Scheduling deteriorating jobs with CON/SLK due date assignment on a single machine

This paper considers the problem of scheduling deteriorating jobs and due date assignment on a single machine. The actual processing time of a job is a linear increasing function of its starting time. The problem is to determine the optimal due dates and the processing sequence simultaneously to minimize costs for earliness, due date assignment and weighted number of tardy jobs. We present polynomial-time algorithms to solve the problem in the case of two popular due date assignment methods: CON and SLK.     

Minimizing mean absolute deviation of completion time about a common due window subject to maximum tardiness for a single machine

This study deals with the problem of scheduling jobs on a single machine to minimize the mean absolute deviation of the job completion time about a large common due window subject to the maximum tardiness constraint. Using the well-known three-field notation, the problem is identified as MAD/large DueWindow/T_max. The common due window is set to be large enough to allow idle time prior to the beginning of a schedule to investigate the effect of the T_max constraint. Penalties arise if a job is completed outside the due window. A branch and bound algorithm and a heuristic are proposed. Many properties of the solutions and precedence relationships are identified. Our computational results reveal that the branch and bound algorithm is capable of solving problems of up to 50 jobs and the heuristic algorithm yields approximate solutions that are very close to the exact solution.     

Non‐Standard “Contingent” Employment and Job Satisfaction: A Panel Data Analysis

Contingent forms of employment are usually associated with low‐quality jobs and, by inference, jobs that workers find relatively unsatisfying. This assumption is tested using data from a representative household panel survey covering a country (Australia) with a high incidence of nonstandard employment. Results from the estimation of ordered logit regression models reveal that among males, both casual employees and labor‐hire (agency) workers (but not fixed‐term contract workers) report noticeably lower levels of job satisfaction, though this association diminishes with job tenure. Negative effects for women are mainly restricted to labor‐hire workers.     

A bicriteria approach to maximize the weighted number of just-in-time jobs and to minimize the total resource consumption cost in a two-machine flow-shop scheduling system

We analyze a two-machine flow-shop scheduling problem in which the job processing times are controllable by the allocation of resources to the job operations and the resources can be used in discrete quantities. We provide a bicriteria analysis of the problem where the first criterion is to maximize the weighted number of just-in-time jobs and the second criterion is to minimize the total resource consumption cost. We prove that although the problem is known to be NP-hard even for constant processing times, a pseudo-polynomial time algorithm for its solution exists. In addition, we show how the pseudo-polynomial time algorithm can be converted into a two-dimensional fully polynomial approximation scheme for finding an approximate Pareto solution.     

Heuristics for operational fixed job scheduling problems with working and spread time constraints

Operational fixed job scheduling problems select a set of jobs having fixed ready and processing times and schedule the selected jobs on parallel machines so as to maximize the total weight. In this study, we consider working time and spread time constrained versions of the operational fixed job scheduling problems. The working time constraints limit the total processing load on each machine. The spread time constraints limit the time between the start of the first job and the finish of the last job on each machine. For the working time constrained problem, we present a filtered beam search algorithm that evaluates the promising nodes of the branch and bound tree. For the spread time constrained problem we propose a two phase algorithm that defines the promising sets for the first jobs and finds a solution for each promising set. The results of our computational tests reveal that our heuristic algorithms perform very well in terms of both solution quality and time.     

A genetic algorithm based heuristic for two machine no-wait flowshop scheduling problems with class setup times that minimizes maximum lateness

Machine scheduling problem has been extensively studied by researchers for many decades in view of its numerous applications on solving practical problems. Due to the complexity of this class of scheduling problems, various approximation solution approaches have been presented in the literature. In this paper, we present a genetic algorithm (GA) based heuristic approach to solve the problem of two machine no-wait flowshop scheduling problems that the setup time on the machines is class dependent, and the objective is to minimize the maximum lateness of the jobs processed. This class of machine scheduling problems has many practical applications in manufacturing industry, such as metal refinery operations, food processing industry and chemical products production processes, in which no interruption between subsequent processes is allowed and the products can be grouped into families. Extensive computation experiments have been conducted to evaluate the effectiveness of the proposed algorithm. Results show the proposed methodology is suitable to be adopted for the development of an efficient scheduling plan for this class of problems in real life application.     

Scheduling a maintenance activity and due-window assignment based on common flow allowance

We study a due-window assignment problem on a single machine. The job-dependent due-windows are obtained by the common flow allowance criterion. The scheduler has the option to perform a maintenance activity which is rate modifying, i.e., improves the processing times of the following jobs. We consider a number of versions of this setting: (i) The maintenance requires a constant time, (ii) The maintenance duration is an increasing function of its starting time (linear deterioration), and (iii) The maintenance duration is position-dependent (general deterioration). We study the standard setting of regular job processing times, and investigate also the extension to position-dependent processing times. The set of potential optimal positions for the maintenance activity is fully characterized. Consequently, the problems based on all the combinations of these settings are shown to be solved in polynomial time.     

Two-machine flow shop scheduling of polyurethane foam production

This paper studies a two-machine flow shop scheduling problem with a supporting precedence relation. The model originates from a real production context of a chemical factory that produces foam-rubber products. We extend the traditional two-machine flow shop by dividing the operations into two categories: supporting tasks and regular jobs. In the model, several different compositions of foam rubber can be mixed at the foam blooming stage, and products are processed at the manufacturing stage. Each job (product) on the second machine cannot start until its supporting tasks (parts) on the first machine are all finished and the second machine is not occupied. The objective is to find a schedule that minimizes the total job completion time. The studied problem is strongly NP-hard. In this paper, we propose a branch-and-bound algorithm incorporating a lower bound and two dominance rules. We also design a simple heuristic and an iterated local search (ILS) algorithm to derive approximate solutions. The performances of the proposed algorithms are examined through computational experiments.     

A hybrid artificial bee colony algorithm for the job shop scheduling problem

The job shop scheduling problem (JSSP) has attracted much attention in the field of both information sciences and operations research. In terms of the objective function, most existing research has been focused on the makespan criterion (i.e., minimizing the overall completion time). However, for contemporary manufacturing firms, the due date related performance is usually more important because it is crucial for maintaining a high service reputation. Therefore, in this study we aim at minimizing the total weighted tardiness in JSSP. Considering the high complexity, a novel artificial bee colony (ABC) algorithm is proposed for solving the problem. A neighborhood property of the problem is discovered, and then a tree search algorithm is devised to enhance the exploitation capability of ABC. According to extensive computational tests, the proposed approach is efficient in solving the job shop scheduling problem with total weighted tardiness criterion.     

Firm‐Size Wage Gaps along the Formal‐Informal Divide: Theory and Evidence

Observationally equivalent workers are paid higher wages in larger firms. This fact is often called the “firm‐size wage gap” and is regarded as a key empirical puzzle. Using microlevel data from Turkey, we document a new stylized fact: The firm‐size wage gap is more pronounced for informal (unregistered) jobs than for formal (registered) jobs. To explain this fact, we develop a two‐stage wage‐posting game with market imperfections and segmented markets, the solution to which produces wages as a function of firm size in a well‐defined subgame‐perfect equilibrium. The model proposes two explanations. First, taxes on formal employment generate a wedge between formal and informal size wage gaps. Thus, government policy can potentially affect the magnitude of the firm‐size wage gaps. The second explanation features a market‐based framework with strategic interactions. Relative to small firms, large firms typically post higher wages for both formal and informal jobs. A high‐wage formal job attracts a larger pool of applicants than a high‐wage informal job. The larger pool of applicants for the formal job, in turn, allows the firm to somewhat lower the initial wage offer, while this second‐round effect is negligible for informal jobs. As a result, size differentials are lower in formal jobs than informal jobs. We argue that the observed patterns in the use of social connections in job search and heterogeneity in job preferences can be used to justify the validity of this second mechanism.     

Particle swarm optimization with cocktail decoding method for hybrid flow shop scheduling problems with multiprocessor tasks

This paper addresses the problem of multiprocessor task-scheduling in a hybrid flow shop (HFS) problem to minimize the makespan. Due to the complex nature of an HFS problem, it is decomposed into the following two sequential decision problems: determining the job permutation in stage 1, followed by a decoding method to assign jobs into each machine in subsequent stages when designing a heuristic algorithm. The decoding method plays a pivotal role for improving the solution quality of any algorithm for the HFS problem. However, the majority of existing algorithms ignores the problem and is only concerned with the first decision problem. This study emphasizes the importance of the decoding method via a small test, and searches for a number of solid decoding methods that can be incorporated into the cocktail decoding method. Then, this study develops a particle swarm optimization (PSO) algorithm that can be combined with the cocktail decoding method. In the PSO, a variety of job sequences are generated using the PSO procedure in stage 1, and the cocktail decoding method is used to assign the jobs to machines in sequential stages. Moreover, a modified lower bound is introduced. Computational results show that the proposed lower bound is competitive, and with the help of the cocktail decoding method, the proposed PSO, and even the adoption of a standard PSO framework, significantly outperforms the majority of existing algorithms in terms of quality of solutions, especially for large problems.     

Scheduling jobs with values dependent on their completion times

The paper concerns two scheduling problems with job values and losses of job values (costs) dependent on job completion times. In the first problem, we consider scheduling jobs with stepwise values in parallel processor environment. In the stepwise value, there is given a number of moments at which the job value decreases and between them the job value is constant (thus, the value deteriorates over time). The maximized criterion is the total job value. We prove strong NP-hardness of a single processor case of the problem and construct a pseudo-polynomial time algorithm for a special case with fixed number of unrelated parallel processors and fixed number of common moments of job value changes. Additionally, for uniform and unrelated parallel processors we construct and experimentally test several heuristic algorithms based on the list strategy. The second problem is a single processor one with piecewise linear losses of job values (the loss increases over time). The minimized criterion is the total loss of job value. We prove strong NP-hardness of the problem and existence of a pseudo-polynomial time exact algorithm for its special case. We also construct some heuristic algorithms for this problem and verify experimentally their efficiency.     

Non-permutation flowshop scheduling in a supply chain with sequence-dependent setup times

In this paper, we consider a flowshop scheduling problem with sequence-dependent setup times and a bicriteria objective to minimize the work-in-process inventory for the producer and to maximize the customers' service level. The use of a bicriteria objective is motivated by the fact that successful companies in today's environment not only try to minimize their own cost but also try to fulfill their customers' need. Two main approaches, permutation and non-permutation schedules, are considered in finding the optimal schedule for a flowshop. In permutation schedules the sequence of jobs remains the same on all machines whereas in non-permutation schedule, jobs can have different sequence on different machines. A linear mathematical model for solving the non-permutation flowshop is developed to comply with all of the operational constraints commonly encountered in the industry, including dynamic machine availabilities, dynamic job releases, and the possibility of jobs skipping one or more machines, should their operational requirements deem that it was necessary. As the model is shown to be NP-hard, a metasearch heuristic, employing a newly developed concept known as the Tabu search with embedded progressive perturbation (TSEPP) is developed to solve, in particular, industry-size problems efficiently. The effectiveness and efficiency of the search algorithm are assessed by comparing the search algorithmic solutions with that of the optimal solutions obtained from CPLEX in solvable small problem instances.     

A note: Multi-machine scheduling with general position-based deterioration to minimize total load

The majority of the papers dealing with scheduling deteriorating jobs ignores general deterioration forms, and considers only special cases. Moreover, most of these papers consider deterioration, most of these papers consider deterioration based on job starting times, and only a few study position-based deterioration. Finally, very few researchers focus on the measure of total load, which becomes important in a setting of deterioration on multi-machines. In this note, we study general, non-decreasing, job-dependent and position-dependent deterioration function. The machine setting is parallel identical machines, and the objective function is total load. We introduce a polynomial time solution for this problem.     

