Heuristic-based Genetic Algorithm Research Articles

An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective

Price based demand response is an important strategy to facilitate energy retailers and end-users to maintain a balance between demand and supply while providing the opportunity to end users to get monetary incentives. In this work, we consider real-time electricity pricing policy to further calculate the incentives in terms of reduced electricity price and cost. Initially, a mathematical model based on the backtracking technique is developed to calculate the load shifted and consumed in any time slot. Then, based on this, the electricity price is calculated for all types of users to estimate the incentives through load shifting profiles. To keep the load under the upper limit, the load is shifted in other time slots in such a way to facilitate end-users regarding social welfare. The user who is not interested in participating load shifting program will not get any benefit. Then the well behaved functional form optimization problem is solved by using a heuristic-based genetic algorithm (GA), wwhich converged within an insignificant amount of time with the best optimal results. Simulation results reflect that the users can obtain some real incentives by participating in the load scheduling process.

A Heuristic-Based Genetic Algorithm for Scheduling of Multiple Projects Subjected to Resource Constraints and Environmental Responsibility Commitments

During the last decades, resource-constrained project scheduling problems have been abundantly presented in extant literature. However, there are still some real-world challenges that have not been adequately considered. These challenges include environmental commitments and constraints related to the procurement of resources (as regards procurement commitment). This calls for the integration of the project planning and forward-reverse supply chain planning systems. To achieve this goal, this paper contributes to the existing literature by presenting a model that incorporates the two issues in the integrated planning system: (1) the procurement commitment objective is met through the just-in-time delivery of non-renewable resources to the project sites while considering the limited supply capacity of suppliers, and (2) the environmental commitment is satisfied by collecting and recycling the waste generated at project sites. A mixed-integer linear formulation of the problem is proposed. Since the model is NP-hard (non-deterministic polynomial time-hard), the paper develops a new heuristic-based genetic algorithm to solve the problem instances. The main parameters of the algorithm are tuned using the Taguchi method. The results show the efficiency of the algorithm in obtaining appropriate solutions in reasonable computational times. The integrated planning model that is proposed in this paper and its novel resolution method would help managers to make more responsive and efficient decisions.

Solution of Cluster Traveling Salesman Problem using Heuristic Based Genetic Algorithm with Risk Constraint

This is a heuristic investigation with risk constraint for solving the traveling salesman problem (TSP) dividing given vertices (nodes) between a prespecified number of clusters. A Heuristic based Genetic Algorithm (HbGA) is applied on each cluster to produce a Hamiltonian path based on prespecified nodes of a cluster. Each cluster must have a unique set of nodes. Finally, all Hamiltonian path of each cluster together prepare a possible Hamiltonian cycle. The efficiency of our proposed algorithm has been tested for a number of symmetric TSPLIB instances of various sizes. The computational results show the proposed algorithm works well in realistic manner.

휴리스틱 기반의 유전 알고리즘을 활용한 경로 탐색 알고리즘

휴리스틱 기반의 유전 알고리즘을 활용한 경로 탐색 알고리즘

Heuristic based genetic algorithms for the re-entrant total completion time flowshop scheduling with learning consideration

AbstractRecently, both the learning effect scheduling and re-entrant scheduling have received more attention separately in research community. However, the learning effect concept has not been introduced into re-entrant scheduling in the environment setting. To fill this research gap, we investigate re-entrant permutation flowshop scheduling with a position-based learning effect to minimize the total completion time. Because the same problem without learning or re-entrant has been proved NP-hard, we thus develop some heuristics and a genetic algorithm (GA) to search for approximate solutions. To solve this problem, we first adopt four existed heuristics for the problem; we then apply the same four methods combined with three local searches to solve the proposed problem; in the last stage we develop a heuristic-based genetic algorithm seeded with four good different initials obtained from the second stage for finding a good quality of solutions. Finally, we conduct experimental tests to evaluate the behavi...

A heuristic-based genetic algorithm for the two-machine flowshop scheduling with learning consideration

In scheduling problems, the learning phenomenon is often seen in some practical applications such as in the processing of certain chemicals in oil refineries and in the steel plates or bars produced by a foundry. A review of the literature reveals that most researchers paid more attention to the scheduling with both the single-machine settings and the learning without a bound. This is at odds with reality and thereby highlights the importance of addressing the issue by different approaches. This paper tackles the issue by considering a two-machine flowshop problem with a truncated learning consideration where the objective function is to minimize the makespan. In order to solve the proposed model, a branch-and-bound algorithm is first developed for the optimal solution. Then four genetic heuristic-based algorithms are proposed for the near-optimal solution. In addition, the experimental results of all proposed algorithms are also provided.

