Yunqing Rao

A very fast TS/SA algorithm for the job shop scheduling problem

The job shop scheduling problem (JSP) is one of the most notoriously intractable NP-complete optimization problems. Over the last 10-15 years, tabu search (TS) has emerged as an effective algorithmic approach for the JSP. However, the quality of solutions found by tabu search approach depends on the initial solution. To overcome this problem and provide a robust and efficient methodology for the JSP, the heuristics search approach combining simulated annealing (SA) and TS strategy is developed. The main principle of this approach is that SA is used to find the elite solutions inside big valley (BV) so that TS can re-intensify search from the promising solutions. This hybrid algorithm is tested on the standard benchmark sets and compared with the other approaches. The computational results show that the proposed algorithm could obtain the high-quality solutions within reasonable computing times. For example, 17 new upper bounds among the unsolved problems are found in a short time.

A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem

Tabu search (TS) algorithms are among the most effective approaches for solving the job shop scheduling problem (JSP) which is one of the most difficult NP-complete problems. However, neighborhood structures and move evaluation strategies play the central role in the effectiveness and efficiency of the tabu search for the JSP. In this paper, a new enhanced neighborhood structure is proposed and applied to solving the job shop scheduling problem by TS approach. Using this new neighborhood structure combined with the appropriate move evaluation strategy and parameters, we tested the TS approach on a set of standard benchmark instances and found a large number of better upper bounds among the unsolved instances. The computational results show that for the rectangular problem our approach dominates all others in terms of both solution quality and performance.

A new hybrid GA/SA algorithm for the job shop scheduling problem

Among the modern heuristic methods, simulated annealing (SA) and genetic algorithms (GA) represent powerful combinatorial optimization methods with complementary strengths and weaknesses. Borrowing from the respective advantages of the two paradigms, an effective combination of GA and SA, called Genetic Simulated Algorithm (GASA), is developed to solve the job shop scheduling problem (JSP). This new algorithm incorporates metropolis acceptance criterion into crossover operator, which could maintain the good characteristics of the previous generation and reduce the disruptive effects of genetic operators. Furthermore, we present two novel features for this algorithm to solve JSP. Firstly, a new full active schedule (FAS) based on the operation-based representation is presented to construct schedule, which can further reduce the search space. Secondly, we propose a new crossover operator, named Precedence Operation Crossover (POX), for the operation-based representation. The approach is tested on a set of standard instances and compared with other approaches. The Simulation results validate the effectiveness of the proposed algorithm.

A novel algorithm for economic load dispatch of power systems

Economic load dispatch (ELD) is of great significance for energy saving and emission reduction in power systems. However, many practical constraints and nonlinear characteristics such as valve point effects make this problem a nonlinear constrained optimization problem which is difficult to be solved by traditional optimization techniques. In order to solve this problem effectively, this paper introduces a novel method, chaotic teaching-learning-based optimization with Levy flight (CTLBO). In the proposed CTLBO, the population is divided into two parts: one is evolved through teaching-learning process, while another part is performed by Levy flight. Then a chaos perturbation is implemented on the randomly chosen part of population in terms of diversification. Moreover, the proposed method is combined with penalty function to address the constraints. Several numerical cases are adopted and solved to illustrate the effectiveness of the proposed algorithm. And the experimental results are analyzed and compared with existing algorithms, which show that the proposed method outperforms other algorithms and has achieved a significant improvement.

Novel multi-objective resource allocation and activity scheduling for fourth party logistics

In order to reduce logistic costs, the scheduling of logistic tasks and resources for fourth party logistics (4PL) is studied. Current scheduling models only consider costs and finish times of each logistic resource or task. Not generally considered are the joint cost and time between two adjacent activities for a resource to process and two sequential activities of a task for two different resources to process are ignored. Therefore, a multi-objective scheduling model aiming at minimizing total operation costs, finishing time and tardiness of all logistic tasks in a 4PL is proposed. Not only are the joint cost and time of logistic activities between two adjacent activities and two sequential activities included but the constraints of resource time windows and due date of tasks are also considered. An improved nondominated sorting genetic algorithm (NSGA-II) is presented to solve the model. The validity of the proposed model and algorithm are verified by a corresponding case study.

