Particle swarm optimization and differential evolution for multi-objective multiple machine scheduling

Production scheduling is one of the most important issues in the planning and operation of manufacturing systems. Customers increasingly expect to receive the right product at the right price at the right time. Various problems experienced in manufacturing, for example low machine utilization and excessive work-in-process, can be attributed directly to inadequate scheduling. In this dissertation a production scheduling algorithm is developed for Optimatix, a South African-based company specializing in supply chain optimization. To address the complex requirements of the customer, the problem was modeled as a flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and production down time. The algorithm development process focused on investigating the application of both particle swarm optimization (PSO) and differential evolution (DE) to production scheduling environments characterized by multiple machines and multiple objectives. Alternative problem representations, algorithm variations and multi-objective optimization strategies were evaluated to obtain an algorithm which performs well against both existing rule-based algorithms and an existing complex flexible job shop scheduling solution strategy. Finally, the generality of the priority-based algorithm was evaluated by applying it to the scheduling of production and maintenance activities at Centurion Ice Cream and Sweets. The production environment was modeled as a multi-objective uniform parallel machine shop problem with sequence-dependent set-up times and unavailability intervals. A self-adaptive modified vector evaluated DE algorithm was developed and compared to classical PSO and DE vector evaluated algorithms. Promising results were obtained with respect to the suitability of the algorithms for solving a range of multi-objective multiple machine scheduling problems. CopyrightDissertation (MEng)--University of Pretoria, 2009.Industrial and Systems Engineeringunrestricte

The heterogeneous meta-hyper-heuristic : from low level heuristics to low level meta-heuristics

Meta-heuristics have already been used extensively for the successful solution of a wide range of real world problems. A few industrial engineering examples include inventory optimization, production scheduling, and vehicle routing, all areas which have a significant impact on the economic success of society. Unfortunately, it is not always easy to predict which meta-heuristic from the multitude of algorithms available, will be best to address a specific problem. Furthermore, many algorithm development options exist with regards to operator selection and parameter setting. Within this context, the idea of working towards a higher level of automation in algorithm design was born. Hyper-heuristics promote the design of more generally applicable search methodologies and tend to focus on performing relatively well on a large set of different types of problems. This thesis develops a heterogeneous meta-hyper-heuristic algorithm (HMHH) for single-objective unconstrained continuous optimization problems. The algorithm development process focused on investigating the use of meta-heuristics as low level heuristics in a hyper-heuristic context. This strategy is in stark contrast to the problem-specific low level heuristics traditionally employed in a hyper-heuristic framework. Alternative low level meta-heuristics, entity-to-algorithm allocation strategies, and strategies for incorporating local search into the HMHH algorithm were evaluated to obtain an algorithm which performs well against both its constituent low level meta-heuristics and four state- of-the-art multi-method algorithms. Finally, the impact of diversity management on the HMHH algorithm was investigated. Hyper-heuristics lend themselves to two types of diversity management, namely solution space diversity (SSD) management and heuristic space diversity (HSD) management. The concept of heuristic space diversity was introduced and a quantitative metric was defined to measure heuristic space diversity. An empirical evaluation of various solution space diversity and heuristic space diversity intervention mechanisms showed that the systematic control of heuristic space diversity has a significant impact on hyper-heuristic performance.Thesis (PhD)--University of Pretoria, 2015.Industrial and Systems EngineeringUnrestricte

Supplier segmentation : a case study of Mozambican cassava farmers

CITATION: Matshabaphala, N. S. & Grobler, J. 2021. Supplier segmentation : a case study of Mozambican cassava farmers. South African Journal of Industrial Engineering, 32(1):196-209, doi:10.7166/32-1-2459.The original publication is available at http://sajie.journals.ac.zaENGLISH ABSTRACT: Over 3 000 Mozambican smallholder farmers supply cassava to Company XYZ. XYZ needs an effective supplier segmentation method to gain insight into how it should direct its resources for the greatest impact. This paper describes the application of the k-means algorithm, agglomerative hierarchical clustering, and a self-organising map with ward clustering to segment these cassava suppliers. The insights gained from the cluster analysis are then used to provide recommendations and suggest suitable intervention strategies to manage each segment of suppliers. The proposed method offers users the basis of a supplier segmentation system that is more robust than commonly used qualitative supplier segmentation models.http://sajie.journals.ac.za/pub/article/view/2459Publisher's versio

Managing the cold chain : a case study at a South African ice cream company

This paper documents the results of a supply chain management (SCM) case study conducted at Centurion Ice Cream and Sweets CC, a producer of ice cream in the greater Gauteng area. The current SCM environment was first analyzed before the distribution function was identified as a prime candidate for further analysis. A Monte Carlo simulation was subsequently performed to investigate the effect of different distribution scenarios. The paper concludes with an investigation into information technology (IT) as the enabler for improved supply chain performance.Hierdie artikel dokumenteer die resultate van ‘n voorsieningskanaalgevallestudie uitgevoer by Centurion Ice Cream and Sweets CC, ‘n roomysvervaardiger in die Gauteng-area. Die voorsieningskanaal is eers ontleed voordat die distribusiefunksie geïdentifiseer is as ‘n kandidaat vir verdere analise. ‘n Monte Carlo-simulasie uitgevoer om die effek van verskillende distribusiescenarios te ondersoek. Die artikel sluit af met ‘n ondersoek na inligtingstegnologie as katalisator vir verbeterde voorsieningskanaalprestasie

System dynamics comparison of three inventory management models in an automotive parts supply chain

BACKGROUND : The automotive parts supply chain measures its success in terms of parts availability and stock required to achieve the availability target, measured as allocation fill rate (AFR). The supply chain strives to achieve an AFR target of 95.5% while maintaining low levels of stock. OBJECTIVE : The first objective of this study is to evaluate the current inventory management approach, namely the maximum inventory position (MIP) method, to understand the difference between the theoretical derivation and the actual implementation. The second objective is to develop and compare the performance of a new stock target setting (STS) method relative to the MIP methods. METHOD : The theoretical and actual equations behind the MIP and STS methods are derived for steady state as well as stochastic conditions. A system dynamics simulation model (SDSM) was developed to describe both the local and imported supply chains. The SDSM was used to simulate and confirm the parameters for the STS method. It was also used to compare the three inventory management methods against a theoretical environment and actual data sets. RESULTS : The STS method requires a damping factor (DF) to ensure it does not cause the bullwhip effect. The SDSM was used to determine that a value equal to the lead time ensures effective damping. In the theoretical environment, the MIPTheory method requires the lowest stock, but also has the lowest AFR. MIPActual achieves the highest AFR, but with significantly higher stock holding. The STS method improves on the AFR achieved by the MIPTheory method, with lower stock holding than the MIPActual method. With the actual demand data sets, the results vary by parts movement type. With fast moving parts, all methods achieve the AFR target, the MIPActual method has a higher stock holding for all cases, and the STS method results in reduced stock holding for 7 of 12 cases. With medium moving parts, the MIPActual method improves on the AFR in all 15 cases, but with significantly higher stock. The STS method increases the AFR in 7 of 15 cases and reduces the stockholding in 11 of 15 cases. With slow moving parts, both the MIPActual and STS methods improve the AFR with increased stock holding. The increase in stock holding for the STS method is significantly lower. With erratic moving parts, the MIPActual method improves on the AFR in all 17 cases, but requires significantly higher stock holding. The STS method achieves lower AFR values in 10 of 17 cases, but also requires lower or equal stock holding in 10 of 17 cases. CONCLUSION : The STS method provides a new approach to inventory management in the automotive supply chain. It provides improved performance for lower stock holding than the implemented MIP method (MIPActual). The results for the different movement category suggest that there is further research to be done to confirm the effectiveness of the various methods with other demand distributions.The study forms part of A.B.’s PhD study. J.G. and V.S.S.Y. contributed as study leaders,http://www.jtscm.co.zaam2018Industrial and Systems Engineerin

Cyber-security : identity deception detection on social media platforms

Social media platforms allow billions of individuals to share their thoughts, likes and dislikes in real-time, without any censorship. This freedom, however, comes at a cyber-security risk. Cyber threats are more difficult to detect in a cyber world where anonymity and false identities are ever-present. The speed at which these deceptive identities evolve calls for solutions to detect identity deception. Cyber-security threats caused by humans on social media platforms are widespread and warrant attention. This research posits a solution towards the intelligent detection of deceptive identities contrived by human individuals on social media platforms (SMPs). Firstly, this research evaluates machine learning models by using attributes such as the “profile image” found on SMPs. To improve on the results delivered by these models, past research findings from the field of psychology, such as that humans lie about their gender, are used. Newly engineered features such as “gender-derived-from-the-profile-image” are evaluated to grasp whether these features detect deception with greater accuracy. Furthermore, research results from detecting non-human (also known as bot) accounts are also leveraged to improve on the initial results. These machine learning results are lastly applied to a proposed model for the intelligent detection and interpretation of identity deception on SMPs. This paper shows that the cyber-security threat of identity deception can potentially be minimized, should the vulnerability in the current way of setting up user accounts on SMPs be re-engineered in the future.http://www.elsevier.com/locate/cose2019-09-01hj2018Computer ScienceIndustrial and Systems Engineerin

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

This paper investigates the application of particle swarm optimization (PSO) to the multi-objective flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and machine down time. To achieve this goal, alternative particle representations and problem mapping mechanisms were implemented within the PSO paradigm. This resulted in the development of four PSO-based heuristics. Benchmarking on real customer data indicated that using the priority-based representation resulted in a significant performance improvement over the existing rule-based algorithms commonly used to solve this problem. Additional investigation into algorithm scalability led to the development of a priority-based differential evolution algorithm. Apart from the academic significance of the paper, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability, and overall competitive advantage