New Solution Approaches for Scheduling Problems in Production and Logistics

The current cumulative PhD thesis consists of six papers published in/submitted to scientific journals. The focus of the thesis is to develop new solution approaches for scheduling problems encountering in manufacturing as well as in logistics. The thesis is divided into two parts: “ma-chine scheduling in production” and “scheduling problems in logistics” each of them consisting three papers. To have most comprehensive overview of the topic of machine scheduling, the first part of the thesis starts with two systematic review papers, which were conducted on tertiary level (i.e., re-viewing literature reviews). Both of these papers analyze a sample of around 130 literature re-views on machine scheduling problems. The first paper use a subjective quantitative approach to evaluate the sample, while the second papers uses content analysis which is an objective quanti-tative approach to extract meaningful information from massive data. Based on the analysis, main attributes of scheduling problems in production are identified and are classified into sever-al categories. Although the focus of both these papers are set to review scheduling problems in manufacturing, the results are not restricted to machine scheduling problem and the results can be extended to the second part of the thesis. General drawbacks of literature reviews are identi-fied and several suggestions for future researches are also provided in both papers. The third paper in the first part of the thesis presents the results of 105 new heuristic algorithms developed to minimize total flow time of a set of jobs in a flowshop manufacturing environ-ment. The computational experiments confirm that the best heuristic proposed in this paper im-proves the average error of best existing algorithm by around 25 percent. The first paper in second part is focused on minimizing number of electric tow-trains responsi-ble to deliver spare parts from warehouse to the production lines. Together with minimizing number of these electric vehicles the paper is also focused to maximize the work load balance among the drivers of the vehicles. For this problem, after analyzing the complexity of the prob-lem, an opening heuristic, a mixed integer linear programing (MILP) model and a taboo-search neighborhood search approach are proposed. Several managerial insights, such as the effect of battery capacity on the number of required vehicles, are also discussed. The second paper of the second part addresses the problem of preparing unit loaded devices (ULDs) at air cargos to be loaded latter on in planes. The objective of this problem is to mini-mize number of workers required in a way that all existing flight departure times are met and number of available places for building ULDs is not violated. For this problem, first, a MILP model is proposed and then it is boosted with a couple of heuristics which enabled the model to find near optimum solutions in a matter of 10 seconds. The paper also investigates the inherent tradeoff between labor and space utilization as well as the uncertainty about the volume of cargo to be processed. The last paper of the second part proposes an integrated model to improve both ergonomic and economic performance of manual order picking process by rotating pallets in the warehouse. For the problem under consideration in this paper, we first present and MILP model and then pro-pose a neighborhood search based on simulated annealing. The results of numerical experiment indicate that selectively rotating pallets may reduce both order picking time as well as the load on order picker, which leads to a quicker and less risky order picking process

Application of computational intelligence to explore and analyze system architecture and design alternatives

Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research contribution is a hybrid fuzzy-EA model that facilitates the exploration and analysis of possible SoS configurations. The second contribution is a hybrid neural network-EA in which the EA explores, analyzes, and evolves the neural network architecture and weights. The third contribution is a multi-objective EA that examines potential installation (i.e. system) infrastructure repair strategies. The final contribution is the introduction of a hierarchical multi-objective evolutionary algorithm (MOEA) framework with a feedback mechanism to evolve and simultaneously evaluate competing subsystem and system level performance objectives. Systems architects and engineers can utilize the frameworks and approaches developed in this research to more efficiently explore and analyze complex system design alternatives --Abstract, page iv

