scholarly journals Integrated day-ahead energy procurement and production scheduling

2018 ◽  
Vol 66 (11) ◽  
pp. 950-963 ◽  
Author(s):  
Egidio Leo ◽  
Sebastian Engell
Keyword(s):  
Production Scheduling ◽  
Programming Model ◽  
Production Capacity ◽  
Electricity Consumption ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Production Schedule ◽  
Sources Of Uncertainty ◽  
Continuous Power ◽  
The Cost

Abstract For the optimal operation of power-intensive plants, a challenge which is addressed in this work is to simultaneously determine the optimal production schedule and the optimal day-ahead electricity commitment. In order to ensure stability of the power grid, the electricity suppliers impose a daily electricity commitment to large consumers. The consumers have to commit one day in advance to the amount of energy they are going to purchase and use for a horizon of 24 hours (with an hourly discretization) and in case the actual electricity consumption differs significantly from the committed profile, the consumer is obliged to pay penalties. Since the consumers have to commit to the electricity suppliers before the actual electricity demand is known, uncertainty needs to be taken into account. A stochastic mixed-integer linear programming model is developed to consider two critical sources of uncertainty: equipment breakdowns and deviation prices. Equipment breakdowns can reduce the production capacity and make the actual electricity consumption deviate from the day-ahead electricity commitment. The application of the proposed approach to a continuous power-intensive plant shows the benefit gained from the solution of the stochastic model instead of the deterministic counterpart in terms of reduction of the cost of the energy.

scholarly journals Minimising negative externalities cost using 0-1 mixed integer linear programming model in e-commerce environment

2017 ◽  
Vol 11 ◽  
Author(s):  
Akyene Tetteh ◽  
Sarah Dsane-Nsor
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Programming Model ◽  
Petroleum Products ◽  
Mixed Integer ◽  
The Internet ◽  
Negative Externalities ◽  
The Government ◽  
The Cost

Background: Although the Internet boosts business profitability, without certain activities like efficient transportation, scheduling, products ordered via the Internet may reach their destination very late. The environmental problems (vehicle part disposal, carbon monoxide [CO], nitrogen oxide [NOx] and hydrocarbons [HC]) associated with transportation are mostly not accounted for by industries.Objectives: The main objective of this article is to minimising negative externalities cost in e-commerce environments.Method: The 0-1 mixed integer linear programming (0-1 MILP) model was used to model the problem statement. The result was further analysed using the externality percentage impact factor (EPIF).Results: The simulation results suggest that (1) The mode of ordering refined petroleum products does not impact on the cost of distribution, (2) an increase in private cost is directly proportional to the externality cost, (3) externality cost is largely controlled by the government and number of vehicles used in the distribution and this is in no way influenced by the mode of request (i.e. Internet or otherwise) and (4) externality cost may be reduce by using more ecofriendly fuel system.

COMBINING PROCESS SELECTION AND LOT SIZING MODELS FOR PRODUCTION SCHEDULING OF ELECTROFUSED GRAINS

2009 ◽  
Vol 26 (03) ◽  
pp. 421-443 ◽  
Author(s):  
JOSÉ ROBERTO DALE LUCHE ◽  
REINALDO MORABITO ◽  
VITÓRIA PUREZA
Keyword(s):  
Customer Service ◽  
Production Scheduling ◽  
Programming Model ◽  
Lot Sizing ◽  
Mixed Integer ◽  
Due Dates ◽  
Process Selection ◽  
Simple Task ◽  
Model Combining ◽  
Constructive Heuristic

This work presents an optimization model to support decisions in the production planning and control of the electrofused grain industry. A case study was carried out in a Brazilian company with the aim of helping to increase productivity and improve customer service concerning meeting deadlines. A mixed integer linear programming model combining known models of process selection and single-stage lot sizing were applied to the production scheduling of electrofused grains. Optimizing this scheduling is not a simple task mainly because of the scale of the equipment setup times, the diversity of the products and the deadlines of the order due dates. A constructive heuristic is also proposed as an alternative solution method, particularly for large-sized instances. The results show that the model and the heuristic can produce better solutions than the ones currently used by the company.

scholarly journals Optimal Operation of Soft Open Points-Based Energy Storage in Active Distribution Networks by Considering the Battery Lifetime

2021 ◽  
Vol 8 ◽  
Author(s):  
Jian Wang ◽  
Niancheng Zhou ◽  
Anqi Tao ◽  
Qianggang Wang
Keyword(s):  
Energy Storage ◽  
Reactive Power ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Operation Model ◽  
Battery Lifetime ◽  
Battery Degradation ◽  
Active Distribution Networks

Soft open point-based energy storage (SOP-based ES) can transfer power in time and space and also regulate reactive power. These characteristics help promote the integration of distributed generations (DGs) and reduce the operating cost of active distribution networks (ADNs). Therefore, this work proposed an optimal operation model for SOP-based ES in ADNs by considering the battery lifetime. First, the active and reactive power equations of SOP-based ES and battery degradation cost were modeled. Then, the optimal operation model that includes the operation cost of ADNs, loss cost, and battery degradation cost was established. The mixed integer nonlinear programming model was transformed to a mixed integer linear programming model derived through linearization treatment. Finally, the feasibility and effectiveness of the proposed optimization model are verified by the IEEE33 node system.

scholarly journals A mixed-integer linear programming model for harvesting, loading and transporting sugarcane. A case study in Peru

