Line-up Competition Algorithm Research Articles

An Improved Line-Up Competition Algorithm for Unrelated Parallel Machine Scheduling with Setup Times

It is well known that with the development of economic globalization and increasing competition in the market, enterprises are facing a huge challenge in the unrelated parallel machine scheduling problem with setup time (UPMST). Determining the processing order of all jobs and assigning machines to production scheduling has become more complex and has research implications. Moreover, a reasonable production scheduling scheme can not only complete the production plan efficiently but also contribute to reducing carbon emissions. In this paper, a mathematical model with the goal of the shortest completion time is studied for the UPMST problem. An improved line-up competition algorithm (ILCA) is proposed to solve this model, and the search accuracy and rate of the algorithm are improved by the newly proposed heuristic workpiece allocation rules and variation strategies. From the perspective of evaluation purposes, the effectiveness and stability of the method are significantly superior to other methods, and it is competitive in solving the UPMST problem.

Biogeography-based optimization algorithm for large-scale multistage batch plant scheduling

The batch process is characterized by many varieties, small batches, redundant production equipment, flexible production process, and high-added-value products. This process is widely used in chemical, plastic, rubber, pharmaceutical, fine chemical, metallurgical, steel, food, and other industries. The optimized scheduling scheme of the batch process can effectively enhance enterprise competitiveness and improve economic benefits. Multistage Multiproduct Scheduling Problem (MMSP) is an important branch of batch scheduling problems. It is difficult to solve MMSP within a reasonable time by traditional mathematical programming, because once the scale of scheduling problems increases, the solution space expands exponentially. This study proposes a metaheuristic approach based on a time key biogeography-based optimization algorithm to solve MMSP. This new time key representation contains two vectors, which represent the processing sequence and equipment allocation of orders respectively. In accordance with the time information in the new representation, we add the preference of equipment processing time to migration and calculate the probability of every mutation value. In addition, the elite solution is combined with the active scheduling technique and modified Nawaz-Enscore-Ham (NEH) algorithm to improve the search accuracy of the proposed algorithm. To test the performance of Improved Time Key Biogeography-Based Optimization (Improved-TKBBO) algorithm, its results are compared with computational results of mathematical programming, Genetic Algorithm (GA), and Line-up Competition Algorithm (LCA). Simulation results show that the proposed Improved-TKBBO can solve the large-scale MMSP with non-identical parallel units effectively.

Plantwide control for optimal operation of industrial-scale crude distillation unit processes

ABSTRACTIn this article, a combination of the wavelet neural network framework and the line-up competition algorithm is used to solve the economic optimization algorithm for an industrial-scale atmospheric distillation column (ADC) process. Compared to the relevant measuring data from Sinopec Wuhan Petroleum Group Company, China, the first optimal operating conditions show that the increments of the duties of furnace and pump-arounds of the ADC can effectively improve oil production. In our approach, the preflash column (PFC) coupled with ADC is denoted as an industrial-scale crude distillation unit (CDU) process. Since the PFC can produce light naphtha and reduce the furnace duty and steam consumption of ADC, it is verified that the CDU process provides the higher economic potential than ADC. Based on the second optimal operating conditions, the plantwide control strategy is employed to operate the system safely as well as regulate the outputs of the plant in the presence of inlet perturbations. Within the plantwide control framework, the inventory control aims to keep the controlled variables close to the desired operating condition and the quality control loops use a combination of inferential predictions and feedforward ratios to effectively suppress the temperature spikes of trays and furnaces. Finally, the simulations show that the product quality is guaranteed due to no offset ASTM D86 distillation temperature responses.

