Supply Chain Simulation: A Survey

This paper provides a survey of simulation in supply chain management.It reviews four types of simulation, namely spreadsheet simulation, system dynamics, discreteevent simulation, and business games.Which simulation type should be applied, depends on the type of managerial question to be answered by the model.Moreover, this paper summarizes novel sensitivity and robustness analyses.This sensitivity analysis yields a shortlist of the truly important factors in large simulation models with (say) a hundred factors.The robustness analysis optimises the important factors controllable by management, while accounting for the noise created by the important non-controllable, environmental factors.Both analyses are illustrated by a case study involving the simulation of a supply chain in the mobile communications industry in Sweden.In general, simulation is important because it may support the quantification of the benefits resulting from supply chain management.simulation;logistics;performance measurement;risk analysis;uncertainty;bifurcation;supply chain management

Scheduling and Control Modelling of HVLV Systems Using Max-Plus Algebra

International audienceThe High-Variety, Low-Volume (HVLV) scheduling problem is one of the most arduous and combinatorial optimization problems. This paper presents an analytical scheduling model using a tropical algebra called (max,+) algebra. The aim is to find an allocation for each operation and to define the sequence of operations on each machine, so that the resulting schedule has a minimal completion time and the due dates of the different jobs (products) are met such that a Just-In-Time (JIT) production will be satisfied. To generate feasible schedules, decision variables are introduced in the model. The algebraic model developed in this work describes the discontinuous operations aspect of HVLV systems as Discrete Event Dynamic Systems (DEDS). It is non-linear in the sense of (max,+) algebra. The focus of this research concerns the development of a static scheduling approach for deterministic and not-decision-free HVLV manufacturing systems. Firstly, using (max, +) algebra, a direct generation of event-timing equations for deterministic and not-decision free HVLV systems is obtained. Then, a non-linear optimization problem in (max, +) algebra is solved. Finally, the validity of the proposed approach is illustrated by simulation examples

Optimization of Surgery Scheduling in Multiple Operating Rooms with Post Anesthesia Care Unit Capacity Constraints

Surgery schedules are subject to disruptions due to duration uncertainty in surgical activities, patient punctuality, surgery cancellation and surgical emergencies. Unavailable recovery resources, such as post-anesthesia care unit (PACU) beds may also cause deviations from the surgical schedule. Such disruptions may result in inefficient utilization of medical resources, suboptimal patient care and patient and staff dissatisfaction. To alleviate these adverse effects, we study three open challenges in the field of surgery scheduling. The case we study is in a surgical suite with multiple operating rooms (ORs) and a shared PACU. The overall objective is to minimize the expected cost incurred from patient waiting time, OR idle time, OR blocking time, OR overtime and PACU overtime.In the first part of this work, we study surgery scheduling with PACU capacity constraints. With surgery sequences predetermined in each OR, a discrete event dynamic system (DEDS) and a DEDS-based stochastic optimization model are devised for the problem. A sample-gradient-based algorithm is proposed for the sample average approximation of our formulation. Numerical experiments suggest that the proposed method identifies near-optimal solutions and outperforms previous methods. It is also shown that considerable cost savings (11.8% on average) are possible in hospitals where PACU beds are a constraint.In the second part, we propose a two-stage solution method for stochastic surgery sequencing and scheduling with PACU capacity constraints. In the first stage, we propose a mixed-integer programming model with a surrogate objective that is much easier to solve than the original problem. The Lagrangian relaxation of the surrogate model can be decomposed by patients into network-structured subproblems which can be efficiently solved by dynamic programming. The first-stage model is solved by the subgradient method to determine the surgery sequence in each OR. Given the surgery sequence, scheduled start times are determined in the second stage using the sample-gradient descent algorithm. Our solution method outperforms benchmark methods that are proposed in the literature by 11% to 43% in numerical experiments. Our sequencing method contributes 45% to 80% of the overall improvement. We also illustrate the improvement on PACU utilization after using our scheduling strategy. In the third part, we propose a proactive and reactive surgery scheduling method for surgery scheduling under surgical disruptions. A surgical schedule considering possible disruptions is constructed prior to the day of surgery, and is then adjusted dynamically in response to disruptions on the day of surgery. The proposed method is based on stochastic optimization and a sample-gradient descent algorithm, which is the first non-metaheuristic approach proposed for this problem. In addition, the to-follow scheduling policy, which is widely used in practice, is considered in this study. This differs from previous surgical scheduling studies which assume no surgery can start before its scheduled start time. The proposed method finds near-optimal solutions and outperforms the scheduling method commonly used in practice

A Developmental Organization for Robot Behavior

This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions of dynamic pattern theory in which behavior is an artifact of coupled dynamical systems with a number of controllable degrees of freedom. In our model, the events that delineate control decisions are derived from the pattern of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential knowledge gathering and representation tasks and provide examples of the kind of developmental milestones that this approach has already produced in our lab

Simulation model to determine ratios between quay, yard and intra-terminal transfer equipment in an integrated container handling system

This paper presents a generic simulation model to determine the equipment mix (quay, yard and intra-terminal transfer) for a Container Terminal Logistics Operations System (CTLOS). The simulation model for the CTLOS, a typical type of discrete event dynamic system (DEDS), consists of three sub-models: ship queue, loading-unloading operations and yard-gate operations. The simulation model is empirically applied to phase 1 of the Yangshan Deep Water Port in Shanghai. This study considers different scenarios in terms of container throughput levels, equipment utilization rates, and operational bottle-necks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions