Consecutive Jobs Research Articles

A study of new labor market entrants' job satisfaction trajectories during a series of consecutive job changes.

Previous research on the psychological effect of job change has revealed a honeymoon-hangover pattern during the turnover process. However, there is a dearth of evidence on how individuals react and adapt to multiple job changes over their working lives. This study distinguishes adaptation to a single job change in the short term from adaptation to the process of job change in the long term. Drawing on two large-scale, long-running panel data sets from Britain and Australia, it examined how job satisfaction trajectory evolved as individuals made a series of consecutive job changes since they first entered the labor market. Our fixed effect analyses show that in both countries, individuals experienced a stronger honeymoon effect with each successive job change, before gradually reverting to their baseline job satisfaction. In short, the amplitude of the honeymoon-hangover effect increased across multiple job changes. By distinguishing "adaptation to change" from "change in adaptation," this study generates original insights into the role of job mobility in facilitating career development and extends set point theory from understanding the impact of single life events to recurring life events. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Supplemental Material for A Study of New Labor Market Entrants’ Job Satisfaction Trajectories During a Series of Consecutive Job Changes

Supplemental Material for A Study of New Labor Market Entrants’ Job Satisfaction Trajectories During a Series of Consecutive Job Changes

A Hybrid Exact–Local Search Approach for One-Machine Scheduling with Time-Dependent Capacity

Machine scheduling is a hard combinatorial problem having many manifestations in real life. Due to the schedule followed, the possibility of installations of machines operating sub-optimally is high. In this work, we examine the problem of a single machine with time-dependent capacity that performs jobs of deterministic durations, while for each job, its due time is known in advance. The objective is to minimize the aggregated tardiness in all tasks. The problem was motivated by the need to schedule charging times of electric vehicles effectively. We formulate an integer programming model that clearly describes the problem and a constraint programming model capable of effectively solving it. Due to the usage of interval variables, global constraints, a powerful constraint programming solver, and a heuristic we have identified, which we call the “due times rule”, the constraint programming model can reach excellent solutions. Furthermore, we employ a hybrid approach that exploits three local search improvement procedures in a schema where the constraint programming part of the solver plays a central role. These improvement procedures exhaustively enumerate portions of the search space by exchanging consecutive jobs with a single job of the same duration, moving cost-incurring jobs to earlier times in a consecutive sequence of jobs or even exploiting periods where capacity is not fully utilized to rearrange jobs. On the other hand, subproblems are given to the exact constraint programming solver, allowing freedom of movement only to certain parts of the schedule, either in vertical ribbons of the time axis or in groups of consecutive sequences of jobs. Experiments on publicly available data show that our approach is highly competitive and achieves the new best results in many problem instances.

On the Influence of AlSi10Mg Powder Recycling Behavior in the LPBF Process and Consequences for Mechanical Properties

By using additive manufacturing techniques like the laser powder bed fusion (LPBF) process, parts can be manufactured with high material efficiency because unfused powder material can be reconditioned and reused in consecutive manufacturing jobs. Nevertheless, process by-products like spatters may influence the powder quality and hence alter the mechanical properties/performance of parts. In order to investigate these dependencies, a methodology and a standard build job for the recycling behavior of the lightweight aluminum alloy AlSi10Mg was developed and built with ageing powder in 10 consecutive jobs with no refreshing between the cycles. The powder properties and mechanical performance of parts at static load for two build directions (horizontally and vertically to substrate plate) was evaluated. The influence of build height effects on mechanical performance was investigated as well. The findings may indicate that the coarsening of the powder material during recycling could lead to improved mechanical properties for the AlSi10Mg alloy.

Introducing preferences in scheduling applications

In some applications like fabric dying, the sequence in which jobs are processed on a single machine can influence product quality, i.e., there is a strong preference to run a certain type of job after another for reasons that do not influence regular scheduling objectives like total completion time. These reasons include minimizing changes in color families on machines (schedule preferences) and reducing the difference in shades between consecutive jobs (shade inconsistency). Nonetheless, the scheduling of jobs on machines must consider sequence-dependent setup times as well. In this paper, a mixed integer linear program has been presented to schedule jobs and account for setup times. The scheduling preferences and shade differences have been modeled as two new objectives, along with traditionally used objectives such as makespan, lateness and total setup time. The MILP is found to be useful only for small problems. To tackle larger problems, several heuristic methods are proposed. In order to obtain solutions quickly, multiple construction heuristics are developed based on SPT/LPT/EDD/LFJ rules and tested on a variety of industrially-inspired data sets. To produce schedules that perform well across multiple objectives at the same time, three additional tailored heuristics are developed. Both the original and tailored heuristics are compared to the optimal solutions for small-sized problems. In addition, a problem size reduction heuristic has been developed which can segment the larger MILP into smaller independently solvable MILPs by reducing the possible machines on which a job can be processed. For these larger problems, the heuristics are compared among themselves as well as against the problem size reduction heuristic.

One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

This paper addresses the problem of scheduling a set of jobs on a machine with time-varying capacity, with the goal of minimizing the total tardiness objective function. This problem arose in the context scheduling the charging times of a fleet of electric vehicles and it is NP-hard. Recent work proposed an efficient memetic algorithm for solving the problem, combining a genetic algorithm and a local search method. The local search procedure is based on swapping consecutive jobs on a C-path, defined as a sequence of consecutive jobs in a schedule. Building on it, this paper develops new memetic algorithms that stem from new local search procedures also proposed in this paper. The local search methods integrate several mechanisms to make them more effective, including a new condition for swapping pairs of jobs, a hill climbing approach, a procedure that operates on several C-paths and a method that interchanges jobs between different C-paths. As a result, the new local search methods enable the memetic algorithms to reach higher-quality solutions. Experimental results show significant improvements over existing approaches.

A hybrid learning-based meta-heuristic algorithm for scheduling of an additive manufacturing system consisting of parallel SLM machines

Additive manufacturing (AM) has been recognised as a promising technology under the context of Industry 4.0, which is reshaping manufacturing paradigms. A prominent type of AM machine is the selective laser melting (SLM) machine, in which several parts may form a job and be produced concurrently. This paper aims to investigate a scheduling problem in an AM system with non-identical parallel SLM machines. Since, in this system, there might be differences in the material types of parts, the required setup time between two consecutive jobs on the relevant machine is dependent on their material types. Accordingly, a bi-objective mathematical model is extended for the problem, considering the makespan and the total tardiness penalty as two objective functions. Due to the high complexity of the problem, an efficient hybrid meta-heuristic algorithm is developed by combining the non-dominated sorting genetic algorithm (NSGA-II) with a novel learning-based local search founded on the k-means clustering algorithm and a regression neural network. The local search enhances the exploitation ability of the NSGA-II while intelligently being taught during the solving procedure. Finally, the superiority of the proposed hybrid algorithm is demonstrated through a computational experiment.

Algorithms for the Calzedonia workload allocation problem

The Workload Allocation Problem consists of assigning a sequence of operations to workers. The order of these operations is fixed. Each operation consists of a batch of B units, hence a total of jobs have to be performed. Each worker is assigned to an ordered subset of consecutive jobs. Workers have different skills, and therefore jobs take a variable time to process, depending on the assigned worker. The study of this problem is rooted in the operations of Calzedonia. In this paper, we briefly introduce the application before presenting algorithms for solving the problem exactly and heuristically. Our computational results compare the performance of a stand-alone mathematical formulation solved by CPLEX, a sequential exact algorithm, and a metaheuristic, with a simple heuristic implemented in the company.

Development of a laser-triggered film switch for nanosecond rectangular pulse generator.

