Multicriteria hybrid flow shop scheduling problem: literature review, analysis, and future research

This research focuses on the Hybrid Flow Shop production scheduling problem, which is one of the most difficult problems to solve. The literature points to several studies that focus the Hybrid Flow Shop scheduling problem with monocriteria functions. Despite of the fact that, many real world problems involve several objective functions, they can often compete and conflict, leading researchers to concentrate direct their efforts on the development of methods that take consider this variant into consideration. The goal of the study is to review and analyze the methods in order to solve the Hybrid Flow Shop production scheduling problem with multicriteria functions in the literature. The analyses were performed using several papers that have been published over the years, also the parallel machines types, the approach used to develop solution methods, the type of method develop, the objective function, the performance criterion adopted, and the additional constraints considered. The results of the reviewing and analysis of 46 papers showed opportunities for future researchon this topic, including the following: (i) use uniform and dedicated parallel machines, (ii) use exact and metaheuristics approaches, (iv) develop lower and uppers bounds, relations of dominance and different search strategiesto improve the computational time of the exact methods,  (v) develop  other types of metaheuristic, (vi) work with anticipatory setups, and (vii) add constraints faced by the production systems itself

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

Design and Analysis of an Estimation of Distribution Approximation Algorithm for Single Machine Scheduling in Uncertain Environments

In the current work we introduce a novel estimation of distribution algorithm to tackle a hard combinatorial optimization problem, namely the single-machine scheduling problem, with uncertain delivery times. The majority of the existing research coping with optimization problems in uncertain environment aims at finding a single sufficiently robust solution so that random noise and unpredictable circumstances would have the least possible detrimental effect on the quality of the solution. The measures of robustness are usually based on various kinds of empirically designed averaging techniques. In contrast to the previous work, our algorithm aims at finding a collection of robust schedules that allow for a more informative decision making. The notion of robustness is measured quantitatively in terms of the classical mathematical notion of a norm on a vector space. We provide a theoretical insight into the relationship between the properties of the probability distribution over the uncertain delivery times and the robustness quality of the schedules produced by the algorithm after a polynomial runtime in terms of approximation ratios

Studying the effect of server side constraints on the makespan of the no-wait flow shop problem with sequence dependent setup times.

Peer ReviewedThis paper deals with the problem of scheduling the no-wait flow-shop system with sequence-dependent set-up times and server side-constraints. No-wait constraints state that there should be no waiting time between consecutive operations of jobs. In addition, sequence-dependent set-up times are considered for each operation. This means that the set-up time of an operation on its respective machine is dependent on the previous operation on the same machine. Moreover, the problem consists of server side-constraints i.e. not all machines have a dedicated server to prepare them for an operation. In other words, several machines share a common server. The considered performance measure is makespan. This problem is proved to be strongly NP-Hard. To deal with the problem, two genetic algorithms are developed. In order to evaluate the performance of the developed frameworks, a large number of benchmark problems are selected and solved with different server limitation scenarios. Computational results confirm that both of the proposed algorithms are efficient and competitive. The developed algorithms are able to improve many of the best-known solutions of the test problems from the literature. Moreover, the effect of the server side-constraints on the makespan of the test problems is explained using the computational results

A multi objective volleyball premier league algorithm for green scheduling identical parallel machines with splitting jobs

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms

An approach for the production scheduling problem when lot streaming is enabled at the operational level

By means of the present work, the production scheduling and the lot streaming problems are simultaneously addressed at flexible manufacturing environments. The proposal is based on a Constraint Programming (CP) formulation that can efficiently tackle the scheduling of manufacturing operations and the splitting of lots into smaller sublots. The approach is capable to define the number of sublots for each lot and the number of parts belonging to each sublot, as well as the assignment of the operations on sublots to machines, with their corresponding start and completion times. The CP model can be easily adapted to cope with different problem issues and several operational policies, which constitutes the main novelty of the contribution. A set of case studies were solved in order to validate the proposal and good quality solutions were found when minimizing the makespan.Sociedad Argentina de Informática e Investigación Operativ

An approach for the production scheduling problem when lot streaming is enabled at the operational level

By means of the present work, the production scheduling and the lot streaming problems are simultaneously addressed at flexible manufacturing environments. The proposal is based on a Constraint Programming (CP) formulation that can efficiently tackle the scheduling of manufacturing operations and the splitting of lots into smaller sublots. The approach is capable to define the number of sublots for each lot and the number of parts belonging to each sublot, as well as the assignment of the operations on sublots to machines, with their corresponding start and completion times. The CP model can be easily adapted to cope with different problem issues and several operational policies, which constitutes the main novelty of the contribution. A set of case studies were solved in order to validate the proposal and good quality solutions were found when minimizing the makespan.Sociedad Argentina de Informática e Investigación Operativ

An approach for the production scheduling problem when lot streaming is enabled at the operational level

By means of the present work, the production scheduling and the lot streaming problems are simultaneously addressed at flexible manufacturing environments. The proposal is based on a Constraint Programming (CP) formulation that can efficiently tackle the scheduling of manufacturing operations and the splitting of lots into smaller sublots. The approach is capable to define the number of sublots for each lot and the number of parts belonging to each sublot, as well as the assignment of the operations on sublots to machines, with their corresponding start and completion times. The CP model can be easily adapted to cope with different problem issues and several operational policies, which constitutes the main novelty of the contribution. A set of case studies were solved in order to validate the proposal and good quality solutions were found when minimizing the makespan.Sociedad Argentina de Informática e Investigación Operativ

Serial-batch scheduling – the special case of laser-cutting machines

The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning