scholarly journals Minimizing setups and waste when printing labels of consumer goods

OR Spectrum ◽  
2021 ◽  
Author(s):  
Herbert Meyr ◽  
Mirko Kiel
Keyword(s):  
Dynamic Programming ◽  
Consumer Goods ◽  
Planning Problem ◽  
Printing Plate ◽  
Setup Costs ◽  
Numerical Tests ◽  
Single Plate ◽  
Job Splitting ◽  
Splitting Problem ◽  
Problem Instances

AbstractA real-world planning problem of a printing company is presented where different sorts of a consumer goods’ label are printed on a roll of paper with sufficient length. The printer utilizes a printing plate to always print several labels of same size and shape (but possibly different imprint) in parallel on adjacent lanes of the paper. It can be decided which sort is printed on which (lane of a) plate and how long the printer runs using a single plate. A sort can be assigned to several lanes of the same plate, but not to several plates. Designing a plate and installing it on the printer incurs fixed setup costs. If more labels are produced than actually needed, each surplus label is assumed to be “scrap”. Since demand for the different sorts may be heterogeneous and since the number of sorts is usually much higher than the number of lanes, the problem is to build “printing blocks”, i.e., to decide how many and which plates to design and how long to run the printer with a certain plate so that customer demand is satisfied with minimum costs for setups and scrap. This industrial application is modeled as an extension of a so-called job splitting problem which is solved exactly and by various decomposition heuristics, partly basing on dynamic programming. Numerical tests compare both approaches with further straightforward heuristics and demonstrate the benefits of decomposition and dynamic programming for large problem instances.

scholarly journals Dynamic Network Planning of Underground Logistics System on Uncertainty Graph

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
YiHua Zhong ◽  
ShiMing Luo ◽  
Min Bao ◽  
XiaoDie Lv
Keyword(s):  
Dynamic Programming ◽  
Programming Model ◽  
Network Planning ◽  
Dynamic Network ◽  
Planning Problem ◽  
Uncertainty Measure ◽  
Uncertain Graph ◽  
Logistics System ◽  
Nanjing City ◽  
Dynamic Programming Model

When designing the underground logistics system, it is necessary to consider the uncertainty of logistics nodes, high cost, and high risk. This paper employed the theories of uncertain graph and dynamic programming to solve the network planning problem of underground logistics system. Firstly, we proposed the concepts of uncertainty measure matrix and vertices structure uncertainty graph by using uncertainty measure and uncertainty graph. Secondly, vertices structure uncertainty graph of the underground logistics system was constructed based on our proposed vertices structure uncertainty graph and the uncertainty of logistics nodes. Thirdly, the dynamic programming model of the underground logistics system was established, and its solution algorithm was also designed by improving simulated annealing. Finally, the correctness and feasibility of the method was validated by using a numerical example of the underground logistics system in Xianlin district, Nanjing City in China.

ONLINE CAPACITY PLANNING FOR REHABILITATION TREATMENTS: AN APPROXIMATE DYNAMIC PROGRAMMING APPROACH

2018 ◽  
Vol 34 (3) ◽  
pp. 381-405
Author(s):  
Ingeborg A. Bikker ◽  
Martijn R.K. Mes ◽  
Antoine Sauré ◽  
Richard J. Boucherie
Keyword(s):  
Dynamic Programming ◽  
Capacity Planning ◽  
Approximate Dynamic Programming ◽  
Capacity Allocation ◽  
Access Time ◽  
Programming Approach ◽  
Planning Problem ◽  
Dynamic Programming Approach ◽  
Negative Effect ◽  
Long Access

AbstractWe study an online capacity planning problem in which arriving patients require a series of appointments at several departments, within a certain access time target.This research is motivated by a study of rehabilitation planning practices at the Sint Maartenskliniek hospital (the Netherlands). In practice, the prescribed treatments and activities are typically booked starting in the first available week, leaving no space for urgent patients who require a series of appointments at a short notice. This leads to the rescheduling of appointments or long access times for urgent patients, which has a negative effect on the quality of care and on patient satisfaction.We propose an approach for allocating capacity to patients at the moment of their arrival, in such a way that the total number of requests booked within their corresponding access time targets is maximized. The model considers online decision making regarding multi-priority, multi-appointment, and multi-resource capacity allocation. We formulate this problem as a Markov decision process (MDP) that takes into account the current patient schedule, and future arrivals. We develop an approximate dynamic programming (ADP) algorithm to obtain approximate optimal capacity allocation policies. We provide insights into the characteristics of the optimal policies and evaluate the performance of the resulting policies using simulation.

Optimization Methods for Lot-Sizing Problem in an Automated Foundry

2013 ◽  
Vol 58 (3) ◽  
pp. 863-866 ◽  
Author(s):  
J. Duda ◽  
A. Stawowy
Keyword(s):  
Production Planning ◽  
Lot Sizing ◽  
Planning Horizon ◽  
Optimization Methods ◽  
Mixed Integer ◽  
Planning Problem ◽  
Lot Size ◽  
Mip Model ◽  
Production Planning Problem ◽  
Problem Instances

Abstract In the paper we studied a production planning problem in a mid-size foundry that provides tailor-made cast products in small lots for a large number of clients. Assuming that a production bottleneck is the furnace, a mixed-integer programming (MIP) model is proposed to determine the lot size of the items and the required alloys to be produced during each period of the finite planning horizon that is subdivided into smaller periods. As using an advanced commercial MIP solvers may be impractical for more complex and large problem instances, we proposed and compared a few computational intelligence heuristics i.e. tabu search, genetic algorithm and differential evolution. The examination showed that heuristic approaches can provide a good compromise between speed and quality of solutions and can be used in real-world production planning.

