Allocation Of Operations Research Articles

Case allocation of extensive operations on head and neck within the German DRG system 2004–2007

Hintergrund Bei Einfuhrung des DRG-Systems bestanden erhebliche Zweifel an einer sachgerechten Abbildung der Patienten mit grosen Eingriffen an Kopf- und Hals. Daher wurden grose Anstrengungen zur Ausdifferenzierung dieser Fallgruppe unternommen. Ziel der vorliegenden Arbeit war es, zu analysieren, inwieweit durch die Ausdifferenzierung des G-DRG-Systems eine sachgerechte Abbildung erreicht werden konnte.

1.3.3 Hybrid System Dynamic, Petri Net, and Agent‐Based Modeling of the Air and Space Operations Center

AbstractIn an earlier paper, an existing Air and Space Operations Center (AOC) process model (i.e., Petri net) and new global and mission models for the environment in which the AOC operates (i.e., System Dynamics) were linked (federated). The focus of this paper is the development of an operator‐environment model (i.e., Agent‐Based Model). An existing systems framework for attention allocation of operators within the AOC has been implemented that supports multiple modeling paradigms. The results for linking the Petri net and System Dynamics models are summarized, and new results for the Agent‐Based Model are presented based on a pilot‐down scenario. It has been observed that many AOC operators can become distracted by a pilot‐down critical event, even if the operator is not able to directly assist in the rescue. Furthermore, this distraction has been hypothesized to have a detrimental effect on the activities the non‐involved operators are currently handling.

Workflow balancing in parallel machine scheduling with precedence constraints using genetic algorithm

PurposeTo propose a methodology based on genetic algorithm (GA) to solve the parallel machine scheduling problems with precedence constraints.Design/methodology/approachWorkflow balancing helps to remove bottlenecks present in a shop floor yielding faster movements of components or jobs. Multiple machines are used in parallel for processing the jobs to meet the demand. In parallel machine scheduling with precedence constraints, there are m machines to which n jobs are assigned using suitable scheduling algorithms. Workflow of a machine is the sum of processing time of all jobs assigned. All the preceding jobs are allocated first to satisfy the constraints. GA is developed to solve parallel machine scheduling problems with precedence constraints based on the objective of workflow balancing. The GA was coded on IBM/PC compatible system in the C++ language for simulation to a standard manufacturing environment.FindingsThe relative percentage of imbalance (RPI) in workloads among the parallel machines is used to evaluate the performance of the GA developed. The proposed GA produces lesser RPI values against the RANDOM heuristic algorithm for a wider range of jobs and machines.Research limitations/implicationsThe performance of GA can be compared with the performance of other meta‐heuristic algorithms to find out the robustness of the results obtained by this research.Practical implicationsThe proposed GA also gives better solution for a case study of assembly scheduling.Originality/valueThe allocation of assembly operations to the operators is modeled into a parallel machine scheduling problem with precedence constraints using the objective of minimizing the workflow among the operators.

A combined fuzzy set theory and MILP approach in integration of planning and scheduling of batch plants—Personnel evaluation and allocation

This paper presents the application of fuzzy set theory (FST) within the context of integrated planning and scheduling. Fuzzy set theory provides a framework for modelling uncertain or ambiguous information, which is commonly encountered in industry. A typical example of an industrial situation wherein uncertainty cannot be avoided due to the type of data required is operator evaluation (grading). This usually requires information on experience, expertise, responsibility, age, etc., which tend to be more qualitative than quantitative, and involve high levels of imprecision. Consequently, traditional mathematical procedures fail to address this issue effectively. Since human intervention is crucial in the operation and performance of batch chemical plants, the issue of operator evaluation demands attention. It is generally accepted that operators with different skills will have different process understanding and response times, which will eventually impact on plant performance. Moreover, different plants will have different requirements in terms of the aforementioned evaluation criteria. Therefore, efficiency of any operator evaluation procedure is entirely dependent on the quality of operator allocation to different plants. Another aspect that should not be overlooked when dealing with operators, is remuneration, which is determined by the grade. Operators with high levels of skill (experience) and expertise (education) tend to command high salaries. Therefore, they should only be hired on mandatory rather than discretionary grounds. The presentation made in this paper demonstrates the application of FST in dealing with qualitative features of plant personnel as well as plant requirements in order to maximise plant performance, whilst accounting for the financial aspects. The FST output can then be incorporated in an MILP formulation to determine the optimal allocation of operators to different plants. The second part of this series presents the impact of personnel allocation on the overall integrated planning and scheduling framework.

Simulation of workflow balancing in assembly shopfloor operations

PurposeTo provide a new model to solve the assembly‐planning problem of a textile machine in a shopfloor which can help researchers and practitioners.Design/methodology/approachThe assembly planning of a textile machine (repetitive manufacturing system) involves the allocation of operations to cross‐trained operators. Workflow is defined as the workloads assigned to the operators. Operators with smaller workloads are selected to be assigned new operations from the list of unscheduled operations. Three different scheduling strategies – random, shortest processing time, and longest processing time – are adopted for the selection of operations to be assigned to operators. Different combinations of these strategies are considered for the selection of both preceding and succeeding operations. A computer simulation program has been coded on an IBM/PC‐compatible system in the C++ language to study the performance of real data from the shopfloor.FindingsThe relative percentage of imbalance is adopted for evaluating the performance of these heuristics. The RL, SL and LL produced well balanced workload schedules with lesser RPI values for all operators other than heuristics.Research limitations/implicationsNon‐traditional approaches like genetic algorithms can be applied to determine the robustness of the results obtained by this research.Practical implicationsThe experiments on simulated and real data clearly indicate that the order of succeeding operations determines the balanced workflows to the assembly of operations among the operators.Originality/valueThe allocation of assembly operations to the operators is modeled into a parallel machine‐scheduling problem with precedence constraints using the objective of minimizing the workflow among the operators.

Robotic cell scheduling with operational flexibility

In this paper, we study the problem of two-machine, identical parts robotic cell scheduling with operational flexibility. We assume that every part to be processed has a number of operations to be completed in these two machines and both machines are capable of performing all of the operations. The decision to be made includes finding the optimal robot move cycle and the corresponding optimal allocation of operations to these two machines that jointly minimize the cycle time. We prove that with this definition of the problem 1-unit robot move cycles are no longer necessarily optimal and that according to the given parameters either one of the 1-unit robot move cycles or a 2-unit robot move cycle is optimal. The regions of optimality are presented.

An integrated model for part-operation allocation and investments in CNC technology

This study addresses the issue of investment appraisal of new technology, specifically computer numerical control (CNC) machine tools in conjunction with optimal allocation of parts and operations on CNC machines as the investments take place. Part-operation allocation is the allocation of parts and operations to either the conventional machines or to the new technology as they are acquired. It is shown that part allocation is an important consideration in the assessment of profitability from investments in CNC technology. A mixed integer programming model is presented to (1) determine the optimal allocation of parts and operations to conventional machines and to new CNC machine tools; and (2) determine the optimal investment sequence and timing of investments in CNC machine tools. The optimality criterion is based on a maximization of net present value (NPV) over a specified planning horizon. The application of the model is illustrated using a numerical example, and the implications for industrial practice are also indicated.

Dynamic operator assignment based on shifting machine loading

This study proposes a way to improve the efficiency of semiconductor wafer fabrications by better allocating operator resources. A semiconductor wafer fabrication facility produces multiple products using unreliable machines, and each product follows a different route. Random machine failures and demand fluctuations make machine loading change from time to time. Thus, the operator assignment needs adjustment accordingly to maintain a high factory throughput rate. We propose methods to make this dynamic adjustment. By using simulation models, we demonstrate that proportionally allocating operators among all work stations based on their utilization requirements can improve overall factory efficiency. We also show that frequent short term dynamic allocation of operators produces more output than conventional long term static allocation.

Short-term manpower management in manufacturing systems: new requirements and DSS prototyping

The short-term planning and scheduling of discrete manufacturing systems has mostly focused in the past on the management of machines, implicitly considered as the critical resources of the workshops. Some of the present schedulers claim to also manage human resources, but perform most of the time a local allocation of operators to machines, these operators having regular working hours. However, it seems clear that the workforce has a specificity that should be better taken into account by short-term planning facilities. Moreover, the variability of the weekly working hours through the year will shortly become a rule and not anymore an exception. On the base of a questionnaire answered by 19 French companies of different sizes and industrial sectors, we have tried to identify more precisely some industrial requirements concerning the short-term management of human resources. The growing interest in annualised hours together with the lack of software tools that allow to implement it practically is one of the results of this questionnaire. We suggest in this article the specification of a decision support system for short-term manpower management under annualised hours, taking into account the competence of the operators. A software prototype has been developed according to these specifications; the results of a simple but representative example are described.

