Multi-stage scheduling for banks by mathematical programming

Despite moves toward electronic funds transfer systems, the United States banking industry must continue to cope with the paper processing requirements associated with a payments system still heavily reliant upon checks. Through the development of MICR (magnetic ink character recognition), computers have been utilized to sort documents and post customer accounts. However, a labor intensive key encoding function is required to prepare documents for processing. An attempt to eliminate the labor intensity is the recent development of optical scanning capture equipment which ‘reads’ printed or hand-written documents and inscribes them with MICR characters. However, the utilization of optical scanning technology requires the scheduling and coordination of five processing activities. A mathematical programming model has been developed which minimizes the combination of payroll and float costs while recognizing machine capacity constraints in the scheduling of optical capture systems. The formulation was specifically designed to respond to the bank check processing scheduling problem. However, with minor modifications, the model is directly transferable to any multi-echelon processing system where penalties are incurred when the units of output fail to meet prespecified deadlines.     

On-line machine scheduling with batch setups

We study a class of scheduling problems with batch setups for the online-list and online-time paradigms. Jobs are to be scheduled in batches for processing. All jobs in a batch start and complete together, and a constant setup is prior to each batch. The objective is to minimize the total completion time of all jobs. We primarily consider the special cases of these problems with identical processing times, for which efficient on-line heuristics are proposed and their competitive performance is evaluated.     

A hybrid genetic algorithm/mathematical programming approach to the multi-family flowshop scheduling problem with lot streaming

This paper presents a hybrid genetic algorithm/mathematical programming heuristic for the n-job, m-machine flowshop problems with lot streaming. The number of sublots for each job and the size of sublots are directly addressed by the heuristic and setups may be sequence-dependent. A new aspect of the problem, the interleaving of sublots from different jobs in the processing sequence, is developed and addressed. Computational results from 12 randomly generated test sets of 24 problems each are presented.     

Single facility scheduling with dual criteria: minimizing maximum tardiness subject to minimum number of tardy jobs

This paper considers the static single machine scheduling problem with the objective of minimizing the maximum tardiness of any job subject to the constraint that the total number of lardy jobs is minimum. Based on simple dominance conditions an o(n²) heuristic algorithm is proposed to find an approximate solution to this problem. The effectiveness of the proposed heuristic algorithm is empirically evaluated by solving a large number of problems and comparing them to the optimal solutions obtained through the branch and bound algorithm.     

不确定条件下不同交货期窗口的Job Shop 调度

研究了具有不同交货期窗口的Job Shop 的提前/ 拖期调度问题,并考虑了处理时间的不确定 性,采用三角模糊数表示处理时间的不确定性,提出了基于遗传算法的求解算法. 仿真实验验证了 算法的有效性.     

A survey of scheduling problems with late work criteria

The paper presents the first complete survey of scheduling problems with the late work criteria. Late work objective functions estimate the quality of a schedule based on durations of late parts of jobs, not taking into account the amount of delay for fully late jobs. The paper provides a formal definition of the late work parameter and compares the criteria based on it with other classical performance measures. It shows the relationship between the late work model and the imprecise computation model known from the hard real-time literature. Moreover, the paper presents a few real world applications of the late work objective function. The paper lists results obtained for nearly forty problems of scheduling jobs on a single machine, parallel (identical and uniform) machines and dedicated machines, investigated in the literature since 1984.     

Single process plan scheduling with genetic algorithms

AIn this paper, a genetic algorithm model for scheduling manufacturing resources is developed for the case when there is only one process plan available per job, hence there is no routeing flexibility. The scheduling objectives considered are minimizing the makespan and mean flow time. Genetic algorithms design issues are discussed and the working of the employed genetic operators is explained in detail. Parameters for the genetic algorithms used for single process plan scheduling SPPS problems are set through extensive experimentation. Finally, the genetic algorithms approach is compared with several other approaches in terms of optimality of solution and computAuthors: ing time. It was observed that in most cases the genetic algorithms approach performed better than other approaches both in terms of finding an optimal or near optimal solution as well as computing time.     

