Towards the development of human work-performance standards in futuristic man-machine systems: A fuzzy modeling approach

Drastic changes in the nature of industrial work force, from manufacturing related industries to service and information industries, and increased automation have led to abundance of jobs for which there are no established performance standards. While work performance standards exist for physical tasks that are routinely performed in manufacturing industries, limits on acceptable levels of work are yet to be developed for mental tasks which are fast becoming common. This paper describes an approach to quantify qualitative human responses and describes a procedure to integrate them with other quantitative responses resulting from physical, mental, behavioral, or physcho-social factors. The theory of fuzzy sets and systems is utilized in developing the modeling concept which can be used in developing work-performance standards or optimizing overall man—machine systems effectiveness.     

Dynamic routing to heterogeneous collections of unreliable servers

We argue the importance of problems concerning the dynamic routing of tasks for service in environments where the servers have diverse characteristics and are subject to breakdown. We propose a general model in which both service times and repair times at each machine are i.i.d.with some general distribution. Routing decisions take account of queue lengths, machine states (up or down), the elapsed processing times of jobs in service and the times to date of any machine repairs in progress. We develop an approach to machine calibration which yields a machine index which is a function of all of the preceding information. The heuristic which routes all tasks to the machine of current smallest index performs outstandingly well. The approach of the paper is flexible and is capable of yielding strongly performing routing policies for a range of variants of the basic model. These include cases where job processing is lost at each breakdown and where the machine state may be only partially observed. AMS Subject Classifications 49L20, 90B22     

Using profit maximizing scheduling models to structure operational trade-offs and manufacturing strategy issues

Manufacturing plays an increasingly important role in determining the competitiveness of the firm. However, corporate strategy is often formulated with little regard for how these decisions affect operations within the manufacturing system. Detailed models provide a necessary link between manufacturing performance and the functional policies followed by the firm, so that the strengths of the manufacturing system can be consistently reflected in strategic decisions.This paper presents a scheduling model that relates the strategic decisions that determine the type of work that must ultimately be processed by the manufacturing system with the detailed decisions that determine how this work should be scheduled. The model accounts for varying processing time, delay penalty, and revenue characteristics among the jobs available for processing by a single facility, with jobs partitioned in multiple classes such that a setup is incurred each time two jobs of different classes are processed in succession. Given limited processing capacity, the objective is to simultaneously determine the subset of jobs to accept for processing and the associated order in which accepted jobs should be sequenced to maximize the total profit realized by the facility. Problem formulations and dynamic programming algorithms are presented for both the special case in which all available work is from a single job class, and the more general case involving multiple job classes. The insight derived from these models concerning the operational implications of strategic decisions is illustrated through a series of example problems, first focusing on the coordination of marketing and manufacturing policy, and finally by considering important issues related to manufacturing focus.     

Hierarchical production planning and scheduling with random demand and production failure

A multiple-objective hierarchical production planning and scheduling model is developed that integrates aggregate type decisions, family disaggregate decisions, lotsizing and scheduling of the jobs. It is assumed that demand and production failure are subject to uncertainties. Stochastic programming with recourse using a constraint sample approximation method is used to incorporate random demand and production failure into the model. The model evaluates final production plans, updates the demand forecasts and proceeds on a rolling horizon manner. Experimental results show that it is sufficient to generate and incorporate into the aggregate type model a small sample of the stochastic constraints from an infinite set of scenarios. A heuristic scheduling algorithm provides detailed information regarding the progress of jobs through work centers. This information is extremely useful in resolving infeasibilities during the production process. Other features of the model are also reported.     

The performance of a memory restricted computer with a state-dependent job admission policy

Congestion and memory occupancy in computer system may be reduced further if new jobs are admitted only when the number of jobs queued at CPU is below CPU run queue cutoff (RQ). In this paper, we prove that response time of a job is invariant with respect toRQ if jobs do not communicate each other. We also demonstrate this invariance property numerically using marix-geometric methods and present an approximate method for the delay due to context switching under time slicing. The approximation suggests that time slicing with constant overhead yields a throughput similar to an FCFS system without overhead.     

Admission control of a service station when the arrivals are internal

All studies in the admission control of a service station make decisions at arrival epochs. When arrivals are internal and are rejected from a queue, the rejected jobs have to be routed to other stations in the system. However the system will not know whether a job will be admitted to a queue or not until its arrival epoch to that queue. Thus, the system has to react dynamically and agilely to the decisions made at a specific queue and may try several queues before finding a queue that admits the job. This paper remedies these difficulties by changing the decision epochs of the admission control from arrival epochs to departure epochs with the actions of switching (keeping) the arrival stream on or off. Thus upstream stations will have information on the admission status of their downstream stations all the time. It is proved that the optimal policy for this revised admission control system is of control limit type for an M/G/1 queue. Comparisons of the optimal values and optimal policies for the admission controls made at arrival epochs and at departure epochs are included in the paper.     

A Method for Solving Crew Scheduling Problems

The method discussed provides an allocation of jobs to men in a transportation system. Each man is assigned a prespecified shift, associated with a base station, and returned to his base station at the end of his shift. The total number of men required is minimized, subject to a minimum time between jobs, a suitable lunch break, and the performance of all the jobs, employing the decomposition principle of linear programming.     

The master-slave paradigm in parallel computer and industrial settings

The master-slave paradigm finds important applications in parallel computer scheduling, semiconductor testing, machine scheduling, transportation, maintenance management and other industrial settings. In the master-slave model considered in this paper a set of jobs is to be processed by a system of processors. Each job consists of a preprocessing task, a slave task and a postprocessing task that must be executed in this order. The pre- and post-processing tasks are to be processed by a master processor while the slave task is processed by a slave processor. In this paper, we motivate the master-slave model and develop bounded performance approximation algorithms for the unconstrained makespan minimization problem as well as for multiple master systems.This work was supported in part by the National Science Foundation under grant MIP-9103379 and the Army Research Office under grant DAA H04-95-1-0111.     

Trading grid services – a multi-attribute combinatorial approach

The Grid is a promising technology for providing access to distributed high-end computational capabilities. Thus, computational tasks can be performed spontaneously by other resources in the Grid that are not under the user’s control. However, one of the key problems in the Grid is deciding which jobs are to be allocated to which resources at what time. In this context, the use of market mechanisms for scheduling and allocating Grid resources is a promising approach toward solving these problems. This paper proposes an auction mechanism for allocating and scheduling computer resources such as processors or storage space which have multiple quality attributes. The mechanism is evaluated according to its economic and computational performance as well as its practical applicability by means of a simulation.     

不确定环境下多自主体系统的分布式估计与控制

多自主体系统是当前系统控制界研究的热点问题. 在实际中, 自主体系统通常并不是在理想的环境下执行任务, 而是面临多源头、多层次和多变化的各类不确定性因素的影响. 它们通过在微观层面上影响各自主体决策的正确性, 从而在宏观上对自主体系统的整体行为产生显著影响. 不确定性因素和多自主体系统分布式信息架构交互耦合, 给系统的设计与分析带来本质性困难. 本文围绕分布式估计与分布式控制问题, 研究在随机通信噪声、数据丢失、量化和系统未知结构参数等不确定因素影响下, 如何为各自主体设计更加鲁棒、更加有效的分布式估计算法及分布式控制律, 以实现全局估计与控制目标, 并对闭环系统性能进行系统分析.     

Optimal threshold policies in a two-class preemptive priority queue with admission and termination control

We consider a two-class 1 preemptive priority queue in which there are two essential, on-line decisions that have to be taken. The first is the decision to either accept or reject new type-1 or type-2 jobs. The second is the decision to abort jobs, i.e., to remove any type-1 or type-2 jobs from the system. We show that there exist optimal threshold policies for these two types of, decisions.     

Heavy-traffic limits for an infinite-server fork–join queueing system with dependent and disruptive services

We study an infinite-server fork–join queueing system with dependent services, which experiences alternating renewal service disruptions. Jobs are forked into a fixed number of parallel tasks upon arrival and processed at the corresponding parallel service stations with multiple servers. Synchronization of a job occurs when its parallel tasks are completed, i.e., non-exchangeable. Service times of the parallel tasks of each job can be correlated, having a general continuous joint distribution function, and moreover, the service vectors of consecutive jobs form a stationary dependent sequence satisfying the strong mixing (\(\alpha \)-mixing) condition. The system experiences renewal alternating service disruptions with up and down periods. In each up period, the system operates normally, but in each down period, jobs continue to enter the system, while all the servers will stop working, and services received will be conserved and resume at the beginning of the next up period. We study the impact of both the dependence among service times and these down times upon the service dynamics, the unsynchronized queueing dynamics, and the synchronized process, assuming that the down times are asymptotically negligible. We prove FWLLN and FCLT for these processes, where the limit processes in the FCLT possess a stochastic decomposition property and the convergence requires the Skorohod \(M_1\) topology.     

Optimization of multi-echelon repairable item inventory systems with simultaneous location of repair facilities

We study a support system for a fleet of technical systems. Our tasks are to determine the amount of spares and test equipment necessary for the support system to operate satisfactorily and to determine where repair should take place. Our decisions are made on an economical basis, or more precisely, we seek the optimal support system configuration that meets a specified life-cycle cost constraint. A mathematical framework is presented which allows for these three tasks to be solved simultaneously.     

FirmNet: the scope of firms and the allocation of task in a knowledge-based economy

The increasing knowledge intensity of jobs, typical of a knowledge economy, highlights the role of firms as integrators of know how and skills. As economic activity becomes mainly intellectual and requires the integration of specific and idiosyncratic skills, firms need to allocate skills to tasks and traditional hierarchical control may result increasingly ineffective. In this work, we explore under what circumstances networks of agents, which bear specific skills, may self-organize in order to complete tasks. We use a computer simulation approach and investigate how local interaction of agents, endowed with skills and individual decision-making rules, may produce aggregate network structure able to perform tasks. To design algorithms that mimic individual decision-making, we borrow from computer science literature and, in particular, from studies addressing protocols that produce cooperation in P2P networks. We found that self-organization depends on imitation of successful peers, competition among agents holding specific skills, and the structural features of, formal or informal, organizational networks embedding both professionals, holding skills, and project managers, holding access to jobs.

Uniqueness of the solution for optimal static routing in open BCMP queueing networks

Optimal static routing problems in open BCMP queueing networks with state-independent arrival and service rates are studied. They include static routing problems in communication networks and optimal static load balancing problems in distributed computer systems. We consider an overall optimal policy that is the routing policy whereby the overall mean response (or sojourn) time of a job is minimized. We obtain the routing decisions of the overall optimal policy and show that they may not be unique, but that the utilization of each service center is uniquely determined by the overall optimal policy. We also consider an individually optimal policy whereby jobs are routed so that each job may feel that its own expected response time is minimized if it knows the mean delay time for each path.     

A review of data science in business and industry and a future view

The aim of this paper is to frame Data Science, a fashion and emerging topic nowadays in the context of business and industry. We open with a discussion about the origin of Data Science and its requirement for a challenging mix of capability in data analytics, information technology, and business know‐how. The mission of Data Science is to provide new or revised computational theory able to extract useful information from the massive volumes of data collected at an accelerating pace. In fact, besides the traditional measurements, digital data obtained from images, text, audio, sensors, etc complement the survey. Then, we review the different and most popular methodologies among the practitioners of Data Science research and applications. In addition, because the emerging field requires personnel with new competences, we attempt to describe the Data Scientist profile, one of the sexiest jobs of the 21st Century according to Davenport and Patil. Most people are aware of the need to embrace Data Science, but they feel intimidated that they do not understand it and they worry that their jobs will disappear. We want to encourage them: Data Science is more likely to add value to jobs and enrich the lives of working people by helping them make better, more informed business decisions. We conclude this paper by presenting examples of Data Science in action in business and industry, to demonstrate the collection of specialist skills that must come together for this new science to be effective.     

Note on scheduling with general learning curves to minimize the number of tardy jobs

Several research studies have confirmed that people and organizations become better at their tasks as the tasks are repeated. The effect of this learning phenomenon on classical scheduling problems has been studied recently. One of the single-machine scheduling problems which seems to become nontrivial when learning effects are introduced is that of minimizing the number of tardy jobs. In this note, we study the special case where all jobs share a common due-date. We show that even when the learning process is assumed to be general and job-dependent, the problem remains polynomially solvable.     

Discrimination decisions for 100,000-dimensional spaces

Discrimination decisions arise in many natural language processing tasks. Three classical tasks are discriminating texts by their authors (author identification), discriminating documents by their relevance to some query (information retrieval), and discriminating multi-meaning words by their meanings (sense discrimination). Many other discrimination tasks arise regularly, such as determining whether a particular proper noun represents a person or a place, or whether a given work from some teletype text would be capitalized if both cases had been used.We [9] introduced a method designed for the sense discrimination problems mentioned.We also discuss areas for research based on observed shortcomings of the method. In particular, an example in the author identification task shows the need for a robust version of the method. Also, the method makes an assumption of independence which is demonstrably false, yet there has been no careful study of the results of this assumption.     

Task allocation in distributed processing systems

Distributed processing in computer networks is a powerful development emanating from technological advances in computer science and communications research. An important unresolved problem in distributed processing is how to optimally allocate jobs among interconnected processors. We develop a dynamic programming algorithm which incorporates many of the essential problem elements. Computational results are encouraging for problems of realistic size.     

Scheduling for a multifunction phased array radar system

A multifunction radar is a new, complex, radar system which combines the previously isolated tasks of searching volumes of space, tracking targets and guiding missiles. This study was instigated by the Defence Research Agency who require scheduling rules for their newly developed multifunction radar system. The primary interest when looking at the operational efficiency of this type of radar system is to schedule the radar jobs effectively. These jobs take the form of a coupled task which consists of two distinct operations that require processing in a predetermined order and at a specified interval apart. In the radar scenario, each job is cyclic in nature with its own due date and processing time. The need for an on-line scheduler restricts the radar controller to use heuristic methods. A detailed functional simulation model, which generates a multifunction radar environment, has been developed to aid the evaluation of the various scheduling heuristics that we have proposed.