Heuristics for hybrid flow shops with controllable processing times and assignable due dates (original) (raw)
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The hybrid flow shop scheduling problem
2010
The scheduling of flow shops with multiple parallel machines per stage, usually referred to as the Hybrid Flow Shop (HFS), is a complex combinatorial problem encountered in many real world applications. Given its importance and complexity, the HFS problem has been intensively studied. This paper presents a literature review on exact, heuristic and metaheuristic methods that have been proposed for its solution. The paper discusses several variants of the HFS problem, each in turn considering different assumptions, constraints and objective functions. Research opportunities in HFS are also discussed.
Invited Review The hybrid flow shop scheduling problem
The scheduling of flow shops with multiple parallel machines per stage, usually referred to as the hybrid flow shop (HFS), is a complex combinatorial problem encountered in many real world applications. Given its importance and complexity, the HFS problem has been intensively studied. This paper presents a literature review on exact, heuristic and metaheuristic methods that have been proposed for its solution. The paper briefly discusses and reviews several variants of the HFS problem, each in turn considering different assumptions, constraints and objective functions. Research opportunities in HFS are also discussed.
Metaheuristic approaches to the hybrid flow shop scheduling problem with a cost-related criterion
In the paper, the flow-shop scheduling problem with parallel machines at each stage (machine center) is studied. For each job its release and due date as well as a processing time for its each operation are given. The scheduling criterion consists of three parts: the total weighted earliness, the total weighted tardiness and the total weighted waiting time. The criterion takes into account the costs of storing semi-manufactured products in the course of production and ready-made products as well as penalties for not meeting the deadlines stated in the conditions of the contract with customer. To solve the problem, three constructive algorithms and three metaheuristics (based one Tabu Search and Simulated Annealing techniques) are developed and experimentally analyzed. All the proposed algorithms operate on the notion of so-called operation processing order, i.e. the order of operations on each machine. We show that the problem of schedule construction on the base of a given operation processing order can be reduced to the linear programming task. We also propose some approximation algorithm for schedule construction and show the conditions of its optimality. r Hybrid flow-shop problems have received considerable attention from researchers during the last two decades. However, the scheduling criterion most frequently considered in those papers was the maximum completion time, see , , , and . Furthermore, these studies propose either an exact algorithm, which can solve only up to moderate size problem instances, or different types of heuristic and approximation algorithms .
Mathematical and Computer Modelling, Vol. 29, 1999, 101 - 126.
In this paper, we propose different heuristic algorithms for flow shop scheduling problems, where the jobs are partitioned into groups or families. Jobs of the same group can be processed together in a batch but the maximal number of jobs in a batch is limited. A setup is necessary before starting the processing of a batch, where the setup time depends on the group of the jobs. In this paper, we consider the case when the processing time of a batch is given by the maximum of the processing times of the operations contained in the batch. As objective function we consider the makespan as well as the weighted sum of completion times of the jobs. For these problems, we propose and compare various constructive and iterative algorithms. We derive suitable neighbourhood structures for such problems with batch setup times and describe iterative algorithms that are based on different types of local search algorithms. Except for standard metaheuristics, we also apply multilevel procedures which use different neighbourhoods within the search. The algorithms developed have been tested in detail on a large collection of problems with up to 120 jobs.
Efficient heuristics for the parallel blocking flow shop scheduling problem
Expert Systems with Applications, 2017
We consider the NP-hard problem of scheduling n jobs in F identical parallel flow shops, each consisting of a series of m machines, and doing so with a blocking constraint. The applied criterion is to minimize the makespan, i.e., the maximum completion time of all the jobs in F flow shops (lines). The Parallel Flow Shop Scheduling Problem (PFSP) is conceptually similar to another problem known in the literature as the Distributed Permutation Flow Shop Scheduling Problem (DPFSP), which allows modeling the scheduling process in companies with more than one factory, each factory with a flow shop configuration. Therefore, the proposed methods can solve the scheduling problem under the blocking constraint in both situations, which, to the best of our knowledge, has not been studied previously. In this paper, we propose a mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines. The proposed constructive procedures use two approaches that are totally different from those proposed in the literature. These methods are used as initial solution procedures of an iterated local search (ILS) and an iterated greedy algorithm (IGA), both of which are combined with a variable neighborhood search (VNS). The proposed constructive procedure and the improved methods take into account the characteristics of the problem. The computational evaluation demonstrates that both of them -especially the IGAperform considerably better than those algorithms adapted from the DPFSP literature. Abstract We consider the NP-hard problem of scheduling n jobs in F identical parallel flow shops, each consisting of a series of m machines, and doing so with a blocking constraint. The applied criterion is to minimize the makespan, i.e., the