Finite Capacity Scheduling Research Articles

Simulação e estudos de caso no ensino de planejamento e controle da produção: um survey com professores da engenharia de produção

Resumo A geração de valor depende das prioridades competitivas (custo, qualidade, flexibilidade, serviço e entrega) que apoiam a estratégia empresarial. O papel dos Sistemas de Planejamento e Controle da Produção é fornecer informação e modelos para gestão eficiente dos recursos produtivos. Esta pesquisa trata do ensino dos tópicos MRP e APS/PF (programação finita) nos cursos de PCP. Os objetivos incluem levantar as práticas de ensino desses tópicos, focando nas estratégias simulação e estudos de caso, e avaliar se os professores diferenciam as duas abordagens de programação. O método empregado foi um survey com professores da Engenharia de Produção. Os resultados indicam que ambos os tópicos são amplamente abordados nos cursos (MRP: 100% abordam e 91% o consideram importante; APS: 81% e 72%, respectivamente). Destaca-se também que 78% consideram importante diferenciar as abordagens e que 85% acreditam que o uso de estudos de caso com software contribui para a melhoria do aprendizado.

Multi-Objectives Finite Capacity Scheduling of Make-and-Pack Production with Options to Adjust Processing Time

Make-and-pack production is characterized by two stages of production namely, “make-stage” and “pack-stage” (Honkomp et al. 2000). Each stage consists of parallel processing units. In make-stage, raw materials are converted into final products by batch processing. Then, the final products are packed into containers in pack-stage. This paper develops finite capacity scheduling (FCS) system of make-and-pack production with multi-objectives and options to adjust processing time (OAPT). Multi-objectives including minimizations of total tardiness, total earliness, total flow time, and total processing costs are conflicting and a compromised solution is needed. Moreover, the processing time can be adjusted by adding some special chemicals. This paper proposes mixed-integer linear programming models to determine the compromised solution by using weighted average of satisfaction levels (WASL) of all objectives as performance measure. The proposed compromised solution method consists of three steps, (1) determining the best and worst values of each objective, (2) determining the initial compromised solution of all objectives when OAPT is not included in the model, and (3) determining the compromised solution with OAPT. The effect of chemical costs to the OAPT is evaluated. The results showed that chemical costs have significant effect to the OAPT. When the chemical cost is increased the OAPT is reduced and then the improvement of performance measure is also less. The proposed FCS system offered a compromised solution between conflicting objectives.

Biopharmaceutical Process Optimization with Simulation and Scheduling Tools.

Design and assessment activities associated with a biopharmaceutical process are performed at different levels of detail, based on the stage of development that the product is in. Preliminary "back-of-the envelope" assessments are performed early in the development lifecycle, whereas detailed design and evaluation are performed prior to the construction of a new facility. Both the preliminary and detailed design of integrated biopharmaceutical processes can be greatly assisted by the use of process simulators, discrete event simulators or finite capacity scheduling tools. This report describes the use of such tools for bioprocess development, design, and manufacturing. The report is divided into three sections. Section One provides introductory information and explains the purpose of bioprocess simulation. Section Two focuses on the detailed modeling of a single batch bioprocess that represents the manufacturing of a therapeutic monoclonal antibody (MAb). This type of analysis is typically performed by engineers engaged in the development and optimization of such processes. Section Three focuses on production planning and scheduling models for multiproduct plants.

Finite Capacity Scheduling of Make-Pack Production: Case Study of Adhesive Factory

This paper developed a Finite Capacity Scheduling (FCS) system for make-pack production based on a real case of an adhesive factory. The FCS determines production quantity of each machine to conform with resource capacities and due date of customer orders while minimizes related total cost. The total cost includes total production, inventory, and cleaning cost. A Mixed Integer Linear Programming (MILP) model is formulated and solved by LINGO software. The computational time is very long since the model has a lot of integer variables. Thus, the model is solved for a reasonable time and the best but not optimal solution is reported with the lower bound. This paper tries fixed horizon and rolling horizon scheduling methods. The fixed horizon plans for an entire horizon of 30 days. The rolling horizon plans for a sub-horizon of 10, 15, and 17 days. An overlapping of sub-horizons is applied to reduce end-of-horizon effect. Three scenarios (high, normal, and low) of demands are considered. The fixed horizon method is applied first to all scenarios of demand. If the best solution is far away from the lower bound, the rolling horizon method is applied. The results indicated that the rolling horizon method may significantly reduce the total cost with the same computational time. Moreover, the rolling horizon method is more applicable for a dynamic situation where customers frequently change orders. The proposed MILP model can generate reasonable solutions and they are useful for scheduling decision of make- pack production.

Implementing finite capacity production scheduling: lessons from a practical case

Many studies that have addressed the production scheduling problem refer to modelling and developing a solution. However, the academic literature presents a research–practice gap in this area with a lack of studies examining the implementation of production scheduling solutions in real contexts. This paper presents the lessons learned from the empirical findings of an action research on a successful implementation of computational simulators for finite capacity scheduling that were designed to help production schedulers in a large production plant of a global multinational motorcycle manufacturer. Although modelling the problem was important and required effort from the researchers, the main challenge revealed from the research was the model’s implementation on the shop floor. Issues regarding human, organisational and technological aspects were highlighted throughout the implementation process and proved essential for the solution to be effective.

Autonomous Planning using the Basic Principles of Gene Transcription Regulatory Control

The vast majority of the research efforts in finite capacity scheduling over the past several years has focused on the generation of precise and almost exact measures for the working schedule presupposing complete information and a deterministic environment. During execution, however, production may be the subject of considerable variability, which may lead to frequent schedule interruptions. Adopting biological control principles refers to the process where a schedule is developed prior to the start of the processing after considering all the parameters requirements at a machine and updated accordingly as the process executes. This research reviews the best practices in gene transcription and translation control methods and adopts these principles in the development of an autonomous finite capacity scheduling control logic aimed at reducing excessive use of manual input in planning tasks. With autonomous decision-making functionality, finite capacity scheduling will as much as practicably possible be able to respond autonomously to process variability by deployment of proactive scheduling procedures that may be used to revise or re-optimize the schedule when variability process requirements is noted. The novelty of this work is the ability for processing machine to autonomous take decisions just as decisions are taken by autonomous entities in the process of gene transcription and translation. The idea has been implemented by the integration of simulation modelling techniques with Taguchi analysis to investigate the contributions of finite capacity scheduling factors, and determination of the ‘what if’ scenarios encountered due to the existence of variability in production processes. The control logic adopts the induction rules as used in gene expression control mechanisms, studied in biological systems. Scheduling factors are identified to that effect and are investigated to find their effects on selected performance metrics for each machine used. How they are used to deal with variability in process is one major objective for this research as it is because of the variability that autonomous decision making becomes of interest. Although different scheduling techniques have been applied and are successful in production planning and control, the results obtained from the inclusion of the autonomous finite capacity scheduling control logic has proved that significant improvement can still be achieved. This research demonstrated that the correct choice of values for finite capacity scheduling factors has a great impact on the performance measurements, such that, high throughput rate can be reached with a bigger batch size, lower percentage of rework, and shorter stoppages. Quicker movement of goods through the facility means better utilisation of assets. Better utilisation of assets creates additional capacity resulting from faster throughput thereby improving customer satisfaction through quicker delivery. It has also been shown that %waiting, %blocking and %stoppage makes it possible to examine different manufacturing constraints as well as the relationship between non-utilisation and different performance measurements.

Finite Capacity Scheduling - Large System Design

This paper presents the design principles for a large finite capacity scheduling system which takes into account alternative tools and machines. The system will produce tightly packed and stable schedules which in many cases will be close to optimal. The system can then be used as a platform for research into the application of decision making techniques, mathematical optimization, and other modern techniques.

Levantamento das práticas de programação detalhada da produção: um survey na indústria paulista

Devido à grande instabilidade dos mercados e a intensa competição entre as empresas, a Programação Detalhada da Produção (PDP) vem se tornando cada vez mais um desafio para a gestão empresarial. As empresas, na busca do aprimoramento das atividades de PCP, estão demandando ferramentas de programação finita, também conhecidas como Advanced Planning and Scheduling (APS). Este artigo visa identificar as necessidades e dificuldades da programação detalhada da produção, as barreiras de implantação e os benefícios dos sistemas APS. O método utilizado foi um survey com as empresas filiadas à Fiesp. Os resultados obtidos mostram: i) a programação detalhada da produção é efetivamente uma atividade complexa para a maioria das empresas, especialmente aquelas com estratégia de produção contra pedido (MTO-make to order); ii) a maioria das empresas ainda utiliza o modelo MRP (capacidade infinita); iii) os objetivos de melhoria de desempenho na entrega poderiam ser mais facilmente alcançados com o uso de sistemas APS; iv) o investimento financeiro e falta de capacitação são ainda impedimentos para a implantação desses sistemas.

Finite capacity scheduling with mixed duration tooling trees

Finite capacity scheduling is a research area which has much practical application and the quality of the schedules has a major impact on the delivery performance, profitiability and finance requirements of companies. This paper proposes a method of finite capacity scheduling where activities have a range of mandatory and alternative tools which can be used, and some of the less preferred tools run at a slower rate. The calculations are presented by example, and in the context of a computer program to implement the algorithms.

The Squeezing Method : Calculating Capacity Requirements for Finite Capacity Scheduling(<Special English Issue>Global Supply Chain Management)

The Squeezing Method : Calculating Capacity Requirements for Finite Capacity Scheduling(<Special English Issue>Global Supply Chain Management)

Advanced planning and scheduling systems in aluminium conversion industry

Standard software packages, known as advanced planning and scheduling systems (APS), are often implemented to compute feasible production plans that can take into account several constraints (such as resources capacity), at different decision levels. In this research, emphasis is put on studying the relevance of APS principles in the specific area of the aluminium conversion industry (ACI). In this particular case, the characteristics of the processes involved (e.g. casting and rolling) are such that certain principles used by APS (e.g. finite capacity scheduling, constraint-based planning, bills of materials) may cause problems. This article extends a previous study related to the relevance of enterprise resource planning (ERP) systems for ACI in the direction of APS. It aims at identifying benefits, but also limitations and problems, which may arise when implementing this type of system. In this respect, the main features of APS systems are recalled. Specific features of ACI that are likely to cause problems are identified. It is shown that, if APS are indeed attractive, in certain cases important limitations exist, which can significantly affect the performances (e.g. increasing the lateness of product delivery, overproducing or increasing scrap and inventories). Several results presented in the current article can be relevant for other types of process industries.

Modified drum–buffer–rope scheduling mechanism for a non-identical parallel machine flow shop with processing-time variation

The drum–buffer–rope (DBR) is a scheduling mechanism under the Theory of Constraints (TOC) philosophy. In DBR, ‘drum’ is a production schedule on the capacity-constrained resources (CCRs), which controls the speed of production for the whole system; ‘rope’ is a mechanism to release the required material to the CCRs; and ‘buffer’ is used to protect the CCRs from starvation due to statistical fluctuations. For a non-identical parallel machine flow-shop environment, estimating an efficient rope and time buffer for DBR implementation is not an easy task because of the complexity of non-identical parallel machine loading. This paper proposes a new scheduling method, which is called the modified DBR (MOD-DBR). It applies a backward finite capacity scheduling technique, including machine loadings and detail scheduling, instead of the rope mechanism in DBR. The scheduling performances of MOD-DBR are evaluated under variable processing time situations. The experimental results indicate that the MOD-DBR without a time buffer outperformed the DBR with a considerable level of buffer on the average flow time, while they have the same performance on tardiness, constraint resource utilization, and throughput.

Lessons learned for building agile and flexible scheduling tool for turbulent environments in the extended enterprise

This paper presents results of a 2.5-year, multidisciplinary, university–industry collaborative effort investigating design of “Internet-based Scheduler for material Optimisation and agile ProducTion In MUlti-Site enterprises in agile manufacturing” (IS-OPTIMUS), a four nation collaborative project aimed at improvements in turbulent manufacturing environments. The focus of this paper is specifically on the content of the work carried out, along with the main benefits and results. Key to achieving the goals is following a complete project-life-cycle path from the initial stages where the industrial users requirements were identified and the system specification took place to the development and tuning of the final system. Design choices for software must strike a balance between the user flexibility required and the representing environment constraints, i.e. finite capacity scheduling, which takes production requirements from existing production planning systems, to schedule production resources like plant workers, critical tools and machines. The system consists of a material optimiser working closely with the finite capacity scheduler and a dynamic scheduler providing automatic reaction to real-time exceptions, thus deriving in solutions of higher performance.

Finite Capacity Material Requirement Planning System for a Multi-stage Automotive-Part Assembly Factory

This paper aimed to develop a practical finite capacity material requirement planning (FCMRP) system based on the needs of an automotive-part manufacturing company in Thailand. The approach includes a linear programming model to determine the optimal start time of each operation to minimize the weighted average of total earliness, total tardiness, and average flow-time considering the finite capacity of all work centers and precedence of operations. Important factors of the proposed FCMRP system were objective function weights and dispatching rules. Effects of these factors on the performance measures were statistically analyzed based on a real situation of an auto-part factory. Statistical results showed that the dispatching rules and objective function weights had significant effects on the performance measures of the proposed FCMRP system. The proposed FCMRP system offered a good trade-off between conflicting performance measures and resulted in the best weighted average performance measure when compared with conventional forward and forward-backward finite capacity scheduling systems.

Do OPT à Teoria das Restrições: avanços e mitos

Ainda que não mais se possa considerar a Teoria das Restrições (TOC) como uma abordagem efetivamente nova de se gerenciar organizações, o volume de falsos entendimentos acerca do assunto é muito grande. Este artigo tem o propósito de cooperar no sentido de esclarecer alguns aspectos bastante particulares da TOC, mais especificamente relacionados com a sua proposta de programação da produção baseada na capacidade finita dos recursos fabris, denominada de Tambor-Pulmão-Corda (TPC). Concomitantemente, a partir das discussões expostas, este artigo pretende mostrar que a lógica inerente ao TPC não necessariamente é a mesma do já difundido software OPT, assim como mostrar que a aplicação da lógica de programação para frente e para trás no TPC também muitas vezes difere de alguns livros-textos sobre o assunto.

