Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research

Scheduling is a decision-making process that is concerned with the allocation of limited resources to competing tasks (operations of jobs) over a time period with the goal of optimising one or more objectives. In theory, the objective is usually to optimise some classical system performance measures such as makespan, tardiness/earliness and flowtime under deterministic and static assumptions. In practice, however, scheduling systems operate in dynamic and stochastic environments. Hence, there is a need to incorporate both uncertainty and dynamic elements into the scheduling process. In this paper, the major issues involved in scheduling decisions are discussed and the basic approaches to tackle these problems in manufacturing environments are analysed. Proactive scheduling is then focused on and several robustness and stability measures are presented. Previous research on scheduling robustness and stability is also reviewed and further research directions are suggested

Automatic Design of Dispatching Rules for Job Shop Scheduling with Genetic Programming

Scheduling is an important planning activity in manufacturing systems to help optimise the usage of scarce resources and improve the customer satisfaction. In the job shop manufacturing environment, scheduling problems are challenging due to the complexity of production flows and practical requirements such as dynamic changes, uncertainty, multiple objectives, and multiple scheduling decisions. Also, job shop scheduling (JSS) is very common in small manufacturing businesses and JSS is considered one of the most popular research topics in this domain due to its potential to dramatically decrease the costs and increase the throughput. Practitioners and researchers have applied different computational techniques, from different fields such as operations research and computer science, to deal with JSS problems. Although optimisation methods usually show their dominance in the literature, applying optimisation techniques in practical situations is not straightforward because of the practical constraints and conditions in the shop. Dispatching rules are a very useful approach to dealing with these environments because they are easy to implement(by computers and shop floor operators) and can cope with dynamic changes. However, designing an effective dispatching rule is not a trivial task and requires extensive knowledge about the scheduling problem. The overall goal of this thesis is to develop a genetic programming based hyper-heuristic (GPHH) approach for automatic heuristic design of reusable and competitive dispatching rules in job shop scheduling environments. This thesis focuses on incorporating special features of JSS in the representations and evolutionary search mechanisms of genetic programming(GP) to help enhance the quality of dispatching rules obtained. This thesis shows that representations and evaluation schemes are the important factors that significantly influence the performance of GP for evolving dispatching rules. The thesis demonstrates that evolved rules which are trained to adapt their decisions based on the changes in shops are better than conventional rules. Moreover, by applying a new evaluation scheme, the evolved rules can effectively learn from the mistakes made in previous completed schedules to construct better scheduling decisions. The GP method using the newproposed evaluation scheme shows better performance than the GP method using the conventional scheme. This thesis proposes a new multi-objective GPHH to evolve a Pareto front of non-dominated dispatching rules. Instead of evolving a single rule with assumed preferences over different objectives, the advantage of this GPHH method is to allow GP to evolve rules to handle multiple conflicting objectives simultaneously. The Pareto fronts obtained by the GPHH method can be used as an effective tool to help decision makers select appropriate rules based on their knowledge regarding possible trade-offs. The thesis shows that evolved rules can dominate well-known dispatching rules when a single objective and multiple objectives are considered. Also, the obtained Pareto fronts show that many evolved rules can lead to favourable trade-offs, which have not been explored in the literature. This thesis tackles one of themost challenging issues in job shop scheduling, the interactions between different scheduling decisions. New GPHH methods have been proposed to help evolve scheduling policies containing multiple scheduling rules for multiple scheduling decisions. The two decisions examined in this thesis are sequencing and due date assignment. The experimental results show that the evolved scheduling rules are significantly better than scheduling policies in the literature. A cooperative coevolution approach has also been developed to reduce the complexity of evolving sophisticated scheduling policies. A new evolutionary search mechanisms and customised genetic operations are proposed in this approach to improve the diversity of the obtained Pareto fronts

