Fulvio Antonio Cappadonna

Joint optimization of a flow-shop group scheduling with sequence dependent set-up times and skilled workforce assignment

Flow-shop sequence-dependent group scheduling (FSDGS) problem has been extensively investigated in the literature also due to many manufacturers who implemented the concept of group technology to reduce set-up costs, lead times, work-in-process inventory costs, and material handling costs. On the other hand, skilled workforce assignment (SWA) to machines of a given shop floor may represent a key issue for enhancing the performance of a manufacturing system. As the body of literature addressing the group scheduling problems ignored up to now the effect of human factor on the performance of serial manufacturing systems, the present paper moves in that direction. In particular, an M-machine flow-shop group scheduling problem with sequence-dependent set-up times integrated with the worker allocation issue has been studied with reference to the makespan minimization objective. First, a Mixed Integer Linear Programming model of the proposed problem is reported. Then, a well-known benchmark arisen from the literature is adopted to carry out an extensive comparison campaign among three properly developed metaheuristics based on a genetic algorithm framework. Once the best procedure among those tested is selected, it is compared with an effective optimization procedure recently proposed in the field of FSDGS problems, being this latter properly adapted to run the SWA issue. Finally, a further analysis dealing with the trade-off between manpower cost and makespan improvement is proposed.

A hybrid genetic algorithm for minimizing makespan in a flow-shop sequence-dependent group scheduling problem

In this paper, the flow-shop sequence-dependent group scheduling (FSDGS) problem is addressed with reference to the makespan minimization objective. In order to effectively cope with the issue at hand, a hybrid metaheuristic procedure integrating features from genetic algorithms and random sampling search methods has been developed. The proposed technique makes use of a matrix encoding able to simultaneously manage the sequence of jobs within each group and the sequence of groups to be processed along the flow-shop manufacturing system. A well-known problem benchmark arisen from literature, made by two, three and six-machine instances has been taken as reference for both tuning the relevant parameters of the proposed procedure and assessing performances of such approach against the two most recent algorithms presented in the body of literature addressing the FSDGS issue. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the FSDGS problem under investigation.

A dual encoding-based meta-heuristic algorithm for solving a constrained hybrid flow shop scheduling problem

Though scheduling problems have been largely investigated by literature over the last 50years, this topic still influences the research activity of many experts and practitioners, especially due to a series of studies which recently emphasized the closeness between theory and industrial practice. In this paper the scheduling problem of a hybrid flow shop with m stages, inspired to a truly observed micro-electronics manufacturing environment, has been investigated. Overlap between jobs of the same type, waiting time limit of jobs within inter-stage buffers as well as machine unavailability time intervals represent just a part of the constraints which characterize the problem here investigated. A mixed integer linear programming model of the problem in hand has been developed with the aim to validate the performance concerning the proposed optimization technique, based on a two-phase metaheuristics (MEs). In the first phase the proposed ME algorithm evolves similarly to a genetic algorithm equipped with a regular permutation encoding. Subsequently, since the permutation encoding is not able to investigate the overall space of solutions, a random search algorithm equipped with an m-stage permutation encoding is launched for improving the algorithm strength in terms of both exploration and exploitation. Extensive numerical studies on a benchmark of problems, along with a properly arranged ANOVA analysis, demonstrate the statistical outperformance of the proposed approach with respect to the traditional optimization approach based on a single encoding. Finally, a comprehensive comparative analysis involving the proposed algorithm and several metaheuristics developed by literature demonstrated the effectiveness of the dual encoding based approach for solving HFS scheduling problems.