Optimization of a crude distillation unit using a combination of wavelet neural network and line-up competition algorithm

Abstract The modeling and optimization of an industrial-scale crude distillation unit (CDU) are addressed. The main specifications and base conditions of CDU are taken from a crude oil refinery in Wuhan, China. For modeling of a complicated CDU, an improved wavelet neural network (WNN) is presented to model the complicated CDU, in which novel parametric updating laws are developed to precisely capture the characteristics of CDU. To address CDU in an economically optimal manner, an economic optimization algorithm under prescribed constraints is presented. By using a combination of WNN-based optimization model and line-up competition algorithm (LCA), the superior performance of the proposed approach is verified. Compared with the base operating condition, it is validated that the increments of products including kerosene and diesel are up to 20% at least by increasing less than 5% duties of intermediate coolers such as second pump-around (PA2) and third pump-around (PA3).

Rule-based scheduling of multi-stage multi-product batch plants with parallel units

Abstract A novel rule-based model for multi-stage multi-product scheduling problem (MMSP) in batch plants with parallel units is proposed. The scheduling problem is decomposed into two sub-problems of order assignment and order sequencing. Firstly, hierarchical scheduling strategy is presented for solving the former sub-problem, where the multi-stage multi-product batch process is divided into multiple sequentially connected single process stages, and then the production of orders are arranged in each single stage by using forward order assignment strategy and backward order assignment strategy respectively according to the feature of scheduling objective. Line-up competition algorithm (LCA) is presented to find out optimal order sequence and order assignment rule, which can minimize total flow time or maximize total weighted process time. Computational results show that the proposed approach can obtain better solutions than those of the literature for all scheduling problems with more than 10 orders. Moreover, with the problem size increasing, the solutions obtained by the proposed approach are improved remarkably. The proposed approach has the potential to solve large size MMSP.

Improved exact and meta-heuristic methods for minimizing makespan of large-size SMSP

The single-stage multi-product scheduling problem (SMSP) in a batch plant with unrelated parallel units has been solved by both exact and meta-heuristic methods. Although new ideas have been put forward continually, large-size instances are still challenging due to the NP-hardness of the problem. This paper extends the fastest mixed integer linear programming (MILP) model so far for SMSP with sequence-dependent changeovers to general cases where unit/order release times need to be considered. Improved versions of the line-up competition algorithm (LUCA) are also proposed. Computational tests with representative examples indicate that the improved versions are more effective in solving large-size SMSP.

Size optimization of stand-alone PV/wind/diesel hybrid power generation systems

Abstract In this paper, a stand-alone PV/wind/diesel hybrid power generation (HPG) system, where the battery bank is assisted to store excess renewable power sources and the diesel generator acts as an emergency backup, is presented. To improve the utilization of the battery bank and avoid the loss of power supply, an improved power management strategy (I-PMS) is proposed. An economic and environmental multi-objective size optimization (EEMSO) problem for HPG systems subject to constraints of battery capacity, energy supply and component size is effectively solved by a multi-objective line-up competition algorithm (MLUCA). A comparison shows that the optimal PV/wind/diesel HPG system is superior to the renewable PV/wind HPG system. Finally, it is also verified that the optimal HPG configuration is robust against large variations of component capacities, costs and CO2-equivalent emissions.

A Lexicographic Optimization and Augmented ϵ-constraint Technique for Short-term Environmental/Economic Combined Heat and Power Scheduling

—In recent years, combined heat and power units have become significant elements in conventional power stations due their numerous merits, including operational cost savings and reduced emissions. In this regard, this article proposes a short-term multi-objective framework for the combined heat and power economic/emission dispatch problem. In addition, to more precisely model the problem, the non-linear forms of fuel cost functions and valve-point loading along with power transmission loss are considered. The objectives of the problem are total cost minimization as well as minimization of pollutant emissions; lexicographic optimization and the augmented epsilon-constraint technique are employed to solve the multi-objective problem. Also, a fuzzy decision making technique has been used to select the most preferred solution among the Pareto solutions. Afterward, a comprehensive comparison is performed between the results obtained from the proposed method and those derived from the non-dominated sorting genetic algorithm II, strength Pareto evolutionary algorithm 2, and multi-objective line-up competition algorithm, verifying the superiority of the presented approach for lower execution time, total cost, and emission. Furthermore, the proposed model is implemented on a large-scale test system while the execution time is rational.

