Hybrid Multi-objective Genetic Algorithm Research Articles

A hybrid multi-objective genetic algorithm for scheduling heterogeneous workover rigs on onshore oil fields

Onshore oil fields are composed of a set of geographically distributed wells that, after some time of operation, might present some malfunction and have their production interrupted. When the oil production of some wells is interrupted, specially-equipped vehicles, also called workover rigs, are deployed to service the wells and guarantee that their activity is restored. Given the limited number of workover rigs and the large number of wells around the oil field, the workover rig problem consists of finding the best scheduling for the workover rigs, so the total production loss of wells is minimized. The scheduling considers some factors such as the production loss rate of each well, the service level required and the planning time horizon in which the schedule must be executed. This research presents a hybrid genetic algorithm to solve the multi-objective workover rig problem with a heterogeneous fleet and a finite time horizon. The hybrid genetic algorithm incorporates a variable neighborhood descent heuristic as a local search procedure to increase the convergence speed of the set of solutions. Both objectives of minimization of the production loss and fleet cost associated with the rent of workover rigs are taken into consideration. The fleet is held variable, so a workover rig depot is included at a strategic position on the oil field to guarantee that new workover rigs, besides the already existent ones on the oil field, might be included in the scheduling when required. The genetic algorithm was tested on a set of practical-sized instances up to 200 wells, 10 workover rigs and 300 as time horizon. Results show a high conflict between the objectives of minimizing the production loss and fleet cost for the workover rig problem, besides important aspects of the solutions obtained by the proposed algorithm.

Research on dyeing workshop scheduling methods for knitted fabric production based on a multi-objective hybrid genetic algorithm

As the most important core process in the dyeing and finishing workshop of knitting companies, the dyeing process has the characteristics of multi-variety, small-batch, parallel machine processing of multiple types, and high cost in equipment cleaning, which render the dyeing scheduling problem a bottleneck in the production management of a dyeing and finishing workshop. In this paper, the dyeing process scheduling problem in dyeing and finishing workshops is described and abstracted, and an optimized mathematical model of dyeing scheduling is constructed with the goal of minimizing the delay cost and switching cost. Constraints such as multiple types of equipment, equipment capacity, weights of orders and equipment cleaning time are considered. For the sub-problem of equipment scheduling in the dyeing scheduling problem, a heuristic rule that considers equipment utilization and order delay is proposed. For the sub-problem of order sorting of the equipment in the dyeing scheduling problem, a hybrid genetic algorithm with a variable neighbourhood search strategy has been designed to optimize sorting. The algorithm proposed in this paper has been demonstrated via case simulation to be effective in solving the scheduling problem in dyeing and finishing workshops.

Energy-efficient steelmaking-continuous casting scheduling problem with temperature constraints and its solution using a multi-objective hybrid genetic algorithm with local search

With the increasing energy price and the urgent demand of manufacturing enterprises for energy conservation, energy-efficient scheduling (EES) technology has been widely investigated and applied in industry and academia. The steelmaking-continuous casting (SCC) production is the main energy-consuming sector and the key process for quality control of steel manufacturing. Due to the high-temperature characteristics of SCC production, the temperature drop deriving from non-processing process could directly lead to energy loss and increase the energy consumption of each procedure, which has important influence on the total energy consumption. Therefore, the energy-efficient steelmaking-continuous casting scheduling problem with temperature constraints (EESCCSPT) was concerned and a multi-objective mathematical programming model was introduced to minimize the penalty of due date deviation and the extra energy consumption measured by temperature drop. Comparing to the general scheduling problem, the constraint of minimum casting superheat and the constraint of target tapping temperature generated by the high-temperature technical requirements were directly considered to ensure schedule feasibility in terms of temperature. A multi-objective hybrid genetic algorithm combined with local search (MOHGALS) was presented, in which the enhanced evolutionary mechanisms combined with the improved genetic operators and the local search were also designed. Results of computational experiments showed that MOHGALS was more feasible and effective than NSGA-II and SPEA2 on the EESCCSPT.

