Multi-Objective Flexible Job-Shop Scheduling Problem in Steel Tubes Production

doi:10.1016/S1874-8651(10)60063-4

Systems Engineering - Theory & Practice

Volume 29, Issue 8, August 2009, Pages 117-126

https://doi.org/10.1016/S1874-8651(10)60063-4 Get rights and content

Abstract

On the basis of the distinct characteristics of steel tube production process, the production scheduling problem of seamless steel tubes is described as flexible job-shop scheduling problem. Considering the parallel machines with capacity and speed constraint, maintenance of machines as well as intermediate inventory restriction, it is formulated as mixed-integer-programming(MIP) model to decide the flexible routes for every job and to optimize the sequence of jobs. The objective is not only to reduce delivery delay, but also to minimize idleness of machines and interruption in production. Given the problem is NP-hard (non-deterministic polynomial-time hard), modified genetic algorithm is proposed, whose effectiveness can be well verified in scheduling decision support system for the production of seamless steel tubes in Baoshan Iron & Steel Complex.

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However, there are many studies on various solution methods considering multiple objectives for FJCS, as summarized in Table 4. Table 3 shows that in almost all multi-objective flexible job shop scheduling problems makespan is the main objective taken into account, except [14,15]. The majority of the studies in our sample survey present solution methods optimizing the objectives of makespan, the total workload of machines, and critical machine workload simultaneously.
The multi-objective flexible job shop scheduling in a cellular manufacturing environment is a challenging real-world problem. This recently introduced scheduling problem variant considers exceptional parts, intercellular moves, intercellular transportation times, sequence-dependent family setup times, and recirculation requiring minimization of makespan and total tardiness, simultaneously. A previous study shows that the exact solver based on mixed-integer nonlinear programming model fails to find an optimal solution to each of the ‘medium’ to ‘large’ size instances considering even the simplified version of the problem. In this study, we present evolutionary algorithms for solving that bi-objective problem and apply genetic and memetic algorithms that use three different scalarization methods, including weighted sum, conic, and tchebycheff. The performance of all evolutionary algorithms with various configurations is investigated across forty-three benchmark instances from ‘small’ to ‘large’ size including a large real-world problem instance. The experimental results show that the transgenerational memetic algorithm using weighted sum outperforms the rest producing the best-known results for almost all bi-objective flexible job shop cell scheduling instances, in overall.
Multi-Objective parameter-less population pyramid for solving industrial process planning problems
2021, Swarm and Evolutionary Computation
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Various real-world industrial scheduling problems were attempted to be solved with customised multi-objective GAs as well. In [57], for example, a real-world manufacturing problem of a steel tube production was described as an extension of a classic Job-Shop Scheduling Problem with compatible resources (aka Flexible Job-Shop Scheduling Problem). A multi-objective GA was applied with two objectives: minimisation of the resources’ idleness and waiting time of orders.
Evolutionary methods are effective tools for obtaining high-quality results when solving hard practical problems. Linkage learning may increase their effectiveness. One of the state-of-the-art methods that employ linkage learning is the Parameter-less Population Pyramid (P3). P3 is dedicated to solving single-objective problems in discrete domains. Recent research shows that P3 is highly competitive when addressing problems with so-called overlapping blocks, which are typical for practical problems. In this paper, we consider a multi-objective industrial process planning problem that arises from practice and is NP-hard. To handle it, we propose a multi-objective version of P3. The extensive research shows that our proposition outperforms the competing methods for the considered practical problem and typical multi-objective benchmarks.
Data-driven oriented optimization of resource allocation in the forging process using Bi-objective Evolutionary Algorithm
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After drilling, the steel rods are heated in induction heaters and extruded through thousands of tons of presses into seamless steel tubes and iron pipes of the size specified by customers. Each product is then tested by an in-line inspection machine to ensure internal and surface quality, providing customers with the perfect finished product quality (Li and Huo, 2009). The forging process is the heart of the overall steel production process, and combines a wide range of raw materials from the upstream steelmaking process, and the finished product specifications required by downstream customers.
Resource allocation in the forging process of the steel production industry is an important element of the material supply in upstream processes, and the subsequent processing of semi-finished products downstream. However, the existing literature rarely discusses issues related to this problem. In this study, the information flow is built by referring to specifications and process logic, which is a kind of pre-processing in data analysis flow in order to break the initial barrier of resource allocation in forging. In addition, Bi-objective Evolutionary Algorithm (BOEA) is proposed to optimize the resource allocation in the forging process. Using the built information flow, the available multiple forging process resources can be effectively connected, and information of available resource combinations can be established for orders. Since real users have preferences for different objectives in practice, experiment results show that the proposed BOEA can deal with these preferences by effectively optimizing both the remnants (the remaining materials) and the execution cost, and the profit contribution is also proved by effective cost savings.