Agile manufacturing system control based on cell re-configuration

The control of an agile manufacturing system (AMS) is expected to be flexible, open, scalable and re-configurable so as to tackle the more complex and uncertain information flows. To meet these requirements, we propose agent-based control architecture for AMS, under which the functions of task planning, scheduling and dynamic control are integrated seamlessly. First of all, this paper introduces the concept of RMC (re-configurable manufacturing cell), based on which, we construct the control architecture for AMS in compliance with multi-agent system (MAS). The whole control process under the architecture comprises two hierarchies, i.e. the upper one for order planning and RMC forming and the lower one for task scheduling within each RMC. For the upper hierarchy, we establish a linear integer programming (LIP)-based mathematical model and a MAS-based dynamic process model, and present a two-step approach to order planning and RMC forming. For the lower hierarchy, we develop the scheduling model, a method based on the bidding mechanism from contract net, and describe the rescheduling mechanism in the control system. To illustrate the methodology proposed in the paper, a simulation study is thoroughly discussed. Our studies demonstrate that the RMC-based control architecture provides an AMS with an optimal, dynamic and flexible mechanism of responding to an unpredictable manufacturing environment, which is crucial to achieve agility for the whole manufacturing system.

Scheduling a single vehicle in the just-in-time part supply for a mixed-model assembly line

This paper focuses on the scheduling of a single vehicle, which delivers parts from a storage centre to workstations in a mixed-model assembly line. In order to avoid part shortage and to cut down total inventory holding and travelling costs, the destination workstation, the part quantity and the departure time of each delivery have to be specified properly according to predetermined assembly sequences. In this paper, an optimisation model is established for the configuration that only one destination workstation is involved within each delivery. Four specific properties of the problem are deduced, then a backward-backtracking approach and a hybrid GASA (genetic algorithm and simulated annealing) approach are developed based on these properties. Both two approaches are applied to several groups of instances with real-world data, and results show that the GASA approach is efficient even in large instances. Furthermore, the existence of feasible solutions (EOFS) is analysed via instances with different problem settings, which are obtained by an orthodox experimental design (ODE). An analysis of variance (ANOVA) shows that the buffer capacity is the most significant factor influencing the EOFS. Besides this, both the assembly sequence length and distances to workstations also have noticeable impacts.

A CORBA- and MAS-based architecture for agile collaborative manufacturing systems

An architecture for building up agile collaborative manufacturing systems (ACMS), which is based on common object request broker architecture (CORBA) and multi-agent system (MAS) paradigm, is proposed in the current paper. First, the diverse manufacturing resources, which may be heterogeneous and physically distributed, are MAS-based agentified and CORBA-based encapsulated as the resource agents. Second, the source agents are registered at the system manager agent (SMA) as member agents and subsequently integrated under the proposed architecture to form an ACMS. Third, the member agents communicate and interact with each other to conduct collaborative manufacturing by means of agent interoperation and human-machine interaction. In order to validate the methodology, an experimental system is developed, where several heterogeneous manufacturing resources are well-integrated via their agents in a plug-and-play manner to establish an ACMS prototype. The experiments demonstrate that the proposed architecture, characterized by distribution and openness, provides a feasible solution for developing ACMS, where integratability, reconfigurability, flexibility and agility are achieved.

RFID-enabled real-time PBS monitoring for automobile assembly factory

In automotive industry, painted body storage (PBS) is generally employed as a buffering and re-sequencing site, which has the capabilities of levelling unmatched production capacity and balancing different optimisation objects between paint shop and trim shop. Takt time becomes shorter with the growth of automotive production, and product configurations become more diverse with the increase of customising requirements. However, PBSs in many automotive enterprises are still manual or semi-manual monitored and controlled. Since manual operation is time consuming and error prone, manually operated PBS has become efficient bottleneck of production in automobile factory. In this article, a radio frequency identification (RFID) enabled real-time monitoring system (RRMS) is developed to improve visualisation and controllability of the PBS in a Chinese multi-purpose vehicle manufacturer. RRMS improves the efficiency of operation and makes PBS into a transparent, open and controllable system. Moreover, integrated with manufacturing execution system, RRMS provides a longer deterministic list of about-to-be-trimmed car-bodies for assembly sequencing and material delivering, which doubles the time in advance for production supervisor and material deliverer. As an indispensable part of the practical application research, this article finally discusses some non-technical issues in the system designing and implementation.

On-Line Simulation for Shop Floor Control in Manufacturing Execution System

This paper presents and discusses the structure and components of a novel on-line simulation system for real-time shop floor control in manufacturing execution system (MES). The framework of the simulation system is constructed in association with a MES. The on-line simulation system can collect data from physical shop floor and communicate with a scheduling controller through the MES. Furthermore, MES can provide much of the dynamic information required by the simulation model, and can also execute the shop floor control strategy which is solved by the simulation system. A multi-pass simulation method is introduced and applied to control the shop floor.