A new game-theoretical multi-objective evolutionary approach for cash-in-transit vehicle routing problem with time windows (A Real life Case)

Cash transfer from a central treasury to bank branches, which is with high security, is one of the crucial processes in the banking system. In this paper, a new multi-objective game theory-based model is developed to increase the security of cash-in-transit. For this purpose and in order to reduce the transportation costs, a bi-objective vehicle routing problem with time window is developed where the risk of transfers (including armed robbers attack and theft) and the distance traveled by vehicles are minimized. In order to better estimate the robber’s performance, the probability of robber’s ambush is calculated by the game theory approach, in such a way that a two-player, zero-sum game is played between the robber and the cash carrier. The probability of theft success is also estimated in the proposed approach through a multiple-criteria decision-making and in order to be further representative of real-life situations. A periodic review is also added to the proposed model to increase the cash transport security in which the previously used links would enjoy less chance of choosing in the current period. Moreover, a new multi-objective hybrid genetic algorithm incorporated with a number of new heuristics and operators is developed to tackle the proposed model. The efficiency and effectiveness of the algorithm are examined through several standard data sets, and the results indicate the effectiveness of the proposed solution algorithm. The wide applicability of our proposed approach in real-life situations is examined with a real case study as well.

Bayesian optimization algorithm for multi-objective scheduling of time and precedence constrained tasks in heterogeneous multiprocessor systems

This paper presents a Bayesian optimization based novel approach for multi-objective scheduling of time and precedence constrained tasks in heterogeneous multiprocessing environments. The proposed approach, termed as multi-objective Bayesian optimization algorithm (moBOA) for real-time scheduling, can suitably produce optimal task schedules without any violation of the timing and precedence constraints. The moBOA utilizes Bayesian networks to learn the task graphs that represent the precedence relationships among the tasks. It first allocates tasks to individual processors and then decides the order of execution on each processor based on the latest deadline first policy. The proposed moBOA may be applied to both homogeneous and heterogeneous multiprocessor systems. Extensive comparative analysis has been made by considering two other existing evolutionary algorithms, namely the multi-objective genetic algorithm (moGA) and multi-objective hybrid genetic algorithm (mohGA) through experimental simulations with benchmark datasets. From the results of the simulation experiments, it is observed that moBOA outperforms both moGA and mohGA in terms of quality of solutions, Pareto-optimalilty and standard performance measures. We demonstrate that the task schedules produced by moBOA are optimal and comply with the timing as well as the precedence constraints. Statistical significant analyses of the results are conducted.

A Hybrid Multi-objective Genetic Algorithm for Bi-objective Time Window Assignment Vehicle Routing Problem

Providing a satisfying delivery service is an important way to maintain the customers’ loyalty and further expand profits for manufacturers and logistics providers. Considering customers’ preferences for time windows, a bi-objective time window assignment vehicle routing problem has been introduced to maximize the total customers’ satisfaction level for assigned time windows and minimize the expected delivery cost. The paper designs a hybrid multi-objective genetic algorithm for the problem that incorporates modified stochastic nearest neighbour and insertion-based local search. Computational results show the positive effect of the hybridization and satisfactory performance of the metaheuristics. Moreover, the impacts of three characteristics are analysed including customer distribution, the number of preferred time windows per customer and customers’ preference type for time windows. Finally, one of its extended problems, the bi-objective time window assignment vehicle routing problem with time-dependent travel times has been primarily studied.

Bi-objective optimization approach for energy aware scheduling considering electricity cost and preventive maintenance using genetic algorithm

Abstract In light of various environmental issues regarding electricity generation and usage, several approaches have been suggested to decrease energy usage. One well-known method on the supplier side is time-of-use (TOU) pricing, through which demand on the customer side is controlled by adjusting electricity prices. Several studies have investigated the scheduling problem in such an environment to efficiently handle energy usage from the customer perspective, and have addressed its effectiveness. However, in most energy-aware scheduling problems considering TOU pricing, machine failure is not considered. This is a significant assumption because it implies that machines are available at all times. In this study, we examined a single machine scheduling problem that reflects preventive maintenance under TOU pricing. To solve this problem, a bi-objective mixed-integer non-linear programming model is designed, and a hybrid multi-objective genetic algorithm (HMOGA) is proposed to handle medium- and large-sized problems. In addition, α - and P-improvement methods are proposed to efficiently generate a better Pareto frontier. The performance of the algorithm is compared with that of a non-dominated sorting genetic algorithm (NSGA)-2 to demonstrate the effectiveness of the proposed HMOGA. Numerical experiments showed that the HMOGA yields better outcomes than the NSGA-2 and faster than Baron solver. The major contribution of this study lies in building a trade-off relationship between the total electricity cost and machine unavailability in a TOU pricing environment.

Multi-Objective Task Scheduling Using Hybrid Genetic-Ant Colony Optimization Algorithm in Cloud Environment

Task allocation within the cloud computing environment is a nondeterministic polynomial time class problem that is laborious to get the best solution. It is an important issue in the cloud computing setting. The usage of cloud based applications and cloud users are increasing tremendously. In order to handle the massive cloud user’s requests, effective multi-objective Hybrid Genetic Algorithm–Ant Colony Optimization (HGA–ACO) based task allocation technique is proposed in this paper. Utility based scheduler identifies the task order and suitable resources to be scheduled. The proposed HGA–ACO considers the utility based scheduler output and finds the best task allocation method based on response time, completion time and throughput. The HGA–ACO algorithm combines Genetic and Ant Colony Optimization algorithms together. Genetic algorithm (GA) initializes the effective pheromone for ant colony optimization (ACO). ACO is used to enhance the GA solutions for crossover operation of GA. The experimental results show that the proposed framework has better performance in task allocation and ensuring quality of service parameters.

Multi-objective optimization of a flow straightener in a large capacity firefighting water cannon

In the present study, a multi-objective optimization of a flow straightener in a firefighting water cannon is performed by using the surrogate modeling and a hybrid multi-objective genetic algorithm to increase the jet range of the water cannon. Based on analysis using the three-dimensional Reynolds-averaged Navier-Stokes equations, the optimization is carried with a surrogate model and the radial basis neural network. Three geometric design variables, i.e., the length, the thickness of the blade, and the radius of the outer pipe of the flow straightener, are selected for the optimization. The pressure drop through the water cannon and the area-averaged turbulent kinetic energy at the outlet of the water cannon, which are closely related to the jet range of the water cannon, are selected as the objective functions to be minimized. The design space is determined through a parametric study, and the Latin hypercube sampling method is used to select the design points in the design space. The Pareto-optimal solutions are obtained through the optimization. Five representative Pareto-optimal solutions are selected to study the trade-off between two objectives.

Optimal Trajectory Scheme for Robotic Welding Along Complex Joints Using a Hybrid Multi-Objective Genetic Algorithm

The problem of trajectory planning is relevant for the proper use of costly robotic systems to mitigate undesirable effects such as vibration and even wear on the mechanical structure of the system. The objective of this study is to design trajectories that are devoid of collision, velocity, acceleration, jerk and snap discontinuities so that the cycle time required to complete the process can be reduced. The trajectory design was constructed for all the six joints, using a 9th order Bezier curve to accommodate the ten boundary conditions required to satisfy the continuity constraints for joints displacement, velocity, acceleration, jerk and snap. The scheme combines the multi-objective genetic algorithm and the multi-objective goal attainment algorithm to solve the problem of total tracking error reduction during arc welding. The use of a hybrid multi-objective algorithm shows an improved average spread, average distance, number of iteration and computational time. Also, it can be concluded from the constraints studied, that the optimal path in terms of the robots dynamic constraints can achieve the expected tracking ability in terms of the optimal joint angles, velocities, acceleration, jerk, snap and torque.

A hybrid multi-objective genetic local search algorithm for the prize-collecting vehicle routing problem

This paper studies the prize-collecting vehicle routing problem (PCVRP), which is a new variant of the vehicle routing problem. In the PCVRP, besides the vehicle assignment and visiting sequencing, customer selection also must be determined because the available vehicles are insufficient to visit all the customers. Two types of PCVRP are often encountered in the real world, namely the PCVRP in which the number of vehicles is predetermined (PCVRP-P) and that in which the number of vehicles is not predetermined (PCVRP-NP). In general, multiple optimization objectives are required to be considered in both the PCVRP-P and PCVRP-NP. Unlike the commonly used weighted-sum method, a Pareto-based evolutionary algorithm featuring a genetic algorithm combined with a local search strategy (denoted as HGLS) is proposed for solving the multi-objective PCVRP-P. Subsequently, based on the proposed HGLS, a decomposition strategy is designed to solve the multi-objective PCVRP-NP problem by decomposing it into multiple PCVRP-P problems. In order to determine the number of PCVRP-P problems in the decomposition strategy, a vehicle-number-range determination approach is proposed to obtain a tight upper bound and lower bound of the number of vehicles. The superiority of the proposed algorithm is demonstrated by conducting several experiments on the benchmark problems.