Modeling and Optimization for Short-term Scheduling of Multipurpose Batch Plants

In the past two decades, short-term scheduling of multipurpose batch plants has received significant attention. Most scheduling problems are modeled using either state-task-network or resource-task-network (RTN) process representation. In this paper, an improved mixed integer linear programming model for short-term scheduling of multipurpose batch plants under maximization of profit is proposed based on RTN representation and unit-specific events. To solve the model, a hybrid algorithm based on line-up competition algorithm and linear programming is presented. The proposed model and hybrid algorithm are applied to two benchmark examples in literature. The simulation results show that the proposed model and hybrid algorithm are effective for short-term scheduling of multipurpose batch plants.

Comment on “Multi-objective optimization for combined heat and power economic dispatch with power transmission loss and emission reduction” Shi B, Yan LX, Wu W [Energy 2013; 56: 226–34

This paper comments on the errors of a recently published paper by Shi B, Yan LX, Wu W Energy 2013; 56: 226–34. The Normal Boundary Intersection (NBI)-based optimization technique has been implemented on a test system and the obtained results have been compared to the ones derived from MLCA (multi-objective line-up competition algorithm). The numerical results verify the performance of the proposed technique compared to other method.

Multi-objective optimization for combined heat and power economic dispatch with power transmission loss and emission reduction

The growing concern over environmental considerations and the increasing fuel cost call for operating the energy station in a more economical and environmentally friendly way. This paper develops a multi-objective model for the CHPED (combined heat and power economic dispatch) problem to conventional energy stations, where the competing fuel cost and environmental impact objectives are simultaneously optimized. To model the problem accurately, both non-linear fuel cost functions with valve-point loading effects and power transmission loss are explicitly considered in this model. A novel MLCA (multi-objective line-up competition algorithm) is proposed to handle the problem as a nonlinear constrained multi-objective optimization problem. Some diversity-preserving mechanisms are employed in the MLCA to produce well-distributed Pareto-optimal solutions. Moreover, a fuzzy decision-making process is applied to extract the best compromise nondominated solution from the Pareto-optimal set. The performance of the proposed method is validated by two typical systems. Comparison results have demonstrated the superiority of the proposed approach and confirmed its potential to solve the larger multi-objective CHPED problem.

Rule-Based Scheduling of Single-Stage Multiproduct Batch Plants with Parallel Units

A new rule-based model for single-stage multiproduct scheduling problem (SMSP) in batch plants with parallel units is proposed. The scheduling problem is decomposed into two subproblems of order assignment and order sequencing. A group of order assignment rules are introduced to select suitable units for customer orders. While flexible constraint handling strategies are proposed to eliminate the constraints of forbidden units and unit release times and convert the hard constraint of forbidden sequences to a soft one, leading to any arbitrary order sequence can be synthesized into a legal schedule. The line-up competition algorithm (LUCA) is presented to obtain optimal order sequence and order assignment rule, which can minimize makespan, total tardiness, or total cost. Through comparative study, the proposed approach has demonstrated its effectiveness for solving a large size SMSP.

Multi-period Optimal Operational Planning of Boiler Steam System Based on Line-up Competition Algorithm

In this paper, a multi-period mixed integer nonlinear programming (MINLP) model for boiler steam system, which accounts for both the operating cost for each period and the changeover cost each between periods of operation, is presented. To find an exact solution for the MINLP problem within an allowable computation time, a new hybrid algorithm combining the line-up competition algorithm (LCA) with nonlinear programming is proposed. A boiler steam system contains six boilers is used for case study. And the results show a significant potential for cost saving.

