Sunney Yung-Sun Leung

Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: a case study based on the apparel industry

An effective job shop scheduling (JSS) in the manufacturing industry is helpful to meet the production demand and reduce the production cost, and to improve the ability to compete in the ever increasing volatile market demanding multiple products. In this paper, a universal mathematical model of the JSS problem for apparel assembly process is constructed. The objective of this model is to minimize the total penalties of earliness and tardiness by deciding when to start each orders production and how to assign the operations to machines (operators). A genetic optimization process is then presented to solve this model, in which a new chromosome representation, a heuristic initialization process and modified crossover and mutation operators are proposed. Three experiments using industrial data are illustrated to evaluate the performance of the proposed method. The experimental results demonstrate the effectiveness of the proposed algorithm to solve the JSS problem in a mixed- and multi-product assembly environment.

Intelligent production control decision support system for flexible assembly lines

In this study, a production control problem on a flexible assembly line (FAL) with flexible operation assignment and variable operative efficiencies is investigated. A mathematical model of the production control problem is formulated with the consideration of the time-constant learning curve to deal with the change of operative efficiency in real-life production. An intelligent production control decision support (PCDS) system is developed, which is composed of a radio frequency identification technology-based data capture system, a PCDS model comprising a bi-level genetic optimization process and a heuristic operation routing rule is developed. Experimental results demonstrated that the proposed PCDS system could implement effective production control decision-making for solving the FAL.

A Genetic-Algorithm-Based Optimization Model for Solving the Flexible Assembly Line Balancing Problem With Work Sharing and Workstation Revisiting

This paper investigates a flexible assembly line balancing (FALB) problem with work sharing and workstation revisiting. The mathematical model of the problem is presented, and its objective is to meet the desired cycle time of each order and minimize the total idle time of the assembly line. An optimization model is developed to tackle the addressed problem, which involves two parts. A bilevel genetic algorithm with multiparent crossover is proposed to determine the operation assignment to workstations and the task proportion of each shared operation being processed on different workstations. A heuristic operation routing rule is then presented to route the shared operation of each product to an appropriate workstation when it should be processed. Experiments based on industrial data are conducted to validate the proposed optimization model. The experimental results demonstrate the effectiveness of the proposed model to solve the FALB problem.

Genetic optimization of order scheduling with multiple uncertainties

In this paper, the order scheduling problem at the factory level, aiming at scheduling the production processes of each production order to different assembly lines is investigated. Various uncertainties, including uncertain processing time, uncertain orders and uncertain arrival times, are considered and described as random variables. A mathematical model for this order scheduling problem is presented with the objectives of maximizing the total satisfaction level of all orders and minimizing their total throughput time. Uncertain completion time and beginning time of production process are derived firstly by using probability theory. A genetic algorithm, in which the representation with variable length of sub-chromosome is presented, is developed to generate the optimal order scheduling solution. Experiments are conducted to validate the proposed algorithm by using real-world production data. The experimental results show the effectiveness of the proposed algorithm.

A hybrid particle swarm optimization and its application in neural networks

In this paper, a novel particle swarm optimization model for radial basis function neural networks (RBFNN) using hybrid algorithms to solve classification problems is proposed. In the model, linearly decreased inertia weight of each particle (ALPSO) can be automatically calculated according to fitness value. The proposed ALPSO algorithm was compared with various well-known PSO algorithms on benchmark test functions with and without rotation. Besides, a modified fisher ratio class separability measure (MFRCSM) was used to select the initial hidden centers of radial basis function neural networks, and then orthogonal least square algorithm (OLSA) combined with the proposed ALPSO was employed to further optimize the structure of the RBFNN including the weights and controlling parameters. The proposed optimization model integrating MFRCSM, OLSA and ALPSO (MOA-RBFNN) is validated by testing various benchmark classification problems. The experimental results show that the proposed optimization method outperforms the conventional methods and approaches proposed in recent literature.

