Inter-Temporal Aggregation for Spatially Explicit Optimal Harvest Scheduling under Area Restrictions

2021 ◽  
Author(s):  
Atsushi Yoshimoto ◽  
Patrick Asante
Keyword(s):  
Main Idea ◽  
Maximum Flow ◽  
Planning Horizon ◽  
Spatial Aggregation ◽  
Temporal Aggregation ◽  
Scheduling Problems ◽  
Harvest Scheduling ◽  
Periodic Harvest ◽  
Over Time ◽  
Maximum Opening

Abstract We propose a new approach to solve inter-temporal unit aggregation issues under maximum opening size requirements using two models. The first model is based on Model I formulation with static harvest treatments for harvest activities. This model identifies periodic harvest activities using a set of constraints for inter-temporal aggregation. The second model is based on Model II formulation, which uses dynamic harvest treatments and incorporates periodic harvest activities directly into the model formulation. The proposed approach contributes to the literature on spatially constrained harvest scheduling problems as it allows a pattern of unit aggregation to change across multiple harvests over time, as inter-temporal aggregation under a maximum opening size requirement over period-specific duration. The main idea of the proposed approach for inter-temporal aggregation is to use a multiple layer scheme for a set of spatial constraints, which is adapted from a maximum flow specification in a spatial forest unit network and a sequential triangle connection to create fully connected feasible clusters. By dividing the planning horizon into period-specific durations for different spatial aggregation patterns, the models can complete inter-temporal spatial aggregation over the planning horizon under a maximum opening size requirement per duration. Study Implications Inter-temporal unit aggregation is important because it provides flexible aggregation patterns for maximum opening size problems with multiple harvests over time. We have proposed a new modeling approach capable of solving spatially constrained harvest scheduling problems by allowing a pattern of unit aggregation to change across multiple harvest periods over time, as inter-temporal aggregation under flexible maximum opening size requirements. Forest managers can benefit from this approach for their future requirements based on the public interests as well as their own.

scholarly journals Randomized Problem-Relaxation Solving for Over-Constrained Schedules

2021 ◽  
Author(s):  
Patrick Rodler ◽  
Erich Teppan ◽  
Dietmar Jannach
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Production Capacity ◽  
Main Idea ◽  
Planning Horizon ◽  
Benchmark Problems ◽  
Scheduling Problems ◽  
Constraint Solver ◽  
Set Optimization Problem ◽  
Constraint Model

Optimal production planning in the form of job shop scheduling problems (JSSP) is a vital problem in many industries. In practice, however, it can happen that the volume of jobs (orders) exceeds the production capacity for a given planning horizon. A reasonable aim in such situations is the completion of as many jobs as possible in time (while postponing the rest). We call this the Job Set Optimization Problem (JOP). Technically, when constraint programming is used for solving JSSPs, the formulated objective in the constraint model can be adapted so that the constraint solver addresses JOP, i.e., searches for schedules that maximize the number of timely finished jobs. However, also highly specialized solvers which proved very powerful for JSSPs may struggle with the increased complexity of the reformulated problem and may fail to generate a JOP solution given practical computation timeouts. As a remedy, we suggest a framework for solving multiple randomly modified instances of a relaxation of the JOP, which allows to gradually approach a JOP solution. The main idea is to have one module compute subset-minimal job sets to be postponed, and another one effectuating that random job sets are found. Different algorithms from literature can be used to realize these modules. Using IBM’s cutting-edge CP Optimizer suite, experiments on well-known JSSP benchmark problems show that using the proposed framework consistently leads to more scheduled jobs for various computation timeouts than a standalone constraint solver approach.

scholarly journals Minimizing the Number of Tardy Jobs on a Single Machine with an Availability Constraint

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ehsan Molaee ◽  
Ghasem Moslehi
Keyword(s):  
Branch And Bound ◽  
Single Machine ◽  
Planning Horizon ◽  
Single Machine Scheduling ◽  
Upper And Lower Bounds ◽  
Scheduling Problems ◽  
Bearing Witness ◽  
Number Of Tardy Jobs ◽  
Problem Instances ◽  
Tardy Jobs

Most scheduling problems are based on the assumption that machines work continuously during the planning horizon. This assumption is not true in many production environments because the machine may not be available during one or more periods such as during breakdowns or maintenance operations. In this paper, the problem of the single machine scheduling with one unavailability period and nonresumable jobs with the aim of minimizing the number of tardy jobs is studied. A number of theorems are proved and a heuristic procedure is developed to solve the problem. A branch-and-bound approach is also presented which includes upper and lower bounds and efficient dominance rules. Computational results for 2680 problem instances show that the branch-and-bound approach is capable of solving 98.7% of the instances optimally, bearing witness to the efficiency of the proposed procedure. Our results also indicate that the proposed approaches are more efficient when compared to other methods.

A BEST POSSIBLE ONLINE ALGORITHM FOR SCHEDULING TO MINIMIZE MAXIMUM FLOW-TIME ON BOUNDED BATCH MACHINES

2014 ◽  
Vol 31 (04) ◽  
pp. 1450030 ◽  
Author(s):  
CHENGWEN JIAO ◽  
WENHUA LI ◽  
JINJIANG YUAN
Keyword(s):  
Competitive Ratio ◽  
Completion Time ◽  
Online Algorithm ◽  
Unit Length ◽  
Maximum Flow ◽  
Flow Time ◽  
Release Time ◽  
Batch Machine ◽  
The Difference ◽  
Over Time

We consider online scheduling of unit length jobs on m identical parallel-batch machines. Jobs arrive over time. The objective is to minimize maximum flow-time, with the flow-time of a job being the difference of its completion time and its release time. A parallel-batch machine can handle up to b jobs simultaneously as a batch. Here, the batch capacity is bounded, that is b < ∞. In this paper, we provide a best possible online algorithm for the problem with a competitive ratio of [Formula: see text].

scholarly journals Harvest scheduling with spatial aggregation for two and three strip cut system under shelterwood management

2011 ◽  
Vol 57 (No. 6) ◽  
pp. 271-277 ◽  
Author(s):  
M. Konoshima ◽  
R. Marušák ◽  
A. Yoshimoto
Keyword(s):  
Spatial Aggregation ◽  
Decision Variable ◽  
Scheduling Problem ◽  
Harvest Scheduling ◽  
Aggregation Method ◽  
Management Units ◽  
Decision Variables ◽  
Optimal Harvest ◽  
Cutting System ◽  
Sequential Cuts

We propose a spatial aggregation method to solve an optimal harvest scheduling problem for strip shelterwood management. Strip shelterwood management involves either a two-cut system with a preparatory-removal cut cycle, or a three-cut system with a preparatory-establishment-removal cut cycle. In this study we consider these connected sequential cuts as one decision variable, then employ conventional adjacency constraints to seek the best combination of sequential cuts over space and time. Conventional adjacency constraints exclude any spatially-overlapped strips in the decision variables. Our results show the proposed approach can be used to analyze a strip shelterwood cutting system that requires "connectivity" of management units.

COMMON DUE-WINDOW ASSIGNMENT AND SCHEDULING OF JOB-DEPENDENT DETERIORATING JOBS AND MULTIPLE DETERIORATING MAINTENANCE ACTIVITIES

2014 ◽  
Vol 31 (01) ◽  
pp. 1450004 ◽  
Author(s):  
XINGONG ZHANG
Keyword(s):  
Operations Management ◽  
Production Scheduling ◽  
Window Size ◽  
Deteriorating Jobs ◽  
Planning Horizon ◽  
Scheduling Problems ◽  
Deteriorating Maintenance ◽  
Due Window ◽  
Maintenance Activities ◽  
Due Window Assignment

Due-window assignment and production scheduling problems are important issues in operations management. In this paper, the problems of common due-window assignment and scheduling of job-dependent deteriorating jobs and multiple deteriorating maintenance activities simultaneously on a single-machine are investigated. We assume that the machine may be subjected to several maintenance activities during the planning horizon. However, due to the restriction of budget of maintenance, the upper bound of the maintenance frequency on the machine is assumed to be known in advance. Moreover, we assume that the duration of each maintenance activity depends on the running time of the machine. Polynomial time algorithms for the all the studied problems are provided, where the objective is to simultaneously minimize the earliness, tardiness, due-window starting time, and due-window size costs.

scholarly journals CACHE HIERARCHY INSPIRED COMPRESSION: A NOVEL ARCHITECTURE FOR DATA STREAMS

2016 ◽  
Vol 2 (1) ◽  
pp. 39-52
Author(s):  
G. Holmes ◽  
B. Pfahringer ◽  
R. Kirkby
Keyword(s):  
Data Streams ◽  
Incremental Learning ◽  
Data Stream ◽  
Main Idea ◽  
Replacement Policy ◽  
Cache Hierarchies ◽  
Meta Level ◽  
Web Cache ◽  
General Architecture ◽  
Over Time

We present an architecture for data streams based on structures typically found in web cache hierarchies. The main idea is to build a meta level analyser from a number of levels constructed over time from a data stream. We present the general architecture for such a system and an application to classification. This architecture is an instance of the general wrapper idea allowing us to reuse standard batch learning algorithms in an inherently incremental learning environment. By artificially generating data sources we demonstrate that a hierarchy containing a mixture of models is able to adapt over time to the source of the data. In these experiments the hierarchies use an elementary performance based replacement policy and unweighted voting for making classification decisions.

scholarly journals Rolling Supply Chain Scheduling considering Suppliers, Production, and Delivery Lot-Size

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Rong-Hwa Huang ◽  
Tung-Han Yu ◽  
Chen-Yun Lee
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Simulated Data ◽  
Planning Horizon ◽  
Scheduling Problems ◽  
Supply Chain Scheduling ◽  
Lot Size ◽  
Delivery Cost ◽  
Chain Management ◽  
Rolling Planning

Supply chain management and integration play a key factor in contemporary manufacturing concept. Companies seek to integrate itself within a cooperative and mutual benefiting supply chain. Supply chain scheduling, as an important aspect of supply chain management, highly emphasizes on minimizing stock costs and delivery costs. Most previous researches on supply chain scheduling problems assume make-to-order production, which includes delivery cost in lot-size. This practice simplifies the complexity of the problem. Instead, this research discusses make-to-contract production, where the supply chain has a rolling planning horizon that changes according to contracts. Within a planning horizon, two types of interval are defined. The first is frozen interval, in which the manufacturing decision cannot be changed. The second is free interval, where schedules can be adjusted depending on new contracts. This research aims to build a robust rolling supply management schedule to satisfy customers’ needs, by considering supplier, production, and delivery lot-size simultaneously. The objective is to effectively decide a combination of supplier, production, and delivery lot-size that minimizes total cost consisting of supplier cost, finish good stock cost, and delivery cost. Based on the concept, this study designs a problem-solving process that combines the methods of rolling planning horizon and genetic algorithm. Delivery size (DS), finish good stock (FS), and early delivery cost (ED) are the three methods applied; each will provide a guideline to produce a feasible solution. By further considering the fluctuations in practical needs and performing an overall evaluation, a robust and optimal supply chain scheduling plan can be decided, including the optimal lot-sizes of supplier, production, and delivery. In the effectiveness test which considers 3 types of customer demands and 11 types of company cost structures, the simulated data test results suggest that the proposed methods in this study have excellent performance.

scholarly journals An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3293
Author(s):  
Huilong Fan ◽  
Zhan Yang ◽  
Shimin Wu ◽  
Xi Zhang ◽  
Jun Long ◽  
...  
Keyword(s):  
Graph Theory ◽  
Spatial Information ◽  
Resource Scheduling ◽  
Maximum Flow ◽  
Resource Planning ◽  
Information Networks ◽  
Planning Problem ◽  
Scheduling Problems ◽  
Time Consumption ◽  
Resource Request

To overcome the low timeliness of resource scheduling problems in spatial information networks, we propose a method based on a dynamic reconstruction of resource request queues and the autonomous coordinated scheduling of resources. First, we construct a small satellite network and combine the graph maximum flow theory to solve the link resource planning problem during inter-satellite data transmission. In addition, we design a multi-satellite resource scheduling algorithm with minimal time consumption based on graph theory. The algorithm is based on graph theory to reallocate the resource request queue to satellites with idle processing resources. Finally, we simulate the efficient resource scheduling capability in the spatial information network and empirically compare our approaches against two representative swarm intelligence baseline approaches and show that our approach has significant advantages in terms of performance and time consumption during resource scheduling.

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