Considering skills evolutions in multi-skilled workforce allocation with flexible working hours

The growing need of responsiveness for manufacturing companies facing market volatility raises a strong demand for flexibility in their organisation. Since the company personnel are increasingly considered as the core of the organisational structures, a strong and forward-looking management of human resources and skills is crucial to performance in many industries. These organisations must develop strategies for the short, medium and long terms, in order to preserve and develop skills. Responding to this importance, this work presents an original model, looking at the line-up of multi-period project, considering the problem of staff allocation with two degrees of flexibility. The first results from the annualising of working time, and relies on policies of changing schedules, individually as well as collectively. The second degree of flexibility is the versatility of the operators, which induces a dynamic view of their skills and the need to predict changes in individual performance as a result of successive assignments. We are firmly in a context where the expected durations of activities are no longer predefined, but result from the performance of the operators selected for their execution. We present a mathematical model of this problem, which is solved by a genetic algorithm. An illustrative example is presented and analysed, and, the robustness of the solving approach is investigated using a sample of 400 projects with different characteristics.     

Scheduling a workforce under annualized hours

Annualized hours is a new idea in aggregate planning for seasonal businesses. Many manufacturing and services organizations in Europe have successfully used annualized hours to meet fluctuating demand. However, literature on scheduling a workforce under annualized hours is lacking. We fill this void by showing how to schedule workers over the year under a practical annualized hours scenario. The proposed scheduling algorithm is surprisingly simple.     

Scheduling job shop associated with multiple routings with genetic and ant colony heuristics

The general job shop problem is one of the well known machine scheduling problems, in which the operation sequence of jobs are fixed that correspond to their optimal process plans and/or resource availability. Scheduling and sequencing problems, in general, are very difficult to solve to optimality and are well known as combinatorial optimisation problems. The existence of multiple job routings makes such problems more cumbersome and complicated. This paper addresses a job shop scheduling problem associated with multiple job routings, which belongs to the class of NP hard problems. To solve such NP-hard problems, metaheuristics have emerged as a promising alternative to the traditional mathematical approaches. Two metaheuristic approaches, a genetic algorithm and an ant colony algorithm are proposed for the optimal allocation of operations to the machines for minimum makespan time criterion. ILOG Solver, a scheduler package, is used to evaluate the performance of the proposed algorithms. The comparison reveals that both the algorithms are capable of providing solutions better than the solution obtained with ILOG Solver.     

Scheduling optimization of design stream line for production research and development projects

In a development project, efficient design stream line scheduling is difficult and important owing to large design imprecision and the differences in the skills and skill levels of employees. The relative skill levels of employees are denoted as fuzzy numbers. Multiple execution modes are generated by scheduling different employees for design tasks. An optimization model of a design stream line scheduling problem is proposed with the constraints of multiple executive modes, multi-skilled employees and precedence. The model considers the parallel design of multiple projects, different skills of employees, flexible multi-skilled employees and resource constraints. The objective function is to minimize the duration and tardiness of the project. Moreover, a two-dimensional particle swarm algorithm is used to find the optimal solution. To illustrate the validity of the proposed method, a case is examined in this article, and the results support the feasibility and effectiveness of the proposed model and algorithm.     

Scheduling research in multiple resource constrained job shops: a review and critique

Over the past several years, a number of survey, classification, and review articles have focused on scheduling research in machine [only] constrained job shops. Barring the work of Treleven (1989), there is no reported research that presents a detailed review of the issues related to scheduling and sequencing in job shops with multiple resource constraints. In his article, Treleven reviewed the research in job shops constrained by machines and labour. Job shops are not only constrained by machines and labour, but by auxiliary resources (in the form of tooling, etc.) as well. This paper extends the work of Treleven by reviewing the literature on scheduling in job shops constrained by more than one resource and comparing the scheduling research in auxiliary resource-constrained job shops with that of labour-constrained job shops. In addition, this article raises some issues for future scheduling research in multiple resource-constrained job shops.     

Sequencing mixed-model assembly lines operating with a heterogeneous workforce

We study the problem of sequencing mixed-model assembly lines operating with a heterogeneous workforce. The practical motivation for this study comes from the context of managing assembly lines in sheltered work centres for the disabled. We propose a general framework in which task execution times are both worker and model dependent. Within this framework, the problem is defined and mathematical mixed-integer models and heuristic procedures are proposed. These include a set of fast constructive heuristics, two local search procedures based on approximate measures using either a solution upper bound or the solution of a linear program and a GRASP metaheuristic. Computational tests with instances adapted from commonly used literature databases are used to validate the proposed approaches. These tests give insight on the quality of the different techniques, which prove to be very efficient both in terms of computational effort and solution quality when compared to other strategies such as a random sampling or the solution of the MIP models using a commercial solver.     

Rule mining for scheduling cross training with a heterogeneous workforce

This paper presents an association rule mining based framework for workforce scheduling to assist managers with robust real-time assignment decisions. We assume heterogeneous individual learning and forgetting behaviours, in which worker productivity changes dynamically. We explore a parallel production system that meets a specified production requirement over a fixed time horizon with the minimum workforce resources based on the number of worker-periods assigned. Three managerial policies are considered including: setting a maximum allowable individual cross-training level, balancing workload among workers and an unconstrained policy. We propose the use of several schedule attributes to quantify key aspects of optimised schedules that may, in turn, aid in determining robust assignment rules and the development of better cross-training policies. Current results indicate that the proposed approach is effective at identifying important rules, many of which add to our knowledge of useful workforce scheduling strategies.     

Optimal specialization of a maintenance workforce

This article develops an analytical method for determining an optimal specialization strategy for a maintenance workforce. The method assumes that maintenance tasks are generated by a system of statistically identical machines that experience random malfunctions and require periodic service. The impact of alternative workforce structures on system performance is evaluated with a queueing network model. Markov decision analysis is employed to determine an optimal assignment of maintenance personnel to pending tasks as the network status varies over time. A linear programming algorithm is derived to enable simultaneous optimization of specific assignment decisions and the overall workforce structure. A manufacturing example demonstrates the applicability of the method to many industrial contexts. The method is also applied to the problem of maximizing military aircraft sortie generation subject to a constraint on maintenance personnel expenditure.     

An Algorithm for a Single Machine Scheduling Problem with Sequence Dependent Setup Times and Scheduling Windows

An enumeration algorithm is presented for solving a scheduling problem similar to the single machine job shop problem with sequence dependent setup times. The scheduling problem differs from the job shop problem in two ways. First, its objective is to select an optimum subset of the available tasks to be performed during a fixed period of time. Secondly, each task scheduled is constrained to occur within its particular scheduling window. The algorithm is currently being used to develop typical observational timelines for a telescope that will be operated in earth orbit. Computational times associated with timeline development are presented.     

An efficient time-windowing procedure for scheduling projects under multiple resource constraints

This paper describes an efficient procedure for resource-constrained project scheduling problems. It starts with a simulated annealing technique to find a base schedule, and then improves the result by a time-windowing process. Every time-window, which is part of the base schedule, is a basis for a small subproject with two sets of specific constraints. Associated subprojects with time-windows are scheduled to optimality and based on their results the base schedule is updated. The overlapping feature of time-windows makes the displacement of an activity possible within the range of the entire project. The process of creating time-windows, scheduling their associated subprojects to optimality and improving the base schedule is controlled by a feed-back based mechanism that realises a trade-off between computational effort and the improvement made. The computational results indicate that the procedure is promising and yields better solutions than several heuristic algorithms presented in the literature.     

Labour flexibility and staffing levels in a dual-resource constrained job shop

In many manufacturing organizations, increasing process flexibility is becoming more important while the reliance on product cost to measure manufacturing performance is being lessened. As a result, companies are placing more emphasis on developing a cross-trained workforce in an effort to improve the flexibility of their operations. Having a cross-trained workforce allows managers to move workers around to adjust to temporary overloads in the shop. Another approach to increasing process flexibility is through the addition of labour to create a capacity buffer. Adding more labour improves flexibility since it reduces the average utilization in the shop thereby reducing the possibility of any overload occurring in the first place. This paper compares the benefits realized by the development of a multi-skilled workforce with the benefits realized by additional workforce staffing. Both strategies exhibit improvement in the simulation of a hypothetical dual resource constrained hybrid job-shop. Results suggest that developing a multi-skilled workforce is a more conservative approach to manufacturing improvement than simply increasing levels of staffing. However, the use of additional labour results in a much more pronounced improvement than cross-training. A combination of the two strategies appears to be the best choice     

Agile workforce evaluation: a framework for cross-training and coordination

This paper outlines approaches for assessing and classifying manufacturing and service operations in terms of their suitability for use of cross-trained (flexible) workers. We refer to our overall framework as agile workforce evaluation. The primary contributions of this paper are: (i) a strategic assessment framework that structures the key mechanisms by which cross-training can support organizational strategy; (ii) a tactical framework that identifies key factors to guide the selection of an architecture and worker coordination policy for implementing workforce agility; (iii) a classification of workforce agility architectures; (iv) a survey of a broad range of archetypical classes of worker coordination policies; (v) a survey of the literature with an operational perspective on workforce agility; and (vi) identification of opportunities for research and development of architectures for specific production environments.     