Application of tabu search to process plan optimization for four-axis CNC turning centres

This paper describes our adaptation of the tabu search methodology to solve the process plan optimization problem for four-axis CNC turning centres. Optimization of a process plan (specifically, the allocation of operations to spindles and turrets) to minimize the cycle time for machining a workpiece is not a trivial problem. This is due to the highly combinatorial nature of the problem that arises from the presence of dual spindles and dual turrets and the machine's ability to perform operations in parallel machining and simultaneous machining modes. Our experiments to date have shown that the tabu search based approach can be very effective in process plan optimization, and is well suited for incorporation in CAD/CAM systems for four-axis CNC turning centres.

Models for warehouse management: Classification and examples

In this paper we discuss warehousing systems and present a classification of warehouse management problems. We start with a typology and a brief description of several types of warehousing systems. Next, we present a hierarchy of decision problems encountered in setting up warehousing systems, including justification, design, planning and control issues. In addition, examples of models supporting decision making at each of these levels are discussed, such as distribution system design, warehouse design, inventory management under space restrictions, storage allocation, and assignment and scheduling of warehouse operations.

Iterative procedures for multi-period order selection and loading problems in flexible manufacturing systems

This paper focuses on multi-period order selection and loading problems in flexible manufacturing systems. The multi-period order selection problem is the problem of selecting orders to be produced in each period during the upcoming planning horizon, and the loading problem involves allocation of operations and required tools to machines for orders selected in each period. The objective is to minimize earliness and tardiness costs and subcontracting costs. The earliness and tardiness costs are incurred if an order is not finished on time, while subcontracting cost is incurred if an order is not selected within the planning horizon (and must be subcontracted) due to limits in processing time capacity and the tool magazine capacity of the machines. To solve the two problems simultaneously, we develop four iterative procedures in which the multi-period order selection and loading problems are solved repeatedly until a good solution is obtained. To compare the four iterative procedures, computational experiments are done on randomly generated test problems and the results are reported.

Assembly planning and plan decomposition in an automated microrobot-based microassembly desktop station

There is an increasing interest in performing assembly of microsystems (i.e., non-destructive transportation, precise manipulation, and exact positioning of microcomponents) by flexible mobile microrobots which provide the necessary accuracy. The spectrum of tasks in microassembly ranges from simple preparatory operations like applying adhesives, drawing adjustment marks, cleaning objects, to the performance of the final assembly and inspection of the finished microsystem. An automated microrobot based microassembly desktop station (MMS) may offer the desired features and versatility. The main problems of realizing an MMS are intelligent assembly planning on the uppermost control level and task-specific allocation of assembly operations to robots and tools to allow the assembly process to be free of error and collision. The basis functions are described for the MMS that is being developed at the University of Karlsruhe. After discussing microassembly planning in an MMS a common microassembly model is introduced, based on geometric reasoning. For all planning steps – the generation of feasible assembly sequences, selection of the best assembly plan and allocation of the assembly operations to the robots employed by an MMS – a detailed description of the algorithms is presented, so they can be used by the practitioner. Finally, the general structure of a microassembly planning system of an MMS is introduced and the function of each system level is explained.

Static and dynamic operator allocation problems in cellular manufacturing systems

This paper addresses the static and dynamic operator allocation problems in cellular manufacturing systems (CMS). An assembly environment is considered where each component going into the final product is manufactured in an individual cell. There are m such cells and it is required to manufacture n varieties of products where n > m . Algorithms for static and dynamic allocation of operators to the cells to balance workload and to minimize makespan are developed and tested.