Finite capacity scheduling—packed placement sequence schedule

This is one of a series of papers which describe the placement sequence approach to finite capacity scheduling of factories. A central aim of the placement sequence approach is to achieve well-packed and stable schedules, with multiple and alternative tools and machines, without the need for an iterative schedule improvement stage within the calculations. This means that calculations are bounded and the technique is suitable for large industrial applications. In this paper we treat schedule packing as the main goal and show how to build a ‘packed placement sequence schedule’. We use a two-stage process to choose the machines and placement sequence constraints. The first stage is a backward infinite capacity schedule and the second stage is a backward finite capacity schedule. While building the backward finite schedule we schedule using the order of event times from the infinite capacity schedule, and read the machine loading from the infinite capacity schedule so we can spread the load and push jobs away from the most heavily loaded machines in the finite capacity schedule. The final result is a well-packed, backward, finite capacity schedule. The paper explains the method, discusses the issues, and works through a detailed example of the calculations.

A rule-based heuristic finite capacity scheduling system for semiconductor backend assembly

Finite capacity scheduling (FCS) has been widely implemented in manufacturing companies for detailed shop floor operation management. Most of the traditional and artificial intelligence-based FCS systems lack the ability to provide a satisfying and practical solution for the large-scale scheduling problem in a complex environment. This paper describes a rule-based FCS system for the daily production scheduling (DPS) of a semiconductor backend assembly company. DPS is the FCS module of the gintic scheduling system (GSS), which has been enhanced and implemented successfully in a semiconductor backend assembly company, which is a world market leader in semiconductor packaging technology. Since the focus of this paper is on the DPS module, the overall framework and the other core modules of GSS are briefly covered. The detailed design of the six DPS modules, namely data input/integration, model configuration, data pre-processing, job prioritization (JP) executive, machine selection (MS) executive and result output, are presented in detail. The inference engines of JP and MS executive modules are discussed, which perform a unique rule-based job prioritization and machine selection process of the work order list and the assignments on the machines to achieve the desired scheduling objectives specified by the end user. Group technology is incorporated into the system to categorise the resources and parts for set-up/changeover reduction consideration. The successful deployment of the system in the industry endorsed the fact that the implemented DPS system has the capability to solve large-scale scheduling problems and performs well in the complex industrial environment. Favourable feedbacks received from the company include reduction in changeover and set-up, improved on-time delivery, increased machine utilization, decreased cumulative cycle time of work order, etc.

How to synchronise the multi plant supply chain

The art of production scheduling is in balancing demand with capacity. Any SME or larger enterprise and its partners need to be synchronized, time aware, flexible and highly organised. This is where a scheduling software solution can prove to be an invaluable investment, and additionally, something that can deliver a rapid return on investment (ROI). With demand, capacity and supply chain flow, a finite capacity scheduling (FCS) or advanced planning and scheduling (APS) package can ensure each supplier, customer and internal department has the appropriate information to hand in transparent, up to the minute form. Many companies fail to consider the availability of capacity as a key factor in determining delivery, which can be even more complex when multiple plants/facilities are part of the supply chain. In this paper, the author discusses the importance of capacity in supply chain management.

Finite capacity scheduling with multiple tools and machines

This is one of a series of papers that describe the placement sequence approach to finite-capacity scheduling of factories. We explain the concept of event networks and use input–output matrices so that complex scheduling calculations can be formalized as operations on the rows and columns of matrices. A central aim of the placement sequence approach is to achieve well-packed and stable schedules without the need for an iterative schedule improvement stage within the calculations. This means that calculations are bounded and it is feasible to apply the technique to large industrial applications. This paper addresses the issue of finite-capacity scheduling where activities can have multiple and alternative combinations of tools and machines. This is often called the alternate routing problem. We represent the tooling combinations as Boolean expressions that include the preferences between tools and machines. These are converted into an input–output matrix, which we call the tooling matrix. The description is presented partly in terms of principles and partly in terms of how to write a computer program to implement the calculations. The paper explains the principles of scheduling multiple and alternative tools and machines and then works through a detailed example of the calculations.

The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of product structure

In this paper, the development of a genetic algorithms based scheduling tool that takes into account multiple resource constraints and multiple levels of product structure is described. The genetic algorithms includes a repair process that rectifies infeasible chromosomes that may be produced during evolution process. The algorithm includes problem encoding, chromosome representation and initialisation, genetic operation, repair process, fitness measurement and chromosome selection. The data structure and algorithm are detailed step by step. The tool generates schedules that minimises the penalties caused by early and late delivery of for components, assemblies and final products. The method is applied using data obtained from a collaborating company that manufactures complex capital goods. It is demonstrated that the schedules produced perform significantly better than those produced by the company using a conventional planning method.