Hierarchical control of semi-automated production systems

An integrated control model of a hierarchical production system is presented where the output can be measured only at preset control points as it is impossible or costly to measure it continuously. Three levels are considered—company, section, production unit—each level faces stochastic optimization problems. Each unit produces a given target amount by a given due date (common to all units) and has several possible speeds, which are subject to disturbances. On the unit level, at each control point, decision-making centres on determining both the next control point and the speed to proceed with up to that point. The section level is faced with problems of either reallocating resources among the section's units or reassigning the remaining target amounts among the units so that the faster one will help the slower one. The company level is faced with similar problems, i.e. reallocating resources or reassigning target amounts among the sections. Two different cases are considered: (1) cost parameters are not taken into account, i.e. there are two conflicting objectives, namely to maximize the probability of completing the production on the due date and to minimize the number of control points, but the first objective is dominant; (2) the objective is to maximize the expected net profit. Various optimization problems at each level are presented. Examples from steel, construction and mining industries are given.     

A profitability model for tactical planning

Simple models reflecting changes in aggregate company behaviour in response to changes imposed by management decisions and/or factors outside the direct control of management provide useful tools for tactical planning purposes.One such model is discussed in this paper. The model enables management to assess explicitly and quantitatively the effect on the rate of return on capital employed of primary causes of change in any of the key variables affecting company performance, such as the unit price, the unit cost, the output level and the capital employed. Simple functional relationships are proposed between the major variables in the model, such as the level of output and the unit cost, the unit price and the volume of sales, the working capital requirements and the general level of the firm's activity, etc. and these are based on analytical considerations, empirical evidence and a series of assumptions. These relationships give rise to a set of parameters which are incorporated in the model and describe the operational (cost structure), financial (capital structure) and marketing (elasticity of demand) position of the company under study.Numerical results obtained from the model seem to suggest that the sensitivity of the rate of return to some of the parameters and underlying assumptions is relatively small, implying that the inter-dependence between some of the variables can be ignored, thus simplifying the problems associated with data collection and model evaluation.Both the model and the methodology of analysis employed are flexible enough to allow: (1) application of the model to alternative sets of functional relationships between variables and/or underlying assumptions; (2) further decomposition of the model to include additional variables depending on the particular objectives of the study; (3) modification of the model to explore the effects of management decisions or those of changes in external factors on productive unit and product profitability; and (4) analysis of models reflecting the effects of changes which are imposed on the system on measures of business performance other than the rate of return on capital employed.     

Customer–supplier relationship management in an intelligent supply chain network

Outsourcing is leading to more and more complex industrial organisations. This can be attributed to the fact that several decision centres interact. As a consequence, changes in customer–supplier relationships can be noticed. In recent years, these relations have strongly evolved to lead to better internal management of each partner and a better general performance to satisfy customers. These evolutions created a new approach to the relationship between companies, called ‘industrial partnership’, in the form of a network. Networks induce a need at customer–supplier relation control level. The contribution and participation of each of the partners are thus fundamental to make supply chain management (SCM) a successful project. The control system of each actor partner must thus be adaptable enough to satisfy the production requirements. Our contribution to the improvement of customer–supplier relationship is a decentralised self-organised control model based on the concept of holon. In this model, the decision system manages a group of actors’ operations who are in a partnership. In this paper in particular a process for the evaluation of the suppliers network is discussed.     

Single cycle policies for the one-warehouse N-retailer inventory/distribution system

We address a multi-echelon inventory system with one-warehouse and N  -retailers. The demand at each retailer is assumed to be known and satisfied by the warehouse. Shortages are not allowed and lead times are negligible. Costs at each facility consist of a fixed charge per order and a holding cost. The goal is to determine single-cycle policies which minimize the average cost per unit time, that is, the sum of the average holding and setup costs per unit time at the retailers and at the warehouse. We propose a O(NlogN)$\mathrm{O}(N\log N)$ heuristic procedure to compute efficient single-cycle policies. This heuristic is compared with other approaches proposed by Schwarz, Graves and Schwarz and Muckstadt and Roundy. We carry out a computational study to test the effectiveness of the heuristic and to compare the performance of the different procedures. From the computational results, it is shown that the new heuristic provides, on average, better single-cycle policies than those given by the Muckstadt and Roundy method.     