Multi-objective evolutionary simulation based optimization mechanism for a novel stochastic reliability centered maintenance problem
2018, Swarm and Evolutionary Computation
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In the classical literature of the FJSP, most studies assume that all machines are available all the time during their working process. Both areas of the optimization problems, i.e. model development [48–53] and solving method extension [54–67] can be found in the classical literature of FJSP. Demir and Isleyen [68] performed a comprehensive evaluation on the various mathematical models presented for the FJSP.
This research develops a novel stochastic reliability-centered maintenance (RCM) mechanism within a new multi-objective joint maintenance and production planning problem. RCM in this integrated problem is an agent that monitors and manages the maintenance functions of a stochastic complex production-planning problem, namely flexible job shop scheduling problem (FJSP). The novel developed RCM takes benefit of stochastic condition based maintenance (CBM) approach that works based on stochastic shocking scheme of machines during their process time. It activates the maintenance activities, including preventive and corrective maintenance, according to the degradation level of system reliability after shocks and not merely according to the predetermined intervals. In addition to the maintenance activation times, the maintenance durations of different kinds are also modeled stochastically. Furthermore, different types of stochastic maintenance costs are also considered alongside system reliability and complementation time (C_max). Moreover, as the problem belongs to the NP-Hard class of optimization problems, four multi-objective simulation based optimization (SBO) algorithms, called multi-objective biogeography based optimization (MOBBO) algorithm, Pareto envelope-based selection algorithm (PESA), new version of non-dominated sorting genetic algorithm (NSGAIII) and multi-objective evolutionary algorithm based on decomposition (MOEAD) are employed to solve the underlying problem. A novel visualization approach joint by Gant chart is also proposed to discuss the whole RCM scheme, systematically. Different test problems, statistical tests and outputs explain the problem and algorithms' performance explicitly.
Flexible job shop scheduling problem for parallel batch processing machine with compatible job families
2017, Applied Mathematical Modelling
Flexible Job-Shop Scheduling Problem (FJSP) with Parallel Batch processing Machine (PBM) is studied. First, a Mixed Integer Programming (MIP) formulation is proposed for the first time. In order to address an NP-hard structure of this problem, the formulation is modified to selectively schedule jobs. Although there are many jobs on a given floor, semiconductor manufacturing is most challenged by priority jobs that promise a significant amount of financial compensation in exchange for an expedited delivery. This modification could leave some non-priority jobs unscheduled. However, it vastly expedites the discovery of improving solutions by first branching on integer variables with higher priority jobs. This study then turns job-dependent processing times into job-independent ones by assuming a machine has an equal processing time on different jobs. This assumption is roughly true or acceptable for the sake of the reduced computational time in the industry. These changes significantly reduce computational time compared to the original model when tested on a set of common problem instances from the literature. Computational results show that this proposed model can generate an effective schedule for large problems. Author encourages other researchers to propose an improved MIP model.
Flexible job shop scheduling problem with parallel batch processing machines: MIP and CP approaches
2016, Computers and Industrial Engineering
A flexible job-shop scheduling problem (FJSP) with parallel batch processing machines (PBM) is addressed. First, we exhibit an enhanced mixed integer programming (MIP) model. Secondly, several valid inequalities are added for a reduction of solution space. Finally, we propose a constraint programming (CP) model, which is likely to be superior in many scheduling problems. Those different approaches are tested on a set of common problem instances from the literature. Computational results find three key lessons: the proposed MIP model significantly reduces computational time compared to the original model from the literature, the valid inequalities further reduce a computational time, and CP incomparably outperforms all three MIP models. Authors also discuss future adoption opportunities of CP, which has not got well deserved attention by OR/IE practitioners yet.

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: Foundation item: Supported by the National Natural Science Foundation (No. 70832005)

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RESEARCH PAPERMulti-Objective Flexible Job-Shop Scheduling Problem in Steel Tubes Production

Abstract

European J of Operational Research

Chinese Journal of Chemical Engineering

International Journal of Production Economics

Computers & Operations Research

European Journal of Operational Research

Research on hot rolling seamless steel tube production scheduling

Journal of System Simulation

Model and algorithm for hot rolling steel tube MPS

Journal of Systems Engineering

A multi-agent system for the integrated dynamic scheduling of steel production

A lot grouping algorithm for a continuous slab caster in an integrated steel mill

Production Planning & Control

Model and algorithm of furnace charge plan for steelmaking-continuous casting production scheduling

Journal of Northeastern University

RESEARCH PAPER
Multi-Objective Flexible Job-Shop Scheduling Problem in Steel Tubes Production