Ching-Jong Liao

A discrete version of particle swarm optimization for flowshop scheduling problems

Particle swarm optimization (PSO) is a novel metaheuristic inspired by the flocking behavior of birds. The applications of PSO to scheduling problems are extremely few. In this paper, we present a PSO algorithm, extended from discrete PSO, for flowshop scheduling. In the proposed algorithm, the particle and the velocity are redefined, and an efficient approach is developed to move a particle to the new sequence. To verify the proposed PSO algorithm, comparisons with a continuous PSO algorithm and two genetic algorithms are made. Computational results show that the proposed PSO algorithm is very competitive. Furthermore, we incorporate a local search scheme into the proposed algorithm, called PSO-LS. Computational results show that the local search can be really guided by PSO in our approach. Also, PSO-LS performs well in flowshop scheduling with total flow time criterion, but it requires more computation times.

Ant colony optimization combined with taboo search for the job shop scheduling problem

In this paper, we present a hybrid algorithm combining ant colony optimization algorithm with the taboo search algorithm for the classical job shop scheduling problem. Instead of using the conventional construction approach to construct feasible schedules, the proposed ant colony optimization algorithm employs a novel decomposition method inspired by the shifting bottleneck procedure, and a mechanism of occasional reoptimizations of partial schedules. Besides, a taboo search algorithm is embedded to improve the solution quality. We run the proposed algorithm on 101 benchmark instances and obtain competitive results and a new best upper bound for one open benchmark instance is found.

An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups

In many real-world production systems, it requires an explicit consideration of sequence-dependent setup times when scheduling jobs. As for the scheduling criterion, the weighted tardiness is always regarded as one of the most important criteria in practical systems. While the importance of the weighted tardiness problem with sequence-dependent setup times has been recognized, the problem has received little attention in the scheduling literature. In this paper, we present an ant colony optimization (ACO) algorithm for such a problem in a single-machine environment. The proposed ACO algorithm has several features, including introducing a new parameter for the initial pheromone trail and adjusting the timing of applying local search, among others. The proposed algorithm is experimented on the benchmark problem instances and shows its advantage over existing algorithms. As a further investigation, the algorithm is applied to the unweighted version of the problem. Experimental results show that it is very competitive with the existing best-performing algorithms.

A discrete particle swarm optimization for lot-streaming flowshop scheduling problem

We consider an n-job, m-machine lot-streaming problem in a flowshop with equal-size sublots where the objective is to minimize the total weighted earliness and tardiness. To solve this problem, we first propose a so-called net benefit of movement (NBM) algorithm, which is much more efficient than the existing linear programming model for obtaining the optimal starting and completion times of sublots for a given job sequence. A new discrete particle swarm optimization (DPSO) algorithm incorporating the NBM algorithm is then developed to search for the best sequence. The new DPSO improves the existing DPSO by introducing an inheritance scheme, inspired by a genetic algorithm, into particles construction. To verify the proposed DPSO algorithm, comparisons with the existing DPSO algorithm and a hybrid genetic algorithm (HGA) are made. Computational results show that the proposed DPSO algorithm with a two-point inheritance scheme is very competitive for the lot-streaming flowshop scheduling problem.

A case study in a two-stage hybrid flow shop with setup time and dedicated machines

Abstract In this paper we address a scheduling problem taken from a label sticker manufacturing company. The production system is a two-stage hybrid flow shop with the characteristics of sequence-dependent setup time at stage 1, dedicated machines at stage 2, and two due dates. The objective is to schedule one days mix of label stickers through the shop such that the weighted maximal tardiness is minimized. A heuristic is proposed to find the near-optimal schedule for the problem. The performance of the heuristic is evaluated by comparing its solution with both the optimal solution for small-sized problems and the solution obtained by the scheduling method currently used in the shop. As the heuristic is beneficial to the company, it will be implemented in the near future.

