Capacity Scheduling Research Articles

Wind/PV/storage independent system capacity configuration model and scheduling policy analysis

Wind/PV/storage system has the advantages of good power supply performance, few geographical limitations, and reasonable investment in equipment, and gradually becomes the priority project of developing independent wind power system preference in most remote areas. Based on the analysis of the constraint conditions of wind/PV/storage independent system, this paper discusses the capacity configuration model, process and strategies of wind/PV/storage independent system in detail, and considers practical solutions to power supply requirements in local areas without electricity, at the same time, it provides technology and practical basis for solving the key technical issues of independent power grid construction in remote areas.

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.

A mixed-integer nonlinear program for the optimal design and dispatch of distributed generation systems

Maturing distributed generation (DG) technologies have promoted interest in alternative sources of energy for commercial building applications due to their potential to supply on-site heat and power at a lower cost and emissions rate compared to centralized generation. Accordingly, we present an optimization model that determines the mix, capacity, and operational schedule of DG technologies that minimize economic and environmental costs subject to the heat and power demands of a building and to the performance characteristics of the technologies. The technologies available to design the system include lead-acid batteries, photovoltaic cells, solid oxide fuel cells, heat exchangers, and a hot water storage tank. Modeling the acquisition and operation of discrete technologies requires integer restrictions, and modeling the variable electric efficiency of the fuel cells and the variable temperature of the tank water introduces nonlinear equality constraints. Thus, our optimization model is a nonconvex, mixed-integer nonlinear programming (MINLP) problem. Given the difficulties associated with solving large, nonconvex MINLPs to global optimality, we present convex underestimation and linearization techniques to bound and solve the problem. The solutions provided by our techniques are close to those provided by existing MINLP solvers for small problem instances. However, our methodology offers the possibility to solve large problem instances that exceed the capacity of existing solvers and that are critical to the real-world application of the model.

An integrated production planning and scheduling framework for make-to-order environment

A capacity constrained integrated production planning and scheduling framework for a manufacturing firm operating in make-to-order environment, has been formalized. It primarily aims at the generation of shop floor schedules that are feasible with production plans generated at a higher level, practical and efficient at the shop floor. The formalized framework integrates capacity planning, loading and scheduling activities of a job-shop with consideration of process plans flexibility. The performance of framework is assessed through two case studies. Experimental investigation reveals that shop utilization level considered at capacity planning level affects master production schedule and system performance. Further, presence of multiple process plans improves system performance; especially, they assist in reducing the number of tardy part-types. Moreover, shortest processing time dispatching rule performs better than earliest due date during scheduling.

Wind/PV/storage independent system capacity configuration model and scheduling policy analysis

Wind/PV/storage independent system capacity configuration model and scheduling policy analysis

Biologically Inspired Networks

Inspired by the nature, the interesting properties of natural organisms can be imitated in many different applications. The nature has been optimizing itself for billions of years and in many cases it simply introduces the most optimal approaches. On the other hand, the task of data collection and aggregation is critical in wireless sensor networks (WSN). The information packets should traverse the network towards the aggregating center i.e. sink node. The problem of finding the best path to route the data has been long under investigation. The authors propose a new method for collecting the data from sensor field based on a biological inspired method adopted from tubular network formation behavior of slime mold where biological organisms efficiently self-organize unreliable and dynamically changing topology, to compensate for the failure of individual components while not relying on explicit central coordination. They show that the emergent network exhibits a widely observed property in natural topologies called scale-free which explains a lot of inherent characteristics in living creatures. At the end the authors show that the data collection time for a biologically inspired network is shorter than uniform channel capacity scheduling.

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.

On Capacity and Optimal Scheduling for the Half-Duplex Multiple-Relay Channel

We study the half-duplex multiple-relay channel (HD-MRC) where every node can either transmit or listen but cannot do both at the same time. We obtain a capacity upper bound based on a max-flow min-cut argument and achievable transmission rates based on the decode-forward (DF) coding strategy, for both the discrete memoryless HD-MRC and the phase-fading HD-MRC. We discover that both the upper bound and the achievable rates are functions of the transmit/listen state (a description of which nodes transmit and which receive). More precisely, they are functions of the time fraction of the different states, which we term a schedule. We formulate the optimal scheduling problem to find an optimal schedule that maximizes the DF rate. The optimal scheduling problem turns out to be a maximin optimization, for which we propose an algorithmic solution. We demonstrate our approach on a four-node multiple-relay channel, obtaining closed-form solutions in certain scenarios. Furthermore, we show that for the received signal-to-noise ratio degraded phase-fading HD-MRC, the optimal scheduling problem can be simplified to a max optimization.

A multilevel integrative approach to hospital case mix and capacity planning

Hospital case mix and capacity planning involves the decision making both on patient volumes that can be taken care of at a hospital and on resource requirements and capacity management. In this research, to advance both the hospital resource efficiency and the health care service level, a multilevel integrative approach to the planning problem is proposed on the basis of mathematical programming modeling and simulation analysis. It consists of three stages, namely the case mix planning phase, the master surgery scheduling phase and the operational performance evaluation phase. At the case mix planning phase, a hospital is assumed to choose the optimal patient mix and volume that can bring the maximum overall financial contribution under the given resource capacity. Then, in order to improve the patient service level potentially, the total expected bed shortage due to the variable length of stay of patients is minimized through reallocating the bed capacity and building balanced master surgery schedules at the master surgery scheduling phase. After that, the performance evaluation is carried out at the operational stage through simulation analysis, and a few effective operational policies are suggested and analyzed to enhance the trade-offs between resource efficiency and service level. The three stages are interacting and are combined in an iterative way to make sound decisions both on the patient case mix and on the resource allocation.

A Multilevel Integrative Approach to Hospital Case Mix and Capacity Planning

Hospital case mix and capacity planning involves the decision making both on patient volumes that can be taken care of at a hospital and on resource requirements and capacity management. In this research, to advance both the hospital resource efficiency and the health care service level, a multilevel integrative approach to the planning problem is proposed on the basis of mathematical programming modeling and simulation analysis. It consists of three stages, namely the case mix planning phase, the master surgery scheduling phase and the operational performance evaluation phase. At the case mix planning phase, a hospital is assumed to choose the optimal patient mix and volume that can bring the maximum overall financial contribution under the given resource capacity. Then, in order to improve the patient service level potentially, the total expected bed shortage due to the variable length of stay of patients is minimized through reallocating the bed capacity and building balanced master surgery schedules at the master surgery scheduling phase. After that, the performance evaluation is carried out at the operational stage through simulation analysis, and a few effective operational policies are suggested and analyzed to enhance the trade-offs between resource efficiency and service level. The three stages are interacting and are combined in an iterative way to make sound decisions both on the patient case mix and on the resource allocation.

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.

Models for Component Commonality in Multistage Production

Use of common parts for different products (commonality) is important methods for managing product variety and preserving competitiveness in the age of mass customization and supply chain competition. In literature, the advantages of inclusion of common components in a product family are well established. Unfortunately, most of the works have been conducted via simulation or conceptual thinking. The mathematical models in the premises are not adequate for production, planning and control in multistage production. This paper focuses on the advancement of venerable manufacturing resources planning models by incorporating the part commonality concept in a multiproduct, multi-period and multistage manufacturing system under a deterministic situation. The models are validated with established MRPII models. The material requirement schedule for the basic MRP II and proposed models are compared. It is really a good matching shown between the two schedules. The later bearing additional information of the location where to be available the parts in a time frame. The effects of commonality on cost, capacity and requirement schedule are discussed based on the outcomes of the mathematical models executed with the available live data.

Mathematical models for component commonality under quality and resources breakdown in multistage production

In the era of mass customization and supply chain rivalry, managing product diversity is essential for survival in business. The use of common part for different products is a vital method of achieving the goal. The advantages of insertion of duplicate component in a product family are stated in literatures. Simulation or conceptual beliefs is employed in the majority of the researches and mainly considered single stage manufacturing. However, the mathematical models in the premises of multistage production are not available. In this paper, the part commonality notion is integrated with venerable manufacturing resources planning models for a multiproduct, multi-period and multistage manufacturing under a deterministic demand and lead time. A random distribution of quality and resources breakdown events glued with the models. The models are validated with real data from a Malaysian company and arbitrarily numerical scheme. The material requirement schedule is generated using the proposed models. The outcomes are compared with the same from the basic MRP II and live archrival data collected from the floor. It is found that the two schedules converge. However, the proposed models are bearing additional information of the location where to be available the parts in a time frame. The effects of commonality on cost, capacity and requirement schedule under the quality and breakdown troubles are discussed. It is observed that the use of common parts in manufacturing is always better over the non-commonality scenario in terms of production cost and capacity requirements. Key words: Component commonality, mathematical model, MRP II, quality, breakdown.

On max–min fair flow optimization in wireless mesh networks

The paper is devoted to modeling wireless mesh networks (WMN) through mixed-integer programming (MIP) formulations that allow to precisely characterize the link data rate capacity and transmission scheduling using the notion of time slots. Such MIP models are formulated for several cases of the modulation and coding schemes (MCS) assignment. We present a general way of solving the max–min fairness (MMF) traffic objective for WMN using the formulated capacity models. Thus the paper combines WMN radio link modeling with a non-standard way of dealing with uncertain traffic, a combination that has not, to our knowledge, been treated so far by exact optimization models. We discuss several ways, including a method based on the so called compatible or independent sets, of solving the arising MIP problems. We also present an extensive numerical study that illustrates the running time efficiency of different solution approaches, and the influence of the MCS selection options and the number of time slots on traffic performance of a WMN. Exact joint optimization modeling of the WMN capacity and the MMF traffic objectives forms the main contribution of the paper.