配额——交易机制下动态批量生产和减排投资策略研究

随着我国碳排放交易市场的建立和发展,在碳排放约束下逐步降低单位产出的碳排放水平成为企业生产经营管理中的中长期约束性目标。本文在一个多周期决策模型中研究了配额-交易机制下企业的最优动态批量生产、碳排放权交易和减排投资联合决策问题。生产商在整个决策周期期初决定是否进行减排投资以及投资规模,根据每个周期的生产计划决定减排设备的运行计划。根据节能减排技术的特点,本文假设生产商运行减排设备时不仅降低了产品的单位生产碳排放量,而且降低了产品的单位生产成本。本文基于广义Benders分解法对模型进行了最优性分析,得到了最优生产决策和最优减排投资决策的一些基本性质,并通过数值实验分析了碳排放配额和碳排放权价格对生产商总成本、总排放以及减排投资决策的影响。本文的数值实验分析结果发现:(1)当碳交易市场上的碳排放权充足时,减少碳排放配额或改变碳排放配额的分配方式并不能影响生产商的碳排放水平;(2)碳排放权价格是影响生产商的碳排放水平和减排投资规模的关键因素;(3)随着碳排放权价格的上升,即使拥有足够的碳排放配额,生产商仍会不断提高减排投资规模以获得减排收益。研究结果对碳排放交易体系下生产企业进行减排技术投资具有较强的管理启示。     

Bridging strategic planning and business model management – A formal control framework to manage business model portfolios and dynamics

Despite the increasing interest in the intersection between strategic planning and business model management, research and practice struggle to integrate the two activities. By applying a rationalist view and drawing on the formal control theory, we develop an integrated management framework that provides a conceptual model of how to achieve consistency between the relevant goal and the planning levels. Therefore, we decompose the complex organizational activities into the level strategy, business model portfolio, tactics, and operations. For each level, we specify the formal activities and objectives and elaborate how the levels determine each other. Furthermore, we explain how higher and lower level formal control loops ensure alignment and that the appropriate level of the organization responds to changes in the environment. Finally, we discuss how these formal control mechanisms allow for proficient management of the dynamics inherent in strategy and business model management.     

Modeling patient arrivals in community clinics

We develop improved methods for modeling and simulating the streams of patients arriving at a community clinic. In previous practice, random (unscheduled) patient arrivals were often assumed to follow an ordinary Poisson process (so the corresponding patient interarrival times were randomly sampled from an exponential distribution); and for scheduled arrivals, each patient's tardiness (i.e., the deviation from the scheduled appointment time) was often assumed to be randomly sampled from a normal distribution. A thorough analysis of patient arrival times, obtained from detailed workflow observations in nine community clinics, indicates these assumptions are not generally valid, and the tardiness data sets for this study are best modeled by unbounded Johnson distributions. We also propose a nonhomogeneous Poisson process to model the random patient arrivals; we review a nonparametric approach to estimating the associated mean-value function; and we describe an algorithm for generating random patient arrivals from the estimated model. The adequacy of this model of random patient arrivals can be assessed by standard goodness-of-fit tests. These findings are important since testable scheduling optimization strategies must be based upon accurate models for both random and scheduled patient arrivals. The impacts on modeling, as well as implications for practice management, are discussed.     

Scheduling Packets with Values and Deadlines in Size-Bounded Buffers

Motivated by providing quality-of-service differentiated services in the Internet, we consider buffer management algorithms for network switches. We study a multi-buffer model. A network switch consists of multiple size-bounded buffers such that at any time, the number of packets residing in each individual buffer cannot exceed its capacity. Packets arrive at the network switch over time; they have values, deadlines, and designated buffers. In each time step, at most one pending packet is allowed to be sent and this packet can be from any buffer. The objective is to maximize the total value of the packets sent by their respective deadlines. A 9.82-competitive online algorithm (Azar and Levy in Lect Notes Comput Sci 4059:5–16 2006) and a 4.73-competitive online algorithm (Li in Lect Notes Comput Sci 5564:265–278, 2009) have been provided for this model, but no offline algorithms have yet been described. In this paper, we study the offline setting of the multi-buffer model. Our contributions include a few optimal offline algorithms for some variants of the model. Each variant has its unique and interesting algorithmic feature.     

Output planning for material requirements under uncertainty

This paper considers a production planning and control system requiring that, for each work station, a time-phased series of input and output values be estimated. It is assumed that the projected input levels can vary significantly across time periods and that anticipated output levels can be determined by management, subject to limits specified by available manpower and subcontracting. The actual input and output levels for each period depart from the planned levels according to a probability distribution determined empirically. An approach is presented that allows management to evaluate a chosen set of output levels to determine the probability that sufficient amounts of work will be available at a work station each period.     

Psychosocial safety climate as an antecedent of work characteristics and psychological strain: A multilevel model

Abstract

Psychosocial safety climate (PSC) refers to a specific organizational climate for the psychological health of workers. It is largely determined by management and at low levels is proposed as a latent pathogen for psychosocial risk factors and psychological strain. Using an extended Job Demands-Control-Support framework, we predicted the (24 month) cross-level effects of PSC on psychological strain via work conditions. We used a novel design whereby data from two unrelated samples of nurses working in remote areas were used across time (N=202, Time 1; N=163, Time 2), matched at the work unit level (N= 48). Using hierarchical linear modelling we found that unit PSC assessed by nurses predicted work conditions (workload, control, supervisor support) and psychological strain in different nurses in the same work unit 24 months later. There was evidence that the between-group relationship between unit PSC and psychological strain was mediated via Time 2 work conditions (workload, job control) as well as Time 1 emotional demands. The results support a multilevel work stress model with PSC as a plausible primary cause, or “cause of the causes”, of work-related strain. The study adds to the literature that identifies organizational contextual factors as origins of the work stress process.     

Hierarchical model to design manufacturing systems applied to a metal-mechanic company

This paper describes the application of a model to design and manage the manufacturing process through an integrated system, using a Hierarchical methodology-by means of different aggregation and disaggregation stages with an appropriate time horizon. The hierarchical model obtained allows interfacing between the master schedule and the schedule of the orders. At each level of the hierarchical model some decisions have to be made in order to design the manufacturing system, but having a model that also takes into consideration the management of the system. To do this it is necessary to use aggregated information concerned with the level at which decisions are made. It is obtained from the database, which contains the information at the highest level of aggregation. This model has been applied to a metal mechanic company, that makes final products such as car washers, etc. The benefits of this approach for the company are addressed in this paper.     

A PTAS for minimum weighted connected vertex cover $$P_3$$ problem in 3-dimensional wireless sensor networks

Given a connected and weighted graph \(G=(V, E)\) with each vertex v having a nonnegative weight w(v), the minimum weighted connected vertex cover \(P_{3}\) problem \((MWCVCP_{3})\) is required to find a subset C of vertices of the graph with minimum total weight, such that each path with length 2 has at least one vertex in C, and moreover, the induced subgraph G[C] is connected. This kind of problem has many applications concerning wireless sensor networks and ad hoc networks. When homogeneous sensors are deployed into a three-dimensional space instead of a plane, the mathematical model for the sensor network is a unit ball graph instead of a unit disk graph. In this paper, we propose a new concept called weak c-local and give the first polynomial time approximation scheme (PTAS) for \(MWCVCP_{3}\) in unit ball graphs when the weight is smooth and weak c-local.     

A PTAS for the minimum weighted dominating set problem with smooth weights on unit disk graphs

In the minimum weighted dominating set problem (MWDS), we are given a unit disk graph with non-negative weight on each vertex. The MWDS seeks a subset of the vertices of the graph with minimum total weight such that each vertex of the graph is either in the subset or adjacent to some nodes in the subset. A?weight function is called smooth, if the ratio of the weights of any two adjacent nodes is upper bounded by a constant. MWDS is known to be NP-hard. In this paper, we give the first polynomial time approximation scheme (PTAS) for MWDS with smooth weights on unit disk graphs, which achieves a (1+ε)-approximation for MWDS, for any ε>0.     

Economic Manufacturing Quantity and Its Integrating Implications

Several contradictions are noted among the Economic Order Quantity (EOQ), Just‐In‐Time (JIT), and Optimized Production Technology (OPT) approaches and the economic framework for profit maximization. A fundamental model referred to as the Economic Manufacturing Quantity (EMO) is developed and examined for its integrating implications for the three approaches. An implication for the classic EOQ approach is that the balance between setup and inventory carrying costs is valid when a production facility is operating at or below a certain critical level but not when operating above that level. An implication for the JIT approach is that one must reduce setup cost at non‐bottlenecks and setup time at bottlenecks to reduce inventory. An implication for the OPT approach is that trade‐offs between setup and inventory carrying costs may indeed be ignored while determining process batch sizes, provided each facility in a production system is operating at or above Its critical level. Economic theoretic analysis of the EMO model provides a basis for unification of JIT which advocates stability in operating level as a key to improved productivity and quality, and OPT that advocates maximizing operating level with resultant emphasis on bottlenecks as a key to increased profits. This unifying basis states that a profit‐maximizing production facility or system will operate at the full and stable level as long as market demand remains relatively sensitive to price and operating at the full (maximum) level provides positive unit contribution.     

An EOQ model with deteriorating items in response to a temporary sale price

In economic order quantity models, it is often assumed that the unit purchase cost is constant. Such an assumption is usually not fulfilled in many practical situations. In practice, it is observed that suppliers sometimes offer temporary price discounts to stimulate demand, boost market share or decrease inventories of certain items. In this paper, a deteriorating inventory model with a temporary sale price has been developed. We shall be concerned with finding the optimal total cost saving for deteriorating items during the special replenishment period. Numerical examples are presented to illustrate the proposed model.     

