83 research outputs found
Multi-objective sequence dependent setup times permutation flowshop: A new algorithm and a comprehensive study
The permutation flowshop scheduling problem has been thoroughly studied in recent decades, both from single objective as well as from multi-objective perspectives. To the best of our knowledge, little has been done regarding the multi-objective flowshop with Pareto approach when sequence dependent setup times are considered. As setup times and multi-criteria problems are important in industry, we must focus on this area. We propose a simple, yet powerful algorithm for the sequence dependent setup times flowshop problem with several criteria. The presented method is referred to as Restarted Iterated Pareto Greedy or RIPG and is compared against the best performing approaches from the relevant literature. Comprehensive computational and statistical analyses are carried out in order to demonstrate that the proposed RIPG method clearly outperforms all other algorithms and, as a consequence, it is a state-of- art method for this important and practical scheduling problemThe authors thank the anonymous referees for their careful and detailed comments which have helped improve this manuscript considerably. This work is partially financed by the Spanish Ministry of Science and Innovation, under the projects "SMPA-Advanced Parallel Multiobjective Sequencing: Practical and Theorerical Advances" with reference DPI2008-03511/DPI and "RESULT-Realistic Extended Scheduling Using Light Techniques" with reference DPI2012-36243-C02-01 and by the Small and Medium Industry of the Generalitat Valenciana (IMPIVA) and by the European Union through the European Regional Development Fund (FEDER) inside the R+D program "Ayudas dirigidas a Institutos Tecnologicos de la Red IMPIVA" during the year 2011, with project numbers IMDEEA/2011/142 and IMDEEA/2012/143.Ciavotta, M.; Minella, GG.; Ruiz García, R. (2013). Multi-objective sequence dependent setup times permutation flowshop: A new algorithm and a comprehensive study. European Journal of Operational Research. 227(2):301-313. https://doi.org/10.1016/j.ejor.2012.12.031S301313227
Makespan Minimization in Re-entrant Permutation Flow Shops
Re-entrant permutation flow shop problems occur in practical applications such as wafer manufacturing, paint shops, mold and die processes and textile industry. A re-entrant material flow means that the production jobs need to visit at least one working station multiple times. A comprehensive review gives an overview of the literature on re-entrant scheduling. The influence of missing operations received just little attention so far and splitting the jobs into sublots was not examined in re-entrant permutation flow shops before. The computational complexity of makespan minimization in re-entrant permutation flow shop problems requires heuristic solution approaches for large problem sizes. The problem provides promising structural properties for the application of a variable neighborhood search because of the repeated processing of jobs on several machines. Furthermore the different characteristics of lot streaming and their impact on the makespan of a schedule are examined in this thesis and the heuristic solution methods are adjusted to manage the problem’s extension
A Pareto-Based Adaptive Variable Neighborhood Search for Biobjective Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time
Different from most researches focused on the single objective hybrid flowshop scheduling (HFS) problem, this paper investigates a biobjective HFS problem with sequence dependent setup time. The two objectives are the minimization of total weighted tardiness and the total setup time. To efficiently solve this problem, a Pareto-based adaptive biobjective variable neighborhood search (PABOVNS) is developed. In the proposed PABOVNS, a solution is denoted as a sequence of all jobs and a decoding procedure is presented to obtain the corresponding complete schedule. In addition, the proposed PABOVNS has three major features that can guarantee a good balance of exploration and exploitation. First, an adaptive selection strategy of neighborhoods is proposed to automatically select the most promising neighborhood instead of the sequential selection strategy of canonical VNS. Second, a two phase multiobjective local search based on neighborhood search and path relinking is designed for each selected neighborhood. Third, an external archive with diversity maintenance is adopted to store the nondominated solutions and at the same time provide initial solutions for the local search. Computational results based on randomly generated instances show that the PABOVNS is efficient and even superior to some other powerful multiobjective algorithms in the literature
An iterated greedy heuristic for no-wait flow shops with sequence dependent setup times, learning and forgetting effects
[EN] This paper addresses a sequence dependent setup times no-wait flowshop with learning and forgetting effects to minimize total flowtime. This problem is NP-hard and has never been considered before. A position-based learning and forgetting effects model is constructed. Processing times of operations change with the positions of corresponding jobs in a schedule. Objective increment properties are deduced and based on them three accelerated neighbourhood construction heuristics are presented. Because of the simplicity and excellent performance shown in flowshop scheduling problems, an iterated greedy heuristic is proposed. The proposed iterated greedy algorithm is compared with some existing algorithms for related problems on benchmark instances. Comprehensive computational and statistical tests show that the presented method obtains the best performance among the compared methods. (C) 2018 Elsevier Inc. All rights reserved.This work is supported by the National Natural Science Foundation of China (Nos. 61572127, 61272377), the Collaborative Innovation Center of Wireless Communications Technology and the Key Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 12KJA630001). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness(MINECO), under the project "SCHEYARD - Optimization of Scheduling Problems in Container Yards" with reference DPI2015-65895-R.Li, X.; Yang, Z.; Ruiz García, R.; Chen, T.; Sui, S. (2018). An iterated greedy heuristic for no-wait flow shops with sequence dependent setup times, learning and forgetting effects. Information Sciences. 453:408-425. https://doi.org/10.1016/j.ins.2018.04.038S40842545
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
The dynamic, resource-constrained shortest path problem on an acyclic graph with application in column generation and literature review on sequence-dependent scheduling
This dissertation discusses two independent topics: a resource-constrained shortest-path problem
(RCSP) and a literature review on scheduling problems involving sequence-dependent setup
(SDS) times (costs).
