S. F. Chan

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.

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 recursive operator allocation approach for assembly line-balancing optimization problem with the consideration of operator efficiency

This paper addresses an optimization model for assembly line-balancing problem in order to improve the line balance of a production line under a human-centric and dynamic apparel assembly process. As the variance of operator efficiency is vital to line imbalance in labor intensive industry, an approach is proposed to balance production line through optimal operator allocation with the consideration of operator efficiency. Two recursive algorithms are developed to generate all feasible solutions for operator allocation. Three objectives, namely, the lowest standard deviation of operation efficiency, the highest production line efficiency and the least total operation efficiency waste, are devised to find out the optimal solution of operator allocation. The method in this paper improves the flexibility of the operator allocation on different sizes of data set of operations and operators, and enhances the efficiency of searching for the optimal solution of big size data set. The results of experiments are reported. The performance comparison demonstrates that the proposed optimization method outperforms the industry practice.

Improve Production Balance for Apparel Supply Chain Adopting VMI Replenishment Strategy

This paper analyzed the replenishment problem faced by manufacturer who adopts the vendor management inventory (VMI) replenishment strategy with different retailers in apparel supply chain. An optimization replenishment model with the consideration of production capacity constraints was proposed. A simulation program was presented to generate the VMI replenishment strategy to satisfy the customer service level (CSL) required by the retailers. With the consideration of the production capacity constraints, a rolling optimization of the VMI replenishment strategy using simulation and genetic algorithm was then formulated to optimize production balance of manufacturers. The experimental results demonstrated that the proposed replenishment strategy could benefit the manufacturers with balanced production, meanwhile maintain the CSL required by different retailers at a certain degree

An Evaluation of the Adoption of Lean Production as a Supply Chain Management Strategy in the Apparel Industry: A Case Study of a Swimwear Manufacturing Company Using Computer Simulation Techniques

This research aims to identify the issues relating to the implementation of lean production (LP) in the apparel industry where manufacturers usually find full implementation to be a challenge. Computer simulation has been used to build models to understand the behavior of the LP system of a selected swimwear company. Experimental investigations demonstrated the responsiveness and limitations of the LP system in meeting short-run contracts demanded by the market. Conclusion is drawn on the directions for full implementation for a seamless supply chain.

The role of radio frequency identification (RFID) technologies in improving garment assembly line operations

Abstract: In this chapter, a production control problem on a flexible assembly line (FAL) with flexible operation assignment and variable operative efficiencies is described. A mathematical model of the production control problem is formulated by considering 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, composed of a radio frequency identification (RFID) technology-based data capture system and 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.

Optimizing apparel production order planning scheduling using genetic algorithms

In this chapter the order scheduling problem at the factory level is investigated. Various uncertainties 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 from probability theory. A genetic algorithm is developed to seek after 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 bi-level genetic algorithm for multi-objective scheduling of multi- and mixed-model apparel assembly lines

In this paper, a multi-objective scheduling problem of the multi- and mixed-model apparel assembly line (MMAAL) is investigated. A bi-level genetic algorithm is developed to solve the scheduling problem, in which a new chromosome representation is proposed to represent the flexible operation assignment including assigning one operation to multiple workstations as well as assigning multiple operations to one workstation. The proposed algorithm is validated using real-world production data and the experimental results show that the proposed algorithm can solve the proposed scheduling problem effectively.