Assessing the factors of green computing adoption among manufacturing employees: an analysis of the electrical and electronic sector

Recent trends and heavy uses of IT products and electronic gadgets have led to a proliferation of green computing studies because these wastes are not biodegradable. A significant amount of previous studies has been performed on green computing at the organizational level with most studies tend to focus on developed countries. The present study examined numerically the most influential factor towards the employees’ intention to adopt green computing and measure the intention level of employees in green computing adoption. The current study explored five adoption factors with five hypotheses have been established. These hypotheses were theorized from Theory of Planned Behaviour with emphasis on environmental concern. Previous studies from Malaysia context have primarily concentrated on green computing in education sector compared to manufacturing sector. Hence the study was conducted at electrical and electronic industries located at southern Malaysia. Questionnaires were purposely distributed to 250 respondents, however only 110 responses were valid that yielded response rate of 56%. Respondents are among the employees in IT and administration department that equipped with ICT application. The finding verifies the most influential factor affecting green computing adoption is environmental concern with 43.8%. All factors were proved to have positive correlation to the green computing intention. This correlation is related to the high intention level of employees in practicing green computing due to headquarters initiatives and the conditions set by the importing countries. Social norms have less impact towards behavioral intention to practice green computing that manifested by the low correlation percentage. The current study contributes to our knowledge on the green computing intention among manufacturing employees in Malaysia besides the implications of the results and future research directions. This study will help the policy makers in promoting environmental awareness among users of computing devices

Quantum algorithms for process parallel flexible job shop scheduling

Flexible Job Shop Scheduling is one of the most difficult optimization problems known. In addition, modern production planning and control strategies require continuous and process-parallel optimization of machine allocation and processing sequences. Therefore, this paper presents a new method for process parallel Flexible Job Shop Scheduling using the concept of quantum computing based optimization. A scientific benchmark and the application to a realistic use-case demonstrates the good performance and practicability of this new approach. A managerial insight shows how the approach for process parallel flexible job shop scheduling can be integrated in existing production planning and control IT-infrastructure

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Development Of A Simulation-Based Decision Support Model For High Precision Component Manufacturer

In this age of global competition, the key to the success and even survival of many manufacturing firms is the ability to respond quickly to the needs of the customers. In the real world, performance measurements are difficult to determine because of the complexity of the manufacturing systems. Simulation has become an indispensable tool which enables engineers, designers, planners and managers, to study, analyze and evaluate complex situations that would not be otherwise possible. In this study the case company operates in a job shop environment characterized by fixed machine configurations but process routes that are different for each job. There are more than 800 different orders with different specifications (process routes, cycle times and lot sizes) that need to be completed for each month. In such configurations, a job may undergo operations only in certain orders of machines and not necessarily at every machine. The complexity is further compounded by the indefinite batch size for each product. With such complex scenario, determination of throughputs, identifications of bottlenecks, completion dates of customer orders and total cost of the customer orders constitute a real challenge to the planners and production managers in the company. Currently the planner of the case company plans the due dates of customer orders by using the manual approach and this has resulted in the inability of the company to meet the due date of all customers’ demands. In the face of the difficulties associated with analytic techniques, this research propose the development of a novel simulation-based decision support model (SDSM) to improve the performance in a job shop environment based on the economic approach. This model will account for the intrinsic characteristics and complexities of the job shop by allowing for usage of judgmental decision making and cost-estimation based on the current job shop status. The SDSM is useful to calculate the waiting cost which is too complicated if modelled by mathematical modeling or algorithms. By incorporating the Activity Based Costing (ABC) system into the simulation model, the costing for customer orders is greatly improved. The developed model shows that the company underestimated the cost of the customer orders. Incorporating ABC system into simulation model helps the company to evaluate costs of customer orders more effectively than Traditional Costing (TC) approach. Moreover, the use of simulation enables the study and investigation of the interactive effect of individual components or variables. The model will allow experimentations by changing the overtime and shift pattern in the company. Throughput of the case company was improved by 20.63% by identifying the bottleneck workstation (Wire Cut and Milling workstation). Waiting times at, utilization of, these workstation are improved by adding 4 hours overtime for all milling technicians and 2 hours overtime for all wire cut technicians with total additional cost of RM5700 per month. In addition, the SDSM is also able to determine the completion date of customer orders and the manpower requirement to achieve the demand by indentifying the bottleneck candidates in the production system. The accuracy for predicting the completion dates of customer orders was improved to 80% by using the simulation model. This because the simulation model provided better understanding about the capacity and the condition of the production flow by including the waiting times

High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization

Disertační práce je zaměřena na optimalizaci průběhu pracovních operací v logistických skladech a distribučních centrech. Hlavním cílem je optimalizovat procesy plánování, rozvrhování a odbavování. Jelikož jde o problém patřící do třídy složitosti NP-težký, je výpočetně velmi náročné nalézt optimální řešení. Motivací pro řešení této práce je vyplnění pomyslné mezery mezi metodami zkoumanými na vědecké a akademické půdě a metodami používanými v produkčních komerčních prostředích. Jádro optimalizačního algoritmu je založeno na základě genetického programování řízeného bezkontextovou gramatikou. Hlavním přínosem této práce je a) navrhnout nový optimalizační algoritmus, který respektuje následující optimalizační podmínky: celkový čas zpracování, využití zdrojů, a zahlcení skladových uliček, které může nastat během zpracování úkolů, b) analyzovat historická data z provozu skladu a vyvinout sadu testovacích příkladů, které mohou sloužit jako referenční výsledky pro další výzkum, a dále c) pokusit se předčit stanovené referenční výsledky dosažené kvalifikovaným a trénovaným operačním manažerem jednoho z největších skladů ve střední Evropě.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.

Job Shop Scheduling Problem: an Overview

The Job-shop scheduling is one of the most important industrial activities, especially in manufacturing planning. The problem complexity has increased along with the increase in the complexity of operations and product-mix. To solve this problem, numerous approaches have been developed incorporating discrete event simulation methodology. The scope and the purpose of this paper is to present a survey which covers most of the solving techniques of Job Shop Scheduling (JSS) problem. A classification of these techniques has been proposed: Traditional Techniques and Advanced Techniques. The traditional techniques to solve JSS could not fully satisfy the global competition and rapidly changing in customer requirements. Simulation and Artificial Intelligence (AI) have proven to be excellent strategic tool for scheduling problems in general and JSS in particular. The paper defined some AI techniques used by manufacturing systems. Finally, the future trends are proposed briefly

Overlap Algorithms in Flexible Job-shop Scheduling

The flexible Job-shop Scheduling Problem (fJSP) considers the execution of jobs by a set of candidate resources while satisfying time and technological constraints. This work, that follows the hierarchical architecture, is based on an algorithm where each objective (resource allocation, start-time assignment) is solved by a genetic algorithm (GA) that optimizes a particular fitness function, and enhances the results by the execution of a set of heuristics that evaluate and repair each scheduling constraint on each operation. The aim of this work is to analyze the impact of some algorithmic features of the overlap constraint heuristics, in order to achieve the objectives at a highest degree. To demonstrate the efficiency of this approach, experimentation has been performed and compared with similar cases, tuning the GA parameters correctly

Decentralized Scheduling of Discrete Production Systems with Limited Buffers

Die Steuerung der Produktion ist eine der Kernaufgaben eines jeden produzierenden Unternehmens. Sie ist insbesondere wichtig, um auf die Anforderungen des Marktes und damit auf die Wünsche der Kunden reagieren zu können. Aktuelle Trends im Markt führen dabei zu einer hochindividualisierten Produktion bei gleichzeitiger Erhöhung der produzierten Stückzahlen. Eine Konsequenz daraus ist, dass Unternehmen über flexiblere und agilere Produktionssysteme verfügen müssen, um auf die sich ständig ändernden Kundenwünsche reagieren zu können. Da starre Fertigungslinien nicht mehr geeignet sind, werden zunehmend komplexere Strukturen wie die der Werkstattfertigung oder Matrixproduktion eingesetzt. Hierfür werden geeignete Steuerungsmethoden für die Produktion benötigt. Diese Arbeit beschäftigt sich mit eben jenen Steuerungsmethoden, genauer gesagt Methoden zur Planung von Produktionsaufträgen in diesen neuen Produktionssystemen. Zur Steuerung eignen sich echtzeitfähige und autonome Entscheidungssysteme, mit denen die Steuerung der neuen Organisationsstruktur der Produktion angepasst ist. Agentenbasierte Systeme bieten genau diese Eigenschaften und erlauben es, komplexe Planungsaufgaben in kleinere Teilprobleme zu zerlegen, die schneller und genauer gelöst werden können. Sie erfordern die Verfügbarkeit von Daten in Echtzeit und eine schnelle Kommunikation zwischen den Agenten, was heute dank der vierten industriellen Revolution zur Verfügung steht. Demgegenüber steht der erhöhte Koordinierungsbedarf, der in diesen Systemen beherrscht werden muss. Das Ziel dieser Arbeit ist es, einen dezentralen Produktionsplanungs-Algorithmus zu entwickeln, der in einem Multi-Agenten-System implementiert ist. Er berücksichtigt begrenzte Verfügbarkeit von Pufferplätzen an jedem Arbeitsplatz, ein Thema, das in der Literatur wenig erforscht ist. Der Algorithmus ist in einer flexiblen Werkstattfertigung anwendbar und zeigt eine große Zeiteffizienz bei der Einplanung größerer Mengen von Aufträgen. Um dieses Ziel zu erreichen, wird zunächst der Produktionsplanungs-Algorithmus ohne das Agentensystem entworfen. Er basiert auf der von \textcite{adams1988} veröffentlichten Shifting Bottleneck Heuristik. Da viele Änderungen notwendig sind, um die geforderten Eigenschaften berücksichtigen zu können, bleibt nur die grundlegende Vorgehensweise gleich, während alle Schritte der Heuristik von Grund auf neu modelliert werden. Anschließend wird ein Multi-Agenten-System entworfen, das die genannten Anforderungen abbildet und den Algorithmus zur Planung verwendet. In diesem System hat jeder Arbeitsplatz einen Arbeitsplatzagenten, der für die Planung und Steuerung seines zugeordneten Arbeitsplatzes zuständig ist, sowie einige zusätzliche Agenten für die Kommunikation, die Datenspeicherung und allgemeine Aufgaben. Der entworfene Algorithmus wird angepasst und in das Multi-Agenten-System implementiert. Da das System im praktischen Einsatz immer eine Lösung finden muss, stellen wir mögliche Fehlerfälle vor und wie mit ihnen umgegangen wird. Abschließend findet eine numerische Evaluierung mit zwei realen Produktionssystemen statt. Da sich diese Systeme in einem wichtigen Merkmal ähneln, werden weitere zufällig erzeugte Beispiele getestet und ausgewertet

Bottleneck Management through Strategic Sequencing in Smart Manufacturing Systems

Nowadays, industries put a significant emphasis on finding the optimum order for carrying out jobs in sequence. This is a crucial element in determining net productivity. Depending on the demand criterion, all production systems, including flexible manufacturing systems, follow a predefined sequence of job-based machine operations. The complexity of the problem increases with increasing machines and jobs to sequence, demanding the use of an appropriate sequencing technique. The major contribution of this work is to modify an existing algorithm with a very unusual machine setup and find the optimal sequence which will really minimize the makespan. This custom machine setup completes all tasks by maintaining precedence and satisfying all other constraints. This thesis concentrates on identifying the most effective technique of sequencing which will be validated in a lab environment and a simulated environment. It illustrates some of the key methods of addressing a circular non permutation flow shop sequencing problem with some additional constraints. Additionally, comparisons among the various heuristics algorithms are presented based on different sequencing criteria. The optimum sequence is provided as an input to a real-life machine set up and a simulated environment for selecting the best performing algorithm which is the basic goal of this research. To achieve this goal, at first, a code using python programming language was generated to find an optimum sequence. By analyzing the results, the makespan is increasing with the number of jobs but additional pallet constraint shows, adding more pallets will help to reduce makespan for both flow shops and job shops. Though the sequence obtained from both algorithms is different, for flow shops the makespan remains same for both cases but in the job shop scenario Nawaz, Enscore and Ham (NEH) algorithms always perform better than Campbell Dudek Smith (CDS) algorithms. For job shops with different combinations the makespan decreases mostly for maximum percentage of easy category jobs combined with equal percentage of medium and complex category jobs