RCSP is often used as a subproblem in column generation because it can be used to
solve many practical problems. This dissertation studies RCSP with multiple resource
constraints on an acyclic graph, because many applications involve this configuration, especially
in column genetation formulations. In particular, this research focuses on a dynamic RCSP
since, as a subproblem in column generation, objective function coefficients are updated using
new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial
solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic
graph with the goal of effectively reoptimizing RCSP in the context of column generation. This
method uses a one-time âÂÂpreliminaryâ phase to transform RCSP into an unconstrained shortest
path problem (SPP) and then solves the resulting SPP after new values of dual variables are used
to update objective function coefficients (i.e., reduced costs) at each iteration. Network
reduction techniques are considered to remove some nodes and/or arcs permanently in the preliminary phase. Specified techniques are explored to reoptimize when only several
coefficients change and for dealing with forbidden and prescribed arcs in the context of a column
generation/branch-and-bound approach. As a benchmark method, a label-setting algorithm is
also proposed. Computational tests are designed to show the effectiveness of the proposed
algorithms and procedures.
This dissertation also gives a literature review related to the class of scheduling
problems that involve SDS times (costs), an important consideration in many practical
applications. It focuses on papers published within the last decade, addressing a variety of
machine configurations - single machine, parallel machine, flow shop, and job shop - reviewing
both optimizing and heuristic solution methods in each category. Since lot-sizing is so
intimately related to scheduling, this dissertation reviews work that integrates these issues in
relationship to each configuration. This dissertation provides a perspective of this line of
research, gives conclusions, and discusses fertile research opportunities posed by this class of
scheduling problems.
since, as a subproblem in column generation, objective function coefficients are updated using
new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial
solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic
graph with the goal of effectively reoptimizing RCSP in the context of column generation. This
method uses a one-tim
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Bi-criteria Scheduling in an Assembly Flow Shop with Limited Buffer Storage and Shift Production
In this research, the comparative performance of permutation and non-permutation schedules is investigated in an assembly flow shop (AFS) with shift production, where a limited buffer storage is available between two machines. Most of the traditional scheduling problems consider continuous production, i.e., production occurs for 24 hours (3 * 8-hour shifts) each day, seven days a week. However, some companies operate only one or two shifts each day, which creates a limited availability constraint on the machines. This causes a discontinuity in production between end and start of two successive production days. To mimic real-life industry practice, dynamic job release and dynamic machine availability times have been considered. Each job considered in a problem can have different weight assigned based on customers’ preferences. The setup times between jobs are assumed to be machine- and sequence-dependent. However, at the start of each production day, setup times are not sequence-dependent but depend on machine startup times such as preheating time, pressure build up, etc. The objective of the problem is to minimize the linear combination of total setup time and weighted tardiness. The minimization of total setup time represents producer’s interest whereas the minimization of weighted tardiness represents customers’ interest. Since these two objectives are not evaluated on a commensurate basis, a normalization factor is used.
The problem is formulated as a mixed-integer linear programming (MILP) model, MILP-1 for permutation schedules and MILP-2 for non-permutation schedules. The MILP models for small-size problem instances are solved to optimality using CPLEX. However, the problem is shown to be NP-hard. As a result, it is not possible to find an optimal solution within a reasonable time, as the problem size increases. Hence, a meta-heuristic search algorithm based on short-term Tabu Search (TS) and Tabu Search/Path-Relinking (TS/PR) are developed. TS represents a local search algorithm, whereas TS/PR represents a hybridization of local search enhanced with population-based search algorithm. Two algorithms each, are developed for both, permutation (PN) and non-permutation (NPN) sequences. One of the algorithms is based on short term TS and the other is based on TS/PR. The developed heuristics are tested on sixteen small-size problems and their solution quality are compared with the optimal solution obtained from CPLEX. The evaluations show that the developed heuristics obtain good quality solutions within much less computational time. For PN sequence, the best algorithm obtained an average deviation of 0.49% compared with the optimal solution and for NPN sequence, the deviation is 0.13%. In addition, a slight improvement of 2.68% was obtained by adopting an NPN sequence over PN sequence for these problem instances.
A statistical designed experiment is conducted to evaluate the difference in performance of the developed heuristics, and permutation and non-permutation schedules. The results show that the TS/PR algorithms outperform short-term TS, in the case of both PN and NPN sequences. The comparison between the solutions from the best PN algorithm and the best NPN algorithm shows that an average improvement of 1.64% is obtained by implementing an NPN sequence over PN sequence. The statistical analysis shows that the improvement offered by NPN sequence is statistically significant for problems with large number of product types and small number of jobs in each product. In addition, it is also shown that the NPN sequence performs better for non-continuous production as compared to continuous production. The efficiency of the algorithms was analyzed using the computational time required by the algorithms. The results show that PN algorithms require a significantly less computational time as compared to NPN algorithms. Hence, it is recommended that NPN sequences be considered only for the problems with large number of product types and small number of jobs in each product. For other problems, only PN sequence should be considered. TS/PR algorithm is recommended for both, PN and NPN sequences
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