Job Deterioration Research Articles

Hukou Stratification and Job Precariousness between the State and Market Sectors in Contemporary China, 2010–2021

Echoing the global expansion of precarious employment, China has experienced a surge in labor flexibility and job deterioration since its integration into global capitalism. Existing research also highlighted the distinct features of the Chinese labor market related to its post-socialist nature. The Chinese household registration system, known as hukou, has been critical in explaining stratification and shaping labor market outcomes in China. However, hukou reforms in the recent decade have further relaxed restrictions on labor mobility and hukou conversion, suggesting that hukou may gradually lose its importance in transitional China. Building upon the global research on precarious work and dynamics of precariousness in employment in the Chinese context, I conceptualize job precariousness in the contemporary Chinese labor market in three dimensions. Using pooled data from the 2010 to 2021 Chinese General Social Survey, I examine how hukou stratification is associated with the three aspects of job precariousness, compare the relative importance of hukou with other market-related factors, and identify the disparities of the state and market sectors. The findings help contextualize global trends of precariousness by broadening the evidence base through a case in China.

Single Machine Scheduling Proportionally Deteriorating Jobs with Ready Times Subject to the Total Weighted Completion Time Minimization

In this paper, we investigate a single machine scheduling problem with a proportional job deterioration. Under release times (dates) of jobs, the objective is to minimize the total weighted completion time. For the general condition, some dominance properties, a lower bound and an upper bound are given, then a branch-and-bound algorithm is proposed. In addition, some meta-heuristic algorithms (including the tabu search (TS), simulated annealing (SA) and heuristic (NEH) algorithms) are proposed. Finally, experimental results are provided to compare the branch-and-bound algorithm and another three algorithms, which indicate that the branch-and-bound algorithm can solve instances of 40 jobs within a reasonable time and that the NEH and SA are more accurate than the TS.

<i>Seru</i> scheduling problems with learning effect and job deterioration during an increasing adjustment period

<i>Seru</i> scheduling problems with learning effect and job deterioration during an increasing adjustment period

Disassembly Line Balancing Problem Under The Effects of Sum-of-Logarithm-Processing-Time-Based Learning and Job Deterioration

Artan çevresel farkındalık, ekonomik kaygılar ve yasal kurallar sayesinde, son zamanlarda ürün geri kazanımı veya yeniden üretimi yoğun ilgi görmektedir. Demontaj, ömrünü tamamlamış ürünlerin geri kazanımı aşamasındaki en önemli süreçlerden biridir. Bu yüzden, etkin ve dengeli kurulmuş demontaj hatları önem taşımaktadır. Bu çalışmada, demontaj hattı dengeleme (DHD) problemleri, eş zamanlı logaritmik işlem süreleri toplamı tabanlı öğrenme ve iş bozulması etkileri altında incelenmiştir. DHD literatüründe, logaritmik işlem süreleri toplamı tabanlı öğrenme ve iş bozulması eş zamanlı olarak ilk kez çalışılmıştır. Herhangi bir iş, istasyondaki sırasına göre, kendisinden önceki işlerin işlem sürelerinin logaritmik toplamından etkilenmektedir. İş bozulması ise, işin işleme başlamasını geciktiren bozulmalardır. Öğrenme işlerin işlem sürelerinin azaltırken, bozulma artırmaktadır. Bu çalışmada amaç fonksiyonu, açılan istasyon sayısı minimizasyonudur. Öğrenme ve bozulma etkisi altındaki DHD probleminin çözümü için hibrit (Parçacık Sürüsü Optimizasyonu- Genetik Algoritma) PSO-GA algoritması geliştirilmiş. Farklı öğrenme ve bozulma oranları için sonuçlar elde edilip karşılaştırmalar yapılmıştır. Öğrenme ve bozulma etkisinin, DHD problemlerinde dikkate alındığında amaç fonksiyonu değerinde iyileşmeler görülmüştür.

Predicting employment deterioration with the Processing Speed Test (PST) and SDMT in multiple sclerosis

Background: Employment deterioration is common in people with multiple sclerosis (PwMS). Clinicians often learn of job loss after its occurrence, leaving no opportunity for preventive measures. Objectives: Identify which neuropsychological measures discriminate between healthy volunteers (HVs) and employed/disabled PwMS at baseline and predict work deterioration over 2 years. Methods: We examined 198 PwMS with computerized tests such as the Processing Speed Test (PST) and conventional tests such as the Symbol-Digit Modalities Test (SDMT), administered at baseline. Employment was assessed via Buffalo Vocational Monitoring Survey. Univariate and regression analyses identified significant predictors of PwMS categorized as work-stable versus work-deteriorated status. Results: PwMS were impaired on all baseline assessments relative to HVs (p’s < 0.001). Post hoc analyses showed that employed PwMS and HVs performed similarly and better than work-disabled PwMS. At the univariate level, both PST and SDMT discriminated between work-deteriorated and work-stable PwMS (p’s < 0.01). The logistic regression model accounting for all measures retained PST and the computerized Walking Speed Test. PwMS with increased negative work events had lower PST (p < 0.001), SDMT (p < 0.001), and BVMT-R (p < 0.01) scores than stable PwMS. The related regression model retained PST and BVMT-R (p < 0.001). Conclusion: Cognition, as measured by the PST and BVMT-R, are predictive of job deterioration in PwMS and may be a useful screening tool to identify those at high risk of unemployment.

A genetic-simulated annealing algorithm for stochastic seru scheduling problem with deterioration and learning effect

ABSTRACT As a flexible and effective production mode, seru production has been adopted successfully in electronic industry. In practice, the processing time may be affected by many stochastic factors, such as worker absence, shortage of resources, and so on. This paper focuses on seru scheduling problems with stochastic processing time, and considers the influence of dynamic resource allocation, job deterioration, learning effecteffect, and setup time simultaneously to minimize the makespan. A genetic-simulated annealing algorithm is proposed, in which a simulated annealing procedure is constructed to re-optimize the optimal individual obtained by geneticthe genetic algorithm. Experiment results validate the effectiveness of proposedthe proposed seru scheduling model and genetic-simulated annealing algorithm for solving large-scale cases, and indicate that the stochastic processing time has a great influence on the makespan whichmakespan that can help production manager to makeproduce more consistent results according to the actual situation.

Reformulation and exact solution method for &lt;i&gt;seru&lt;/i&gt; scheduling problem with learning effect and job deterioration

Reformulation and exact solution method for &lt;i&gt;seru&lt;/i&gt; scheduling problem with learning effect and job deterioration

Reformulation and exact solution method for seru scheduling problem with learning effect and job deterioration

Reformulation and exact solution method for seru scheduling problem with learning effect and job deterioration

A Coordination Mechanism for Scheduling Game on Parallel-Batch Machines with Deterioration Jobs

In this study, we consider a parallel-batch machines scheduling game problem with deterioration jobs. The processing time of a job is a simple linear function of its starting time. Each of the parallel-batch machines can process up to B jobs simultaneously as a batch. The processing time of a batch is the processing time of the job with the longest deteriorating rate in the batch. All jobs in the same batch start and complete at the same time. Each job as an agent and its individual cost is the completion time of the job. We present a coordination mechanism for the scheduling game problem with social cost of minimizing the makespan in this paper, namely fully batch longest deteriorating rate. For this problem, we precisely quantify the inefficiency of Nash equilibrium by the logarithm price of anarchy. It is defined to be the ratio between the logarithm of social cost of the worst Nash equilibrium and the logarithm of social cost of an optimum schedule. In addition, we discuss the existence of Nash equilibrium and present an upper bound and lower bounds on the logarithm price of anarchy of the coordination mechanism. We show that the mechanism has a logarithm price of anarchy at most 2 − 1 / 3 m a x m , B − 2 / 3 B .