A particle swarm optimization algorithm for hybrid flow-shop scheduling with multiprocessor tasks

The multistage hybrid flow-shop scheduling problem with multiprocessor tasks has been found in many practical situations. Due to the essential complexity of the problem, many researchers started to apply metaheuristics to solve the problem. In this paper, we address the problem by using particle swarm optimization (PSO), a novel metaheuristic inspired by the flocking behaviour of birds. The proposed PSO algorithm has several features, such as a new encoding scheme, an implementation of the best velocity equation and neighbourhood topology among several different variants, and an effective incorporation of local search. To verify the PSO algorithm, computational experiments are conducted to make a comparison with two existing genetic algorithms (GAs) and an ant colony system (ACS) algorithm based on the same benchmark problems. The results show that the proposed PSO algorithm outperforms all the existing algorithms for the considered problem.

Makespan minimization for two parallel machines with an availability constraint

Abstract In this paper, we consider a two parallel machine problem where one machine is not available during a time period. The unavailable time period is fixed and known in advance. A machine is not available probably because it needs preventive maintenance or periodical repair. The objective of the problem is to minimize the makespan. For both nonresumable and resumable cases, we partition the problem into four sub-problems, each of which is solved optimally by an algorithm. Although all the algorithms have exponential time complexities, they are quite efficient in solving large-sized problems.

Minimizing total tardiness on a single machine with controllable processing times

The concept of time-cost trade-off is commonly considered in PERT/CPM, but it is seldom considered in the scheduling area. Such concept implies that the processing times of jobs are controllable by increasing or decreasing the available resources, such as manpower and equipment. In this paper, we focus on the single machine total tardiness problem with controllable processing times. First, a mixed-integer programming (MIP) model is formulated to find the optimal solution. Then, we propose both a linear programming model and a net benefit of compression (NBC) algorithm to obtain a set of optimal amounts of compression for a given sequence. To solve medium- to large-size problem instances, we develop a heuristic based on the NBC algorithm. To verify the proposed heuristic, the MIP model is used as a comparison for small-size problem instances, whereas for medium- to large-size instances the variable neighborhood search, a useful local search method, is employed. Computational results show that the proposed heuristic has a good performance.

A performance evaluation of permutation vs. non-permutation schedules in a flowshop

It has been pointed out that a permutation schedule can be improved by a non-permutation schedule in a flowshop with completion-time based criteria, but there is a lack of comprehensive analyses. This paper presents an extensive computational investigation concerning the performance comparison between permutation and non-permutation schedules. The computational results indicate that in general, there is little improvement made by non-permutation schedules over permutation schedules with respect to completion-time based criteria. But the improvement is significant with respect to due-date based criteria, including total tardiness and total weighted tardiness. The results provide practitioners a guideline as to when to adopt a non-permutation schedule, which may exhibit better performance but require additional computational and control efforts.

Multi-attribute approach to sustainable supply chain management under uncertainty

– The purpose of this paper is to enhance the understanding of sustainable supply chain management (SSCM) and provide a comprehensive and quantitative method to assess performance. , – The study applied interval-valued triangular fuzzy numbers associated with grey relational analysis to improve the insufficient information and overcome the incomplete system under uncertainty. , – The findings support the argument that the triple bottom line is insufficient to cover the entire concept of SSCM; in particular, the aspects of operations, stakeholders and resilience have not been addressed in previous studies. , – The results reveal that the triple bottom line concept is insufficient to illustrate the principles of SSCM and to provide an extensive basis for theory development. The aspects and criteria considered in the study only relate to the studied company and may need to be reviewed when applied to other industries. , – The methodology and findings of the study demonstrate the core applications of criteria ranking and identify priority areas that utilize less investment but that may maintain the studied company’s current performance. Suggestions for the prioritization of criteria to enhance SSCM performance are provided. , – The present study provides three valuable contributions. First, it adopts collaboration theory to furnish a theoretical foundation for SSCM. Second, the proposed hybrid method is able to overcome uncertainty and subsequently evaluate SSCM while utilizing incomplete and imprecise information. Third, the evaluation provides significant results for consideration in decision making by the studied company.