Tsung Che Chiang

A memetic algorithm for minimizing total weighted tardiness on parallel batch machines with incompatible job families and dynamic job arrival

This paper addresses a scheduling problem motivated by scheduling of diffusion operations in the wafer fabrication facility. In the target problem, jobs arrive at the batch machines at different time instants, and only jobs belonging to the same family can be processed together. Parallel batch machine scheduling typically consists of three types of decisions-batch forming, machine assignment, and batch sequencing. We propose a memetic algorithm with a new genome encoding scheme to search for the optimal or near-optimal batch formation and batch sequence simultaneously. Machine assignment is resolved in the proposed decoding scheme. Crossover and mutation operators suitable for the proposed encoding scheme are also devised. Through the experiment with 4860 problem instances of various characteristics including the number of machines, the number of jobs, and so on, the proposed algorithm demonstrates its advantages over a recently proposed benchmark algorithm in terms of both solution quality and computational efficiency.

NNMA: An effective memetic algorithm for solving multiobjective permutation flow shop scheduling problems

Abstract The permutation flow shop scheduling problem is addressed in this paper. Two objectives, minimization of makespan and total flow time, are considered. We propose a memetic algorithm, called NNMA, by integrating a general multiobjective evolutionary algorithm (NSGA-II) with a problem-specific heuristic (NEH). We take NEH as a local improving procedure in NNMA and propose several adaptations including the acceptance criterion and job-insertion ordering to deal with multiple objectives and to improve its performance. We test the performance of NNMA using 90 public problem instances with different problem scales, and compare its performance with 23 algorithms. The experimental results show that our NNMA provides close performance for 30 small-scale instances and better performance for 50 medium- and large-scale instances. Furthermore, more than 70% of the net set of non-dominated solutions is updated by NNMA for these 50 instances.

Modeling, scheduling, and performance evaluation for wafer fabrication: a queueing colored Petri-net and GA-based approach

In this paper, we propose a modeling tool named Queueing Colored Petri nets (QCPN) for performance evaluation and scheduling for wafer fabrication. The main idea of this tool is to combine colored timed Petri nets with the queueing systems, and it aims to make simulation over the model more efficient. Due to the wide acceptance of priority rules in the wafer manufacturing industry, we also proposed a mechanism to realize priority rules in the QCPN models. Since it is known that no single rule can dominate in any circumstance, we proposed a genetic algorithm (GA) to search for the optimal combination of a number of priority rules based on the status and performance measures of the fab. Our approach can be considered as taking the advantage of the lot execution sequence generated by priority rules to guide the search. This approach can reduce the solution space and help us find the good solution more quickly. In addition, the QCPN-based GA scheduler can greatly reduce the computation time so that this GA scheduler can meet the need for a rapidly changing environment. Note to Practitioners-Performance evaluation and scheduling are two functions required by fab managers and engineers. This paper proposed a tool which consists of a simulator and a scheduler. By connecting to the Manufacturing Execution System (MES) and providing the scheduling rules, we can see how the fab runs virtually with the simulator. General information such as throughput and average cycle time and specific information like lot activity history can be obtained. This can be used for decision making, delivery prediction, bottleneck seeking, and testing of newly developed heurisitcs. The implementation cost is only on data communication between the MES and the simulator and the incorporation of rule modules. The scheduler, which takes the simulator as the performance evaluation module, can generate the suitable scheduling rule based on the current fab status, preference of performance criteria, and rule candidates. There is almost no extra cost after the simulator is connected to the MES. The scheduler can be easily made faster by common parallelization techniques.

A new paradigm for rule-based scheduling in the wafer probe centre

This paper addresses the scheduling problem in the wafer probe centre. The proposed approach is based on the dispatching rule, which is popularly used in the semiconductor manufacturing industry. Instead of designing new rules, this paper proposes a new paradigm to utilize these rules. The proposed paradigm formulates the dispatching process as a 2-D assignment problem with the consideration of information from multiple lots and multiple pieces of equipment in an integrated manner. Then, the dispatching decisions are made by maximizing the gains of multiple possible decisions simultaneously. Besides, we develop a genetic algorithm (GA) for generating good dispatching rules through combining multiple rules with linear weighted summation. The benefits of the proposed paradigm and GA are verified with a comprehensive simulation study on three due-date-based performance measures. The experimental results show that under the proposed paradigm, the dispatching rules and GA can perform much better than under the traditional paradigm.

A knowledge-based evolutionary algorithm for the multiobjective vehicle routing problem with time windows

This paper addresses the multiobjective vehicle routing problem with time windows (MOVRPTW). The objectives are to minimize the number of vehicles and the total distance simultaneously. Our approach is based on an evolutionary algorithm and aims to find the set of Pareto optimal solutions. We incorporate problem-specific knowledge into the genetic operators. The crossover operator exchanges one of the best routes, which has the shortest average distance, the relocation mutation operator relocates a large number of customers in non-decreasing order of the length of the time window, and the split mutation operator breaks the longest-distance link in the routes. Our algorithm is compared with 10 existing algorithms by standard 100-customer and 200-customer problem instances. It shows competitive performance and updates more than 1/3 of the net set of the non-dominated solutions.

MOEA/D-AMS: Improving MOEA/D by an adaptive mating selection mechanism

In this paper we propose a multiobjective evolutionary algorithm based on MOEA/D [1] for solving multiobjective optimization problems. MOEA/D decomposes a multiobjective optimization problem into many single-objective subproblems. The objective of each subproblem is a weighted aggregation of the original objectives. Using evenly distributed weight vectors on subproblems, solutions to subproblems form a set of well-spread approximated Pareto optimal solutions to the original problem. In MOEA/D, each individual in the population represents the current best solution to one subproblem. Mating selection is carried out in a uniform and static manner. Each individual/subproblem is selected/solved once at each generation, and the mating pool of each individual is determined and fixed based on the distance between weight vectors on the objective space. We propose an adaptive mating selection mechanism for MOEA/D. It classifies subproblems into solved ones and unsolved ones and selects only individuals of unsolved subproblems. Besides, it dynamically adjusts the mating pools of individuals according to their distance on the decision space. The proposed algorithm, MOEA/D-AMS, is compared with two versions of MOEA/D using nine continuous functions. The experimental results confirm the benefits of the adaptive mating selection mechanism.

A two-stage hybrid memetic algorithm for multiobjective job shop scheduling

In this paper we address multiobjective job shop scheduling problems. After several decades of research in scheduling problems, a variety of heuristics have been developed. The proposed algorithm is a hybrid of three frequently applied ones: the dispatching rule, the shifting bottleneck procedure, and the evolutionary algorithm. It is a two-stage algorithm, which integrates a rule-based memetic algorithm in the first stage and a re-optimization procedure of shifting bottleneck in the second. We conduct experiments using benchmark instances found in the literature to assess the performance of the proposed method. The experimental results show that the proposed method is effective and efficient for multiobjective scheduling problems.

Using a family of critical ratio-based approaches to minimize the number of tardy jobs in the job shop with sequence dependent setup times

This paper addresses the job shop scheduling problem to minimize the number of tardy jobs, considering the sequence dependent setup time. This problem is taken as a sequencing problem, and a family of approaches with different levels of intricacy is proposed. The simplest form is a critical ratio-based dispatching rule, which leads to satisfactory solutions by taking into account the group information rather than only the individual information of jobs. Then, an enhanced approach consisting of an iterative schedule refining mechanism will be given. Its feature is to iteratively adjust the estimation of the remaining processing times of jobs in a dynamic and operation-specific manner. Finally, a genetic algorithm which takes the dispatching rule and the refining mechanism as the core is proposed. The performance of these approaches is carefully examined by a comprehensive experimental study.

A rule-centric memetic algorithm to minimize the number of tardy jobs in the job shop

This paper addresses the job shop-scheduling problem with minimizing the number of tardy jobs as the objective. This problem is usually treated as a job-sequencing problem, and the permutation-based representation of solutions was commonly used in the existing search-based approaches. In this paper, the flaw of the permutation-based representation is discussed, and a rule-centric concept is proposed to deal with it. A memetic algorithm is then developed to realize the proposed idea by tailored genome encoding/decoding schemes and a local search procedure. Two benchmark approaches, a multi-start hill-climbing approach and a simulated annealing approach, are compared in the experiments. The results show that the proposed approach significantly outperforms the benchmarks.

A two-phase evolutionary algorithm for multiobjective mining of classification rules

Classification rule mining, addressed a lot in machine learning and statistics communities, is an important task to extract knowledge from data. Most existing approaches do not particularly deal with data instances matched by more than one rule, which results in restricted performance. We present a two-phase multiobjective evolutionary algorithm which first aims at searching decent rules and then takes the rule interaction into account to produce the final rule sets. The algorithm incorporates the concept of Pareto dominance to deal with trade-off relations in both phases. Through computational experiments, the proposed algorithm shows competitive to the state-of-the-art. We also study the effect of a niching mechanism.