Scheduling Hybrid Flow Lines of Aerospace Composite Manufacturing Systems

Composite manufacturing is a vital part of aerospace manufacturing systems. Applying effective scheduling within these systems can cut the costs in aerospace companies significantly. These systems can be characterized as two-stage Hybrid Flow Shops (HFS) with identical, non-identical and unrelated parallel discrete-processing machines in the first stage and non-identical parallel batch-processing machines in the second stage. The first stage is normally the lay-up process in which the carbon fiber sheets are stacked on the molds (tools). Then, the parts are batched based on the compatibility of their cure recipe before going to the second stage into the autoclave for curing. Autoclaves require enormous capital investment and maximizing their utilization is of utmost importance. In this thesis, a Mixed Integer Linear Programming (MILP) model is developed to maximize the utilization of the resources in the second stage of this HFS. CPLEX, with an underlying branch and bound algorithm, is used to solve the model. The results show the high level of flexibility and computational efficiency of the proposed model when applied to small and medium-size problems. However, due to the NP-hardness of the problem, the MILP model fails to solve large problems (i.e. problems with more than 120 jobs as input) in reasonable CPU times. To solve the larger instances of the problem, a novel heuristic method along with a Genetic Algorithm (GA) are developed. The heuristic algorithm is designed based on a careful observation of the behavior of the MILP model for different problem sets. Moreover, it is enhanced by adding a number of proper dispatching rules. As its output, this heuristic algorithm generates eight initial feasible solutions which are then used as the initial population of the proposed GA. The GA improves the initial solutions obtained from the aforementioned heuristic through its stochastic iterations until it reaches the satisfactory near-optimal solutions. A novel crossover operator is introduced in this GA which is unique to the HFS of aerospace composite manufacturing systems. The proposed GA is proven to be very efficient when applied to large-size problems with up to 300 jobs. The results show the high quality of the solutions achieved by the GA when compared to the optimal solutions which are obtained from the MILP model. A real case study undertaken at one of the leading companies in the Canadian aerospace industry is used for the purpose of data experiments and analysis

Algorithms for Scheduling Problems

This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms

Genetic algorithms in timetabling and scheduling

Thio thesis investigates the use of genetic algorithms (GAs) for solving a range of timetabling and scheduling problems. Such problems arc very hard in general, and GAs offer a useful and successful alternative to existing techniques.A framework is presented for GAs to solve modular timetabling problems in edu¬ cational institutions. The approach involves three components: declaring problemspecific constraints, constructing a problem specific evaluation function and using a problem-independent GA to attempt to solve the problem. Successful results are demonstrated and a general analysis of the reliability and robustness of the approach is conducted. The basic approach can readily handle a wide variety of general timetabling problem constraints, and is therefore likely to be of great practical usefulness (indeed, an earlier version is already in use). The approach rclicG for its success on the use of specially designed mutation operators which greatly improve upon the performance of a GA with standard operators.A framework for GAs in job shop and open shop scheduling is also presented. One of the key aspects of this approach is the use of specially designed representations for such scheduling problems. The representations implicitly encode a schedule by encoding instructions for a schedule builder. The general robustness of this approach is demonstrated with respect to experiments on a range of widely-used benchmark problems involving many different schedule quality criteria. When compared against a variety of common heuristic search approaches, the GA approach is clearly the most successful method overall. An extension to the representation, in which choices of heuristic for the schedule builder arc also incorporated in the chromosome, iG found to lead to new best results on the makespan for some well known benchmark open shop scheduling problems. The general approach is also shown to be readily extendable to rescheduling and dynamic scheduling

A study of current and possible future industrial engineering methodologies used to increase energy effieciency

Thesis (MScEng)--Stellenbosch University, 2012.ENGLISH ABSTRACT: Energy-related costs are increasing steadily. This is especially true in South Africa where we have been dealing with an energy crisis during the past couple of years. The increase in energy-related costs puts energy dependent industries under financial pressure. It is therefore imperative to find ways to improve the efficiency with which energy is being consumed in order to decrease the amount of money that has to be allocated to energy costs. The efficient consumption of energy at a facility is crucial and to increase that efficiency, Energy Management Programs (EMPs) should be implemented. An important component of EMPs is ascertaining the current energy consumption of a facility in order to identify areas where possible improvements can be made. This is done by completing an energy audit at the facility. After the energy audit has been completed and Energy Conservation Methods (ECMs) have been identified, the implementation of these methods should commence. The aim of this study is to determine how Industrial Engineering (IE) methods can play a more integral role in making South Africa more energy efficient. As part of this study, research was done to identify current EMCs being implemented in different areas and for different equipment in facilities. This information was compared to IE methods to identify the possible relationship between the ECMs and IE methods. Content analyses were completed on both IE and energy efficiency corpora using the Content Analysis Toolkit (CAT) program. These analyses identified important topics in these corpora and correlations between these topics in order to show correlations between the IE and energy efficiency fields. The most significant correlations identified, were between statistical methods and various energy efficiency topics. A case study was completed at a company in the Western Cape that manufactures electronic and integrated circuit products to implement the relevant ECMs. As part of the case study, an energy audit was completed at the facility. The implementation of a number of the ECMs has shown reductions in the daily kilowatt hours (kWh) consumptions. These reductions were obtained through the implementation of a Shut Down Management program, which highlights the importance of management in an energy conservation project. The application of optimisation algorithms for energy efficient design was examined through the optimisation of lighting design, using a Genetic Algorithm. It was found that a Genetic Algorithm is applicable to lighting design but requires further refinement in order to generate the most optimal design solutions.AFRIKAANSE OPSOMMING: Kostes verbonde aan energieverbruik is voortdurend besig om toe te neem.Dit is veral relevant in Suid-Afrika waar ons tans ’n energiekrisis beleef. Hierdie toename in energieverwante kostes plaas energie-afhanklike industrieë onder groot finansiële druk. Dit is daarom belangrik om maniere te vind om energieverbruik meer effektief te maak sodat die bedrag geld wat aan energieverwante kostes toegestaan word, verminder kan word. Effektiewe energieverbruik by ’n fasiliteit is kritiek en om hierdie effektiwiteit te verbeter behoort ’n energiebestuursprogram by die fasiliteit geïmplimenteer te word. ’n Belangrike komponent van energiebestuursprogramme is die bepaling van die huidige energieverbruik en dit word gebruik om die areas te identifiseer waar moontlike verbeteringe aangebring kan word. Die energieverbruik word bepaal deur ’n energie-oudit. Nadat die energie-oudit voltooi en die energiebesparingsmetodes bepaal is, moet hierdie metodes by die fasiliteit geïmplementeer word. Hierdie studie probeer vasstel hoe bedryfsingenieurswesemetodes ‘n groter rol kan speel in die proses om Suid-Afrika meer energie-effektief te maak. Navorsing is gedoen oor energiebesparingsmetodes wat in verskillende areas en vir verskillende toerusting in fasiliteite geïmplementeer word. Hierdie inligting is daarna vergelyk met bedryfsingenieurswesemetodes om juis die moontlike verhouding tussen hierdie twee tipe metodes te identifiseer. Analises was gedoen in bedryfsingenieurswese en energie-effektiwiteitskorpusse met die gebruik van die ‘Content Analysis Toolkit’ program. Belangrike onderwerpe en verwantskappe tussen hierdie onderwerpe in die korpusse is identifiseer om sodoende korrelasies tussen die bedryfsingenieurswese- en energie-effektiwiteitsveld uit te lig. Die mees betekenisvolle korrelasies was tussen statistiese metodes en verskeie energieeffektiwiteitsonderwerpe identifiseer. ’n Gevallestudie is by ’n maatskappy in die Wes-Kaap wat geïntegreerde elektroniese stroombane vervaardig gedoen, om die relevante energiebesparingsmetodes te implementeer. ’n Energie-oudit is as deel van die gevallestudie by die fasiliteit gedoen. Die aantal energiebesparingsmetodes wat wel geïmplementeer is, het ’n verlaging in die kilowatture (kWh) teweeggebring. Hierdie verlagings is verkry deur die implementering van ’n afskakelingsbestuursprogram wat die belangrikheid van bestuur in ’n energiebesparingsprogram uitlig. Die toepaslikheid van optimiseringsalgoritmes vir energie-effektiewe ontwerp is getoets deur die optimisering van ’n liguitlegontwerp met behulp van ’n genetiese algoritme. Daar is gevind dat ’n genetiese algoritme wel toegepas kan word, maar dat dit verbeteringe benodig

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms