Win-Chin Lin

An order scheduling problem with position-based learning effect

The order scheduling problem is receiving increasing attention in the relatively new but creative area of scheduling research. In order scheduling, several orders are processed on multiple machines, and each order comprises multiple components. The order completion time is defined as the time at which all components in an order are completed. In previous studies, the processing times of all components were fixed in order scheduling problems. This is unreasonable because a steady decline in processing time usually occurs when the same task is performed repeatedly in practical situations. Therefore, we propose a multiple-machine order scheduling problem with a learning effect to minimize the total tardiness. We develop a branch-and-bound algorithm incorporating certain dominance rules and three lower bounds for obtaining the optimal solution. Subsequently, we propose simulated annealing, particle swarm optimization, and order-scheduling MDD algorithms for obtaining a near-optimal solution. In addition, the experimental results of all proposed algorithms are provided. We propose a multiple-machine order scheduling problem with a learning effect to minimize the total tardiness.We derive several dominance rules and three lower bounds applied in a branch-and-bound algorithm for an optimal solution.We propose simulated annealing and particle swarm optimization algorithms for obtaining a near-optimal solution.

Particle swarm optimization and opposite-based particle swarm optimization for two-agent multi-facility customer order scheduling with ready times

We study a two-agent multi-facility order scheduling with ready times.We derive several dominance properties and a lower bound on the optimal solution.We present algorithms based on particle swarm optimization (PSO) and opposite-based particle swarm optimization (O-PSO) to obtain near-optimal solutions. Recently, multi-agent scheduling and customer order scheduling have separately received much attention in scheduling research. However, the two-agent concept has not been introduced into order scheduling in the multi-facility setting. To fill this research gap, we consider in this paper two-agent multi-facility order scheduling with ready times. The objective is to minimize the total completion time of the orders of one agent, with the restriction that the total completion time of the orders of the other agent cannot exceed a given limit. We first develop a branch-and-bound algorithm incorporating several dominance rules and a lower bound to solve this intractable problem. We then propose a particle swarm optimization algorithm (PSO), an opposite-based particle swarm optimization (OPSO) algorithm, and a particle swarm optimization algorithm with a linearly decreasing inertia weight (WPSO) to obtain near-optimal solutions. Applying two levels of number of particles and number of neighbourhood improvements for the PSO and OPSO algorithms, we execute them at a fixed inertia weight, and execute WPSO at a varying decreasing inertia weight. We perform a one-way analysis of variance of the performance of the five PSO algorithms in tackling the problem with small and big orders. We demonstrate through extensive computational studies that the proposed PSO algorithms are very efficient in quickly finding solutions that are very close to the optimal solutions.

An iterated local search for the multi-objective permutation flowshop scheduling problem with sequence-dependent setup times

Display Omitted The multiple objectives and the sequence-dependent setup times are considered in the permutation flowshop scheduling problem.The extension of conventional single-objective iterated local search (ILS) to solve multi-objective combinatorial optimization problem.A Pareto based variable depth search is designed to act as the multi-objective local search phase in the multi-objective ILS.The experimental results on some benchmark problems show that the proposed multi-objective ILS outperforms several powerful multi-objective evolutionary algorithms in the literature.A multi-objective iterated local search is proposed. Due to its simplicity yet powerful search ability, iterated local search (ILS) has been widely used to tackle a variety of single-objective combinatorial optimization problems. However, applying ILS to solve multi-objective combinatorial optimization problems is scanty. In this paper we design a multi-objective ILS (MOILS) to solve the multi-objective permutation flowshop scheduling problem with sequence-dependent setup times to minimize the makespan and total weighted tardiness of all jobs. In the MOILS, we design a Pareto-based variable depth search in the multi-objective local search phase. The search depth is dynamically adjusted during the search process of the MOILS to strike a balance between exploration and exploitation. We incorporate an external archive into the MOILS to store the non-dominated solutions and provide initial search points for the MOILS to escape from local optima traps. We compare the MOILS with several multi-objective evolutionary algorithms (MOEAs) shown to be effective for treating the multi-objective permutation flowshop scheduling problem in the literature. The computational results show that the proposed MOILS outperforms the MOEAs.

