Set Of Jobs Research Articles

HUMANS REPLACED BY AI IN HR PRACTICES?-APERSPECTIVE SCANNER

This paper presents a panorama of the research that is based on 20 subsisting types of research presupposing that a paradoxical situation is breathing, ergo the humans are being supplanted from performing human resource practices in essence. Researchers are skeptical about the superseding of various jobs from the liveware to the software, indicatively, Artificial Intelligence software. The research is performed leaning on the secondary data collected. Study shows that the inclusion of AI in HR practices may fish out many of the jobs, contrarily, it might also lead to the hatching of a fresh set of jobs summoning highly digitalized intelligence. Human intelligence and artificial intelligence need to be in consociation and apportion responsibilities, in such wise, that expert systems will guard peculiar processes in HR practices, whereas, intelligent retrievals will concentrate on creative cognitive skills, like leadership and problem-solving. Many researchers advocate that a harmonious association between AI and HR is an absolute necessity for the refinement of existing HR practices. Therefore, this paper aims to provide the literature analysis on the active studies with regard to AI and HR practices, where, neither the former overpowers the latter nor the latter surpasses the former, rather there exists an equipoise of intellectuality, be it human or artificial.

HR-ENGINEERING IN THE ASPECT OF QUANTITATIVE ASSESSMENT ABILITY OF WORKERS TO WORK

The article presents the main theoretical approaches and practical recommendations for the quantitative assessment of workers’ abilities to work. The essence of the assessment, principles and content of its conduct are revealed. Methodological recommendations have been developed for determining the index of the worker’s ability to work, which is a relative indicator that characterizes the ratio of normative labor costs that are absolutely necessary for a worker of some average ability to work to perform a specified job or a set of jobs in the operation cycle in the given organizational and technical conditions of production. to the labor costs that are actually spent by a specific worker with his inherent labor abilities when performing the same work or a set of works under similar production conditions. A step-by-step method of conducting a quantitative assessment is proposed and thorough methodological recommendations are given for determining the individual workload for each worker at different intervals of time, which he is able to systematically perform according to his work abilities for a certain time in certain organizational and technical conditions of production and exchange for a certain amount salary. It has been proven that the establishment of an individual regulatory workload for each worker requires the simultaneous division into component elements of both technological and labor processes, taking into account the personal abilities of workers to work. The role and significance of the quantitative assessment of workers’ abilities to work in the aspect of the most complete and effective use of each worker’s labor resource and the achievement of the highest labor productivity are highlighted.

Parallel-batch scheduling with rejection: Structural properties and approximation algorithms

In this paper, we consider a parallel-batch machine scheduling (PBMS) model that unifies several existing scheduling models in the extant literature. In our scheduling model, a given set of jobs with different release dates are scheduled onto a machine that can process multiple jobs simultaneously in a batch. However, the number of jobs in a batch is limited. Each job can be rejected subject to a job-dependent penalty cost, but the total penalty cost of the jobs rejected is required to be no greater than a given bound. The objective is to minimize the weighted sum of the makespan of accepted jobs and the total rejection penalty of rejected jobs. We provide some important structural properties and develop fast approximation algorithms. We also present an efficient polynomial time approximation scheme (EPTAS) with near-linear-time complexity. Our results significantly improve both the time complexities and performance bounds of several existing algorithms and approximation schemes.

A Genetic-Algorithm-Based Approach for Optimizing Tool Utilization and Makespan in FMS Scheduling

This paper proposes a genetic algorithm approach to solve the identical parallel machines problem with tooling constraints in job shop flexible manufacturing systems (JS-FMSs) with the consideration of tool wear. The approach takes into account the residual useful life of tools and allocates a set of jobs with specific processing times and tooling requirements on identical parallel machines. Two metrics are introduced to evaluate the scheduling decisions and optimize the scheduling process, with the competitive goal of maximizing tool utilization and minimizing production makespan. The proposed approach searches for a set of optimal solutions on the Pareto front that offers the best possible balance between these two objectives, achieving optimal local performance in terms of both makespan and tool utilization. The approach is implemented with a customized genetic algorithm and validated on a real case study from a company operating in the aerospace sector, which confirms its effectiveness in increasing tool utilization and reducing the makespan. The results show that the proposed approach has significant practical implications for the manufacturing industry, particularly in the production of high-value materials such as those in the aerospace sector that require costly tools. This paper contributes to the operational research community by providing advanced scheduling algorithms that can optimize both the makespan and the tool utilization concurrently, improving production efficiency and maintaining competitiveness in the manufacturing industry.

Preemptive scheduling of parallel jobs of two sizes with controllable processing times

AbstractIn parallel machine scheduling, a size of a job is defined as the number of machines that are simultaneously required for its processing. This paper considers a scheduling problem in which the set of jobs consists of jobs of two sizes: the conventional jobs (each job requires a single machine for its processing) and parallel jobs (each job to be simultaneously processed on more than one machine). The processing times are controllable, and they have to be chosen from given intervals in order to guarantee the existence of a preemptive schedule in which all jobs are completed by a common deadline. The objective is to minimize the total compression cost which reflects possible reductions in processing times. Unlike problems of classical scheduling with conventional jobs, the model under consideration cannot be directly handled by submodular optimization methods. We reduce the problem to maximizing the total weighted work of all jobs parametrized with respect to total work of the parallel jobs. The solution is delivered by separately finding the breakpoints for the maximum total weighted work of the parallel jobs and for the maximum total weighted work of the conventional jobs. This results in a polynomial-time algorithm that is no slower than needed to perform sorting of jobs’ parameters.

Rescheduling for new orders on a single machine with rejection

We consider a single machine rescheduling problem where a set of original jobs have been scheduled to minimize the total weighted completion time. However, before formal processing begins, a new set of jobs arrives and creates a disruption. The decision maker can reject a subset of the new jobs by paying certain rejection penalties, and reschedule the original and the remaining new jobs without excessively disrupting the original schedule, which is measured by the maximum completion time deviation for any original jobs between the original and adjusted schedules. The objective is to minimize the sum of total weighted completion time of the original jobs and the accepted new jobs in the adjusted schedule, the weighted maximum completion time deviation and the total rejection cost. We first provide a dynamic programming-based exact algorithm running in pseudo-polynomial time, and then propose a fully polynomial time approximation scheme. Given the NP-hardness for the studied problem, our result is the best possible from the approximation algorithm perspective. In addition, we conduct extensive computational experiments to evaluate the performance of our proposed methods, and provide some managerial insights on their applicability under different situations.

Gated‐Attention Model with Reinforcement Learning for Solving Dynamic Job Shop Scheduling Problem

Job shop scheduling problem (JSSP) is one of the well‐known NP‐hard combinatorial optimization problems (COPs) that aims to optimize the sequential assignment of finite machines to a set of jobs while adhering to specified problem constraints. Conventional solution approaches which include heuristic dispatching rules and evolutionary algorithms has been largely in use to solve JSSPs. Recently, the use of reinforcement learning (RL) has gained popularity for delivering better solution quality for JSSPs. In this research, we propose an end‐to‐end deep reinforcement learning (DRL) based scheduling model for solving the standard JSSP. Our DRL model uses attention‐based encoder of Transformer network to embed the JSSP environment represented as a disjunctive graph. We introduced Gate mechanism to modulate the flow of learnt features by preventing noise features from propagating across the network to enrich the representations of nodes of the disjunctive graph. In addition, we designed a novel Gate‐based graph pooling mechanism that preferentially constructs the graph embedding. A simple multi‐layer perceptron (MLP) based action selection network is used for sequentially generating optimal schedules. The model is trained using proximal policy optimization (PPO) algorithm which is built on actor critic (AC) framework. Experimental results show that our model outperforms existing heuristics and state of the art DRL based baselines on generated instances and well‐known public test benchmarks. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Effective upper and lower bounds for a two-stage reentrant flexible flow shop scheduling problem

Flow shop scheduling is important in modern industrial manufacturing to improve production efficiency. This paper studies a realistic two-stage reentrant flexible flow shop scheduling problem (TSRFFS) with broad applications in aircraft scheduling, manufacturing, and the medical industry, etc. Given a flow shop with a single machine in Stage 1, a set of parallel machines in Stage 2, and a set of jobs to be processed, the TSRFFS aims to determine the completion time of jobs in Stage 1 and then that in Stage 2, and finally returns to Stage 1, as well as determine the job-to-machine assignment in Stage 2 such that all jobs are served and the total processing time of jobs (makespan) is minimized. The optimal solution properties are investigated, based on which a mixed integer programming mathematical model and a greedy random constructive heuristic for near optimal solutions are proposed. By solving series of a revised parallel machine scheduling problem (Pm||Cmax), a lower bound method is developed. Extensive numerical experiments on 1560 random instances with up to 1000 jobs and 50 realistic airport simulation instances were conducted to demonstrate the effectiveness of the proposed algorithms. The average gap between the proposed upper bound and the best lower bounds is approximately 1.78%, and the average gap between the proposed lower bound and the best upper bounds is 0.91%, which far outperforms state-of-the-art approaches in terms of solution quality and computational time.

Energy-efficient single-machine scheduling with release dates under time-of-use electricity tariffs

The global trend of increasing resource utilization in industry has drawn great attention from researchers and practitioners. Industrial managers are increasingly optimizing their production strategies based on time-of-use (TOU) electricity tariffs to reduce energy consumption costs. This study investigates a new energy-efficient single-machine scheduling problem with release dates under TOU electricity tariffs. It consists in sequencing a set of jobs with release dates to a machine under TOU pricing to minimize the total energy cost given a bounded maximum completion time. We first formulate the problem using the widely used time-indexed mixed-integer linear programming (T-MILP). Due to its time-consuming disadvantage, a new period-based MILP (P-MILP) model is developed based on the characteristics of the problem. Given the NP-hardness of the problem, a two-stage heuristic (TSH) algorithm is proposed to solve practical-sized problems. Specifically, in the first stage, a constructive heuristic is designed to obtain an initial solution, and in the second one, a tailored tabu search is devised to yield a better satisfactory solution. Numerical experiments are conducted on a real-life case and 1150 randomly generated instances with up to 1000 jobs. Computational results show that (i) the proposed models can save the total electricity cost by about 30% compared with an existing empirical scheduling method when solving small-sized instances; (ii) the efficiency of P-MILP is about 140.94% higher than that of T-MILP; and (iii) the proposed TSH algorithm can efficiently obtain high-quality solutions for practical-sized instances with gaps of less than 7% compared to the lower bounds, which can effectively support production managers to save the energy costs in practical production.

An ordered precedence constrained flow shop scheduling problem with machine specific preventive maintenance

In reality, the machines may interrupt because of the nature of deterioration of the machines. Thus, it is inevitable to perform maintenance alongside production planning. The preventive maintenance is a schedule of strategic operations that are performed prior to the failure occurring, to retain the system operating at the preferred level of consistency. Thus, preventive maintenance plays a significant role in flow shop scheduling models. With its practical significance, this study addresses a practical three-machine n jobs flow shop-scheduling problem (FSSP) in which machine specific preventive maintenance, where each machine is given with a maintenance schedule is considered. In addition, a practical ordered precedence constraint in which some set of jobs has to process in the specified order irrespective of their processing times is also considered. The problem’s goal is to establish the optimal job sequence and preventive maintenance such that the overall cost of tardiness and preventive maintenance is as minimum as possible. An efficient heuristic approach is designed to tackle the present model, resulting in total cost savings. A comparative analysis is not conducted due to absence of studies on the current problem in the literature. However, Computational experiments are carried out on some test instances and results are reported. The reported results may be useful for future studies.

Impact of Work from Home on Health and Productivity level of Women Employees Working in IT Sector

Objective: This study examines the impact of WFH (occupational and home office environment) on the mental & physical health and productivity level of women employees working in IT sector, Pune. Methods: A questionnaire was deployed to women employees working in the IT sector, Pune from September 2021-March 2022 and 159 valid responses were obtained. ANOVA tests were used to understand the impact of the WFH (occupational and home office environment) on the mental and physical health and productivity level. Results: There is a significant impact of WFH (occupational and home office environment) on the mental & physical health and productivity level of women employees. Respondents who received adequate support from co-workers/supervisors, effective cooperation from supervisors and co-workers, clarity in job, flexible working hours, workspace privacy at home, assistance from organisation for home office set up,proper collaboration with the team had better health and increase in productivity level. Decreased health and productivity level was associated with, lack of clarity in job, less support from co-workers and managers, inadequate cooperation from co-workers and managers, strict working hours, poor home office set up, and collaboration with the team. Conclusion: This study highlights occupational and home office environment factors and its impact on women employees' physical & mental health and productivity level of respondents. This study illuminates that importance of home office set ,lack of support, cooperation, collaboration, clarity in job and poor internet set up are the most important factors that need to be considered by the employers, HR personnel while developing WFH strategies for promoting a better working environment in near future. Novelty: This study focuses on the problems faced by the women employees while working home and its effect on health and productivity level. This will help government and employers to devise supportive Work from Home policies considering Hybrid model as the option of future work.

The synchronized multi-assignment orienteering problem

<p style='text-indent:20px;'>We introduce the Synchronized Multi-Assignment Orienteering Problem (SMOP), a vehicle routing problem that requires jointly selecting a set of jobs while synchronizing the assignment and transportation of agents to roles to form ad-hoc teams at different job locations. Agents must be assigned only to roles for which they are qualified. Each job requires a certain number of agents in each role within a time window and contributes a reward score if selected. The task is to maximize the total reward attained. SMOP can model many real-world scenarios requiring coordinated transportation of resources and accommodates traditional depot-based workforces, depot workforces supplemented by ad-hoc workers, and fully ad-hoc workforces alike. The same problem formulation can be used for initial planning and mid-course replanning. We develop a mixed integer programming formulation (MIP) and an Adaptive Large Neighborhood Search algorithm (ALNS). In computational experiments covering a range of considerations, ALNS consistently found very near-optimal solutions on smaller problems and surpassed a commercial MIP solver substantially on larger problems. ALNS also found 24 new best solutions on a set of benchmark problems from the literature for the related Cooperative Orienteering Problem with Time Windows.</p>

Minimizing the expected waiting time of emergency jobs

We consider a scheduling problem where a set of known jobs needs to be assigned to a set of given parallel resources such that the expected waiting time for a set of uncertain emergency jobs is kept as small as possible. On the basis of structural insights from queuing theory, we develop deterministic scheduling policies that reserve resource capacity in order to increase the likelihood of resource availability whenever an emergency job arrives. Applications of this particular scheduling problem are, for instance, found in the field of surgical operations scheduling in hospitals, where high-priority but uncertain emergencies compete for scarce operating room capacity with elective surgeries of lower priority. We compare our approaches with other policies from the literature in a comprehensive simulation study of a surgical operations unit.

Belief Propagation for Unbalanced Assignment Problem

The unbalanced assignment problem (UAP) aims to distribute a set of jobs to some workers. The cost of the jobs is different when they are distributed to different workers. The goal is: (1) minimizing the total cost of arranging jobs to workers; (2) making the distribution of jobs as even as possible among all the workers. We transform the UAP into a min-cost network flow problem with squared terms, and apply the belief propagation (BP) algorithm to deal with it. We prove that, when the min-cost network flow problem has a unique optimal solution, the BP algorithm converges to the optimal solution within [Formula: see text] iterations, where [Formula: see text] represents the number of vertices of the flow network, [Formula: see text] is the difference between value of the optimal solution and the second optimal solution and [Formula: see text] is the maximum value of the terms of the objective function. Next, we prove that BP converges to the optimal solution in [Formula: see text] operations, where [Formula: see text] represents the number of edges and [Formula: see text] is the tight upper bound of the slope of the terms of the objective function.

Busy-Time Scheduling on Heterogeneous Machines: Algorithms and Analysis

We study a generalized busy-time scheduling model on heterogeneous machines. The input to the model includes a set of jobs and a set of machine types. Each job has a size and a time interval during which it should be processed. Each job is to be placed on a machine for execution. Different types of machines have distinct capacities and cost rates. The total size of the jobs running on a machine must always be kept within the machine's capacity, giving rise to placement restrictions for jobs of various sizes among the machine types. Each machine used is charged according to the time duration in which it is busy, i.e., it is processing jobs. The objective is to schedule the jobs into machines to minimize the total cost of all the machines used. We develop an <inline-formula><tex-math notation="LaTeX">$O(1)$</tex-math></inline-formula> -approximation algorithm in the offline setting and an <inline-formula><tex-math notation="LaTeX">$O(\mu)$</tex-math></inline-formula> -competitive algorithm in the online setting (where <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> is the max/min job length ratio), both of which are asymptotically optimal. This article significantly improves the analysis of the algorithms over our preliminary work.

Well-behaved online load balancing against strategic jobs

In the online load balancing problem on related machines, we have a set of jobs (with different sizes) arriving online, and the task is to assign each job to a machine immediately upon its arrival, so as to minimize the makespan, i.e., the maximum completion time. In classic mechanism design problems, we assume that the jobs are controlled by selfish agents, with the sizes being their private information. Each job (agent) aims at minimizing its own cost, which is its completion time plus the payment charged by the mechanism. Truthful mechanisms guaranteeing that every job minimizes its cost by reporting its true size have been well studied (Aspnes et al. JACM 44(3):486–504, 1997, Feldman et al. in: EC, 2017). In this paper, we study truthful online load balancing mechanisms that are well-behaved (machine with higher speed has larger workload). Good behavior is important as it guarantees fairness between machines and implies truthfulness in some cases when machines are controlled by selfish agents. Unfortunately, existing truthful online load balancing mechanisms are not well behaved. We first show that to guarantee producing a well-behaved schedule, any online algorithm (even non-truthful) has a competitive ratio at least \(\varOmega (\sqrt{m})\), where m is the number of machines. Then, we propose a mechanism that guarantees truthfulness of the online jobs and produces a schedule that is almost well-behaved. We show that our algorithm has a competitive ratio of \(O(\log m)\). Moreover, for the case when the sizes of online jobs are bounded, the competitive ratio of our algorithm improves to O(1). Interestingly, we show several cases for which our mechanism is actually truthful against selfish machines.

Interval scheduling with economies of scale

Motivated by applications in cloud computing, we study interval scheduling problems exhibiting economies of scale. An instance is given by a set of jobs, each with start time, end time, and a function representing the cost of scheduling a subset of jobs on the same machine. Specifically, we focus on the max-weight function and non-negative, non-decreasing concave functions of total schedule weight. The goal is a partition of the jobs that minimizes the total schedule cost, where overlapping jobs cannot be processed on the same machine. We propose a set cover formulation and a column generation algorithm to solve its linear relaxation. For the max-weight function, which is already NP-hard, we give a polynomial-time pricing algorithm; for the more general case of a function of the total weight, we have a pseudo-polynomial algorithm. To obtain integer solutions, we extend the column generation approach using branch-and-price. We computationally evaluate our methods on two different functions, using both random instances and instances derived from cloud computing data; our algorithm significantly outperforms known integer programming formulations (when these are available) and is able to provably optimize instances with hundreds of jobs.

Optimized scheduling of multi‐user Map‐Reduce jobs in heterogeneous environment

SummaryMap‐Reduce is a programming paradigm widely used for data intensive applications in distributed computing. Divisible load theory (DLT) is another model to partition divisible load for parallel processing. In this work, a new job scheduler named virtual job scheduler (VJS) is developed to schedule the MapReduce jobs based on DLT. VJS constructs a virtual job set from the queue of jobs awaiting execution by considering the CPU and IO resource utilization levels of each job. The core of VJS is in its partitioning algorithm. Two novel partitioning algorithms, namely, two level successive partitioning (TLSP) and predictive partitioning (PRED) have been proposed. TLSP applies DLT to both map and reduce phase in succession. This second load partitioning performed during the comparatively longer reduce phase, exploits the advantage of DLT better. PRED is a modification of TLSP aimed at optimizing the overall schedule length, by taking the idle time of the reducers into consideration. Evaluation of these two models across various execution environments has indicated a profound decrease in the wait time of reducers and as a result has shown a significant reduction in the makespan of the whole job as such. VJS altered to incorporate PRED partitioning algorithm has proved to be suitable for heterogeneous environment, with high resource utilization compared with the default Hadoop MapReduce scheduler.

Job shop scheduling using heuristics through Python programming and excel interface

Job shop scheduling problem (JSSP) has remained a challenge both for the practitioners and the researchers. A JSSP consists of multiple number of machines (m) and jobs (n). As the number of jobs increases, the complexity of the problem increases exponentially and it becomes difficult to schedule manually. Practitioners use their experience to schedule jobs in ad hoc sessions resulting in inefficient allocation of jobs and machines. In this paper, a job shop scheduling problem under static and dynamic conditions is solved using heuristic approaches using python programming with an MS Excel user interface. For a supplier of automotive parts with a set of jobs and machines, priority dispatching rules, viz., Shortest Processing Time (SPT), Earliest Due Date (EDD), First-In First-Out (FIFO), Critical Ratio (CR) and Slack Per Remaining Operation (S/RO) are evaluated. The obtained performance metrics such as makespan, and tardiness are compared between the heuristics to select an optimal schedule by the job shop. The user inputs the jobs, machines, start and due dates through the MS Excel interface and obtains faster, practically usable results. This reduces the time taken for job scheduling and helps in making faster productivity-based decisions to maximize resource utilization and the total time to produce the product.

Stability of a schedule minimising the makespan for processing jobs on identical machines

ABSTRACT A set of jobs has to be processed on identical machines. Every job may be processed on any available machine without preemptions. The criterion is to minimise the makespan (i.e. the completion time of the last job in a schedule). During the realisation of a schedule, durations of some jobs may deviate from the initial values estimated before scheduling. Other jobs have fixed durations that are known before scheduling. We conduct a stability analysis of the optimal semi-active schedule. First, we derive necessary and sufficient conditions for an optimal schedule to be unstable with respect to infinitely small variations of the non-fixed durations (the stability radius of an unstable schedule is equal to zero). Second, we show that the stability radius of an optimal schedule could be infinitely large. Furthermore, several lower and upper bounds on the stability radius have been established. Third, we derive a formula and develop an algorithm for calculating stability radii.