A Convergence Criterion for Stochastic Dual Dynamic Programming: Application to the Long-Term Operation Planning Problem

2018 ◽  
Vol 33 (4) ◽  
pp. 3678-3690 ◽  
Author(s):  
Rafael Bruno S. Brandi ◽  
Andre Luis Marques Marcato ◽  
Bruno Henriques Dias ◽  
Tales Pulinho Ramos ◽  
Ivo Chaves da Silva Junior
Keyword(s):  
Dynamic Programming ◽  
Convergence Criterion ◽  
Planning Problem ◽  
Operation Planning ◽  
Stochastic Dual Dynamic Programming ◽  
Term Operation ◽  
Dual Dynamic Programming

Trajectory planning of mobile manipulators using dynamic programming approach

Robotica ◽  
2012 ◽  
Vol 31 (4) ◽  
pp. 643-656 ◽  
Author(s):  
M. H. Korayem ◽  
M. Irani ◽  
A. Charesaz ◽  
A. H. Korayem ◽  
A. Hashemi
Keyword(s):  
Dynamic Programming ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Vision System ◽  
Optimal Path ◽  
Optimization Method ◽  
Mobile Manipulator ◽  
Programming Approach ◽  
Planning Problem ◽  
Mobile Manipulators

SUMMARYThis paper presents a solution for optimal trajectory planning problem of robotic manipulators with complicated dynamic equations. The main goal is to find the optimal path with maximum dynamic load carrying capacity (DLCC). Proposed method can be implemented to problems of both motion along a specified path and point-to-point motion. Dynamic Programming (DP) approach is applied to solve optimization problem and find the positions and velocities that minimize a pre-defined performance index. Unlike previous attempts, proposed method increases the speed of convergence by using the sequential quadratic programming (SQP) formulation. This formulation is used for solving problems with nonlinear constraints. Also, this paper proposes a new algorithm to design optimal trajectory with maximum DLCC for both fixed and mobile base mechanical manipulators. Algorithms for DLCC calculations in previous works were based on indirect optimization method or linear programming approach. The proposed trajectory planning method is applied to a linear tracked Puma and the mobile manipulator named Scout. Application of this algorithm is confirmed and simulation results are compared with experimental results for Scout robot. In experimental test, results are obtained using a new stereo vision system to determine the position of the robot end-effector.

scholarly journals Solving the Torpedo Scheduling Problem

2019 ◽  
Vol 66 ◽  
pp. 1-32
Author(s):  
Martin Josef Geiger ◽  
Lucas Kletzander ◽  
Nysret Musliu
Keyword(s):  
Material Flow ◽  
Simulated Annealing Algorithm ◽  
Steel Production ◽  
Experimental Investigations ◽  
Planning Problem ◽  
Scheduling Problem ◽  
Solution Approach ◽  
Annealing Algorithm ◽  
Problem Instances ◽  
Continuous Material

The article presents a solution approach for the Torpedo Scheduling Problem, an operational planning problem found in steel production. The problem consists of the integrated scheduling and routing of torpedo cars, i. e. steel transporting vehicles, from a blast furnace to steel converters. In the continuous metallurgic transformation of iron into steel, the discrete transportation step of molten iron must be planned with considerable care in order to ensure a continuous material flow. The problem is solved by a Simulated Annealing algorithm, coupled with an approach of reducing the set of feasible material assignments. The latter is based on logical reductions and lower bound calculations on the number of torpedo cars. Experimental investigations are performed on a larger number of problem instances, which stem from the 2016 implementation challenge of the Association of Constraint Programming (ACP). Our approach was ranked first (joint first place) in the 2016 ACP challenge and found optimal solutions for all used instances in this challenge.

scholarly journals Multivalued action languages with constraints in CLP(FD)

2010 ◽  
Vol 10 (2) ◽  
pp. 167-235 ◽  
Author(s):  
AGOSTINO DOVIER ◽  
ANDREA FORMISANO ◽  
ENRICO PONTELLI
Keyword(s):  
Planning Problem ◽  
Slight Variation ◽  
Action Languages ◽  
Conditional Effects ◽  
Electronic Transactions ◽  
Action Language ◽  
Problem Instances ◽  
Description Languages ◽  
Multivalued Action ◽  
Causal Laws

AbstractAction description languages, such asand ℬ (Gelfond and Lifschitz,Electronic Transactions on Artificial Intelligence, 1998, vol. 2, pp. 193—210), are expressive instruments introduced for formalizing planning domains and planning problem instances. The paper starts by proposing a methodology to encode an action language (with conditional effects and static causal laws), a slight variation of ℬ, usingConstraint Logic Programming over Finite Domains. The approach is then generalized to raise the use of constraints to the level of the action language itself. A prototype implementation has been developed, and the preliminary results are presented and discussed.

Sign in / Sign up

Export Citation Format

Share Document