Impulsive pinning synchronization of stochastic discrete-time networks

The pinning synchronization problem of stochastic impulsive networks (SIN) is investigated. Using Lyapunov stability theory and pinning method, some sufficient criteria are derived for asymptotical synchronization and exponential synchronization of such dynamical networks in mean square. Illustrative examples are provided to verify the effectiveness of the proposed approach.

Optimal reorder decision-making in the agent-based apparel supply chain

The application of agent technology in the apparel supply chain management has gained increasing interest. Agents can help automate a variety of tasks and facilitate decision-making in the supply chain. Compared with other industries, there are more uncertainties existing in the fashion industry such as market needs, fashion change and seasonality, which increases the difficulty of managing the apparel supply chain especially in the ordering process. Thus, it is necessary to increase the coordination in the apparel supply chain processes and develop optimal decision-making strategy for the apparel supply chain under the dynamic environment. In this paper, unified modeling language (UML) is applied to simulate the supply chain processes and describe the relationships between agents. This paper also applies genetic algorithm (GA) and fuzzy inference theory to the dynamic reorder strategy for the supply chain agent to make optimal decision about replenishment quantity and reorder point in order to minimize the inventory cost correspondingly.

A fashion mix-and-match expert system for fashion retailers using fuzzy screening approach

In todays fashion retailing business, providing fashion mix-and-match or fashion coordination recommendations is a must strategy to enhance customer service and improve sales. In this study, a fashion mix-and-match expert system is developed to provide customers with professional and systematic mix-and-match recommendations automatically. The system can capture the knowledge and emulate the decisions of fashion designers on apparel coordination and its knowledge base can store the literal form of information. A set of attributes of the apparel for coordination are identified and formulated; their corresponding importance is also defined with designers opinions using ordered weighted averaging operators. The Fashion Coordination Satisfaction Index is devised and computed using the fuzzy screening approach to represent the satisfaction degree of the coordinating pairs of apparel product items. The experimental results demonstrate that the proposed system can generate effective mix-and-match recommendations and is now integrated with a smart dressing system used effectively in a fashion chain store company in Hong Kong.

Multiple-objective genetic optimization of the spatial design for packing and distribution carton boxes

Packing and cutting problems, which dealt with filling up a space of known dimension with small pieces, have been an attractive research topic to both industry and academia. Comparatively, the number of reported studies is smaller for container spatial design, i.e., defining the optimal container dimension for packing small pieces of goods with known sizes so that the container space utilization is maximized. This paper aims at searching an optimal set of carton boxes for a towel manufacturer so as to lower the overall future distribution costs by improving the carton space utilization and reducing the number of carton types required. A multi-objective genetic algorithm (MOGA) is used to search the optimal design of carton boxes for a one-week sales forecast and a 53-week sales forecast. Clustering techniques are then used to study the order pattern of towel products in order to validate the genetically generated results. The results demonstrate that MOGA effectively search the best carton box spatial design to reduce unfilled space as well as the number of required carton types. It is important to note that the proposed methodology for optimal container design is not limited to the apparel industry but practically attractive and applicable to every industry which aims for distribution costs reduction.

A hybrid intelligent model for order allocation planning in make-to-order manufacturing

This paper investigated a multi-objective order allocation planning problem in make-to-order manufacturing with the consideration of various real-world production features. A novel hybrid intelligent optimization model, integrating a multi-objective memetic optimization (MOMO) process, a Monte Carlo simulation technique and a heuristic pruning technique, is developed to tackle this problem. The MOMO process, combining a NSGA-II optimization process with a tabu search, is proposed to provide Pareto optimal solutions. Extensive experiments based on industrial data are conducted to validate the proposed model. Results show that (1) the proposed model can effectively solve the investigated problem by providing effective production decision-making solutions; (2) the MOMO process has better capability of seeking global optimum than an NSGA-II-based optimization process and an industrial method.