Seru scheduling problems with learning effect and job deterioration during an increasing adjustment period

Seru scheduling problems with learning effect and job deterioration during an increasing adjustment period

Scheduling with deterioration effects and maintenance activities under parallel processors

ABSTRACT This article considers unrelated parallel processor scheduling problems with deterioration effects and processor maintenance activities. In this case, deterioration occurs during processing. A maintenance activity on a parallel processor is allowed after a job is processed, and the maintenance time is a fixed constant. After the maintenance work is completed, the processor is restored to the initial state, and job deterioration starts again. The goal of this article is to determine the location of the processor maintenance activity and to find the optimal sequence to minimize the total completion time and total machine load. It is shown in this article that these problems are polynomial-time solvable and the corresponding algorithms are provided.

Minimizing total load on parallel machines with linear deterioration

We study a scheduling problem with linear deterioration of job processing times. We consider parallel identical machines. The objective is minimum total load, i.e., minimum total processing times on all the machines. We propose a simple heuristic of a greedy type for this NP-hard problem. A lower bound based on the geometric mean of the job deterioration factors is also introduced. The greedy heuristic is shown to guarantee an optimal solution for small size problems, and our numerical tests indicate that in general it produces very close-to-optimal schedules.

A Policy Reform Aimed at Improving the Job Quality of Graduates

The research investigates the employment opportunities and outcomes for Italian graduates in business economics, after two decades from the beginning of the Bologna process. Preliminarily, the study identifies the predictors of the employment status of graduates one year after graduation. Subsequently, the job quality of the employed graduates is assessed. Both kinds of analysis evidence problems of mismatch between education and occupation, leading to bad job quality. To overcome job deterioration, the research suggests a new policy measure. According to the proposed solution, higher education institutions should be funded by governments not only for graduation rates or for employment rates of their students, but, above all, for the job quality of their employed graduates. Without such adjustments, the strategy of the Bologna process, although valuable for its purpose, will remain an incomplete reform failing to address the main question: the job quality, rather than just the quantity, of graduates.

Recessionary changes at work and employee well-being: The protective roles of national and workplace institutions

The recession following the 2008 financial crisis brought major changes to employees’ experiences at work. We investigate the adverse effects of two of such changes: perceived organizational distress and job deterioration. We also examine the extent to which institutions at national level (employment protection legislation and collective bargaining coverage) and at workplace level (employment contract and union membership) may act as buffers against these effects. Using data from 21 European countries, we show that recessionary changes were associated with reduced psychological well-being and greater levels of work–nonwork interference.

An uncertain parallel machine problem with deterioration and learning effect

An uncertain uniform parallel machine scheduling problem with job deterioration and a learning effect is considered. Job processing times, due dates, deterioration rates and learning rates are assumed to be uncertain variables. The objective functions are the total weight earliness, tardiness and makespan. Three mathematical programming models are presented, i.e., expected value model, pessimistic value model and measure chance model. These models can be converted into equivalent crisp models by the inverse uncertainty distribution method. A hybrid algorithm mixed with dispatching rules based on structural features is employed to solve the problem. Finally, computational experiments are presented to illustrate the effectiveness of proposed algorithms.

Parallel-batching scheduling with nonlinear processing times on a single and unrelated parallel machines

Parallel-batching processing and job deterioration are universal in the real industry. Scholars have deeply investigated the problem of parallel-batching scheduling and the problem of scheduling with deteriorating jobs separately. However, the situations where both parallel-batching processing and job deterioration exist simultaneously were seldom considered. This paper studies the parallel-batching scheduling problem with nonlinear processing times on a single machine, and proposes several structural properties and an optimal algorithm to solve it. Based on the above properties and optimal algorithm for the single machine setting, we further study the problem of parallel-batching scheduling with nonlinear processing times under the unrelated parallel machine setting. Since the unrelated parallel machines scheduling problem is NP-hard, a hybrid SFLA-VNS algorithm combining Shuffle Frog Leap Algorithm (SFLA) with Variable Neighborhood Search Algorithm (VNS) is proposed. Computational experiments and comparison are finally conducted to demonstrate the effectiveness of the proposed algorithm.