Multi-Depot Vehicle Routing Optimization Considering Energy Consumption for Hazardous Materials Transportation

Focusing on the multi-depot vehicle routing problem (MDVRP) for hazardous materials transportation, this paper presents a multi-objective optimization model to minimize total transportation energy consumption and transportation risk. A two-stage method (TSM) and hybrid multi-objective genetic algorithm (HMOGA) are then developed to solve the model. The TSM is used to find the set of customer points served by each depot through the global search clustering method considering transportation energy consumption, transportation risk, and depot capacity in the first stage, and to determine the service order of customer points to each depot by using a multi-objective genetic algorithm with the banker method to seek dominant individuals and gather distance to keep evolving the population distribution in the second stage, while with the HMOGA, customer points serviced by the depot and the serviced orders are optimized simultaneously. Finally, by experimenting on two cases with three depots and 20 customer points, the results show that both methods can obtain a Pareto solution set, and the hybrid multi-objective genetic algorithm is able to find better vehicle routes in the whole transportation network. Compared with distance as the optimization objective, when energy consumption is the optimization objective, although distance is slightly increased, the number of vehicles and energy consumption are effectively reduced.

A method to optimize mobile crane and crew interactions to minimize construction cost and time

Abstract Cranes often play a central role in transporting materials on building construction sites and are therefore critical to project cost and schedule. This paper presents a new model to simulate the interactions between mobile cranes and associated work crews onsite. The model considers crane type and position, the sequence of components transported, and the number and size of crews at the demand point. A novel hybrid multi-objective Genetic Algorithm (MOGA) is utilized to identify optimal crane and crew configurations that minimize construction cost and duration. The proposed method is demonstrated on an example problem involving the installation of curtain wall panels for a mid-rise office building. The results indicate that considering crane and crew decisions in parallel reduces installation cost by 19.5% and duration by 1.7% compared to considering these decisions sequentially. Furthermore, the number of crews used and the number of crane stops had the most significant impact on project cost and schedule, respectively.

Nash-Pareto Genetic Algorithm for the Frequency Assignment Problem

This paper presents a hybrid multi-objective genetic algorithm which combines the main notion of game theory Nash equilibrium with Pareto-optimality to solve the multi-objective Frequency Assignment Problem (FAP) in mobile networks. The game is coupled with genetic algorithm to accelerate convergence and produce Nash equilibrium and Pareto non-dominated solutions simultaneously. The proposed hybrid approach produces high quality solutions as proved by several performed tests and corroborated by the comparison with the most referred multi-objective optimization algorithms such as NSGA-II and SPEA2 on well-known Philadelphia and COST259 FAP instances. Furthermore, the effect of some parameters is discussed.

Multiobjective optimization–based fault‐tolerant flight control system design

SummaryThe loss of measurements used for controller scheduling or envelope protection in modern flight control systems due to sensor failures leads to a challenging fault‐tolerant control law design problem. In this article, an approach to design such a robust fault‐tolerant control system, including full envelope protections using multiobjective optimization techniques, is proposed. The generic controller design and controller verification problems are derived and solved using novel multiobjective hybrid genetic optimization algorithms. These algorithms combine the multiobjective genetic search strategy with local, single‐objective optimization to improve convergence speed. The proposed strategies are applied to the design of a fault‐tolerant flight control system for a modern civil aircraft. The results of an industrial controller verification and validation campaign using an industrial benchmark simulator are reported.

A bi-criteria hybrid Genetic Algorithm with robustness objective for the course timetabling problem

Traditional methods of generating timetables may yield high-quality solutions, but they may not yield robust solutions that may easily be adapted to changing inputs. Incorporating late changes by making minimum modifications on the final timetable is an important need in many practical applications of timetabling. In this study, we focus on a subset of course timetabling problems, the curriculum-based timetabling problem. We first define a robustness measure for the problem, and then try to find a set of good solutions in terms of both penalty and robustness values. We model the problem as a bi-criteria optimization problem and solve it by a hybrid Multi-objective Genetic Algorithm, which makes use of Hill Climbing and Simulated Annealing algorithms in addition to the standard Genetic Algorithm approach. The algorithm is tested on the well known ITC-2007 instances and shown to identify high quality Pareto fronts.