Multi-period part selection and loading problems in flexible manufacturing systems

We consider multi-period part selection and loading problems in flexible manufacturing systems with the objective of minimizing subcontracting costs. The part selection problem is to select sets of part types and to determine their quantities to be produced during the upcoming planning horizon while satisfying due dates of all orders for the parts, and the loading problem involves allocation of operations and required tools to machines. Production demands should be satisfied for periods through subcontracting if production demands cannot be satisfied by the system due to machine capacity or tool magazine capacity constraints. For the part selection and loading problems, we develop three iterative algorithms, called the forward algorithm, the backward algorithm and the capacity approximation algorithm, that solve the part selection and loading problems iteratively for each period. To compare the three algorithms, a series of computational experiments is done on randomly generated test problems.

Machine loading in flexible manufacturing systems considering routeing flexibility

The key competitive strength of a manufacturing system lies in its flexibility, which represents the ability to respond effectively to changing circumstances. Flexible manufacturing systems (FMSs) react appropriately to change. The relevance of a particular type of flexibility depends upon the system and problem being considered. In this paper, the machine loading problem in a flexible manufacturing system is addressed. The loading problem is concerned with the allocation of part operations and required tools to the machines, so as to optimise some objective(s) subject to some technological constraints. Several objectives have been considered in the past such as maximisation of the utilisation of resources, minimisation of processing and tooling costs and maximisation of throughput rates. In such procedures, it is quite probable that a rigid loading schedule is obtained and in cases of machine breakdowns and tool failures alternate routeings are often not available. Therefore, in this paper, we develop a mathematical model which considers maximisation of the production routeings available for the parts and hence increases the routeing flexibility. The model is illustrated by examples.

A new architecture for the Viterbi decoder for code rate k/n

A novel VLSI architecture is proposed for implementing a long constraint length Viterbi decoder (VD) for code rate k/n. This architecture is based on the encoding structure where k input bits are shifted into k shift registers in each cycle. The architecture is designed in a hierarchical manner by breaking the system into several levels and designing each level independently. The tasks in the design of each level range from determining the number of computation units, and the interconnection between the units, to the allocation and scheduling of operations. Additional design issues such as in-place storage of accumulated path metrics and trace back implementation of the survivor memory have also been addressed. The resulting architecture is regular, has a foldable global topology and is very flexible. It also achieves a better than linear trade-off between hardware complexity and computation time.

A New Architecture for the Viterbi Decoder for Code Rate k/n1

A novel VLSI architecture is proposed for implementing a long constraint length Viterbi decoder (VD) for code rate k/n. This architecture is based on the encoding structure where k input bits are shifted into k shift registers in each cycle. The architecture is designed in a hierarchical manner by breaking the system into several levels and designing each level independently. The tasks in the design of each level range from determining the number of computation units, and the interconnection between the units, to the allocation and scheduling of operations. Additional design issues such as in-place storage of accumulated path metrics and trace back implementation of the survivor memory have also been addressed. The resulting architecture is regular, has a foldable global topology and is very flexible. It also achieves a better than linear trade-off between hardware complexity and computation time.

Allocating data and operations to nodes in distributed database design

The allocation of data and operations to nodes in a computer communications network is a critical issue in distributed database design. An efficient distributed database design must trade off performance and cost among retrieval and update activities at the various nodes. It must consider the concurrency control mechanism used as well as capacity constraints at nodes and on links in the network. It must determine where data will be allocated, the degree of data replication, which copy of the data will be used for each retrieval activity, and where operations such as select, project, join, and union will be performed. We develop a comprehensive mathematical modeling approach for this problem. The approach first generates units of data (file fragments) to be allocated from a logical data model representation and a characterization of retrieval and update activities. Retrieval and update activities are then decomposed into relational operations on these fragments. Both fragments and operations on them are then allocated to nodes using a mathematical modeling approach. The mathematical model considers network communication, local processing, and data storage costs. A genetic algorithm is developed to solve this mathematical formulation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

CLEAN COASTAL WATERS COST PARTICIPATION FORMULA

ABSTRACT Clean Coastal Waters, Inc.'s (CCW) cost participation formula (COSTPART™) is a computerized formula which calculates the cost allocation of CCW operations and equipment purchases to member companies. Costs are allocated on the basis of oil volumes handled during specific oil-handling activities such as terminal transfers, production operations, and pipeline operations. The formula accounts for the relative risks of spill occurrence for the various activities, using a relative risk analysis of historical spills resulting from activities similar to those performed by CCW members. COSTPART is statistically based and has been adapted for use on IBM-compatible personal computers.