Hybrid flow shop scheduling problems have gained an increasing attention in recent years because of its wide applications in real-world production systems. Most of the prior studies assume that the processing time of jobs is deterministic and constant. In practice, jobs’ processing time is usually difficult to be exactly known in advance and can be influenced by many factors, e.g., machines’ abrasion and jobs’ feature, thereby leading to their uncertain and variable processing time. In this paper, a dual-objective stochastic hybrid flow shop deteriorating scheduling problem is presented with the goal to minimize makespan and total tardiness. In the formulated problem, the normal processing time of jobs follows a known stochastic distribution, and their actual processing time is a linear function of their start time. In order to solve it effectively, this paper develops a hybrid multiobjective optimization algorithm that maintains two populations executing the global search in the whole solution space and the local search in promising regions, respectively. An information sharing mechanism and resource allocating method are designed to enhance its exploration and exploitation ability. The simulation experiments are carried out on a set of instances, and several classical algorithms are chosen as its peers for comparison. The results demonstrate that the proposed algorithm has a great advantage in dealing with the investigated problem.

Scheduling Dual-Objective Stochastic Hybrid Flow Shop With Deteriorating Jobs via Bi-Population Evolutionary Algorithm

Part I, Fundamentals.- Fundamentals.- Preliminaries.- Problems and Algorithms.- NP-Complete Problems.- Part II, Scheduling Models.- The Classical Scheduling Theory.- The Modern Scheduling Theory.- The Time-Dependent Scheduling.- Part III, Polynomial Problems.- Polynomial Single Machine Problems.- Polynomial Parallel Machine Problems.- Polynomial Dedicated Machine Problems.- Part IV, NP-Hard Problems.- NP-Hard Single Machine Problems.- NP-Hard Parallel Machine Problems.- NP-Hard Dedicated Machine Problems.- Part V, Algorithms.- Exact Algorithms.- Approximation Algorithms and Schemes.- Greedy Algorithms Based on Signatures.- Heuristic Algorithms.- Local Search and Meta-heuristic Algorithms.- Part VI, Advanced Topics.- Time-Dependent Scheduling Under Precedence Constraints.- Matrix Methods in Time-Dependent Scheduling.- Bi-criteria Time-Dependent Scheduling.- New Topics in Time-Dependent Scheduling.- App. A, Open Time-Dependent Scheduling Problems.- List of Algorithms.- List of Figures.- List of Tables.- Symbol Index.- Subject Index.

Models and Algorithms of Time-Dependent Scheduling

Scheduling with multiple agents has been widely studied. However, a little work has been done on multi-agent scheduling with availability constraints. This paper addresses a two-agent parallel machine scheduling problem with tool change activities. The aim is to minimise the total completion time of all jobs while keeping the maximum makespan of agent two’s jobs below a fixed level. A mathematical model is presented to solve the problem optimally in small-sized instances. Then, an imperialist competitive algorithm ICA is developed to solve large-sized instances of the problem. For further enhancement, the proposed ICA is hybridised with a simple but efficient local search algorithm. A set of experimental instances are carried out to evaluate the algorithm. The proposed algorithm is carefully evaluated for its performance against an available algorithm. The results of computational experiments show the desirable performance of the proposed algorithm.

A parallel machine scheduling problem with two-agent and tool change activities: an efficient hybrid metaheuristic algorithm

Competitive two-agent scheduling with deteriorating jobs on a single parallel-batching machine

In this paper, we consider single machine scheduling problems with controllable processing time (resource allocation), truncated job-dependent learning and deterioration effects. The goal is to find the optimal sequence of jobs and the optimal resource allocation separately for minimizing a cost function containing makespan (total completion time, total absolute differences in completion times) and/or total resource cost. For two different processing time functions, i.e., a linear and a convex function of the amount of a common continuously divisible resource allocated to the job, we solve them in polynomial time respectively.

Scheduling jobs with controllable processing time, truncated job-dependent learning and deterioration effects

Two-agent single-machine scheduling with deteriorating 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.

Jun Liu

Papers

Two-agent single-machine scheduling with deteriorating jobs

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