Minimising total flowtime in a no-wait flow shop (NWFS) using genetic algorithms

This paper considers a no-wait flow shop scheduling (NWFS) problem, where the objective is to minimise the total flowtime. We propose a genetic algorithm (GA) that is implemented in a spreadsheet environment. The GA functions as an add-in in the spreadsheet. It is demonstrated that with proposed approach any criteria can be optimised without modifying the GA routine or spreadsheet model. Furthermore, the proposed method for solving this class of problem is general purpose, as it can be easily customised by adding or removing jobs and machines. Several benchmark problems already published in the literature are used to demonstrate the problem-solving capability of the proposed approach. Benchmark problems set ranges from small (7-jobs, 7 machines) to large (100-jobs, 10-machines). The performance of the GA is compared with different meta-heuristic techniques used in earlier literature. Experimental analysis demonstrate that solutions obtained in this research offer equal quality as compared to algorithms already developed for NWFS problems

Robustness and stability measures for scheduling policies in a single machine environment

Cataloged from PDF version of article.Scheduling is a decision making process that concerns allocation of limited resources (machines, material handling equipment, operators, tools, fixtures, etc.) to competing tasks (operations of jobs) over time with the goal of optimizing one or more objectives. The output of this decision process is time/machine/operation assignments. In classical scheduling theory, the objective is generally maximizing some measure of system performance. In addition to classical performance measures two new criteria are used in modern scheduling literature: "robustness" and "stability". In this thesis, we propose several robustness and stability measures and policies. Two new surrogate measures are also developed since the exact measures are difficult to calculate. These surrogate measures are embedded in a tabu search algorithm to generate robust and stable schedules for a single machine subject to random machine breakdowns. We show that our surrogate measures are better than well-known and commonly used average slack method.Gören, SelçukM.S

An intelligent manufacturing system for heat treatment scheduling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly

The distributed assembly permutation flowshop scheduling problem

Nowadays, improving the management of complex supply chains is a key to become competitive in the twenty-first century global market. Supply chains are composed of multi-plant facilities that must be coordinated and synchronised to cut waste and lead times. This paper proposes a Distributed Assembly Permutation Flowshop Scheduling Problem (DAPFSP) with two stages to model and study complex supply chains. This problem is a generalisation of the Distributed Permutation Flowshop Scheduling Problem (DPFSP). The first stage of the DAPFSP is composed of f identical production factories. Each one is a flowshop that produces jobs to be assembled into final products in a second assembly stage. The objective is to minimise the makespan. We present first a Mixed Integer Linear Programming model (MILP). Three constructive algorithms are proposed. Finally, a Variable Neighbourhood Descent (VND) algorithm has been designed and tested by a comprehensive ANOVA statistical analysis. The results show that the VND algorithm offers good performance to solve this scheduling problem.Ruben Ruiz is partially supported by the Spanish Ministry of Science and Innovation, under the project 'RESULT - Realistic Extended Scheduling Using Light Techniques' with reference DPI2012-36243-C02-01. Carlos Andres-Romano is partially supported by the Spanish Ministry of Science and Innovation, under the project 'INSAMBLE' - Scheduling at assembly/disassembly synchronised supply chains with reference DPI2011-27633.Hatami, S.; Ruiz García, R.; Andrés Romano, C. (2013). The distributed assembly permutation flowshop scheduling problem. International Journal of Production Research. 51(17):5292-5308. https://doi.org/10.1080/00207543.2013.807955S529253085117Basso, D., Chiarandini, M., & Salmaso, L. (2007). Synchronized permutation tests in replicated designs. Journal of Statistical Planning and Inference, 137(8), 2564-2578. doi:10.1016/j.jspi.2006.04.016Biggs, D., De Ville, B., & Suen, E. (1991). A method of choosing multiway partitions for classification and decision trees. Journal of Applied Statistics, 18(1), 49-62. doi:10.1080/02664769100000005Chan, F. T. S., Chung, S. H., Chan, L. Y., Finke, G., & Tiwari, M. K. (2006). Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach. Robotics and Computer-Integrated Manufacturing, 22(5-6), 493-504. doi:10.1016/j.rcim.2005.11.005Chan, F. T. S., Chung, S. H., & Chan, P. L. Y. (2006). Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems. International Journal of Production Research, 44(3), 523-543. doi:10.1080/00207540500319229Liao, C.-J., & Liao, L.-M. (2008). Improved MILP models for two-machine flowshop with batch processing machines. Mathematical and Computer Modelling, 48(7-8), 1254-1264. doi:10.1016/j.mcm.2008.01.001Framinan, J. M., & Leisten, R. (2003). An efficient constructive heuristic for flowtime minimisation in permutation flow shops. Omega, 31(4), 311-317. doi:10.1016/s0305-0483(03)00047-1Gao, J., & Chen, R. (2011). A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem. International Journal of Computational Intelligence Systems, 4(4), 497-508. doi:10.1080/18756891.2011.9727808Hansen, P., & Mladenović, N. (2001). Variable neighborhood search: Principles and applications. European Journal of Operational Research, 130(3), 449-467. doi:10.1016/s0377-2217(00)00100-4Hariri, A. M. A., & Potts, C. N. (1997). A branch and bound algorithm for the two-stage assembly scheduling problem. European Journal of Operational Research, 103(3), 547-556. doi:10.1016/s0377-2217(96)00312-8Jia, H. Z., Fuh, J. Y. H., Nee, A. Y. C., & Zhang, Y. F. (2002). Web-based Multi-functional Scheduling System for a Distributed Manufacturing Environment. Concurrent Engineering, 10(1), 27-39. doi:10.1177/1063293x02010001054Jia, H. Z., Nee, A. Y. C., Fuh, J. Y. H., & Zhang, Y. F. (2003). Journal of Intelligent Manufacturing, 14(3/4), 351-362. doi:10.1023/a:1024653810491Jia, H. Z., Fuh, J. Y. H., Nee, A. Y. C., & Zhang, Y. F. (2007). Integration of genetic algorithm and Gantt chart for job shop scheduling in distributed manufacturing systems. Computers & Industrial Engineering, 53(2), 313-320. doi:10.1016/j.cie.2007.06.024Kass, G. V. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics, 29(2), 119. doi:10.2307/2986296Lee, C.-Y., Cheng, T. C. E., & Lin, B. M. T. (1993). Minimizing the Makespan in the 3-Machine Assembly-Type Flowshop Scheduling Problem. Management Science, 39(5), 616-625. doi:10.1287/mnsc.39.5.616Morgan, J. N., & Sonquist, J. A. (1963). Problems in the Analysis of Survey Data, and a Proposal. Journal of the American Statistical Association, 58(302), 415-434. doi:10.1080/01621459.1963.10500855Pan, Q.-K., & Ruiz, R. (2012). Local search methods for the flowshop scheduling problem with flowtime minimization. European Journal of Operational Research, 222(1), 31-43. doi:10.1016/j.ejor.2012.04.034Potts, C. N., Sevast’janov, S. V., Strusevich, V. A., Van Wassenhove, L. N., & Zwaneveld, C. M. (1995). The Two-Stage Assembly Scheduling Problem: Complexity and Approximation. Operations Research, 43(2), 346-355. doi:10.1287/opre.43.2.346Ruiz, R., & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-2049. doi:10.1016/j.ejor.2005.12.009Ruiz, R., Şerifoğlu, F. S., & Urlings, T. (2008). Modeling realistic hybrid flexible flowshop scheduling problems. Computers & Operations Research, 35(4), 1151-1175. doi:10.1016/j.cor.2006.07.014Ruiz, R., & Andrés-Romano, C. (2011). Scheduling unrelated parallel machines with resource-assignable sequence-dependent setup times. The International Journal of Advanced Manufacturing Technology, 57(5-8), 777-794. doi:10.1007/s00170-011-3318-2Stafford, E. F., Tseng, F. T., & Gupta, J. N. D. (2005). Comparative evaluation of MILP flowshop models. Journal of the Operational Research Society, 56(1), 88-101. doi:10.1057/palgrave.jors.2601805Tozkapan, A., Kırca, Ö., & Chung, C.-S. (2003). A branch and bound algorithm to minimize the total weighted flowtime for the two-stage assembly scheduling problem. Computers & Operations Research, 30(2), 309-320. doi:10.1016/s0305-0548(01)00098-3Tseng, F. T., & Stafford, E. F. (2008). New MILP models for the permutation flowshop problem. Journal of the Operational Research Society, 59(10), 1373-1386. doi:10.1057/palgrave.jors.260245