Task Reallocating for Responding to Design Change in Complex Product Design

Abstract In the real-world complex product design (CPD) process, task allocating is an ongoing reactive process where the presence of unexpected design change is usually inevitable. Therefore, reallocating is necessary to respond to design change positively as a procedure to repair the affected task plan. General reallocating literature addressed the reallocating versions with fixed executing time. In this paper, a multi-objective reallocation model is developed with a feasible assumption that the task executing time is controllable. To illustrate this idea, a compressing executing time strategy (CETS) is proposed in CPD process, where the executing time can be controlled with a non-linear compression cost. When design change occurs during the executing, task-resource reallocating is required to absorb the interference effects. Reallocating implies an increase in design cost and system instability; the proposed method CETS can address this issue effectively. CETS considers three objectives: completing time, stability, and change-adaptation cost. An adaptive multi-objective hybrid genetic algorithm and tabu search (AMOGATS) is developed to solve this mathematical method. The computational results of specific simulation examples verify the superiority. It shows that CETS is sensitive to design change, and the proposed algorithm AMOGATS can be effective to achieve the allocating by coordinating the objective consistency.

Integrated modeling and solving the resource allocation problem and task scheduling in the cloud computing environment

Cloud computing is considered to be a new service provider technology for users and businesses. However, the cloud environment is facing a number of challenges. Resource allocation in a way that is optimum for users and cloud providers is difficult because of lack of data sharing between them. On the other hand, job scheduling is a basic issue and at the same time a big challenge in reaching high efficiency in the cloud computing environment. In this paper, “the cloud resources management problem” is investigated that includes allocation and scheduling of computing resources, such that providers achieve the high efficiency of resources and users receive their needed applications in an efficient manner and with minimum cost. For this purpose, a group technology based non-linear mathematical model is presented with an aim at minimization of load difference of servers, number of transfers between servers, number of active virtual machines, maximum construction time, the cost of performing jobs and active servers energy consumption. To solve the model, a meta-heuristic multi-objective hybrid Genetic and Particle Swarm Optimization algorithm is proposed for resource allocation and scheduling. In order to demonstrate the validity and efficiency of the algorithm, a number of problems with different dimensions are randomly created and accordingly the efficiency and convergence capability of the suggested algorithm is investigated. The results indicated that the proposed hybrid method has had an acceptable performance in generating high quality, diverse and sparse solutions.

A reacting approach for design task allocating under resource unavailability

Resource unavailability (RU) is inevitable in complex product design (CPD) process, therefore, it is quite difficult to achieve an optimal solution. Based on the controllable characteristic of execution time, a multi-objective model with compressing execution time strategy (CETS) is considered in CPD. There are two stages in this model, in the first stage, we obtain an original allocation with the goal of minimizing completion time. In the second stage, CETS is utilized in response to RU considering the trade-off between compensatory cost and stability. An adaptive multi-objective hybrid genetic algorithm and tabu search (AMOGATS) is developed to solve the first mathematical step, CETS is performed by a basic genetic algorithm (GA). The computational results verify the superiority of hybrid genetic algorithm and proposed strategy.

Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing scheduling

In real manufacturing systems there are many combinatorial optimization problems (COP) imposing on more complex issues with multiple objectives. However it is very difficult for solving the intractable COP problems by the traditional approaches because of NP-hard problems. For developing effective and efficient algorithms that are in a sense “good,” i.e., whose computational time is small as within 3min, we have to consider three issues: quality of solution, computational time and effectiveness of the nondominated solutions for multiobjective optimization problem (MOP).In this paper, we focus on recent hybrid evolutionary algorithms (HEA) to solve a variety of single or multiobjective scheduling problems in manufacturing systems to get a best solution with a smaller computational time. Firstly we summarize multiobjective hybrid genetic algorithm (Mo-HGA) and hybrid sampling strategy-based multiobjective evolutionary algorithm (HSS-MoEA) and then propose HSS-MoEA combining with differential evolution (HSS-MoEA-DE). We also demonstrate those hybrid evolutionary algorithms to bicriteria automatic guided vehicle (B-AGV) dispatching problem, robot-based assembly line balancing problem (R-ALB), bicriteria flowshop scheduling problem (B-FSP), multiobjective scheduling problem in thin-film transistor-liquid crystal display (TFT-LCD) module assembly and bicriteria process planning and scheduling (B-PPS) problem. Also we demonstrate their effectiveness of the proposed hybrid evolutionary algorithms by several empirical examples.