Production capacity planning and scheduling in a no-wait environment with controllable processing times: An integrated modeling approach

This paper develops an integrated model between a production capacity planning and an operational scheduling decision making process in which a no-wait job shop (NWJS) scheduling problem is considered incorporating with controllable processing times. The duration of any operations are assumed to be controllable variables based on the amount of capacity allocated to them, whereas in classical NWJS it is assumed that the machine capacity and hence processing times are fixed and known in advance. The suggested problem which is entitled no-wait job shop crashing (NWJSC) problem is decomposed into the crashing, sequencing and timetabling subproblems. To tackle the addressed NWJSC problem, an improved hybrid timetabling procedure is suggested by employing the concept of both non-delay and enhanced algorithms which provides better solution than each one separately. Furthermore, an effective two-phase genetic algorithm approach is devised integrating with hybrid timetabling to deal with the crashing and sequencing components. The results obtained from experimental evaluations support the outstanding performance of the proposed approach.

An advanced load adjustment approach without task interruption for flow-shops

This paper presents a new load adjustment approach by overlapping, for flow-shop organised firms, guaranteeing the existence of a limited capacity schedule without scheduling (with no task interruption). Firstly, we present a literature review concerning finite capacity planning approaches. Secondly, we introduce the overlapping load adjustment approach. Thirdly, we propose a solution for the problem of task interruption. Subsequently, we present an original heuristic when using this approach in the case of flow-shop organised firms. This approach is based on a judicious distribution of the manufacturing order’s margins between tasks in a just-in-time context. Then, we expound the results of the experiments. Finally, we discuss a more general use of this approach.

Decision support tool for patient recruitment in a hadrontherapy center

AbstractFor a Carbon-ion therapy center in the design phase, patient recruitment is one of the most important challenges that involve healthcare laws and regulations, financial management, and technical concepts. Numerous constraints related to tumor treatment protocols, priority assignment, decision making and capacity analysis should be taken into account. The main objective of this research work is to identify better admission criteria that drive improved performance and optimized efficiency in the Carbon-ion therapy center. A decision support tool has been developed through constraint programming to optimize patient recruitment, which particularly belongs to online scheduling problems. Being given periodically a list of admission demands characterized by latest beginning day, treatment priority, and number of therapy sessions with their durations, this tool is capable to generate an optimized admission list according to capacity schedule and aiming at increasing economic efficiency. The generated admission list is useful not only for planning treatment activities in the center, but also for regulating both domestic and international demands.

Electricity market price forecasting using support vector machines

Due to the electricity market deregulation, the techniques used for load forecasting have gradually improved over the years. Deregulation in the power system industry has caused rising requirement in planning, operating and controlling electric energy systems, which brings electricity load forecasting to a crucial level. Therefore, adequate techniques are desired for accurately predicting the load and hence assisting power companies in generating capacity scheduling, maintenance, energy planning and procurement, etc. An accurate forecast can greatly help power distribution companies to improve their electricity marketing strategies and avoid over or under unitisation of generating capacity and therefore optimises energy prices. But, to predict the load demand in real time requires a considerable amount of efforts. This paper presents a design methodology for a short term load forecasting useful for distribution companies, which are capable of interacting with users, gathering historical load data, performing a statistical analysis on the historical data and plotting graphs of the predicted load using the support vector machine (SVM). SVM is the chosen forecasting technique because many studies have concluded that SVMs produce the optimum accuracy as compared to other methods such as Naive Bayesian, but SVM has not been optimised for the domain.

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.

Learning curve analysis of mitral valve repair using telemanipulative technology

To determine if the time required to perform mitral valve repairs using telemanipulation technology decreases with experience and how that decrease is influenced by patient and procedure variables. A single-center retrospective review was conducted using perioperative and outcomes data collected contemporaneously on 458 mitral valve repair surgeries using telemanipulative technology. A regression model was constructed to assess learning with this technology and predict total robot time using multiple predictive variables. Statistical analysis was used to determine if models were significantly useful, to rule out correlation between predictor variables, and to identify terms that did not contribute to the prediction of total robot time. We found a statistically significant learning curve (P<.01). The institutional learning percentage∗ derived from total robot times† for the first 458 recorded cases of mitral valve repair using telemanipulative technology is 95% (R(2)=.40). More than one third of the variability in total robot time can be explained through our model using the following variables: type of repair (chordal procedures, ablations, and leaflet resections), band size, use of clips alone in band implantation, and the presence of a fellow at bedside (P<.01). Learning in mitral valve repair surgery using telemanipulative technology occurs at the East Carolina Heart Institute according to a logarithmic curve, with a learning percentage of 95%. From our regression output, we can make an approximate prediction of total robot time using an additive model. These metrics can be used by programs for benchmarking to manage the implementation of this new technology, as well as for capacity planning, scheduling, and capital budget analysis.