Random Keys Genetic Algorithms Scheduling and Rescheduling Systems for Common Production Systems

The majority of scheduling research deals with problems in specific production environments with specific objective functions. However, in many cases, more than one problem type and/or objective function exists, resulting in the need for a more generic and flexible system to generate schedules. Furthermore, most of the published scheduling research focuses on creating an optimal or near optimal initial schedule during the planning phase. However, after production processes start, circumstances like machine breakdowns, urgent jobs, and other unplanned events may render the schedule suboptimal, obsolete or even infeasible resulting in a rescheduling problem, which is typically also addressed for a specific production environment, constraints, and objective functions. This dissertation introduces a generic framework consisting of models and algorithms based on Random Keys Genetic Algorithms (RKGA) to handle both the scheduling and rescheduling problems in the most common production environments and for various types of objective functions. The Scheduling system produces predictive (initial) schedules for environments including single machines, flow shops, job shops and parallel machine production systems to optimize regular objective functions such as the Makespan and the Total Tardiness as well as non-regular objective functions such as the Total Earliness and Tardiness. To deal with the rescheduling problem, and using as a basis the same RKGA, a reactive Rescheduling system capable of repairing initial schedules after the occurrence of unexpected events is introduced. The reactive Rescheduling system was designed not only to optimize regular and non-regular objective functions but also to minimize the instability, a very important aspect in rescheduling to avoid shop chaos due to disruptions. Minimizing both schedule inefficiency and instability, however, turns the problem into a multi-objective optimization problem, which is even more difficult to solve. The computational experiments for the predictive model show that it is able to produce optimal or near optimal schedules to benchmark problems for different production environments and objective functions. Additional computational experiments conducted to test the reactive Rescheduling system under two types of unexpected events, machine breakdowns and the arrival of a rush job, show that the proposed framework and algorithms are robust in handling various problem types and computationally reasonable

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

The three stage assembly permutation flowshop scheduling problem

[ENG] The assembly flowshop scheduling problem has been studied recently due to its applicability in real life scheduling problems. It arises when various fabrication operations are performed concurrently in one stage. It was firstly introduced by Lee et al. (1993) in a flowshop environment. Later, Potts et al. (1995) considered the two-stage assembly flowshop problem with m concurrent operations in the first stage and an assembly operation in the second stage with the makespan objective, they showed that the considered problem is NP-hard in the strong sense even when the number of machines in the first stage is equal to two. Allahverdi et al. (2007) and Al-Anzi et al. (2009) considered two bicriteria two-stage assembly flowshop scheduling problems and proposed some metaheuristics. Previously, Al- Anzi et al. (2007) had considered the two-stage assembly flowshop scheduling problem with consideration of separate setup times from processing times and tried to minimize maximum lateness as objective function. Koulamas et al. (2007) extended the two-stage assembly flowshop to three-stage assembly flowshop scheduling problem with the objective of minimizing the makespan. The first stage manufactures various fabrication operations concurrently, the second one collected and transported them into an assembly stage as final stage for an assembly operation. They analyzed the worst-case ratio bound for several heuristics for the considered problem and they also analyzed the worst-case absolute bound for a heuristic based on compact vector summation techniques. In this paper we considered the three-stage assembly flowshop problem with sequences dependent setup time (SDST) on first and third stages with the objective of minimizing total completion time. The problem is described in detail in the next section, and a mathematical model is proposed and tested in Section 3. Finally the summary of the work is presented in section 4

Flow shop scheduling with earliness, tardiness and intermediate inventory holding costs

We consider the problem of scheduling customer orders in a flow shop with the objective of minimizing the sum of tardiness, earliness (finished goods inventory holding) and intermediate (work-in-process) inventory holding costs. We formulate this problem as an integer program, and based on approximate solutions to two di erent, but closely related, Dantzig-Wolfe reformulations, we develop heuristics to minimize the total cost. We exploit the duality between Dantzig-Wolfe reformulation and Lagrangian relaxation to enhance our heuristics. This combined approach enables us to develop two di erent lower bounds on the optimal integer solution, together with intuitive approaches for obtaining near-optimal feasible integer solutions. To the best of our knowledge, this is the first paper that applies column generation to a scheduling problem with di erent types of strongly NP-hard pricing problems which are solved heuristically. The computational study demonstrates that our algorithms have a significant speed advantage over alternate methods, yield good lower bounds, and generate near-optimal feasible integer solutions for problem instances with many machines and a realistically large number of jobs

Heuristic and Exact Algorithms for the Two-Machine Just in Time Job Shop Scheduling Problem

The problem addressed in this paper is the two-machine job shop scheduling problem when the objective is to minimize the total earliness and tardiness from a common due date (CDD) for a set of jobs when their weights equal 1 (unweighted problem). This objective became very significant after the introduction of the Just in Time manufacturing approach. A procedure to determine whether the CDD is restricted or unrestricted is developed and a semirestricted CDD is defined. Algorithms are introduced to find the optimal solution when the CDD is unrestricted and semirestricted. When the CDD is restricted, which is a much harder problem, a heuristic algorithms proposed to find approximate solutions. Through computational experiments, the heuristic algorithms\u27 performance is evaluated with problems up to 500 jobs