Chin Soon Chong

A bee colony optimization algorithm to job shop scheduling

In the face of globalization and rapidly shrinking product life cycle, manufacturing companies are trying different means to improve productivity through management of machine utilization and product cycle-time. Job shop scheduling is an important task for manufacturing industry in terms of improving machine utilization and reducing cycle-time. However, job shop scheduling is inherently a NP-hard problem with no easy solution. This paper describes a novel approach that uses the honey bees foraging model to solve the job shop scheduling problem. Experimental results comparing the proposed honey bee colony approach with existing approaches such as ant colony and tabu search are presented

A Bee Colony Optimization Algorithm for Traveling Salesman Problem

A bee colony optimization (BCO) algorithm for traveling salesman problem (TSP) is presented in this paper. The BCO model is constructed algorithmically based on the collective intelligence shown in bee foraging behaviour. Experimental results comparing the proposed BCO model with some existing approaches on a set of benchmark problems are presented.

BEE COLONY OPTIMIZATION WITH LOCAL SEARCH FOR TRAVELING SALESMAN PROBLEM

Many real world industrial applications involve finding a Hamiltonian path with minimum cost. Some instances that belong to this category are transportation routing problem, scan chain optimization and drilling problem in integrated circuit testing and production. This paper presents a bee colony optimization (BCO) algorithm for traveling salesman problem (TSP). The BCO model is constructed algorithmically based on the collective intelligence shown in bee foraging behaviour. The model is integrated with 2-opt heuristic to further improve prior solutions generated by the BCO model. Experimental results comparing the proposed BCO model with existing approaches on a set of benchmark problems are presented.

A simulation based analysis of cycle time distribution, and throughput in semiconductor backend manufacturing

Abstract This paper presents a preliminary analysis of the relationship between selected input and output variables in semiconductor backend manufacturing system, using a data driven discrete event simulation model. This study is of interest and importance for a better understanding of the controllable input variables in an effort to reduce factory cycle time and distribution. Our analysis quantifies the effect of varying the input variables of lot release controls, heuristic machine dispatching rules, elimination of selected processes, material handling time, set-up time, and machine up time on selected output variables of throughput, cycle time and cycle time spread. Simulation model of a semiconductor site based in Singapore is used as the base case and the effects are quantified against the base model.

An efficient Bee Colony Optimization algorithm for Traveling Salesman Problem using frequency-based pruning

In a bee colony, bees perform waggle dance in order to communicate the information of food source to their hive mates. This foraging behaviour has been adapted in a Bee Colony Optimization (BCO) algorithm together with 2-opt local search to solve the Traveling Salesman Problem (TSP) [1]. To reduce the high overhead incurred by 2-opt in the BCO algorithm proposed previously, two mechanisms named frequency-based pruning strategy (FBPS) and fixed-radius near neighbour (FRNN) 2-opt are presented. FBPS suggests that only a subset of promising solutions are allowed to perform 2-opt based on the accumulated frequency of its building blocks recorded in a matrix. FRNN 2-opt is an efficient implementation of 2-opt which exploits the geometric structure in a permutation of TSP sequence. Both mechanisms are tested on a set of TSP benchmark problems and the results show that they are able to achieve a 58.42% improvement while maintaining the solution quality at 0.02% from known optimal.

Bee colony optimization algorithm with big valley landscape exploitation for job shop scheduling problems

Scheduling is a crucial activity in semiconductor manufacturing industry. Effective scheduling in its operations leads to improvement in the efficiency and utilization of its equipment. Job shop scheduling is an NP-hard problem which is closely related to some of the scheduling activities in this industry. This paper presents an improved bee colony optimization algorithm with big valley landscape exploitation as a biologically inspired approach to solve the job shop scheduling problem. Experimental results comparing our proposed algorithm with shifting bottleneck heuristic, tabu search algorithm and bee colony algorithm with neighborhood search on Taillard JSSP benchmark show that it is comparable to these approaches.

Using A Bee Colony Algorithm For Neighborhood Search In Job Shop Scheduling Problems

This paper describes a population-based approach that uses a honey bees foraging model to solve job shop scheduling problems. The algorithm applies an efficient neighborhood structure to search for feasible solutions and iteratively improve on prior solutions. The initial solutions are generated using a set of priority dispatching rules. Experimental results comparing the proposed honey bee colony approach with existing approaches such as ant colony, tabu search and shifting bottleneck procedure on a set of job shop problems are presented. The results indicate the performance of the proposed approach is comparable to other efficient scheduling approaches.

Bee colony optimisation algorithm with big valley landscape exploitation for job shop scheduling problems

Job shop scheduling problem (JSSP) is an NP-hard problem that is closely related to scheduling activities in manufacturing industry. This paper presents an improved bee colony optimisation algorithm with Big Valley landscape exploitation (BCBV) as a biologically inspired algorithm to solve the JSSP problem. The BCBV algorithm mimics the bee foraging behaviour where information of newly discovered food source is communicated via waggle dances. In the algorithm, the dances are treated as clusters of solutions to the JSSP. These clusters of solutions are distributed as a Big Valley landscape structure. Via a dance accumulation strategy as well as an effective search in multiple clusters in the entire landscape, the proposed algorithm is able to generate relatively good solutions for the JSSP. Experimental results comparing our proposed algorithm with the shifting bottleneck heuristic (SBP), the tabu search algorithm (TS) and the parameter-free genetic algorithm (PfGA) on the Taillard JSSP benchmark show that it is comparable to these approaches.

Bee Colony Optimization with local search for traveling salesman problem

Many real world industrial applications involve finding a Hamiltonian path with minimum cost. Some instances that belong to this category are transportation routing problem, scan chain optimization and drilling problem in integrated circuit testing and production. This paper presents a bee colony optimization (BCO) algorithm for traveling salesman problem (TSP). The BCO model is constructed algorithmically based on the collective intelligence shown in bee foraging behaviour. The model is integrated with 2-opt heuristic to further improve prior solutions generated by the BCO model. Experimental results comparing the proposed BCO model with existing approaches on a set of benchmark problems are presented.

Development and analysis of a customer-demand-driven semiconductor supply chain model using the High Level Architecture

Effective supply chain management (SCM) enables organisations to be more competitive in the current world of global manufacturing. Simulation can help by evaluating the feasibility of alternative policies and arrangements for managing a supply chain. However, detailed simulation of the supply chain can be complex and computationally intensive. We develop a distributed simulation model that can be used to study such a supply chain. We fine tune the execution speed of the model, and then use the model to investigate on how the frequency of inventory updates and demand changes affect the on-time-delivery (OTD) performance of the entire supply chain.