An efficient simulation–neural network–genetic algorithm for flexible flow shops with sequence-dependent setup times, job deterioration and learning effects

This study presents an integrated approach based on artificial neural network (ANN), genetic algorithm (GA) and computer simulation to explore all the solution space in stochastic flexible flow shop with sequence-dependent setup times, job deterioration and learning effects. The objective of this study is minimizing total tardiness of jobs in the sequences. In this study, the outputs of ANN are inputted to GA and outputs of simulation model are inputted to ANN. We consider learning effects in this problem which means that workers become more experienced with the passage of time, and thus, the processing duration decreases. Deterioration of job means that processing time is a decreasing function of its execution start time. It is not possible to propose a mathematical optimization model for the stated problem; therefore, a simulation optimization approach based on ANN–GA is introduced for a relatively large problem. Finally, actual experiments are conducted to show the applicability of the proposed novel algorithm in finding near-optimal solutions with normal, uniform and exponential processing and setup times. This is the first study that presents an integrated intelligent approach for optimal solution of stochastic flexible flow shop problem with sequence-dependent setup times, job deterioration and learning effects in a real case study.

Design of a Hybrid Genetic Algorithm for Parallel Machines Scheduling to Minimize Job Tardiness and Machine Deteriorating Costs with Deteriorating Jobs in a Batched Delivery System

This paper studies the parallel machine scheduling problem subject to machine and job deterioration in a batched delivery system. By the machine deterioration effect, we mean that each machine deteriorates over time, at a different rate. Moreover, job processing times are increasing functions of their starting times and follow a simple linear deterioration. The objective functions are minimizing total tardiness, delivery, holding and machine deteriorating costs. The problem of total tardiness on identical parallel machines is NP-hard, thus the under investigation problem, which is more complicated, is NP-hard too. In this study, a mixed-integer programming (MILP) model is presented and an efficient hybrid genetic algorithm (HGA) is proposed to solve the concerned problem. A new crossover and mutation operator and a heuristic algorithm have also been proposed depending on the type of problem. In order to evaluate the performance of the proposed model and solution procedure, a set of small to large test problems are generated and results are discussed. The related results show the effectiveness of the proposed model and GA for test problems.

Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects

This paper investigates a multi-objective parallel machine scheduling problem under fully fuzzy environment with fuzzy job deterioration effect, fuzzy learning effect and fuzzy processing times. Due dates are decision variables for the problem and objective functions are to minimise total tardiness penalty cost, to minimise earliness penalty cost and to minimise cost of setting due dates. Due date assignment problems are significant for Just-in-Time (JIT) thought. A JIT company may want to have optimum schedule by minimising cost combination of earliness, tardiness and setting due dates. In this paper, we compare different approaches for modelling fuzzy mathematical programming models with a local search algorithm based on expected values of fuzzy parameters such as job deterioration effect, learning effect and processing times.

Total completion time minimization for scheduling of two-machine flow shop with deterioration jobs and setup time

This article studies a scheduling problem of a two-machine flow shop with both time-dependent deteriorating jobs and sequence-independent setup time. It is assumed that the deterioration of processing time and setup time is of a linear increasing function with respect to the starting time. The objective is to minimize the total completion time of all jobs. For such a scheduling problem, a mixed integer programming model is developed to find an optimal solution for small-sized problems. For medium- and large-sized problems, a modified variable neighborhood search algorithm is presented. To improve the performance of the algorithm, a greedy algorithm is proposed to find a good initial solution. Also, lower bounds are presented for performance evaluation. Numerical experiments are used to evaluate the effectiveness and efficiency of the proposed method, and the results show that an optimal or near-optimal solution can be obtained.