Energy-aware feedback control for production scheduling and capacity control

In this paper, we propose an energy-aware feedback control model for production scheduling and capacity control. Specifically, we integrate functions of production scheduling and capacity control, taking into account the costs of energy consumption and machine maintenance varied by production capacity, and the penalty cost imposed by just-in-time production requirements. Continuous control variables are used to adjust the system and the resulting dynamics are modelled. Computational experiments show that interrelated dynamics among these three performance factors are well explained by the proposed controllers, and considerably better energy performance, about 20.0-40.0% improvement, in an energy-aware production compared to a conventional strategy

Just-in-time delivery for green fleets: A feedback control approach

•Dynamic models for controlling vehicle speed and the departure time are developed.•just-in-time and fuel performances are examined for VRPSTW.•Impact of load weight and JIT penalty cost on a routing schedule is examined.•Drivers can flexibly change the routing schedules considering JIT and fuel performance. With increasing attention being paid to greenhouse gas (GHG) emissions, the transportation industry has become an important focus of approaches to reduce GHG emissions, especially carbon dioxide equivalent (CO2e) emissions. In this competitive industry, of course, any new emissions reduction technique must be economically attractive and contribute to good operational performance. In this paper, a continuous-variable feedback control algorithm called GEET (Greening via Energy and Emissions in Transportation) is developed; customer deliveries are assigned to a fleet of vehicles with the objective function of Just-in-Time (JIT) delivery and fuel performance metrics akin to the vehicle routing problem with soft time windows (VRPSTW). GEET simultaneously determines vehicle routing and sets cruising speeds that can be either fixed for the entire trip or varied dynamically based on anticipated performance. Dynamic models for controlling vehicle cruising speed and departure times are proposed, and the impact of cruising speed on JIT performance and fuel performance are evaluated. Allowing GEET to vary cruising speed is found to produce an average of 12.0–16.0% better performance in fuel cost, and −36.0% to +16.0% discrepancy in the overall transportation cost as compared to the Adaptive Large Neighborhood Search (ALNS) heuristic for a set of benchmark problems. GEET offers the advantage of extremely fast computational times, which is a substantial strength, especially in a dynamic transportation environment

Distributed Feedback Control for Production, Inventory, and CO2 Emissions in an Assemble-To-Order System

We study continuous variable feedback control of an assemble-toorder system with multiple components and multiple workstations to analyse interrelationships among the production system and corresponding CO2 emissions. The proposed dynamic models are designed by proportional and integral control laws, and represent assembly job arrivals along with the component consumption rates at each workstation for controlling finished-goods assembly schedule, and component production rate for controlling component stock levels, respectively. We also develop the unified feedback control algorithm whose objective is to minimize due date deviation from the customer-requested final assembly due date and component inventory discrepancy in respect to the optimum inventory level, simultaneously. Using numerical simulations, we show how dynamics between the inventory and production systems are interrelated, and resulting CO2 emission variation by the production system

Simulation-based control for green transportation with high delivery service

Shipping operations are facing increasing pressures for tighter delivery service levels and green transportation, which conflict with each other and requires trade-off between fuel consumption and delivery service. Furthermore, responsiveness to customer demand requires rapid generation of good quality solutions. This paper presents a simulation-based feedback control algorithm for real-time vehicle route planning which considers delivery timeliness and fuel efficiency. The proposed control theoretic algorithm uses feedback from simulation to adjust the planned routes for timeliness and adaptively adjust the vehicle speed within an allowable range to improve fuel efficiency. The formulation results in a multi-variable continuous variable control system with non-linear dynamics. The control algorithm extends prior work in distributed arrival time control, which is used as a basis to derive analytical insights into this computational intractable optimization problem. Performance of the algorithm is evaluated using a simulation model of an industrial distribution center

A Dynamic Algorithm for Distributed Feedback Control for Manufacturing Production, Capacity, and Maintenance

We propose a dynamic algorithm for distributed feedback control which unifies the functions of production and maintenance scheduling at the shop floor level, and machinery capacity control at the CNC level, which are usually considered in isolation in practice. A continuous-time control theoretic approach is used to model dynamics of these three functions in a unified manner, considering stochastic machine failures and a corresponding maintenance interval. Theories of nonlinear control and discontinuous differential equations are used to analytically predict the system dynamics including the resulting discontinuous dynamics

Real-time Feedback Control for Production, Maintenance, and Capacity

In this paper, we develop dynamic models for production scheduling, machine maintenance planning, and machine capacity control. Specifically, differential equation based models are used to characterize real-time dynamics that span from controlling machinery capacity to production and maintenance planning. Based on these dynamic models, distributed feedback control algorithm which is performed upon discrete-event simulation using each part's and machine's local information is developed to determine the production and the machine maintenance event scheduling while controlling the machine capacity in a unified manner

A Mixed Integer Programming Model for Patient Appointment in an Infusion Center

Infusion centers in hospitals are experiencing higher demand, resulting in substantially long patient wait times. Also, scheduling chemotherapy is not a straightforward task due to several factors such as constraints on patient appointments for infusion and doctor visits. Limited resource capacity in an infusion center and physician unavailability are also important factors that increase appointment scheduling complexity. In this study, we develop a Mixed-Integer Programming (MIP) model based on real work flow data of an infusion center and generate balanced appointment schedules for chemotherapy. The aim of the proposed model is to reduce patient's wait times and the makespan which will improve the satisfaction levels for timely services and will bring more balanced resource utilization, resulting in cost reduction

Learning-based logistics planning and scheduling for crowdsourced parcel delivery

•A method is proposed to control orders and schedule routings of parcel pickup and delivery.•An optimal policy for controlling orders to maximize profitability is derived.•A reinforcement learning with an artificial neural network is utilized to solve the problem.•A continuous-variable feedback control approach is used to schedule multiple vehicle routings.•The proposed approach is beneficial when only limited number of vehicles is available. Today many domains have begun dealing with more complex and practical problems thanks to advances in artificial intelligence. In this paper, we study the crowdsourced parcel delivery problem, a new type of transportation, with consideration of complex and practical cases, such as multiple delivery vehicles, just-in-time (JIT) pickup and delivery, minimum fuel consumption, and maximum profitability. For this we suggest a learning-based logistics planning and scheduling (LLPS) algorithm that controls admission of order requests and schedules the routes of multiple vehicles altogether. For the admission control, we utilize reinforcement learning (RL) with a function approximation using an artificial neural network (ANN). Also, we use a continuous-variable feedback control algorithm to schedule routes that minimize both JIT penalty and fuel consumption. Computational experiments show that the LLPS outperforms other similar approaches by 32% on average in terms of average reward earned from each delivery order. In addition, the LLPS is even more advantageous when the rate of order arrivals is high and the number of vehicles that transport parcels is low

Continuous variable control approach for home care crew scheduling

The home care crew scheduling problem (HCCSP) is defined as a dynamic routing and scheduling problem with caretakers' fixed appointments, and therefore has many similarities with the vehicle routing problem with time windows. Considering frequent demand changes regarding resource priorities, appointment alterations, and time windows in HCCSP, the control theoretic approach with discrete event distributed simulation provide substantial benefits by offering real-time response to demand changes. We develop dynamic models for HCCSP with dynamic patient appointments, and explain dynamics that span from controlling crew work times to home-visit scheduling. Also, the real-time feedback control algorithm is proposed to solve HCCSP, where it is performed based on the time-scaled approach that possibly eliminates the need for directly synchronizing events and thereby eliminates the complexity associated with discrete event distributed simulation approaches

Energy-Aware Models for Warehousing Operations

Part 1: Knowledge-Based SustainabilityInternational audienceThere is a growing need in industries worldwide to become more sustainable and energy efficient. Due to rapid increase in demand of goods, there has been a rise in demand of logistics and operational services. This necessitates needs for a large number of warehouses and distribution centers to satisfy demand. It is imperative that warehouses follow the same sustainable development model practiced in other industries. This paper extends energy efficiency techniques suggested for manufacturing to warehousing. Specifically, warehouses are modeled as M/M/c queues where forklifts are servers and this model is used to evaluate performance of energy control policies. The model is then extended to general distribution queues. Experiments based on real-world data yield results that indicate that for system utilization values between 40% and 100%, as the number of servers in the system increases by a factor of 4, energy consumption increases by a factor of 3.78