problem included addressing real-world issues where more than one salesman needed to be responsible for. This paper covered the use of heuristic approaches to tackle 180 cities and six travelling salesmen to reduce the path distances. To transform an mTSP into a TSP, a K-means clustering algorithm was used. Genetic algorithm (GA) was applied to the cluster after the clustering was done and iterated to provide the best possible value for distance following convergence. Now, with the ant colony optimisation (ACO) algorithm, every cluster was once again solved to determine the optimum distance value as a TSP. Once the two heuristic methods were applied, it became evident that due to the thorough analysis and constructive design of the algorithm, the ant colony optimisation algorithm yielded better results and more efficient tour than the genetic algorithm.

Designing vehicle routes for a mix of different request types, under time windows and loading constraints

The job-shop scheduling problem (JSSP) is considered to be one of the most complex combinatorial optimisation problems. In our previous attempt, we hybridised a Genetic Algorithm (GA) with a local search technique to solve JSSPs. In this research, we propose an improved local search technique, Shifted Gap-Reduction (SGR), which improves the performance of GAs when solving relatively difficult test problems. We also modify the new algorithm for JSSPs with machine unavailability and breakdowns. We consider two scenarios of machine unavailability. First, where the unavailability information is available in advance (predictive) and, secondly, where the information is known after a real breakdown (reactive). We show that the revised schedule is mostly able to recover if the interruptions occur during the early stages of the schedules.

A cooperative coevolutionary optimization design of urban transit network and operating frequencies