Improved mixed-integer programming models for the multiprocessor scheduling problem with communication delays

We revise existing and introduce new mixed-integer programming models for the Multiprocessor scheduling problem with communication delays. The basis for both is the identification of two major modeling strategies one of which can be considered ordering-based, and the other assignment-based. We first reveal redundancies in the encoding of feasible solutions found in present formulations and discuss how they can be avoided. For the assignment-based approach, we propose new inequalities that lead to provably stronger continuous relaxations and better performance in practice. Moreover, we derive a third, novel modeling strategy and show how to more compactly linearize assignment formulations with quadratic constraints. In a comprehensive experimental comparison of representative models that reflect the state-of-the-art in terms of strength and size, we evaluate not only running times but also the obtained lower and upper bounds on the makespan for the harder instances of a large scale benchmark set.     

Job insecurity in temporary versus permanent workers: Associations with attitudes, well-being, and behaviour

Recent research suggests that the relationship between job insecurity and psychological outcomes is more negative among permanent compared with temporary workers. We investigate possible interaction effects between job insecurity and type of contract (temporary versus permanent) for various psychological outcomes (job satisfaction, organizational commitment, life satisfaction, and self-rated performance), some of which have received little attention. We aim to explain these interaction effects, while taking into account the heterogeneous nature of temporary workers in terms of tenure, employment prospects, and wish to do temporary employment. We argue that permanent workers expect higher levels of job security; job insecurity breaches permanent workers' but not temporary workers' expectations. This may relate to unfavourable outcomes. Similarly, the heterogeneous nature of temporary workers may relate to job security expectations and thus to reactions to job insecurity. This study was conducted on a sample of 477 temporary and permanent workers from various occupational sectors in Belgium. The results suggested that the interaction effect between job insecurity and contract type may be limited to job satisfaction and organizational commitment. Furthermore, permanent workers had higher expectations about job security. Breach of these expectations furthermore mediated the relationship between job insecurity and all outcomes, except for self-rated performance. However, the heterogeneity indicators were found to be unrelated to job security expectations.     

A mathematical model for job shop scheduling with multiple process plan consideration per job

In this paper, a mixed integer programming model is formulated for scheduling a set of jobs through a shop when each job is supplied or provided with multiple process plans or process routings. Simultaneous selection of a process plan for each job and the sequencing of the jobs through the machines in the shop based on the set of selected process plans is addressed. The procedure developed seeks to integrate the selection of machines for each job and the sequencing of jobs on each machine based on the objective of minimizing production makespan. the application of the procedure is demonstrated with an example problem. The following conclusions were drawn as a result of the research: (1) the procedure developed produces optimal or near optimal solution; (2) the benefit from the developed approach is that it allows a shop to adaptively select process plans for jobs to optimize on production makespan. By combining solution quality with scheduling flexibility and efficiency, the productivity of a shop can be greatly enhanced.     

A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand

This paper describes an employee scheduling system for retail outlets; it is a constraint-based system that exploits forecasts and stochastic techniques to generate schedules meeting the demand for sales personnel. Uncertain scenarios due to fluctuating demand are taken into account to develop a stochastic operational optimization of staffing levels. Mathematically, the problem is stated as a mixed-integer linear programming problem. Simulations with store data belonging to a major Swiss retailer show the effective performance of the proposed approach. The schedule quality is assessed through comparison with a deterministic scheduling package, which has been used at several outlets in Switzerland.     

遗传规划研究与应用中的若干问题

简要介绍了遗传规划的基本内容,结合具体例子论述了遗传规划成功应用的实际领域的特征,指出研究与应用遗传规划时常见的两个问题,并从理论上给出了相应的对策,以期提高遗传规划的效率     

Joint supplier selection and scheduling of customer orders under disruption risks: Single vs. dual sourcing

This paper presents a stochastic mixed integer programming approach to integrated supplier selection and customer order scheduling in the presence of supply chain disruption risks, for a single or dual sourcing strategy. The suppliers are assumed to be located in two different geographical regions: in the producer's region (domestic suppliers) and outside the producer's region (foreign suppliers). The supplies are subject to independent random local disruptions that are uniquely associated with a particular supplier and to random semi-global (regional) disruptions that may result in disruption of all suppliers in the same geographical region simultaneously. The domestic suppliers are relatively reliable but more expensive, while the foreign suppliers offer competitive prices, however material flows from these suppliers are more exposed to unexpected disruptions. Given a set of customer orders for products, the decision maker needs to decide which single supplier or which two different suppliers, one from each region, to select for purchasing parts required to complete the customer orders and how to schedule the orders over the planning horizon, to mitigate the impact of disruption risks. The problem objective is either to minimize total cost or to maximize customer service level. The obtained combinatorial stochastic optimization problem will be formulated as a mixed integer program with conditional value-at-risk as a risk measure. The risk-neutral and risk-averse solutions that optimize, respectively average and worst-case performance of a supply chain are compared for a single and dual sourcing strategy and for the two different objective functions. Numerical examples and computational results are presented and some managerial insights on the choice between the two sourcing strategies are reported.     

Job Seekers with Musculoskeletal or Sensory Disabilities: Barriers and Facilitators of Job Search

Drawing on interviews with job seekers and expert informants, we outline a model of the job search experience of people with disabilities. This model specifies the sequence of events involved in the pursuit of paid work and the contextual features that inhibit or facilitate job search attitudes, behaviours, intermediate search outcomes and employment outcomes. By contrasting the experiences of job seekers with musculoskeletal and sensory disabilities, and outlining the influence of major stakeholders in the form of employment agencies and family members, our model provides the basis for a more nuanced understanding of the job search process. Finally, we recommend points of intervention that are grounded in data for improving job search outcomes for people with disabilities in general, and for job seekers with musculoskeletal or sensory disabilities specifically.     

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.     

Minimizing tardiness on a single processor with sequence-dependent setup times: a simulated annealing approach

In today's fast-paced Just-In-Time and mass customization manufacturing in a sequence-dependent setup environment, the challenge of making production schedules to meet due-date requirements is becoming a more complex problem. Unfortunately, much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. This paper considers the problem of minimizing tardiness, a common measure of due-date performance, in a sequence-dependent setup environment. Simulated annealing was used to solve the sequencing problem, and its performance was compared with random search. Our experimental results show that the algorithm can find a good solution fairly quickly, and thus can rework schedules frequently to react to variations in the schedule. The algorithm is invaluable for ‘on-line’ production scheduling and ‘last-minute’ changes to production schedule. The results of this research also suggest ways in which more complex and realistic job shop environments, such as multiple machines with a higher number of jobs in the sequence, and other scheduling objectives can be modeled. This research also investigates computational aspects of simulated annealing in solving complex scheduling problems.     

A mathematical model and genetic algorithm-based approach for parallel two-sided assembly line balancing problem

Assembly lines are usually constructed as the last stage of the entire production system and efficiency of an assembly line is one of the most important factors which affect the performance of a complex production system. The main purpose of this paper is to mathematically formulate and to provide an insight for modelling the parallel two-sided assembly line balancing problem, where two or more two-sided assembly lines are constructed in parallel to each other. We also propose a new genetic algorithm (GA)-based approach in alternatively to the existing only solution approach in the literature, which is a tabu search algorithm. To the best of our knowledge, this is the first formal presentation of the problem as well as the proposed algorithm is the first attempt to solve the problem with a GA-based approach in the literature. The proposed approach is illustrated with an example to explain the procedures of the algorithm. Test problems are solved and promising results are obtained. Statistical tests are designed to analyse the advantage of line parallelisation in two-sided assembly lines through obtained test results. The response of the overall system to the changes in the cycle times of the parallel lines is also analysed through test problems for the first time in the literature.     

Job insecurity and emotional exhaustion: Testing psychological contract breach versus distributive injustice as indicators of lack of reciprocity

This study examines the processes underlying the association between job insecurity and emotional exhaustion, proposing that lack of reciprocity in the organizational exchange relationship is a main theoretical explanation for this association. Specifically, based on the social exchange perspective, we compared and tested two distinct mediating mechanisms: psychological contract breach and distributive injustice. These two indicators of lack of reciprocity constitute the explanatory process through which job insecurity relates to emotional exhaustion, the primary component of burnout. Data analyses from a sample of 322 blue-collar workers in Italy confirmed the mediational hypotheses. A contrast test revealed that distributive injustice was the indicator with more explanatory strength. The results contribute to research on the theoretical explanations of the adverse consequences of job insecurity, considering the nature and antecedents of psychological distress from an organizational exchange perspective.