Genetic algorithm to production planning and scheduling problems for manufacturing systems

Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.     

The permutation flow shop problem with just in time production considerations

This research presents a variation to the permutation flow shop problem where Just In Time (JIT) production requirements are taken into account. The model developed in this research employs dual objectives. In addition to the traditional objective of minimizing the production makespan, minimization of Miltenburg's material usage rate is also incorporated. In this model, multiple units of any product are permitted in the production sequence. However, the minimization of material usage rates attempts to prevent batch scheduling of products and allows unit flow of products as required in demand flow manufacturing. A solution method is proposed for determining an optimal production sequence via an efficient frontier approach and Simulated Annealing (SA). Test problems and specific performance criteria are used to assess the solutions generated by the proposed method. Experimental results presented in this paper show that the use of the efficient frontier and SA provide solutions that approach the optimal solution for the performance measures used in this research.     

A simulated annealing algorithm for job shop scheduling

In this paper, the job shop scheduling problem is considered with the objective of minimization of makespan time. We first reviewed the literature on job shop scheduling using meta-heuristics. Then a simulated annealing algorithm is presented for scheduling in a job shop. To create neighbourhoods, three perturbation schemes, viz. pairwise exchange, insertion, and random insertion are used, and the effect of them on the final schedule is also compared. The proposed simulated annealing algorithm is compared with existing genetic algorithms and the comparative results are presented. For comparative evaluation, a wide variety of data sets are used. The proposed algorithm is found to perform well for scheduling in the job shop.     

A genetic algorithm for scheduling on a single machine with set-up times and due dates

This paper presents a genetic algorithm for a single machine-scheduling problem with the objective of minimizing total tardiness. Each job has its own due date and the set-up times are sequence dependent. The parameters of the genetic algorithm are determined by a statistical method. For small problems, the solutions given by the proposed method are compared with solutions provided by a commercial package, and for larger problems, with those obtained by a heuristic proposed in the literature.     

Heuristic for scheduling in a two-machine bicriteria dynamic flowshop with setup and processing times separated

This paper addresses the two-machine bicriteria dynamic flowshop problem where setup time of a job is separated from its processing time and is sequenced independently. The performance considered is the simultaneous minimization of total flowtime and makespan, which is more effective in reducing the total scheduling cost compared to the single objective. A frozen-event procedure is first proposed to transform a dynamic scheduling problem into a static one. To solve the transformed static scheduling problem, an integer programming model with N ² + 5N variables and 7N constraints is formulated. Because the problem is known to be NP-complete, a heuristic algorithm with the complexity of O (N ³) is provided. A decision index is developed as the basis for the heuristic. Experimental results show that the proposed heuristic algorithm is effective and efficient. The average solution quality of the heuristic algorithm is above 99%. A 15-job case requires only 0.0235 s, on average, to obtain a near or even optimal solution.     

Dynamic selection of dispatching rules for job shop scheduling

Although the academic contribution to job shop scheduling is abundant, its impact on practice has been minimal. The most preferred approach to job shop scheduling in the industry is dispatching rules. A major criticism against dispatching rules is that there is no single universal rule. The effective choice of dispatching rules depends on the scheduling criterion and existing job shop conditions. In this paper, the authors have proposed a scheduling method based on the analytic hierarchy process, that dynamically selects the most appropriate dispatching rule from several candidate rules. The selection is based on the existing job shop conditions. This method is applied to two formal job shop problems, and the results for single dispatching rules are inferior to the method proposed in this paper.     

A FRAMEWORK FOR MACHINE SCHEDULING PROBLEMS WITH CONTROLLABLE PROCESSING TIMES

This is a study of single and parallel machine scheduling problems with controllable processing time for each job. The processing time for job j depends on the position of the job in the schedule and is a function of the number of resource units allocated to its processing. Processing time functions and processing cost functions are allowed to be nonlinear. The scheduling problems considered here have important applications in industry and include many of the existing scheduling models as special cases. For the single machine problem, the objective is minimization of total compression costs plus a scheduling measure. The scheduling measures include makespan, total flow time, total differences in completion times, total differences in waiting times, and total earliness and tardiness with a common due date for all jobs. Except when the total earliness and tardiness measure is involved, each case the problem is solved efficiently. Under an assumption typically satisfied in just-in-time systems, the problem with total earliness and tardiness measure is also solved efficiently. Finally, for a large class of processing time functions; parallel machine problems with total flow time and total earliness and tardiness measures are solved efficiently. In each case we reduce the problem to a transportation problem.     

Multimode time-cost-robustness trade-off project scheduling problem under uncertainty

The time/cost trade-off problem is a well-known project scheduling problem that has been extensively studied. In recent years, many researchers have begun to focus on project scheduling problems under uncertainty to cope with uncertain factors, such as resource idleness, high inventory, and missing deadlines. To reduce the disturbance from uncertain factors, the aim of robust scheduling is to generate schedules with time buffers or resource buffers, which are capped by project makespan and project cost. This paper addresses a time-cost-robustness trade-off project scheduling problem with multiple activity execution modes under uncertainty. A multiobjective optimization model with three objectives (makespan minimization, cost minimization, and robustness maximization) is constructed and three propositions are proposed. An epsilon-constraint method-based genetic algorithm along with three improvement measures is designed to solve this NP-hard problem and to develop Pareto schedule sets, and a large-scale computational experiment on a randomly generated dataset is performed to validate the effectiveness of the proposed algorithm and the improvement measures. The final sensitivity analysis of three key parameters shows their distinctive influences on the three objectives, according to which several suggestions are given to project managers on the effective measures to improve the three objectives.

     

Utilizing job resources: Qualitative evidence of the roles of job control and social support in problem solving

Abstract

In resource-based models of job design, job resources, such as control and social support, are thought to help workers to solve problems. Few studies have examined this assumption. We analyzed 80 qualitative diary entries (N=29) and interviews (N=37) concerned with the in-role requirements of medical technology designers in the UK for problem solving. Four themes linked to using the resources of job control and social support for problem solving emerged. These were: (1) eliciting social support to solve problems; (2) exercising job control to solve problems; (3) co-dependence between eliciting social support and exercising job control to solve problems; and (4) using job resources to regulate affect. The results were largely supportive of the assumptions underpinning resource-based models of job design. They also indicated that the explanatory power of resource-based models of job design may be enhanced by considering interdependencies between various factors: how different job resources are used, workers' motivation to use resources, workers' knowledge of how to use resources and the use of resources from across organizational boundaries. The study provides qualitative support for the assumption that social support and job control are used to cope with demands.     

Online scheduling with rejection and reordering: exact algorithms for unit size jobs

We study an online scheduling problem with rejection on \(m\ge 2\) identical machines, in which we deal with unit size jobs. Each arriving job has a rejection value (a rejection cost or penalty for minimization problems, and a rejection profit for maximization problems) associated with it. A buffer of size \(K\) is available to store \(K\) jobs. A job which is not stored in the buffer must be either assigned to a machine or rejected. Upon the arrival of a new job, the job can be stored in the buffer if there is a free slot (possibly created by evicting other jobs and assigning or rejecting every evicted job). At termination, the buffer must be emptied. We study four variants of the problem, as follows. We study the makespan minimization problem, where the goal is to minimize the sum of the makespan and the penalty of rejected jobs, and the \(\ell _p\) norm minimization problem, where the goal is to minimize the sum of the \(\ell _p\) norm of the vector of machine completion times and the penalty of rejected jobs. We also study two maximization problems, where the goal in the first version is to maximize the sum of the minimum machine load (the cover value of the machines) and the total rejection profit, and in the second version the goal is to maximize a function of the machine completion times (which measures the balance of machine loads) and the total rejection profit. We show that an optimal solution (an exact solution for the offline problem) can always be obtained in this environment, and determine the required buffer size. Specifically, for all four variants we present optimal algorithms with \(K=m-1\) and prove that in each case, using a buffer of size at most \(m-2\) does not allow the design of an optimal algorithm, which makes our algorithms optimal in this respect as well. The lower bounds hold even for the special case where the rejection value is equal for all input jobs.     

