Heuristic algorithm for RCPSP with the objective of minimizing activities' cost

Resource-constrained project scheduling problem（RCPSP） is an important problem in research on project management. But there has been little attention paid to the objective of minimizing activities＇ cost with the resource constraints that is a critical sub-problem in partner selection of construction supply chain management because the capacities of the renewable resources supplied by the partners will effect on the project scheduling. Its mathematic model is presented firstly, and analysis on the characteristic of the problem shows that the objective function is non-regular and the problem is NP-complete following which the basic idea for solution is clarified. Based on a definition of preposing activity cost matrix, a heuristic algorithm is brought forward. Analyses on the complexity of the heuristics and the result of numerical studies show that the heuristic algorithm is feasible and relatively effective.

Designing an optimal stope layout for underground mining based on a heuristic algorithm

An optimal layout or three-dimensional spatial distribution of stopes guarantees the maximum profitability over life span of an underground mining operation.Thus,stope optimization is one of the key areas in underground mine planning practice.However,the computational complexity in developing an optimal stope layout has been a reason for limited availability of the algorithms offering solution to this problem.This article shares a new and efficient heuristic algorithm that considers a three-dimensional ore body model as an input,maximizes the economic value,and satisfies the physical mining and geotechnical constraints for generating an optimal stope layout.An implementation at a copper deposit demonstrates the applicability and robustness of the algorithm.A parallel processing based modification improving the performance of the original algorithm in terms of enormous computational time saving is also presented.

A new heuristic algorithm for general integer linear programming problems

A new heuristic algorithm is proposed for solving general integer linear programming problems. In the algorithm, the objective function hyperplane is used as a cutting plane, and then by introducing a special set of assistant sets, an efficient heuristic search for the solution to the integer linear program is carried out in the sets on the objective function hyperplane. A simple numerical example shows that the algorithm is efficient for some problems, and therefore, of practical interest.

Analysis of Petri net model and task planning heuristic algorithms for product reconfiguration

Reconfiguration planning is recognized as an important factor for reducing the cost of manufacturing reconfigurable products, and the associated main task is to generate a set of optimal or near-optimal reconfiguration sequences using some effect algorithms. A method is developed to generate a Petri net as the reconfiguration tree to represent two-state-transit of product, which solved the representation problem of reconfiguring interfaces replacement. Relating with this method, two heuristic algorithms are proposed to generate task sequences which considering economics to search reconfiguration paths effectively. At last, an objective evaluation is applied to compare these two heuristic algorithms to other ones. The developed reconfiguration task planning heuristic algorithms can generate better strategies and plans for reconfiguration. The research finds are exemplified with struts reconfiguration of reconfigurable parallel kinematics machine （RPKM）.

A Multi-stage Heuristic Algorithm for Matching Problem in the Modified Miniload Automated Storage and Retrieval System of E-commerce

E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can＇t fulfill the storage and picking demands of e-commerce sufficiently. In this paper, a modified miniload automated storage/retrieval system is designed to fit these new characteristics of e-commerce in logistics. Meanwhile, a matching problem, concerning with the improvement of picking efficiency in new system, is studied in this paper. The problem is how to reduce the travelling distance of totes between aisles and picking stations. A multi-stage heuristic algorithm is proposed based on statement and model of this problem. The main idea of this algorithm is, with some heuristic strategies based on similarity coefficients, minimizing the transportations of items which can not arrive in the destination picking stations just through direct conveyors. The experimental results based on the cases generated by computers show that the average reduced rate of indirect transport times can reach 14.36% with the application of multi-stage heuristic algorithm. For the cases from a real e-commerce distribution center, the order processing time can be reduced from 11.20 h to 10.06 h with the help of the modified system and the proposed algorithm. In summary, this research proposed a modified system and a multi-stage heuristic algorithm that can reduce the travelling distance of totes effectively and improve the whole performance of e-commerce distribution center.

High speed ghost imaging based on a heuristic algorithm and deep learning

We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.

A Heuristic Algorithm for the Two-Machine Flowshop Group Scheduling Problem

This paper presents the two-machine flowshop group scheduling problem with the optimal objective of maximum lateness. A dominance rule within group and a dominance rule between groups are established. These dominance rules along with a previously established dominance rule are used to develop a heuristic algorithm. Experimental results are given and analyzed.

Heuristic Algorithm for Minimizing the Electricity Cost of Smart House

This framework proposes a heuristic algorithm based on LP （linear programming） for optimizing the electricity cost in large residential buildings, in a smart grid environment. Our heuristic tackles large multi-objective energy allocation problem （large number of appliances and high time resolution）. The primary goal is to reduce the electricity bills, and discomfort factor. Also, increase the utilization of domestic renewable energy, and reduce the running time of the optimization algorithm. Our heuristic algorithm uses linear programming relaxation, and two rounding strategies. The first technique, called CR （cumulative rounding）, is designed for thermostatic appliances such as air conditioners and electric heaters, and the second approach, called MCR （minimum cost rounding）, is designed for other interruptible appliances. The results show that the proposed heuristic algorithm can be used to solve large MILP （mixed integer linear programming） problems and gives a decent suboptimal solution in polynomial time.

HEURISTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR AIR COMBAT DECISION-MAKING ON CMTA

Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.

Heuristic algorithm based on the principle of minimum total potential energy(HAPE):a new algorithm for nesting problems

We present a new algorithm for nesting problems.Many equally spaced points are set on a sheet,and a piece is moved to one of the points and rotated by an angle.Both the point and the rotation angle constitute the packing attitude of the piece.We propose a new algorithm named HAPE(Heuristic Algorithm based on the principle of minimum total Potential Energy) to find the optimal packing attitude at which the piece has the lowest center of gravity.In addition,a new technique for polygon overlap testing is proposed which avoids the time-consuming calculation of no-fit-polygon(NFP).The detailed implementation of HAPE is presented and two computational experiments are described.The first experiment is based on a real industrial problem and the second on 11 published benchmark problems.Using a hill-climbing(HC) search method,the proposed algorithm performs well in comparison with other published solutions.

A two-stage metaheuristic algorithm for the dynamic vehicle routing problem in Industry 4.0 approach

Industry 4.0 is a concept that assists companies in developing a modern supply chain(MSC)system when they are faced with a dynamic process.Because Industry 4.0 focuses on mobility and real-time integration,it is a good framework for a dynamic vehicle routing problem(DVRP).This research works on DVRP.The aim of this research is to minimize transportation cost without exceeding the capacity constraint of each vehicle while serving customer demands from a common depot.Meanwhile,new orders arrive at a specific time into the system while the vehicles are executing the delivery of existing orders.This paper presents a two-stage hybrid algorithm for solving the DVRP.In the first stage,construction algorithms are applied to develop the initial route.In the second stage,improvement algorithms are applied.Experimental results were designed for different sizes of problems.Analysis results show the effectiveness of the proposed algorithm.

A Heuristic Algorithm for P-Cycles Configuration in WDM Optical Networks

Aiming at minimizing spare capacity for optical WDM networks, we propose a new heuristic algorithm for preconfigured protection cycle (p-cycle) design. Numerical results show that the spare capacity obtained by our new algorithm is very close to the optimal solution.

Enterprise-wide optimization of integrated planning and scheduling for refinery-petrochemical complex with heuristic algorithm

This paper focuses on the integrated problem of long-term planning and short-term scheduling in a largescale refinery-petrochemical complex,and considers the overall manufacturing process from the upstream refinery to the downstream petrochemical site.Different time scales are incorporated from the planning and scheduling subproblems.At the end of each discrete time period,additional constraints are imposed to ensure material balance between different time scales.Discrete time representation is applied to the planning subproblem,while continuous time is applied to the scheduling of ethylene cracking and polymerization processes in the petrochemical site.An enterprise-wide mathematical model is formulated through mixed integer nonlinear programming.To solve the problem efficiently,a heuristic algorithm combined with a convolutional neural network(CNN),is proposed.Binary variables are used as the CNN input,leading to the integration of a data-driven approach and classical optimization by which a heuristic algorithm is established.The results do not only illustrate the detailed operations in a refinery and petrochemical complex under planning and scheduling,but also confirm the high efficiency of the proposed algorithm for solving large-scale problems.

A Heuristic Algorithm for Task Scheduling Based on Mean Load on Grid

Efficient task scheduling is critical to achieving high performance on grid computing environment. The task scheduling on grid is studied as optimization problem in this paper. A heuristic task scheduling algorithm satisfying resources load balancing on grid environment is presented. The algorithm schedules tasks by employing mean load based on task predictive execution time as heuristic information to obtain an initial scheduling strategy. Then an optimal scheduling strategy is achieved by selecting two machines satisfying condition to change their loads via reassigning their tasks under the heuristic of their mean load. Methods of selecting machines and tasks are given in this paper to increase the throughput of the system and reduce the total waiting time. The efficiency of the algorithm is analyzed and the performance of the proposed algorithm is evaluated via extensive simulation experiments. Experimental results show that the heuristic algorithm performs significantly to ensure high load balancing and achieve an optimal scheduling strategy almost all the time. Furthermore, results show that our algorithm is high efficient in terms of time complexity.

Multilocation Inventory Models and their Heuristic Algorithm

In this paper, we first construct multilocation inventory models and design a heuristic approximate algorithm of the inventory models. Finally we analyze timing results for algorithm′s implementation on a parallel computer.

Heuristics Scheduling Algorithm on the Links Scheduling of Satellite-Ground Clock Synchronization and Ephemeris Uploading in COMPASS System

The execution process of satellite-ground clock synchronization and ephemeris uploading in the system is analyzed,as well as their characterized operation and their relationship.Based on the analysis of the scheduling goal and constraint character,a heuristics rule-based multi-stage link scheduling algorithm was put forward.The algorithm distinguishes the on-off-frontier satellites from the others and schedules them by turns.The paper presented the main flow as well as the detailed design of the rule.Finally based on the current COMPASS global system,some typical resources and constraints are selected to generate an instance.Then the comparison analysis between the heuristics scheduling algorithm and three other traditional scheduling strategies are carried out.The result shows the validity and reasonability of the multi-stage strategy.

Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm(OCA) with that of a heuristic rules-based algorithm(HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that(i) capacity under the HRA(denoted as C_(H)) is smaller than capacity under the OCA;(ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA;(iii) when the inflow rate is smaller than C_(H), the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost;(iv) when the inflow rate is larger than C_(H), the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.

Heuristic Reduction Algorithm Based on Pairwise Positive Region

To guarantee the optimal reduct set, a heuristic reduction algorithm is proposed, which considers the distinguishing information between the members of each pair decision classes. Firstly the pairwise positive region is defined, based on which the pairwise significance measure is calculated between the members of each pair classes. Finally the weighted pairwise significance of attribute is used as the attribute reduction criterion, which indicates the necessity of attributes very well. By introducing the noise tolerance factor, the new algorithm can tolerate noise to some extent. Experimental results show the advantages of our novel heuristic reduction algorithm over the traditional attribute dependency based algorithm.

A Fuzzy Reasoning System and Its Heuristic Inference Algorithm

Based on a presented inference algorithm of fuzzy reasoning, a fuzzy reasoning system is made up. A method of modeling the fuzzy reasoning system, and the setting up of the reasoning knowledge based and reasoning rules are studied in this paper. Then a heuristic inference algorithm is presented according to the system.

HEURISTIC STUDY OF FLOWSHOP SCHEDULING TO MINIMIZE MEAN FLOW TIME WITH LOT TRANSFER CONSIDERED

Lot scheduling problem with idle time transfer between processes to minimize mean flow time is very important because to minimize mean flow time is to minimize work in process. But the problem is NP hard and no polynomial algorithm exists to guarantee optimal solution. Based the analysis the mathematical structure of the problem, the paper presents a new heuristic algorithm. Computer simulation shows that the proposed heuristic algorithm performs well in terms of both quality of solution and execution speed.