Deadline constraint heuristic-based genetic algorithm for workflow scheduling in cloud

Task scheduling and resource allocation are the key challenges of cloud computing. Compared with grid environment, data transfer is a big overhead for cloud workflows. So, the cost arising from data transfers between resources as well as execution costs must also be taken into account during scheduling based upon user's Quality of Service QoS constraints. In this paper, we present Deadline Constrained Heuristic based Genetic Algorithms HGAs to schedule applications to cloud resources that minimise the execution cost while meeting the deadline for delivering the result. Each workflow's task is assigned priority using bottom-level b-level and top-level t-level. To increase the population diversity, these priorities are then used to create the initial population of HGAs. The proposed algorithms are simulated and evaluated with synthetic workflows based on realistic workflows. The simulation results show that our proposed algorithms have a promising performance as compared to Standard Genetic Algorithm SGA.

Integrating truck arrival management into tactical operation planning at container terminals

Abstract Truck arrival management (TAM) has been recognized as an effective solution to alleviate the gate congestion at container terminals. To further utilize TAM in improving the overall terminal performance, this study integrates TAM with the other terminal operations at a tactical level. An integrated planning model and a sequential planning model are presented to coordinate the major terminal planning activities, including quayside berth allocation, yard storage space allocation and TAM. A heuristic-based genetic algorithm is developed to solve the models. A range of numerical examinations are performed to compare two planning models. The result shows that: the integrated model can improve the terminal performance significantly from the sequential model alone, particularly when the gate capacity and the yard capacity are relatively low; whereas the sequential model is more efficient than the integrated model in terms of computational time.

Hybrid Genetic Algorithm (GA) for job shop scheduling problems and its sensitivity analysis

The Job Shop Scheduling Problem (JSSP) is a hard combinatorial optimisation problem. This paper presents a heuristic-based Genetic Algorithm (GA) or Hybrid Genetic Algorithm (HGA) with the aim of overcoming the GA deficiency of fine tuning of solution around the optimum, and to achieve optimal or near optimal solutions for benchmark JSSP. The paper also presents a detail GA parameter analysis (also called sensitivity analysis) for a wide range of benchmark problems from JSSP. The findings from the sensitivity analysis or best possible parameter combination are then used in the proposed HGA for optimal or near optimal solutions. The experimental results of the HGA for several benchmark problems are encouraging and show that HGA has achieved optimal solutions for more than 90% of the benchmark problems considered in this paper. The presented results will provide a reference for selection of GA parameters for heuristic-based GAs for JSSP.

M-machine SDST flow shop scheduling using modified heuristic genetic algorithm

Bi-criteria flow shop scheduling problems with sequence dependent set up time (SDST) have seen an increasing attention of managers and researchers in recent years. A very restricted research has been reported on bi-criteria SDST flow shop scheduling problems dealing with due date related performance measures. In the present work, a modified heuristic based genetic algorithm (MHGA) has been developed for the aforesaid scheduling problem subject to the minimization of weighted sum of total weighted squared tardiness and makespan criterion. The modified heuristic algorithms, along with other available heuristics and dispatching rules in the literature have also being developed to solve the problem instances given by Taillard. A computational analysis has been made to evaluate the performance of the proposed MHGA for upto 200 jobs and 20 machines problems. Comparative analysis with the help of defined performance index known as relative percentage deviation (RPD) verifies that it is viable and effective approach when compared with others heuristic/dispatching rules based genetic algorithms for the SDST flow shop scheduling, especially for larger sized problems. Keywords: Flow shop scheduling, Modified Heuristic Genetic algorithm (MHGA), Sequence dependent set up time, makespan, total weighted squared tardiness.

A heuristic-based genetic algorithm for workload smoothing in assembly lines

Workload smoothing in assembly lines has many beneficial features: it established the sense of equity among workers, and, more importantly, contributes to increasing the output. Although assembly line balancing has been studied extensively, workload smoothing as the objective has been relatively neglected in the literature. This study presents a new heuristic procedure based on genetic algorithm to balance an assembly line with the objective of maximizing workload smoothness. To improve the capability of searching good solutions, our genetic algorithm puts emphasis on the utilization of problem-specific information and heuristics in the design of representation scheme and genetic operators. Extensive computational experiments are performed for the algorithm. The advantages of incorporating problem-specific heuristic information into the algorithm are demonstrated. The performance comparison of our genetic algorithm with three existing heuristics and with an existing genetic algorithm is made. The experimental results show that our algorithm outperforms the existing heuristics and the compared genetic algorithm. In many cases, our algorithm also improves cycle time.