Relationship between job control,work pressure and strain: Studies in the USA and in The Netherlands

The purpose of this study was to test a model of job control, work pressure and strain in two samples in the USA and in The Netherlands. According to the proposed model, work pressure has a mediating role between job control and worker strain. The present model specifies three types of job control: task or instrumental control, conceptual or resource control, and decision organization control. Task and resource control are assumed to reduce the stressor of work pressure which, in turn, is related to high strain. Organization control is assumed to be positively related to work pressure. The model was tested in two samples : one US sample of 273office employees and one Dutch sample of 958 employees in a variety of jobs. The same measure of job control was used in both samples. Resultspartially confirm the proposed model. As predicted by the proposed model, high task control was related to low work pressure, whereas high organization control was related to high work pressure. The effects of task control and organization control on strain were mediated by work pressure for the measure of anxiety in the US sample and for the measure of stress in the Dutch sample. However, the mediating effect of work pressure was not found for job satisfaction in both samples, for the measure of mood disturbances in the US sample and for the measure of sick days in the Dutch sample. The proposed model is only partially confirmed by the results. These results confirm the importance of differentiating between different dimensions of job control.     

Specific relationships between psychosocial job conditions and job-related stress: A three-level analytic approach

This cross-sectional questionnaire study presents a multi-level analysis on 2565 workers in 188 departments in 36 organizations in the Netherlands. A three-level model is used in which individual workers are nested within departments, which in turn are nested within organizations. Research questions concern (1) the amount and distribution of variance in job-related stress explained for the three levels in the study (individuals, departments, organizations), and (2) the specificity of relationships between psychosocial job demands and job-related stress in the three-level model. Well-being showed slightly more raw variance to be explained at supra-individual levels than strain. The full regression model explained about 35% of the total variance in both work-related strain and well-being. Psychosocial job conditions did not exceed the expected amount of 10 to 15% contribution to this explained variance. These results do not differ from comparable studies that do not use multi-level analysis. The variance distribution in the full model, however, showed unexplained variance to be located at the individual level for both strain and well-being, and at the departmental level only for well-being. This last finding shows a direction for possible improvement of work stress models. Specificity of relationships was also shown: psychological job demands were more strongly related to strain, whereas job content variables (i.e. job variety, job control) were more strongly related to well-being. Results also suggested that social support was more strongly associated with well-being than with strain. Well-being appeared to have a more widely varying range of predictors than strain.     

The drivers of work engagement: A meta-analytic review of longitudinal evidence

ABSTRACT

Work engagement is currently one of the most popular outcomes in occupational health psychology. According to the motivational process within the job demands-resources (JD-R) framework, job resources stimulate work engagement, which in turn fosters job performance. While the general positive impact of job resources on work engagement is well established, it remains unclear how different types of job resources differentially predict work engagement over time. In our meta-analytic review, we identified 55 longitudinal studies that investigate the impact of various job resources on work engagement. To uncover the drivers of work engagement, we examined the differential impact of job resources on work engagement at group level, leader level, and organisational level via meta-analytic structural equation modelling. The findings suggest that job resources at each of the three levels predict work engagement over time. However, organisational-level resources (reflecting how the work is organised, designed and managed) contribute much more strongly to work engagement than group-level, and leader-level resources. All three levels of job resources and work engagement are highly stable. We advocate for interventions at any of the three levels. However, interventions at the organisational-level are most promising for enhancing work engagement and we thus strongly recommend strengthening those job resources.     

Semi-online scheduling with “end of sequence” information

We study a variant of classical scheduling, which is called scheduling with “end of sequence” information. It is known in advance that the last job has the longest processing time. Moreover, the last job is marked, and thus it is known for every new job whether it is the final job of the sequence. We explore this model on two uniformly related machines, that is, two machines with possibly different speeds. Two objectives are considered, maximizing the minimum completion time and minimizing the maximum completion time (makespan). Let s be the speed ratio between the two machines, we consider the competitive ratios which are possible to achieve for the two problems as functions of s. We present algorithms for different values of s and lower bounds on the competitive ratio. The proposed algorithms are best possible for a wide range of values of s. For the overall competitive ratio, we show tight bounds of ϕ + 1 ≈ 2.618 for the first problem, and upper and lower bounds of 1.5 and 1.46557 for the second problem. The authors would like to dedicate this paper to the memory of our colleague and friend Yong He who passed away in August 2005 after struggling with illness. D. Ye: Research was supported in part by NSFC (10601048).     

An integrated simulation model for effective planning of maintenance operations for Saudi Arabian Airlines (SAUDIA)

An effective maintenance system is essential for SAUDIA in order to meet its set objectives. These objectives include minimal flight cancellations, minimal delays, minimal repair turn time, and effective utilization of maintenance resources. In this paper, the elements of an integrated simulation model for SAUDIA have been described. The integrated model consists of several modules. These are planning and scheduling, organization, supply, quality control and performance measures. The required data and software for such a model have been described. Also, the utility of such a model for SAUDIA has been outlined.     

Application of interactive possibilistic linear programming to aggregate production planning with multiple imprecise objectives

Aggregate production planning (APP) addresses matching supply to forecast demand, with varying customer orders over the intermediate planning horizon. In real-world APP problems, input data and related parameters are commonly imprecise because information is incomplete or unavailable, and the decision maker (DM) must simultaneously consider conflicting objectives. This study develops an interactive possibilistic linear programming (i-PLP) approach to solve multi-product and multi-time period APP problems with multiple imprecise objectives and cost coefficients by triangular possibility distributions in uncertain environments. The imprecise multi-objective APP model designed here seeks to minimise total production costs and changes in work-force level with reference to imprecise demand, cost coefficients, available resources and capacity. Additionally, the proposed i-PLP approach provides a systematic framework that helps the decision-making process to solve fuzzy multi-objective APP problems, enabling a DM to interactively modify the imprecise data and parameters until a set of satisfactory solutions is derived. An industrial case demonstrates the feasibility of applying the proposed approach to a practical multi-objective APP problem.     

A heuristic for scheduling in a flowshop with the bicriteria of makespan and maximum tardiness minimization

This article deals with the development of a heuristic for scheduling in a flowshop with the objective of minimizing the makespan and maximum tardiness of a job. The heuristic makes use of the simulated annealing technique. The proposed heuristic is relatively evaluated against the existing heuristic for scheduling to minimize the weighted sum of the makespan and maximum tardiness of a job. The results of the computational evaluation reveal that the proposed heuristic performs better than the existing one.     

Nurses: Training for new job demands

Abstract

This paper argues that both the present and future job demands faced by nurses are likely to exceed the resources of the training which is currently provided for them. This argument will be supported by reference to the authors' studies of stress in nursing, as well as a variety of other sources. The common themes underlying reactions to both present and future demands include first, awareness of high stress levels to be found amongst nurses, and second, an emphasis on communication as both the problem and as the proposed solution. Common responses to these two themes are outlined, including training in stress management, and communication training. Some of the problems and unexamined assumptions (for example that hospital management actually wants to lower stress levels and improve communication in nursing) are examined. Some suggestions are made for developing training in health care contexts, to deal more adequately with likely future demands.     

Online scheduling with a buffer on related machines

Online scheduling with a buffer is a semi-online problem which is strongly related to the basic online scheduling problem. Jobs arrive one by one and are to be assigned to parallel machines. A buffer of a fixed capacity K is available for storing at most K input jobs. An arriving job must be either assigned to a machine immediately upon arrival, or it can be stored in the buffer for unlimited time. A stored job which is removed from the buffer (possibly, in order to allocate a space in the buffer for a new job) must be assigned immediately as well. We study the case of two uniformly related machines of speed ratio s≥1, with the goal of makespan minimization.     

The cost of selfishness for maximizing the minimum load on uniformly related machines

Consider the following scheduling game. A set of jobs, each controlled by a selfish agent, are to be assigned to m uniformly related machines. The cost of a job is defined as the total load of the machine that its job is assigned to. A job is interested in minimizing its cost, while the social objective is maximizing the minimum load (the value of the cover) over the machines. This goal is different from the regular makespan minimization goal, which was extensively studied in a game theoretic context. We study the price of anarchy (poa) and the price of stability (pos) for uniformly related machines. The results are expressed in terms of s, which is the maximum speed ratio between any two machines. For uniformly related machines, we prove that the pos is unbounded for s>2, and the poa is unbounded for s≥2. For the remaining cases we show that while the poa grows to infinity as s tends to 2, the pos is at most 2 for any s≤2.