Application of Line-up Competition Algorithm with Variable Weight for Tree Pipe Network Layout Optimization

As a problem of network flows, pipe network layout optimization is a combinatorial problem with variable weight. This paper takes flow variation by different layout scheme into account to the pipeline optimization and obtains the irrigation pipe network layout optimization with variable weight. Line-up competition algorithm (LCA) is introduced to optimize the tree pipe network layout. The corresponding fitness function, encoded mode and mutation are designed on the basis of the characteristic of tree graph expressed by adjacency matrix. It is testified by case study that LCA can obtain the optimal result, and is better in search efficiency and much stable compared with other algorithms.

Apply a novel evolutionary algorithm to the solution of parameter selection problems

In this study, a modified line-up competition algorithm (LCA) is used to solve parameter selection problems. The so-called parameter selection problems contain parameter identification problems and optimal control problems. Once the later problems are transformed by control parametrization, the parameters embedded in both problems are selected by the proposed method under the framework of integration approach. Two parameter identification problems and one optimal control problem are given to demonstrate the use of LCA. The results show that in addition to being insensitive to the initial conditions, LCA is very efficient in solving highly nonlinear parameter selection problems.

Parallel Line-Up Competition Algorithm for Continuous Optimization

This paper presents a new parallel line-up competition algorithm (PLCA) for solving continuous optimization problems. A coarse-grained parallel model has been used to implement the PLCA. In this model, the whole family is equally divided into several subfamily groups that evolve independently in single processor. When migration condition is reached, a portion of fit families in each subfamily group migrate to the neighboring node according to a ring migration topology. The proposed algorithm has been evaluated by solving a set of well-known test functions. Simulation results show that PLCA has good parallel efficiency and superior performance.

The solution of singular optimal control problems using the modified line-up competition algorithm with region-relaxing strategy

In this study, singular optimal control problems are solved by line-up competition algorithm (LCA) under the framework of control parametrization. Two steps that promote the convergence quality of LCA are taken. One is to use normal (Gaussian) sampling policy to replace uniform sampling policy to accelerate initial convergence, while the other is to introduce region-relaxing strategy to enhance the refinement of solutions in final convergence. Four typical examples are given to illustrate the proposed algorithm. The results show that such modifications make LCA more robust and efficient in the solutions of singular optimal control problems.

The solutions of time-delayed optimal control problems by the use of modified line-up competition algorithm

In this article, line-up competition algorithm (LCA), a brand-new nonlinear programming method based on the principle of evolution, is applied to solve time-delay optimal control problems (TDOCPs). The problems are first discretized based on the concept of control vector parametrization, and then solved by LCA. Since the delay differential equations are directly integrated without using the auxiliary procedures such as model conversion and data interpolation, most TDOCPs can be solved very conveniently under such solution framework. Meanwhile, a more efficient sampling strategy is adopted to promote the too slow convergence of the basic LCA. By solving six typical examples, including five pure mathematical problems and one chemical engineering problem, the modified LCA demonstrates a robust and efficient property in optimizing time-delay unsteady systems.

The Application and Improvement of Line-Up Competition Algorithm in Solving Parameter Estimation Problems of Nonlinear Chemical Systems

In this study, line-up computational algorithm (LCA) is applied to estimate the parameters of nonlinear chemical systems under the framework of the integration approach. Four benchmark problems are provided in this paper to demonstrate the robustness and efficiency of LCA. The strategy for improving unsatisfactory convergence of LCA in some problem is also proposed. Additionally, the differences in convergence between LCA and differential evolution are also discussed.

Integrating Controlled Random Search into the Line-Up Competition Algorithm To Solve Unsteady Operation Problems

In this work, a line-up competition algorithm (LCA) is applied to solve the dynamic optimization problems derived from unsteady chemical systems. The problems are first converted ito nonlinear programming problems using the concept of control vector parametrization. The parameters embedded in the converted problems then are selected by LCA. To improve numerical accuracy, the normal (Gaussian) sampling policy is introduced to replace the uniform sampling policy used in basic LCA. Variable step input (VSI) and variable ramp input (VRI) are respectively considered to rebuild the control policy in solutions. Some typical examples are provided to demonstrate the robustness and efficiency of this modification.