Genetic Programming Hyper-heuristics for Job Shop Scheduling

Scheduling problems arise whenever there is a choice of order in which a number of tasks should be performed; they arise commonly, daily and everywhere. A job shop is a common manufacturing environment in which a schedule for processing a set of jobs through a set of machines needs to be constructed. Job shop scheduling (JSS) has been called a fascinating challenge as it is computationally hard and prevalent in the real-world. Developing more effective ways of scheduling jobs could increase profitability through increasing throughput and decreasing costs. Dispatching rules (DRs) are one of the most popular scheduling heuristics. DRs are easy to implement, have low computational cost, and cope well with the dynamic nature of real-world manufacturing environments. However, the manual development of DRs is time consuming and requires expert knowledge of the scheduling environment. Genetic programming (GP) is an evolutionary computation method which is ideal for automatically discovering DRs. This is a hyper-heuristic approach, as GP is searching the search space of heuristic (DR) solutions rather than constructing a schedule directly. The overall goal of this thesis is to develop GP based hyper-heuristics for the efficient evolution (automatic generation) of robust, reusable and effective scheduling heuristics for JSS environments, with greater interpretability. Firstly, this thesis investigates using GP to evolve optimal DRs for the static two-machine JSS problem with makespan objective function. The results show that some evolved DRs were equivalent to an optimal scheduling algorithm. This validates both the GP based hyper-heuristic approach for generating DRs for JSS and the representation used. Secondly, this thesis investigates developing ``less-myopic'' DRs through the use of wider-looking terminals and local search to provide additional fitness information. The results show that incorporating features of the state of the wider shop improves the mean performance of the best evolved DRs, and that the inclusion of local search in evaluation evolves DRs which make better decisions over the local time horizon, and attain lower total weighted tardiness. Thirdly, this thesis proposes using strongly typed GP (STGP) to address the challenging issue of interpretability of DRs evolved by GP. Several grammars are investigated and the results show that the DRs evolved in the semantically constrained search space of STGP do not have (on average) performance that is as good as unconstrained. However, the interpretability of evolved rules is substantially improved. Fourthly, this thesis investigates using multiobjective GP to encourage evolution of DRs which are more readily interpretable by human operators. This approach evolves DRs with similar performance but smaller size. Fragment analysis identifies popular combinations of terminals which are then used as high level terminals; the inclusion of these terminals improved the mean performance of the best evolved DRs. Through this thesis the following major contributions have been made: (1) the first use of GP to evolve optimal DRs for the static two-machine job shop with makespan objective function; (2) an approach to developing less-myopic DRs through the inclusion of wider looking terminals and the use of local search to provide additional fitness information over an extended decision horizon; (3) the first use of STGP for the automatic discovery of DRs with better interpretability and semantic validity for increased trust; and (4) the first multiobjective GP approach that considers multiple objectives investigating the trade-off between scheduling behaviour and interpretability. This is also the first work that uses analysis of evolved GP individuals to perform feature selection and construction for JSS

An intelligent manufacturing system for heat treatment scheduling

This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A critical analysis of job shop scheduling in context of industry 4.0

Scheduling plays a pivotal role in the competitiveness of a job shop facility. The traditional job shop scheduling problem (JSSP) is centralized or semi-distributed. With the advent of Industry 4.0, there has been a paradigm shift in the manufacturing industry from traditional scheduling to smart distributed scheduling (SDS). The implementation of Industry 4.0 results in increased flexibility, high product quality, short lead times, and customized production. Smart/intelligent manufacturing is an integral part of Industry 4.0. The intelligent manufacturing approach converts renewable and nonrenewable resources into intelligent objects capable of sensing, working, and acting in a smart environment to achieve effective scheduling. This paper aims to provide a comprehensive review of centralized and decentralized/distributed JSSP techniques in the context of the Industry 4.0 environment. Firstly, centralized JSSP models and problem-solving methods along with their advantages and limitations are discussed. Secondly, an overview of associated techniques used in the Industry 4.0 environment is presented. The third phase of this paper discusses the transition from traditional job shop scheduling to decentralized JSSP with the aid of the latest research trends in this domain. Finally, this paper highlights futuristic approaches in the JSSP research and application in light of the robustness of JSSP and the current pandemic situation