DYNA ◽  
2016 ◽  
Vol 83 (195) ◽  
pp. 173-179 ◽  
Author(s):  
Marcela María Morales-Chávez ◽  
José A. Soto-Mejía ◽  
William Ariel Sarache
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Programming Model ◽  
Raw Material ◽  
Linear Programming Model ◽  
Mixed Integer ◽  
Production Plant ◽  
Integer Linear Programming Model ◽  
The Cost

Due to opportunities for economic and social development in the biofuels market, improvement to the supply chain has become a relevant matter. In agro-industrial supply chains, procurement costs are highly relevant. Since sugarcane is a high performance raw material for ethanol production, this paper proposes a Mixed-Integer Linear Programming Model for cost optimization for harvesting, loading and transportation operations. The model determines the quantity of machines and workers to meet the biofuel plant requirements. Costs of resources for harvesting and loading as well as transportation costs from the land parcel to the production plant are minimized. Also, the model calculates the cost of penalties for shortages (unmet demand) and the cost of equipment idle time. The implementation of the model in a Peruvian biofuels company, showed a cost reduction of around 11 % when compared to the current costs.

CONFLICT IDENTIFICATION AND RECONCILIATION IN A COLLABORATIVE MANUFACTURING SCHEDULING TASK

2008 ◽  
Vol 07 (01) ◽  
pp. 147-174 ◽  
Author(s):  
CLAUDIO F. ABREU ◽  
JERROLD H. MAY ◽  
WILLIAM E. SPANGLER ◽  
LUIS G. VARGAS
Keyword(s):  
Production Scheduling ◽  
Group Process ◽  
Programming Model ◽  
Mathematical Formulation ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Intelligent Decision Support System ◽  
Intelligent Decision ◽  
Multiple Experts ◽  
Intelligent Decision Support

We studied the process of production scheduling in a large chemical plant. Scheduling in that environment is inherently a group process because multiple experts are needed to construct a schedule and to manage its execution. A mathematical formulation of the production scheduling problem yields a mixed-integer linear programming model too large to solve in a reasonable time with current technology. We therefore use an intelligent decision support system (DSS) to heuristically find a satisficing solution to the production scheduling problem. Our DSS is based on a model of the collaborative nature of the task, and it focuses on the communication, argumentation, and reconciliation strategies undertaken by individuals. Using actual production schedules, we show that our DSS can lead to measurable improvements over humanly-designed plans, where the quality of the schedule is measured using the objective function of the mathematical formulation of the problem.

scholarly journals Production Scheduling and Customer Orders Assignment in a Three-Level Supply Chain

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
F. Sadeghi Naieni Fard ◽  
B. Naderi ◽  
A. A. Akbari
Keyword(s):  
Production Scheduling ◽  
Variable Neighborhood Search ◽  
Programming Model ◽  
Search Algorithm ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Distribution Centers ◽  
Proposed Model ◽  
Production Distribution ◽  
Neighborhood Search Algorithm

In the classical production-distribution centers problem, only assignment of customers, distribution centers, and suppliers is determined. This paper extends the problem of production-distribution centers assignment by considering sequencing decisions in the supply network. Nowadays, meeting delivery time of products is a competitive benefit; therefore, the objective is to minimize total tardiness. This problem is mathematically formulated by a mixed integer programming model. Then, using the proposed model, small instances of the problem can be optimally solved by GAMS software. Moreover, two metaheuristics based on variable neighborhood search and simulated annealing are proposed to solve large instances of the problem. Finally, performance of the proposed metaheuristics is evaluated by two sets of balanced and unbalanced instances. The computational results show the superiority of the variable neighborhood search algorithm.

scholarly journals MILP of multitask scheduling of geographically distributed maintenance tasks

2022 ◽  
Vol 13 (1) ◽  
pp. 119-134 ◽  
Author(s):  
Hamed Allaham ◽  
Doraid Dalalah
Keyword(s):  
Preventive Maintenance ◽  
Mixed Integer Linear Programming ◽  
Programming Model ◽  
Commercial Buildings ◽  
Mixed Integer ◽  
Service Levels ◽  
Minimum Number ◽  
Geographically Distributed ◽  
Time Utilization ◽  
The Cost

Due to its proactive impact on the serviceability of components in a system, preventive maintenance plays an important role particularly in systems of geographically spread infrastructure such as utilities networks in commercial buildings. What makes such systems differ from the classical schemes is the routing and technicians' travel times. Besides, maintenance in commercial buildings is characterized by its short tasks’ durations and spatial distribution within and between different buildings, a class of problems that has not been suitably investigated. Although it is not trivial to assign particular duties solely to multi-skilled teams under limited time and capacity constraints, the problem becomes more challenging when travel routes, durations and service levels are considered during the execution of the daily maintenance tasks. To address this problem, we propose a Mixed Integer Linear Programming Model that considers the above settings. The model exact solution recommends collaborative choices that include the number of maintenance teams, the selected tasks, routes, tasks schedules, all detailed to days and teams. The model will reduce the cost of labor, replacement parts, penalties on service levels and travel time. The optimization model has been tested using different maintenance scenarios taken from a real maintenance provider in the UAE. Using CPLEX solver, the findings demonstrate an inspiring time utilization, schedules of minimal routing and high service levels using a minimum number of teams. Different travel speeds of diverse assortment of tasks, durations and cost settings have been tested for further sensitivity analysis.

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