An Investigation of Single-Machine Due-Window Assignment with Time-Dependent Processing Times and a Controllable Rate-Modifying Activity

School of Mathematics and Systems Science, Shenyang Normal University, Shenyang, Liaoning 110034, People’s Republic of China Department of Industrial Engineering and Management, Nan Kai University of Technology, Nantou, Taiwan Department of Statistics, Feng Chia University, Taichung, Taiwan Department of Healthcare Management, Yuanpei University of Medical Technology, Hsinchu, Taiwan Corresponding author: cchwu@fcu.edu.tw

A combined approach for two-agent scheduling with sum-of-processing-times-based learning effect

This paper considers a scheduling model involving two agents, job release times, and the sum-of-processing-times-based learning effect. The sum-of-processing-times-based learning effect means that the actual processing time of a job of either agent is a decreasing function of the sum of the processing times of the jobs already scheduled in a given schedule. The goal is to seek for an optimal schedule that minimizes the total weighted completion time of the first agent, subject to no tardy job for the second agent. We first provide a branch-and-bound method to solve the problem. We then develop an approach that combines genetic algorithm and simulated annealing to seek for approximate solutions for the problem. We carry on extensive computational tests to assess the performance of the proposed algorithms.

Heuristic based genetic algorithms for the re-entrant total completion time flowshop scheduling with learning consideration

AbstractRecently, both the learning effect scheduling and re-entrant scheduling have received more attention separately in research community. However, the learning effect concept has not been introduced into re-entrant scheduling in the environment setting. To fill this research gap, we investigate re-entrant permutation flowshop scheduling with a position-based learning effect to minimize the total completion time. Because the same problem without learning or re-entrant has been proved NP-hard, we thus develop some heuristics and a genetic algorithm (GA) to search for approximate solutions. To solve this problem, we first adopt four existed heuristics for the problem; we then apply the same four methods combined with three local searches to solve the proposed problem; in the last stage we develop a heuristic-based genetic algorithm seeded with four good different initials obtained from the second stage for finding a good quality of solutions. Finally, we conduct experimental tests to evaluate the behavi...

A two-machine flowshop scheduling problem with precedence constraint on two jobs

Job precedence can be found in some real-life situations. For the application in the scheduling of patients from multiple waiting lines or different physicians, patients in the same waiting line for scarce resources such as organs, or with the same physician often need to be treated on the first-come, first-served basis to avoid ethical or legal issues, and precedence constraints can restrict their treatment sequence. In view of this observation, this paper considers a two-machine flowshop scheduling problem with precedence constraint on two jobs with the goal to find a sequence that minimizes the total tardiness criterion. In searching solutions to this problem, we build a branch-and-bound method incorporating several dominances and a lower bound to find an optimal solution. In addition, we also develop a genetic and larger-order-value method to find a near-optimal solution. Finally, we conduct the computational experiments to evaluate the performances of all the proposed algorithms.

Single-machine common/slack due window assignment problems with linear decreasing processing times

ABSTRACT This paper studies linear non-increasing processing times and the common/slack due window assignment problems on a single machine, where the actual processing time of a job is a linear non-increasing function of its starting time. The aim is to minimize the sum of the earliness cost, tardiness cost, due window location and due window size. Some optimality results are discussed for the common/slack due window assignment problems and two O(n log n) time algorithms are presented to solve the two problems. Finally, two examples are provided to illustrate the correctness of the corresponding algorithms.

A Multi-Machine Order Scheduling with Learning Using the Genetic Algorithm and Particle Swarm Optimization

The assembly of numerous applications can proceed only if all the parts for assembly are available. The completion time is determined largely by the time of manufacture of the final component. The setup times are included in the job processing time. It is unreasonable to assume that the setup process dominates the overall production process. Such activities are frequently encountered in process manufacturing, in which an initial setup is followed by a lengthy, uninterrupted production process. Motivated by these observations, we examine a multi-machine order scheduling problem with a sum-of-job-processing-times-based learning environment to minimize the number of tardy jobs. Dominance rules and a lower bound are first derived and applied in the branch-and-bound algorithm to identify the optimal solution. Afterward, a genetic algorithm and the particle swarm optimization method are employed to find a near-optimal solution. In addition, the experimental results of all proposed algorithms are provided.

A two-stage three-machine assembly flow shop scheduling with learning consideration to minimize the flowtime by six hybrids of particle swarm optimization

Abstract There have been many applications of two-stage three-machine assembly flow shop in query scheduling, such as fire engine assembly, personal computer manufacturing, and distributed database system. Moreover, learning phenomenon has been shown present in many two-stage assembly flow shop environments. In conjunction with this learning phenomenon, we addressed, in this study, a two-stage three-machine flow shop scheduling problem with a cumulated learning function. Our objective was to search an optimal sequence for minimizing the flowtime (or total completion time). We developed some dominance propositions with a lower bound used in a branch-and-bound algorithm for small-size jobs. We also proposed six versions of hybrid particle swam optimization (PSO) algorithms to find approximate solutions for small-size and big-size jobs, and for three different data types. In addition, analysis of variance (ANOVA) was employed to examine the performances of the six PSOs for each data type. Subsequently, Fishers least significant difference tests were carried out to further make pairwise comparisons among the performances of the six algorithms.