Fixed or variable demand? Does it matter when locating a facility?

In most competitive location models available in the literature, it is assumed that the demand is fixed independently of market conditions. However, demand may vary depending on prices, distances to the facilities, etc., especially when the goods are not essential. Taking variable demand into consideration increases the complexity of the problem and, therefore, the computational effort needed to solve it, but it may make the model more realistic. In this paper, a new planar competitive location and design problem with variable demand is presented. By using it, it is shown numerically for the first time in the literature that the assumption of fixed demand influences the location decision very much, and therefore the selection of the type of demand (fixed or variable) must be made with care when modeling location problems. Finally, two methods are presented to cope with the new model, an exact interval branch-and-bound method and an evolutionary algorithm called UEGO (Universal Evolutionary Global Optimizer).     

On Strategy and Management Control: The Importance of Classifying the Strategy of the Business

The point of departure for this paper is a number of contingency-theory studies on the relationship between business strategy and the design and use of management control. In these studies strategy has been operationalized in different ways – a major reason why the findings are ambiguous and difficult to integrate. Thus there is a strong need for a common frame of reference for classifying business strategy. In view of the multifaceted nature of the concept of strategy, however, it is neither desirable nor possible to arrive at a single method of classification that would be appropriate in all situations. Rather, the task is to integrate different strategic variables such as strategic pattern, strategic position and strategic mission. In this paper we show how these three variables may be assumed to influence, and be influenced by, what characterizes changes in strategy and how business units manage their product offerings. Unlike most previous studies in the field, this paper discusses how the strategic variables taken together may be assumed to influence the classification of strategy and thus the design and use of the management-control system. Our deductive analysis, and the hypotheses used in connection with it, show that studies which consider only one strategic variable may lead to erroneous conclusions about the relationship between strategy and management control.     

A precision pump schedule optimization for the water supply networks with small buffers

The study addresses the problem of finding an energy-efficient pump and valve configuration of a water supply network. It is motivated particularly by scantily buffered networks which require both method precision and algorithm efficiency that tightly chase notably dynamic water demand. The model unprecedentedly comprehends physical and hydraulic laws that govern pump-valve configuration along with ensuing flow and energy level. The solution method combines hydraulic relaxation of individual pump characteristic curves to a station curve and an LP-based search performing optimal trade-off between water head and discharge beyond the minimum energy level for each station. With a hamming-distance-type shortest path formulation incorporated into the search steps, it can also minimize both frequency and magnitude of pump-valve reconfiguration. The proposed method achieved an energy-cost saving of 5.9% and did so more consistently than manual operation, while retaining computation time suitable for unit period as short as 15 min.     

Design requirements for health care production control systems

This paper addresses the issue of determining design requirements for production control in health care organizations, with a restriction to the internal production control of hospitals. Hospital management has limited possibilities to control hospital production, as hospital production processes are driven by medical specialists who, however, do not manage that process. We consider therefore the hospital as a virtual organization, consisting of a number of relatively independent businesses in a common framework. Each business unit functions as a focused factory for a range of more or less homogeneous products. Production control principles can be applied to each of these businesses, but not to the system as a whole. A number of elements from classical production control theory can be also applied to health care, i.e. the use of decoupling points, the bottleneck-oriented approach, and the operational control between production and market. However, important factors that need to be considered in health production control are that often specifications on quality are not available at the start of the process, and that there is strong interaction between the patient and the process. Our conclusion is that a dedicated framework for approaching hospital production control is necessary. The specific characteristics of hospital care and its state of production control development are the main arguments for this dedicated framework.     

Improving spare parts inventory control at a repair shop

We study spare parts inventory control for an aircraft component repair shop. Inspection of a defective component reveals which spare parts are needed to repair it, and in what quantity. Spare part shortages delay repairs, while aircraft operators demand short component repair times. Current spare parts inventory optimization methods cannot guarantee the performance on the component level, which is desired by the operators. To address this shortfall, our model incorporates operator requirements as time-window fill rate requirements for the repair turnaround times for each component type. In alignment with typical repair shop policies, spare parts are allocated on a first come first served basis to repairs, and their inventory is controlled using (s, S) policies. Our solution approach applies column generation in an integer programming formulation. A novel method is developed to solve the related pricing problem. Paired with efficient rounding procedures, the approach solves real-life instances of the problem, consisting of thousands of spare parts and components, in minutes.A case study at a repair shop reveals how data may be obtained in order to implement the approach as an automated method for decision support. We show that the implementation ensures that inventory decisions are aligned with performance targets.