Yajie Tian

A tabu search with a new neighborhood search technique applied to flow shop scheduling problems

In this paper, a tabu search algorithm with a new neighborhood technique is proposed for solving flow shop scheduling problems. An idea obtained from a simulation study on the adaptive behavior of a fish school is used to determine the neighborhood search technique. The cooperation and the diversity of job data are defined for describing the data structure by using the given information. According to the definitions, job data are classified into four types, and two of them are treated in this paper. A large number of computational experiments are carried out to study the assignments of both local effort and global effort under a limited search time. The result shows that the allocation of the local and the global effort depends on the job data characteristics, such as cooperation and diversity. The validity of the tabu search algorithm is examined by comparing it with the genetic algorithm. It is found that the proposed tabu search algorithm has better convergence and much shorter computational time than the genetic algorithm.

Bounded optimization of resource allocation among multiple agents using an organizational decision model

Multi-agent System (MAS) can be used to dispose bounded optimization problems with dynamically changing resources because its autonomous distributed management model aspect is fitted to dealing with such external disturbances. One of the problems in multi-agent optimizations is that it is difficult to rigorously define the optimization criteria with respect to the global optimization in advance. Rather, it may depend much on more situated factors such as temporal availability of resources and coexistence of current conflicts, conflicts among what has been already scheduled and what is to be scheduled. In this paper, an organizational model called Garbage Can Model (GCM) is introduced. In GCM, through its three decision-making strategies and the fluidities of problems and resources, solutions made by an individual agent are concerned with several agents that are co-existing in the environment. The problems allocated to each agent are solved not only by an agents own efforts, but also by the change of problem solving status of other agents. Our simulation experiment shows that GCM is a preferred framework for multi-agent optimization problems in dealing with the above difficulties.

Case‐based automatic programming in robotic assembly production

Purpose – The purpose of this paper is to present a case‐based system for offline automatic programming in robotic assembly production. This system can reuse past learned robot programs to generate programs for new assembly tasks.Design/methodology/approach – The approach used in this paper is case‐based reasoning. The assembly knowledge acquired from the robot program for an assembly task is retained in a case, which is composed of the primitive task description and the corresponding robot program schema. The retained cases are retrieved by matching features of their primitive task descriptions, and are reused to automatically program for new tasks by instantiating their robot program schemata.Findings – A case not only can be reused as a whole, but also can be reused partly by synthesizing different parts of several cases to generate a program for a new task in a variant environment.Practical implications – The teaching time of robots can be greatly reduced. This helps to introduce robots into small and...

A relaxation method for the three-dimensional loading capacitated vehicle routing problem

This paper addresses the three-dimensional loading capacitated vehicle routing problem which is strongly NP-hard for it generalizes two of the most well known NP-hard problems: the container loading problem and the vehicle routing problem. A relaxation method is proposed for solving the problem. In the proposed method, the loading constraint is replaced by the constraint of volume ratio, which greatly reduces the computing time. The method achieves excellent results for public available test cases with reasonable computing time.

A priority-considering approach for the multiple container loading problem

A priority-considering heuristic approach is proposed to solve the multiple container loading problem, i.e., the problem of packing a given set of three-dimensional rectangular items into multiple containers to make the maximum use of the container space. The items with large volume are usually difficult to make efficient use of the container space. These items are set higher priority over other items, and preferentially assigned into the containers. Within the proposed approach an algorithm is used for solving the single container loading problem under the constraint of priority. The proposed approach achieves excellent results for the test cases suggested by Ivancic et al. and Mohanty et al. with reasonable computing time.

EXPLANATION-BASED MANIPULATOR LEARNING: ACQUISITION OF ASSEMBLING TECHNIQUE THROUGH OBSERVATION

This paper describes a robot manipulator system currently under development which learns assembling technique from observation of skilled workers demonstrations. The system is an application of EBL (Explanation-Based Learning) to the robot manufacturing domain. The system improves the operating capability of the robot manipulator through observing, analyzing the skilled workers assembling operation, and learning the assembling technique, that is, the tacit knowledge of skilled workers which makes them complete the assembling task more efficiently. The learning process is a knowledge-based deduction approach, requiring sufficient background knowledge, to understand the observed sequence.

Cooperative negotiation strategy in multi-agent system

Cooperation is considered to be very important for the negotiation among autonomous agents in a multi-agent system (MAS). Papers on cooperation and negotiation in MAS have provided a variety of approaches. However, the case of cooperation and negotiation in dealing with real-world problems with many complicated restrictions has not been thoroughly examined. In this paper, a cooperative negotiation strategy (CNS) is proposed for solving real-world problems such as the loading, allocating and scheduling problem (LASP). CNS is based on the idea of drum-buffer-rope (DBR) in the theory of constraints (TOC), heuristic algorithms, and the knowledge of skilled workers in the real world. Experimental results show that the proposed CNS is valid for relaxing the conflicts among agents and excluding deadlock states.

A local search based on simulations of fish school behavior and its application to an optimal scheduling problem

In this paper, an improved local search (ILS) method is proposed based on the idea obtained from the simulations of fish school behavior and is applied to an optimal scheduling problem of parallel machines. A rough definition of cooperation and diversity of job data is given for describing the characteristics of the system. A neighborhood of a solution is defined based on the system characteristics. A checking set is presented for avoiding overlapping local searches and unnecessary computation efforts. Computation results show that the quality of the suboptimal solution critically depends on the size of the neighborhood. By comparing ILS with the autonomous decentralized (ADS) algorithm and the genetic algorithm (GA), we observe that the proposed ILS has better convergence property than ADS and GA under an assumption that the total number of search points is limited and fixed irrespective of algorithm.

A simulation study on adaptability to environmental variations based on ecological systems

Many ecological systems have an ability to adapt themselves to environmental variations and the adaptability varies with the structures of systems. In the paper, as an example of ecological systems, two kinds of fish school models with different characteristics are presented for investigating the adaptability to environmental variations. The model parameters are estimated by using the observation data obtained from water tank experiments. Various simulations are carried out for the case where an obstacle is set in the behavior space. The adaptability to environmental variations is examined by using the simulation results.

A TOC-based heuristic algorithm for solving a two-row pattern container loading problem

The container loading problem, a real hard problem, is usually difficult to obtain even a suboptimal solution because of not only multiple complicated restrictions but also of multiple objectives. In this paper, a heuristic algorithm is proposed for solving the two-row pattern of the container loading problem in real-world. The algorithm is based on Drum-Buffer-Rope (DBR) presented in the Theory of Constraints (TOC) and the multiagent cooperative negotiation model. A particular attention is focused on improving the constrained agent by striving for the trade-off of restrictions and cooperative negotiations, so that the final solution can arrive its biggest profit.