M Murat Firat

An improved MIP-based approach for a multi-skill workforce scheduling problem

This paper deals with scheduling complex tasks with an inhomogeneous set of resources. The problem is to assign technicians to tasks with multi-level skill requirements. Here, the requirements are merely the presence of a set of technicians that possess the necessary capabilities. An additional complication is that a set of combined technicians stays together for the duration of a work day. This typically applies to scheduling of maintenance and installation operations. We build schedules by repeated application of a flexible matching model that selects tasks to be processed and forms groups of technicians assigned to combinations of tasks. The underlying mixed integer programming (MIP) model is capable of revising technician-task allocations and performs very well, especially in the case of rare skills.

A parametric fuzzy logic approach to dynamic part routing under full routing flexibility

Manufacturing flexibility is a competitive weapon for surviving todays highly variable and volatile markets. It is critical therefore, to select the appropriate type of flexibility for a given manufacturing system, and to design effective strategies for using this flexibility in a way to improve the system performance. This study focuses on full routing flexibility which includes not only alternative machines for operations but also alternative sequences of operations for producing the same work piece. Upon completion of an operation, an on-line dispatching decision called part routing is required to choose one of the alternatives as the next step. This study introduces three new approaches, including a fuzzy logic approach, for dynamic part routing. The fuzzy part routing system adapts itself to the characteristics of a given flexible manufacturing system (FMS) installation by setting the key parameters of the membership functions as well as its Takagi-Sugeno type rule base, in such a way to capture the bottlenecks in the environment. Thus, the model does not require a search or training for the parameter set. The proposed approaches are tested against several crisp and fuzzy routing algorithms taken from the literature, by means of extensive simulation experiments in hypothetical FMS environments under variable system configurations. The results show that the proposed fuzzy approach remains robust across different system configurations and flexibility levels, and performs favourably compared to the other algorithms. The results also reveal important characteristic behaviour regarding routing flexibility.

Analysis of the dial-a-ride problem of Hunsaker and Savelsbergh

Hunsaker and Savelsbergh [B. Hunsaker, M. Savelsbergh, Efficient feasibility testing for dial-a-ride problems, Operations Research Letters 30 (2002) 169-173] discussed feasibility testing for a dial-a-ride problem under maximum wait time and maximum ride time constraints. We show that this feasibility test can be expressed as a shortest path problem in vertex-weighted interval graphs, which leads to a simple linear time algorithm.

Stable multi-skill workforce assignments

This paper analyzes stability in multi-skill workforce assignments of technicians and jobs. In our stability analysis, we extend the notion of blocking pairs as stated in the Marriage model of Gale-Shapley to the multi-skill workforce assignment. It is shown that finding stable assignments is NP-hard. A special case turns out to be solvable in polynomial time. For the general case, we give a characterization of the set of stable assignments by means of linear inequalities involving binary variables. We propose an integer programming (IP) model to construct optimal stable assignments with several objectives. In the computational results, we observe that it is easier to attain stability in instances with easy jobs and we consider a range of instances to show how fast the solution time increases. Open questions and further directions are discussed in the conclusion section.

A Branch-and-Price algorithm for stable workforce assignments with hierarchical skills

This paper deals with assigning hierarchically skilled technicians to jobs by considering preferences. We investigate stability definitions in multi-skill workforce assignments stemming from the notion of blocking pairs as stated in the Marriage model of Gale–Shapley. We propose a Branch-and-Price approach to find a stable workforce assignment in which no technician and job pair can be better off by replacing an already assigned technician in current team of the job. As base for our exact algorithm, we give a reformulation of the problem which constructs a stable assignment by selecting teams from a base set. Then, the pricing problem accounts finding a team to a job. We provide details of the algorithm and show its efficiency by means of a computational study. We also show that checking stability becomes NP-hard, if replacing groups of technicians is considered in defining stability.

Human performance-aware scheduling and routing of a multi-skilled workforce

Planning human activities within business processes often happens based on the same methods and algorithms as are used in the area of manufacturing systems. However, human behaviour is quite different from machine behaviour. Their performance depends on a number of factors, including workload, stress, personal preferences, etc. In this article we describe an approach for scheduling activities of people that takes into account business rules and dynamic human performance in order to optimise the schedule. We formally describe the scheduling problem we address and discuss how it can be constructed from inputs in the form of business process models and performance measurements. Finally, we discuss and evaluate an implementation for our planning approach to show the impact of considering dynamic human performance in scheduling.

Design and optimization of a fuzzy-neural hybrid controller for an artificial muscle robotic arm using genetic algorithms

Humanoids are increasingly used in the service sectors around the world to work with, or assist humans. However current humanoid designs place limitations on direct engagement with the human in terms of safety and usability. In this paper, we present an approach for the control of hybrid, high-speed and safe human-robot interaction systems with highly non-linear dynamic behavior. The proposed approach comprises the three soft computing techniques, namely back propagation neural network, fuzzy and genetic algorithms. This open-loop controller was applied to a Bridgestone Hybrid Robot Arm (BHRA). BHRA has three electric motors and four artificial muscles, arranged in an agonist/antagonist, and opposing pair configuration, that drive the five-degrees of freedom of the robot arm. The behaviors of the artificial muscles are observed under the effects of the links driven by the electric motors and it is shown that the proposed biologically-plausible controller could produce more accurate trajectories at higher speeds when compared to conventional PID and stand alone or combined versions of Neural Network and Fuzzy controllers.