A rectangular pulse generator plays an important role in the calibration of impulse measurement as well as the investigation into electromagnetic pulse effect. In addition, the characteristics of an output switch have a direct impact on the generator's output waveform. Following the basic requirements for the switch in a rectangular generator, this paper proposes a new type of laser-triggered film switch with a compact structure and high repeatability. The mechanical structure and operating mechanism of this switch have been elaborated. To verify the feasibility of this switch model, the irradiation experiments were carried out to study the interaction between the laser and the film, and the inductance of the switch has been estimated. Then, to test the characteristics of the switch, a Blumlein rectangular pulse generator based on this switch has been constructed. The experiment results show that the generator worked well in consecutive jobs and had a wild adjustable amplitude range from 2 kV to 10 kV, with a minimum rise-time of less than 1 ns. The effect of the laser loophole and the thickness of the film on the switch have also been investigated. Finally, the plasma developing process of the laser-triggered film switch has been discussed.

Reliability-Aware Energy-Efficient Scheduling for (m, k)-Constrained Real-Time Systems Through Shared Time Slots

While energy consumption is the primary concern for the design of real-time systems, reliability and Quality of Service (QoS) are becoming increasingly important in the development of today’s pervasive computing systems. In this paper, we present a reliability-aware energy management (RAEM) scheme for minimizing the energy consumption for (m, k)-constrained real-time systems, which requires that at least m out of any k consecutive jobs of a real-time task meet their deadlines. In order to meet the (m, k)-constraints while preserving the system reliability, we propose to reserve shared recovery space for real-time tasks based on window-constraints that could be transferred to the corresponding (m, k)-constraints. Moreover, we propose efficient online scheduling techniques to boost the energy saving through shared slack reclamation. Through extensive simulations, our experimental results demonstrate that the proposed techniques significantly outperformed the previous research in reducing energy consumption for real-time systems while satisfying the (m, k)-constraints under the reliability requirement.

Home Health Care Scheduling and Routing with Temporal Dependencies and Continuity of Care

Due to facing an acute shortage of beds in hospitals, the danger of getting involved in hospital infections and high-cost hospitals care, the Home Health Care industry has encountered high demands in recent years. Different stakeholders with various interests are involved in home health care that makes the process of planning and scheduling of nurses, who offered services, challenging. This paper, therefore, focuses on scheduling and routing nurses traveled to the patient’s home by considering the main features of the problem such as Continuity of Care and temporal dependencies. A new formulation for adjusting the time distance between two consecutive jobs performed by a nurse is presented. A feasible solution has to consider nurse and patient’s preferences, time windows for jobs, nurse’s qualification, and waiting time. A genetic algorithm is proposed to solve the problem. The computational results show the efficiency of the proposed algorithm, especially for large-size instances. Finally, the effect of the nurse’s dispatching policy on the objective function, waiting, and traveling times is examined.

Electromyographic Assessment of a Shoulder Support Exoskeleton During on-Site Job Tasks

OCCUPATIONAL APPLICATIONSWe performed an ergonomic assessment of a passive upper body exoskeleton at on-site job tasks, using surface electromyography (EMG) to quantify muscular demands at the shoulder. Six workers were assessed during cab assembly, hydraulic assembly, parts painting, parts hanging, and frame welding tasks, both with and without an exoskeleton. Workers were evaluated during consecutive job tasks, at beginning and at end of a shift. EMG amplitudes were measured and compared to recommended threshold limit values (TLVs) to determine fatigue risk values. Wearing the exoskeleton significantly reduced anterior deltoid EMG amplitudes and fatigue risk values during the consecutive job cycles, with no significant effects on the biceps brachii, superior trapezius, or lumbar erector spinae. Reduced EMG amplitudes were found at five of the six job tasks, and in three of the tasks fatigue risk values were reduced from above the TLV without the exoskeleton to below the TLV with the exoskeleton. Workers indicated that they would recommend the exoskeleton to others who perform overhead job tasks, but expressed concern that it might interfere with tasks that involve tight spaces, sitting, and bending over. We suggest evaluating specific job tasks when making decisions about exoskeleton usage.

Parallel machine scheduling with time constraints on machine qualifications

This paper studies the scheduling of jobs of different families on parallel machines, where not all machines are qualified (eligible) to process all job families. Originating from semiconductor manufacturing, an important constraint imposes that the time between the processing of two consecutive jobs of the same family on a machine does not exceed a given time limit. Otherwise, the machine becomes disqualified for this family. The goal is to minimize both the flow time and the number of disqualifications of job families on machines. To solve this problem, an integer linear programming model and a constraint programming model are proposed, as well as two improvement procedures of existing heuristics: A Recursive Heuristic and a Simulated Annealing algorithm. Numerical experiments on randomly generated instances compare the performances of each method.

Satisfaction-driven bi-objective multi-skill workforce scheduling problem

Abstract with the global disappearance of the demographic dividend and the Industry 4.0, the workforce related to the manufacturing processes is increasingly important. Most existing works considering the workforce scheduling usually assume that the workers can be treated as specific machines and one machine is operated by one worker. However, in various practical manufacturing processes, the number workers available to run the machines is less than that of machines. Besides, worker’s cooperation may be required to switch consecutive jobs on a machine, i.e., the setup tasks. Therefore, this paper investigates a satisfaction-driven bi-objective workforce scheduling problem, in which the workers process different skills to operate the available parallel machines. The workforce satisfaction and the number of on-time job (i.e., service level) are maximized simultaneously. For the problem, a bi-objective mixed integer programming (MIP) formulation is first proposed. To obtain the Pareto solutions, ϵ-constraint method is applied. A case study is conducted and analyzed to evaluate the applicability of the proposed model.

The Longest Processing Time rule for identical parallel machines revisited

We consider the $$P_m || C_{\max }$$ scheduling problem where the goal is to schedule n jobs on m identical parallel machines $$(m < n)$$ to minimize makespan. We revisit the famous Longest Processing Time (LPT) rule proposed by Graham in 1969. LPT requires to sort jobs in non-ascending order of processing times and then to assign one job at a time to the machine whose load is smallest so far. We provide new insights into LPT and discuss the approximation ratio of a modification of LPT that improves Graham’s bound from $$\left( \frac{4}{3} - \frac{1}{3m} \right) $$ to $$\left( \frac{4}{3} - \frac{1}{3(m-1)} \right) $$ for $$m \ge 3$$ and from $$\frac{7}{6}$$ to $$\frac{9}{8}$$ for $$m=2$$. We use linear programming to analyze the approximation ratio of our approach. This performance analysis can be seen as a valid alternative to formal proofs based on analytical derivation. Also, we derive from the proposed approach an $$O(n \log n)$$ time complexity heuristic. The heuristic splits the sorted job set in tuples of m consecutive jobs ($$1,\dots ,m; m+1,\dots ,2m;$$ etc.) and sorts the tuples in non-increasing order of the difference (slack) between largest job and smallest job in the tuple. Then, given this new ordering of the job set, list scheduling is applied. This approach strongly outperforms LPT on benchmark literature instances and is competitive with more involved approaches such as COMBINE and LDM.

Hospital-wide therapist scheduling and routing: Exact and heuristic methods

We address the problem of scheduling and routing physical therapists hospital-wide. At the beginning of a day, physiotherapy jobs are known to a hospital’s scheduler, who decides for each job when, where and by which therapist it is performed. If a therapist is assigned to a sequence which contains two consecutive jobs that must take place in different treatment rooms, then transfer times must be considered. We propose three approaches to solve the problem. First, an Integer Program (IP) simultaneously schedules therapies and routes therapists. Second, a cutting plane algorithm iteratively solves the therapy scheduling problem without routing constraints and adds cuts to exclude schedules which have no feasible routes. Since hospitals are interested in obtaining quick solutions, we also propose a heuristic algorithm, which schedules therapies sequentially by simultaneously checking routing and resource constraints. Using real-world data from a hospital, we compare the performance of the three approaches. Our computational analysis reveals that our IP formulation fails to solve test instances that have more than 30 jobs to optimality in an acceptable solution time. Our cutting plane algorithm can solve instances with more than 100 jobs optimally. The heuristic approach can be used to quickly generate large-scale solutions.

Reliability-Aware Energy Management for Embedded Real-Time Systems with (m, k)-Hard Timing Constraint

While energy consumption and Quality of Service (QoS) are primary concerns for the design of embedded systems, reliability requirement has become increasingly important in the development of today’s pervasive computing systems. In this paper, we present a reliability-aware energy management (RAEM) scheme for reducing the energy consumption for (m, k)-hard embedded real-time systems, which requires that at least m out of any k consecutive jobs of a real-time task meet their deadlines. In order to ensure the (m, k)-hard requirement while preserving the system reliability, we propose to reserve recovery space for real-time jobs in an adaptive way based on the mandatory/optional job partitioning strategy. Moreover, efficient off-line/online scheduling techniques are proposed to reduce the overall energy consumption while satisfying the reliability requirement. Through extensive simulations, our experiment results demonstrate that the proposed techniques significantly outperformed the previous research in reducing energy consumption for (m, k)-hard embedded real-time systems while preserving the system reliability.

Reliability-aware scheduling for reducing system-wide energy consumption for weakly hard real-time systems

Abstract While energy consumption is the primary concern for the design of real-time embedded systems, reliability and Quality of Service (QoS) are becoming increasingly important in the development of today’s pervasive computing systems. In this paper, we present a reliability-aware energy management (RAEM) scheme for reducing the energy consumption for weakly hard real-time systems with (m, k)-constraints, which requires that at least m out of any k consecutive jobs of a task meet their deadlines. In order to ensure the (m, k)-constraints while preserving the system reliability, we propose to partition the real-time jobs into mandatory and optional ones as well as to reserve recovery space for mandatory ones in an adaptive way. Moreover, efficient on-line scheduling techniques are proposed to reduce the system-wide energy, which is consumed not only by the processor but also by other peripheral devices at run-time. Through extensive simulations, our experiment results demonstrate that the proposed techniques can significantly outperform the previous research in reducing system-wide energy consumption for weakly hard real-time systems while preserving the system reliability.

Gender Differences in Unemployment Dynamics and Initial Wages Over the Business Cycle

Using administrative data from Spanish Social Security for the period 2002-2013, we explore differences between unemployed men and women in their probabilities to find a job, their initial wages if they find a new job, and the likelihood to fall back into unemployment. We estimate bivariate proportional hazard models for unemployment duration and for the consecutive job duration for men and women separately, and decompose the gender gap using a non-linear Oaxaca decomposition. Gender differentials in labour market outcomes are procyclical, probably due to the procyclical nature of typically male occupations. While a higher level of education protects women in particular from unemployment, having children hampers women’s employment and initial wages after unemployment. There are lower gender gaps in the public sector and in high technology- firms. Decompositions show that the gender gaps are not explained by differences in sample composition. Indeed, if women had similar characteristics to men, the gender gap would be even wider.

A pipeline approach to single-particle processing in RELION.

The formal concept of a workflow to single-particle analysis of cryo-electron microscopy (cryo-EM) images in the RELION program is described. In this approach, the structure-determination process is considered as a graph, where intermediate results in the form of images or metadata are the vertices, and different functionalities of the program are the edges. The new implementation automatically logs all user actions, facilitates file management and disk cleaning, and allows convenient browsing of the history of a project. Moreover, new functionality to iteratively execute consecutive jobs allows on-the-fly image processing, which will lead to more efficient data acquisition by providing faster feedback on data quality. The possibility of exchanging data-processing procedures among users will contribute to the development of standardized image-processing procedures, and hence increase accessibility for new users in this rapidly expanding field.