Minimizing the total completion time on a parallel machine system with tool changes

We study a parallel-machine tool-change scheduling problem.We develop a mixed-integer linear programming model.We develop a hybrid genetic algorithm.We compare the proposed metaheuristic with other methods.A statistical analysis emphasizes the effectiveness of the proposed approach. In this paper, the identical parallel machine scheduling problem with periodic tool changes due to wear is addressed under the total completion time minimization objective. Due to machine availability restrictions induced by tool replacement operations, the problem is NP-hard in the strong sense. A mixed integer linear programming (MILP) model has been developed with the aim to provide the global optimum for small-sized test cases. Furthermore, a hybrid metaheuristic procedure based on genetic algorithms has been specifically designed to cope with larger instances. A comprehensive experimental analysis supported by a non-parametric statistical test has been fulfilled to select the best metaheuristic configuration in terms of decoding strategy and parameters driving the search mechanism as well. Then, the proposed optimization procedure has been compared with three alternative methods arising from the relevant literature on the basis of a wide benchmark of test cases. The obtained results, also supported by a proper statistical analysis, demonstrate the effectiveness of the proposed approach for solving the tool change scheduling problem at hand.

A Hybrid Metaheuristic Approach for Minimizing the Total Flow Time in A Flow Shop Sequence Dependent Group Scheduling Problem

Production processes in Cellular Manufacturing Systems (CMS) often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS) problem or, whether setup times are sequence-dependent, the Flow-Shop Sequence-Dependent Group Scheduling (FSDGS) problem. This paper addresses the FSDGS issue, proposing a hybrid metaheuristic procedure integrating features from Genetic Algorithms (GAs) and Biased Random Sampling (BRS) search techniques with the aim of minimizing the total flow time, i.e., the sum of completion times of all jobs. A well-known benchmark of test cases, entailing problems with two, three, and six machines, is employed for both tuning the relevant parameters of the developed procedure and assessing its performances against two metaheuristic algorithms recently presented by literature. The obtained results and a properly arranged ANOVA analysis highlight the superiority of the proposed approach in tackling the scheduling problem under investigation.

Total tardiness minimization in a parallel machine system with flexible periodic maintenance

Abstract This paper addresses the total tardiness minimization problem in a manufacturing environment made by m uniform parallel processors subjected to regular maintenance activities. According to the so-called flexible periodic maintenance strategy, the time between two consecutive maintenance periods on each machine must be lower than or equal to a certain time value. In order to cope with such scheduling issue, a hybrid metaheuristic procedure integrating features from genetic algorithm and local search is proposed. The devised optimization algorithm is compared against two alternative metaheuristics on the basis of two separate benchmarks of test cases, involving small- and large-sized instances, respectively. For small-sized examples, optimal solutions provided by a specifically developed mixed integer linear programming model are taken as reference. Numerical results, also supported by a non-parametric statistical analysis, demonstrate the superiority of the proposed optimization algorithm in solving the investigated scheduling problem.

Scheduling Jobs Families with Learning Effect on the Setup

The present paper aims to address the flow-shop sequence-dependent group scheduling problem with learning effect (FSDGSLE). The objective function to be minimized is the total completion time, that is, the makespan. The workers are required to carry out manually the set-up operations on each group to be loaded on the generic machine. The operators skills improve over time due to the learning effects; therefore the set-up time of a group under learning effect decreases depending on the order the group is worked in. In order to effectively cope with the issue at hand, a mathematical model and a hybrid metaheuristic procedure integrating features from genetic algorithms (GA) have been developed. A well-known problem benchmark risen from literature, made by two-, three- and six-machine instances, has been taken as reference for assessing performances of such approach against the two most recent algorithms presented by literature on the FSDGS issue. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the FSDGSLE problem under investigation.

Heuristics for Single Machine Scheduling Problem with Release Dates, Deteriorating Effect and Skilled Workforce with Learning Ability

In this paper, the single machine total weighted completion time scheduling problem is studied. The jobs have nonzero release time and processing time increases during the production due to the effect of deterioration on the machine. An operator sets up the machine and manually loads the job in the machine and unloads it at the end of the working time. The setup time and the removal time are influenced by the ability of the worker due to his work experience and learning capacity. Heuristic algorithms are proposed to solve the scheduling problem, and their efficiency is evaluated on a wide benchmark.