Henry Y. K. Lau

Integrated scheduling of handling equipment at automated container terminals

Abstract To improve the productivities of an automated container terminal, it is important to schedule different types of handling equipment in an integrated way. A mixed-integer programming model, which considers various constraints related to the integrated operations between different types of handling equipment, is formulated. A heuristic algorithm, called multi-layer genetic algorithm (MLGA) is developed with a view to overcome the computation difficulty for solving the mathematical model. A numerical experimentation is carried out in order to evaluate the performance of the algorithm.

An AIS-based hybrid algorithm for static job shop scheduling problem

A static job shop scheduling problem (JSSP) is a class of JSSP which is a combinatorial optimization problem with the assumption of no disruptions and previously known knowledge about the jobs and machines. A new hybrid algorithm based on artificial immune systems (AIS) and particle swarm optimization (PSO) theory is proposed for this problem with the objective of makespan minimization. AIS is a metaheuristics inspired by the human immune system. Its two theories, namely, clonal selection and immune network theory, are integrated with PSO in this research. The clonal selection theory builds up the framework of the algorithm which consists of selection, cloning, hypermutation, memory cells extraction and receptor editing processes. Immune network theory increases the diversity of antibody set which represents the solution repertoire. To improve the antibody hypermutation process to accelerate the search procedure, a modified version of PSO is inserted. This proposed algorithm is tested on 25 benchmark problems of different sizes. The results demonstrate the effectiveness of the PSO algorithm and the specific memory cells extraction process which is one of the key features of AIS theory. By comparing with other popular approaches reported in existing literatures, this algorithm shows great competitiveness and potential, especially for small size problems in terms of computation time.

Immunity-based autonomous guided vehicles control

The human immune system is a self-organizing and highly distributed multi-agent system. These properties impart a high degree of robustness and performance that has created great interest in implementing engineering systems. This adopted engineering analogue is called Artificial Immune System (AIS). This paper presents an immunity-based control framework which has the ability to detect changes, adapt to dynamic environment and coordinate vehicles activities for goals achievement, to deploy a fleet of AGVs for material handling in an automated warehouse. A robust and flexible automated warehousing system is achieved through the non-deterministic and fully decentralized origination of AGVs.

A decentralized control framework for modular robots

Distributed control paradigm offers robustness, scalability, and simplicity to the control and organization of module based systems. MSR (modular self-reconfigurable) robot is a class of robot that best demonstrate the effectiveness of distributed systems, as all modules in the robot are individuals that perform their own actuation and computation; the behavior of the complete robot is a collective behavior of all independent modules. In this paper, a general control framework, named general suppression framework, is proposed and a distributed control system based on the framework is presented. The control system is designed to control a set of MSR robots configured into a planar manipulator arm. All modules in the manipulator arm contain their own processing and actuation units, which allow them to evaluate and react to the environment independently. The modules can perform passive communication with their immediate neighbors and can exhibit aggressive or tolerant behavior based on the environment change to generate emergent group behaviors. A simulation program is developed to demonstrate the effectiveness of the distributed system in controlling the module based planar manipulator arm.

An AIS-based hybrid algorithm with PDRs for multi-objective dynamic online job shop scheduling problem

The dynamic online job shop scheduling problem (JSSP) is formulated based on the classical combinatorial optimization problem - JSSP with the assumption that new jobs continuously arrive at the job shop in a stochastic manner with the existence of unpredictable disturbances during the scheduling process. This problem is hard to solve due to its inherent uncertainty and complexity. This paper models this class of problem as a multi-objective problem and solves it by hybridizing the artificial intelligence method of artificial immune systems (AIS) and priority dispatching rules (PDRs). The immune network theory of AIS is applied to establish the idiotypic network model for priority dispatching rules to dynamically control the dispatching rule selection process for each operation under the dynamic environment. Based on the defined job shop situations, the dispatching rules that perform best under specific environment conditions are selected as antibodies, which are the key elements to construct the idiotypic network. Experiments are designed to demonstrate the efficiency and competitiveness of this model.

Immunity-based evolutionary algorithm for optimal global container repositioning in liner shipping

Global container repositioning in liner shipping has always been a challenging problem in container transportation as the global market in maritime logistics is complex and competitive. Supply and demand are dynamic under the ever changing trade imbalance. A useful computation optimization tool to assist shipping liners on decision making and planning to reposition large quantities of empty containers from surplus countries to deficit regions in a cost effective manner is crucial. A novel immunity-based evolutionary algorithm known as immunity-based evolutionary algorithm (IMEA) is developed to solve the multi-objective container repositioning problems in this research. The algorithm adopts the clonal selection and immune suppression theories to attain the Pareto optimal front. The proposed algorithm was verified with benchmarking functions and compared with four optimization algorithms to assess its diversity and spread. The developed algorithm provides a useful means to solve the problem and assist shipping liners in the global container transportation operations in an optimized and cost effective manner.

A cooperative control model for multiagent-based material handling systems

This paper presents an Artificial Immune System (AIS)-based model for cooperative control of multiagent systems. This cooperative control model describes collective behaviors of autonomous agents, known as the AIS agents that are exemplified by the regulated activities performed by individual agents under the computation paradigm of Artificial Immune System. The regulations and emergence of agent behaviors are derived from the immune threshold measures that determine those activities performed by the AIS agents at an individual level. These threshold measures together with the collective behavioral model defined the cooperative control of the AIS-based control framework under which AIS agents behave and act strategically according to the changing environment. The cooperative control model is presented under the three domains, namely exploration, achievement and cooperation domains where AIS agents operate. In this research, we implemented the proposed cooperative control model with a case study of automated material handling with a group of AIS agents that cooperate to achieve the defined tasks.

An adaptive large neighborhood search heuristic for solving a robust gate assignment problem

Robust gate assignment problem with transfer passengers and tows is considered.A large neighborhood search heuristic is proposed to solve the integrated problem.Extensive experiments are conducted to justify the performance of our approach. With the rapid growth of air traffic demand, airport capacity becomes a major bottleneck within the air traffic control systems. Minor disturbances may have a large impact on the airport surface operations due to the overly tight schedules, which results in frequent gate conflict occurrences during airports daily operations. A robust gate schedule that is resilient to disturbances is essential for an airport to maintain a good performance. Unfortunately, there is no efficient expert system available for the airport managers to simultaneously consider the traditional cost (the aircraft tow cost, transfer passenger cost) and the robustness. To fill this gap, in this paper, we extend the traditional gate assignment problem and consider a wider scope, in which the traditional costs and the robustness are simultaneously considered. A mathematical model is first built, which leads to a complex non-linear model. To efficiently solve this model, an adaptive large neighborhood search (ALNS) algorithm is then designed. We novelly propose multiple local search operators by exploring the characteristics of the gate assignment problem. The comparison with the benchmark algorithm shows the competitiveness of proposed algorithm in solving the considered problem. Moreover, the proposed methodology also has great potential from the practical perspective since it can be easily integrated into current expert systems to help airport managers make satisfactory decisions.

A Load Scattering Algorithm for Dynamic Routing of Automated Material Handling Systems

An agent-based dynamic routing strategy for a generic automated material handling systems (AMHS) is developed. The strategy employs an agent-based paradigm in which the control points of a network of AMHS components are modelled as cooperating node agents. With the inherent features of route discovery a set of shortest and near-shortest path, an average-flow route selection algorithm is developed to scatter the load of an AMHS. Their performance is investigated through a detailed simulation study. The performance of the proposed dynamic routing strategy is benchmarked with the shortest path algorithm. The results of the simulation experiments are presented and their performance compared under a number of performance indices including the hop count, flow and ability to balance network loading

Immunology-based control framework for multi-jointed redundant manipulators

Artificial immune system (AIS) has recently been actively researched with a number of emerging engineering applications that has capitalized from its characteristics including self-organization, distributive control, knowledge mapping and fault tolerance. This paper reports the development of an AIS paradigm for the distributive control of a multi-jointed, redundant manipulator. Traditionally, manipulator control is achieved by analytical solutions. By adopting a multi-agent-based control paradigm, a multi-jointed manipulator can be thought of as a group of separately controlled agents. In this paper, we investigate the viability of a multi-agent immunology-based control framework for the trajectory control of a multi-jointed redundant manipulator.