maximum completion time of all the jobs in F flow shops (lines). The Parallel Flow Shop Scheduling Problem (PFSP) is conceptually similar to another problem known in the literature as the Distributed Permutation Flow Shop Scheduling Problem (DPFSP), which allows modeling the scheduling process in companies with more than one factory, each factory with a flow shop configuration. Therefore, the proposed methods can solve the scheduling problem under the blocking constraint in both situations, which, to the best of our knowledge, has not been studied previously. In this paper, we propose a mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines. The proposed constructive procedures use two approaches that are totally different from those proposed in the literature. These methods are used as initial solution procedures of an iterated local search (ILS) and an iterated greedy algorithm (IGA), both of which are combined with a variable neighborhood search (VNS). The proposed
A Heuristic Search Algorithm for Flow-Shop Scheduling
Informaticasi, 2008
This article describes the development of a new intelligent heuristic search algorithm (IHSA*) which guarantees an optimal solution for flow-shop problems with an arbitrary number of jobs and machinesprovided the job sequence is constrained to be the same on each machine. The development is described in terms of 3 modifications made to the initial version of IHSA*. The first modification concerns thechoice of an admissible heuristic function. The second concerns the calculation of heuristic estimates as the search for an optimal solution progresses, and the third determines multiple optimal solutions whenthey exist. The first 2 modifications improve performance characteristics of the algorithm and experimental evidence of these improvements is presented as well as instructive examples which illustrate the use of initial and final versions of IHSA*.
Scheduling of Permutation Flow Shops Using a New Hybrid Algorithm
2018
In the current situation, modern engineering and industrial built-up units are encountering a jumble of issues in a variety of areas, including machining time, electricity, manpower, raw materials, and client restraints. One of the most important industrial behaviors, particularly in manufacturing planning, is job-shop scheduling. This study provides a new updated suggested approach of johnson's algorithm as well as the gupta's heuristic algorithm to solve the permutation flow shop sequencing problem with the goal of making the makespan as little as possible. This work is about determining the processing order of n tasks in m machines. Although, because the problem is np-hard for three or more computers, this results in a near-optimal solution to the given issue. The suggested approach is straightforward and easy to comprehend, and it is accompanied with a numerical example.
This paper investigates a difficult scheduling problem on a specialized two-stage hybrid flow shop with multiple processors that appears in semiconductor manufacturing industry, where the first and second stages process serial jobs and parallel batches, respectively. The objective is to seek job-machine, job-batch, and batch-machine assignments such that makespan is minimized, while considering parallel batch, release time, and machine eligibility constraints. We first propose a mixed integer programming (MIP) formulation for this problem, then gives a heuristic approach for solving larger problems. In order to handle real world large-scale scheduling problems, we propose an efficient dispatching rule called BFIFO that assigns jobs or batches to machines based on first-in-first-out principle, and then give several reoptimization techniques using MIP and local search heuristics involving interchange, translocation and transposition among assigned jobs. Computational experiments indicate our proposed re-optimization techniques are efficient. In particular, our approaches can produce good solutions for scheduling up to 160 jobs on 40 machines at both stages within 10 min.
Heuristic algorithms for scheduling hybrid flow shops with machine blocking and setup times
Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2018
We investigate a new variant of the hybrid flow shop problem (HFSP) considering machine blocking and both sequence-independent and sequence-dependent setup times. Since the HFSP is NP-hard, we propose heuristic algorithms along with priority rules based on the traditional SPT and LPT rules. We carried out computational experiments on simulated problem instances to test the performance of the priority rules. The objective function adopted was makespan minimization, and we used the rate of success and the relative deviation as performance criteria. The results indicate superiority of LPT-based rules. On instances with sequence-independent setup times, the LP rule, which is based on the non-increasing sorting of total processing times, outperformed other rules in most tested instances. In instances with sequence-dependent setup times, the LPS rule, which is based on the non-increasing sum of processing time and average setup times, outperformed other rules in most tested instances.
Most scheduling problems are combinatorial optimization problems which are too difficult to be solved optimally, and hence heuristics are used to obtain good solutions in reasonable times. The specific goal of this paper is to investigate scheduling heuristics to seek the minimum of a positively weighted convex sum of makespan and the number of tardy jobs in a static hybrid flow shop environment, where at least one production stage is made up of unrelated parallel machines. In addition, sequence -and machine -dependent setup times are considered. Some simple dispatching rules and flow shop makespan heuristics are adapted for the sequencing problem under consideration. The improvement heuristic algorithm proposed is a reinsertion algorithm. A simulated annealing algorithm is presented in this paper. Three basic parameters (i.e., cooling schedules, neighborhood structures, and initial temperatures) of a simulated annealing algorithm are briefly discussed in this paper. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages.