Huaping Chen

Makespan minimization on single batch-processing machine via ant colony optimization

This paper investigates the problem of minimizing makespan on a single batch-processing machine, and the machine can process multiple jobs simultaneously. Each job is characterized by release time, processing time, and job size. We established a mixed integer programming model and proposed a valid lower bound for this problem. By introducing a definition of waste and idle space ( WIS ), this problem is proven to be equivalent to minimizing the WIS for the schedule. Since the problem is NP-hard, we proposed a heuristic and an ant colony optimization (ACO) algorithm based on the theorems presented. A candidate list strategy and a new method to construct heuristic information were introduced for the ACO approach to achieve a satisfactory solution in a reasonable computational time. Through extensive computational experiments, appropriate ACO parameter values were chosen and the effectiveness of the proposed algorithms was evaluated by solution quality and run time. The results showed that the ACO algorithm combined with the candidate list was more robust and consistently outperformed genetic algorithm (GA), CPLEX, and the other two heuristics, especially for large job instances.

A novel mobile recommender system for indoor shopping

With the widespread usage of mobile terminals, the mobile recommender system is proposed to improve recommendation performance, using positioning technologies. However, due to restrictions of existing positioning technologies, mobile recommender systems are still not being applied to indoor shopping, which continues to be the main shopping mode. In this paper, we develop a mobile recommender system for stores under the circumstance of indoor shopping, based on the proposed novel indoor mobile positioning approach by using received signal patterns of mobile phones, which can overcome the disadvantages of existing positioning technologies. Especially, the mobile recommender system can implicitly capture users preferences by analyzing users positions, without requiring users explicit inputting, and take the contextual information into consideration when making recommendations. A comprehensive experimental evaluation shows the new proposed mobile recommender system achieves much better user satisfaction than the benchmark method, without losing obvious recommendation performances.

Metaheuristics to minimise makespan on parallel batch processing machines with dynamic job arrivals

Batch processing machines that can process a group of jobs simultaneously are often encountered in semiconductor manufacturing and metal heat treatment. This research investigates the scheduling problem on parallel batch processing machines in the presence of dynamic job arrivals and non-identical job sizes. The processing time and ready time of a batch are equal to the largest processing time and release time among all jobs in the batch, respectively. This problem is NP-hard in the strong sense, and hence two lower bounds were proposed to evaluate the performance of approximation algorithms. An ERT-LPT heuristic rule was next presented to assign batches to parallel machines. Two metaheuristics, a genetic algorithm (GA) and an ant colony optimisation (ACO) are further proposed using ERT-LPT to minimise makespan. The performances of the two approaches, along with a BFLPT-ERTLPT (BE) heuristic were compared by computational experiments. The results show that both metaheurisitcs outperform BE. GA is able to obtain better solutions when dealing with small-job instances compared to ACO, whereas ACO dominates GA in large-job instances.

Scheduling a batch processing machine with non-identical job sizes: A clustering perspective

Batch processing machines that process a group of jobs simultaneously are often encountered in semiconductor manufacturing and metal heat treatment. This paper considered the problem of scheduling a batch processing machine from a clustering perspective. We first demonstrated that minimising makespan on a single batching machine with non-identical job sizes can be regarded as a special clustering problem, providing a novel insight into scheduling with batching. The definition of WRB (waste ratio of batch) was then presented, and the objective function of minimising makespan was transformed into minimising weighted WRB so as to define the distance measure between batches in a more understandable way. The equivalence of the two objective functions was also proved. In addition, a clustering algorithm CACB (constrained agglomerative clustering of batches) was proposed based on the definition of WRB. To test the effectiveness of the proposed algorithm, the results obtained from CACB were compared with those from the previous methods, including BFLPT (best-fit longest processing time) heuristic and GA (genetic algorithm). CACB outperforms BFLPT and GA especially for large-scale problems.

Extending analytical target cascading for optimal supply chain network configuration of a product family

Platforming is not only a powerful approach to new product development by sharing common and modular components and processes but also allows the supply chain to gain benefits from risk-pooling effect through shared resources. Analytical target cascading (ATC) is a decentralised method suitable for configuring a hierarchical supply chain of an assembled product while accommodating necessary degree of decision autonomy and information privacy of individual enterprises. Because product variants in a family share platform components, the supply chain structure becomes a weakly networked hierarchy where a small number of elements are laterally linked. In addition, multiple customers who share a common platform component may require the corresponding supplier to use different strategies such as just-in-time (JIT) and lowest price to supply the component. As a result, different decision variables may be involved in the interaction between a shared lower-level element and its different parental elements. This paper develops a new ATC method suitable for dealing with these two special characteristics in supply chain configuration (SCC) for a product family. Numerical results demonstrate that the new method produces better results than those obtained from using the ATC method that only allows a supplier to optimise the supply of the platform component to all its customers with the same strategy.

Priority-based constructive algorithms for scheduling agile earth observation satellites with total priority maximization

We study an Earth observation scheduling problem from Chinas satellite platform.We develop priority-based indicators based on a cost-benefit analysis method.We employ a sequential construction procedure to generate feasible solutions.We evaluate the performance of the proposed algorithms in various scenarios. This paper investigated an Earth observation scheduling problem for agile satellites under a time window constraint and resource constraints of limited on-board memory capacity and consecutive working time. We assumed that different observation tasks may have priority levels, and the objective is to maximize the total priority of selected tasks. To address the problem, we first presented a detailed problem description and developed a mathematical programming model. Considering the over-constrained feature of the problem, we developed constructive algorithms to solve the problem, which adopt a priority-based sequential construction procedure to avoid conflicts and generate feasible solutions. The proposed sequential construction procedure contributes to eliminating the need for extra constraint handling techniques, and helps to reduce the complexity of feasibility checking. By analyzing the competitive relationship of various resources, we proved the condition of mutual exclusion of time windows and then developed new priority-based indicators to evaluate the benefits and opportunity costs of different positioning decisions, which is a key component to be used in the proposed constructive algorithms. Through extensive computational experiments on various scenarios including real-world data from Chinas satellite platform, the effectiveness of the developed constructive algorithms was verified.

Two-agent scheduling on a single batch processing machine with non-identical job sizes

A new scheduling model in which both two-agent and a batch processing machine with non-identical job sizes exist is considered in this paper. Two agents compete to process their respective job sets on a common single batch processing machine. The objectives of the two agents are both to minimize the makespan. It is proved in the literature[7] that the complexity of minimizing makespan of one agent on a single batch processing machine with non-identical job sizes is NP-hard in the strong sense. We developed an improved ant colony optimal algorithm to search for the Pareto optimal solutions. The experimental results showed that the proposed algorithm could get better non-dominated solutions compared with the non-dominated sorting genetic algorithm (NSGA-II) which was widely used in solving the multi-objective optimization problem.

Scheduling unrelated parallel batch processing machines with non-identical job sizes

Scheduling unrelated parallel batch processing machines to minimize makespan is studied in this paper. Jobs with non-identical sizes are scheduled on batch processing machines that can process several jobs as a batch as long as the machine capacity is not violated. Several heuristics based on best fit longest processing time (BFLPT) in two groups are proposed to solve the problem. A lower bound is also proved to evaluate the quality of the heuristics. Computational experiments were undertaken. These showed that J_SC-BFLPT, considering both load balance of machines and job processing times, was robust and outperformed other heuristics for most of the problem categories.

An improved particle swarm optimization for multi-objective flexible job-shop scheduling problem

This paper presents an improved particle swarm optimization(PSO) algorithm to solve the multi-objective flexible job-shop scheduling problem, which integrates the global search ability of PSO and the superiority of escaping from a local optimum with chaos. Firstly, the parameters of PSO are self-adaptively adjusted to balance the exploration and the exploitation abilities efficiently. Secondly, during the search of PSO, a chaotic local optimizer is adopted to improve its resulting precision and convergence rate. Experiments with typical problem instances are conducted to compare the performance of the proposed method with some other methods. The experimental analysis indicates that the proposed method performs better than the others in terms of the quality of solutions and computational time.

A Boltzmann-Based Estimation of Distribution Algorithm for a General Resource Scheduling Model

Most researchers employed common functional models when managing scheduling problems with controllable processing times. However, in many complicated manufacturing systems with a high diversity of jobs, these functional resource models fail to reflect their specific characteristics. To fulfill these requirements, we apply a more general model, the discrete model. Traditional functional models can be viewed as special cases of such model. In this paper, the discrete model is implemented on a problem of minimizing the weighted resource allocation subject to a common deadline on a single machine. By reducing the problem to a partition problem, we demonstrate that it is NP-complete, which addresses the difficult issue of the guarantee of both the solution quality and time cost. In order to tackle the problem, we develop an estimation of distribution algorithm based on an approximation of the Boltzmann distribution. The approximation strategy represents a tradeoff between complexity and solution accuracy. The results of the experiments conducted on benchmarks show that, compared with other alternative approaches, the proposed algorithm has competitive behavior, obtaining 74 best solutions out of 90 instances.