Scheduling a single batch processing machine with arbitrary job sizes and incompatible job families

Motivated by a bottleneck operation in a multi-layer ceramic capacitor production line, we study a scheduling problem of batch processing machine in which a number of jobs are processed simultaneously as a batch. The performance measures considered include makespan, total completion time, and total weighted completion time. We first present a new simple integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems. However, since the problem is NP-hard and the size of a real problem is very large, we propose a number of heuristic algorithms and design a hybrid genetic algorithm to solve practical big-size problems in a reasonable computational time. To verify performance of the algorithms, we compare them with lower bounds for the problems. From the results of these computational experiments the heuristic algorithms including the genetic algorithm show different performances for the three problems.     

Occupational Stereotypes and Gender‐Specific Job Satisfaction

Using representative data containing information on job satisfaction and workers’ gender‐specific prejudices, we investigate the relationship between stereotyping and job satisfaction. We show that women in stereotypically male jobs are significantly less satisfied with their work climate and job content than in stereotypically female jobs but more satisfied with their income in those same jobs. Our findings indicate that women trade off their higher income satisfaction against the negative consequences of stereotyping. As long as we take into account that stereotypically male jobs are physically more demanding than stereotypically female jobs, men are generally more satisfied with stereotypically male jobs.     

A unified solution approach for the due date assignment problem with tardy jobs

We analyze a number of due date assignment problems with the weighted number of tardy jobs objective and show that these problems can be solved in O(n²) time by dynamic programming. We show that the effects of learning or the effects of past-sequence-dependent setup times can be incorporated into the problem formulation at no additional computational cost. We also show that some single-machine due date assignment problems can be extended to an identical parallel machine setting. Finally, we improve the complexity of the solution algorithms for two other due date assignment problems.     

R&D professionals' reactions to the career plateau: Mediating effects of supervisory behaviours and job characteristics

A career plateau is defined as that point in a person's career which represents a cessation of both lateral and upward movement between jobs. The present study elaborates the idea that a fuller understanding of individual reactions to the career plateau can be achieved by considering supervisory behaviours and job characteristics as variables transmitting the influence of career plateauing to behavioural and attitudinal reaction measures. It is suggested that supervisors invest less attention and resources in plateaued employees and that plateaued employees are assigned to jobs with less motivating potential. Analyses of questionnaire data from 618 R&D professionals in 11 large West German firms show that there are significant differences between plateaued R&D professionals (i. e. those subjects with at least 10 years of job tenure) and a nonplateaued comparison group (i. e. respondents with not more than 6 years of job tenure) controlling for age as a potential confounding influence. Specifically, plateaued R&D professionals indicate less working hours, to be less satisfied with their career and their work, to be less involved in their work, and they tend to produce less publications and patents per year of company tenure. Furthermore, considerable differences are found between one's immediate supervisor's behaviours (e. g. provision of job-related performance feedback) reported by plateaued professionals and their non-plateaued counterparts. However, even after controlling the mediating effects of supervisory behaviours and job characteristics variables plateauing still had a small, but significant detrimental effect on 3 out of 6 reaction measures. Implications are outlined for organizational career management activities.     

Workers' Views of the Impact of Trade on Jobs

This article uses new data to examine how workers' perceptions of the impact of trade on jobs like theirs are related to economic variables representing their career paths, job characteristics, and local labor market conditions. We find that only 17 percent of workers think trade creates jobs. And even fewer workers (4–7 percent) in any industry think trade has created better jobs. We find that workers' perceptions do not reflect their job characteristics or the movability of their jobs. Their perceptions of trade primarily reflect local labor market conditions (hiring and separation rates) and education. The determinants of workers' perceptions of trade present a different pattern compared with their perceptions of job security.