Scheduling with alternatives: a link between process planning and scheduling

The objective of this research is to develop and evaluate effective, computationally efficient procedures for scheduling jobs in a large-scale manufacturing system involving, for example, over 1000 jobs and over 100 machines. The main performance measure is maximum lateness; and a useful lower bound on maximum lateness is derived from a relaxed scheduling problem in which preemption of jobs is based on the latest finish time of each job at each machine. To construct a production schedule that minimizes maximum lateness, an iterative simulation-based scheduling algorithm operates as follows: (a) job queuing times observed at each machine in the previous simulation iteration are used to compute a refined estimate of the effective due date (slack) for each job at each machine; and (b) in the current simulation iteration, jobs are dispatched at each machine in order of increasing slack. Iterations of the scheduling algorithm terminate when the lower bound on maximum lateness is achieved or the iteration limit is reached. This scheduling algorithm is implemented in Virtual Factory, a Windows-based software package. The performance of Virtual Factory is demonstrated in a suite of randomly generated test problems as well as in a large furniture manufacturing facility. To further reduce maximum lateness, a second scheduling algorithm also incorporates a tabu search procedure that identifies process plans with alternative operations and routings for jobs. This enhancement yields improved schedules that minimize manufacturing costs while satisfying job due dates. An extensive experimental performance evaluation indicates that in a broad range of industrial settings, the second scheduling algorithm can rapidly identify optimal or nearly optimal schedules.     

Scheduling in a Multistage Production System with Set-up and Inventory Costs

This paper considers a multistage production system with constant known demand rate across an infinite horizon. A unit of product passes through the stages sequentially, so each stage draws from the in-process inventory of the previous stage. Each stage of the process is assumed to produce at a fixed, known, finite rate greater than the demand rate, so that it must be shut down and restarted periodically. Different stages may produce at different rates. This paper describes a procedure to find the start-up and shut-down schedule of each production stage which minimizes the sum of inventory costs and set-up costs for the system. This problem is solved using dynamic programming.     

Scheduling a manufacturing process with restrictions on resource availability

This paper examines the scheduling of a drug packaging operation which requires assigning resources to production lines during set-up in order to satisfy product specifications. We present mixed integer programming models of the problem and develop a Lagrangian heuristic to solve the models. Real data from a drug company and randomly generated data are used to test the models. Results show the efficacy of our solution approach to find good feasible schedules. We discuss some of the practical benefits and potential issues about the production schedules obtained using the models.     

Scheduling of a job-shop problem with limited output buffers

ABSTRACT

This article addresses a job-shop problem with limited output buffers (JS-LOB) with the objective of minimizing the process makespan. An integer nonlinear mathematical programming model is proposed to describe this problem. Based on the model, a two-stage algorithm consisting of obtaining feasible solutions and a local search is proposed to solve the JS-LOB problem. The local search has two operators: the first is a neighbourhood structure based on a disjunctive graph model, and the second is similar to crossover in the genetic algorithm to avoid falling into local optima. Computational results are presented for a set of benchmark tests. The results show the effectiveness of the proposed algorithm and indicate whether the processing time of the job conforms to a uniform distribution. When the proportion between the capacity of the buffer and the number of jobs is larger than 20%, the influence of the buffer becomes very small.     

Scheduling a batch processing machine with non-identical job sizes

The problem of scheduling batch processors is important in some industries and, at a more fundamental level, captures an element of complexity common to many practical scheduling problems. We describe a branch and bound procedure applicable to a batch processor model with arbitrary job processing times, job weights and job sizes. The scheduling objective is to minimize total weighted completion time. We find that the procedure returns optimal solutions to problems of up to 25 jobs in reasonable CPU time, and can be adapted for use as a heuristic for larger problems.     

Optimal Shift Scheduling with a Global Service Level Constraint

We study a shift scheduling problem for call centers with an overall service level objective. We prove a property of this problem, called multimodularity, that ensures that a local search algorithm terminates with a globally optimal solution. We report on computations performed using real call center data.     

Scheduling with multiple-job-on-one-processor pattern

Most scheduling literature considers a “one-job-on-one-processor” pattern, which assumes that a processor processes exactly one job at a time. In this paper we consider a new scheduling problem with a “multiple-job-on-one-processor” pattern, where several jobs can be processed by a single processor simultaneously, provided that the total size of the jobs being processed does not exceed the capacity of the processor at any point in time. This problem is motivated by the operation of berth allocation, which is to allocate vessels (jobs) to a berth (processor), where the vessels, if small in dimension, may share the berth with some other vessels for loading/unloading the goods. We consider the problem to minimize the makespan of the schedule. The well-known First-Fit Decreasing heuristic is generalized and applied to several variations of the problem, and the worst-case behavior of the generalized heuristics is studied. Worst-case error bounds are obtained for those models. Computational experiments are conducted to test the heuristics. The results suggest that the heuristics are effective in producing near-optimal solutions.