Constraint-based Scheduling for Paint Shops in the Automotive Supply Industry

2021 ◽  
Vol 12 (2) ◽  
pp. 1-25
Author(s):  
Felix Winter ◽  
Nysret Musliu
Keyword(s):  
Real Life ◽  
Daily Basis ◽  
Full Time ◽  
Scheduling Problem ◽  
Manufacturing Companies ◽  
Paint Shop ◽  
Shop Scheduling ◽  
Supply Industry ◽  
Automotive Supply Industry ◽  
Automotive Supply

Factories in the automotive supply industry paint a large number of items requested by car manufacturing companies on a daily basis. As these factories face numerous constraints and optimization objectives, finding a good schedule becomes a challenging task in practice, and full-time employees are expected to manually create feasible production plans. In this study, we propose novel constraint programming models for a real-life paint shop scheduling problem. We evaluate and compare our models experimentally by performing a series of benchmark experiments using real-life instances in the industry. We also show that the decision variant of the paint shop scheduling problem is NP-complete.

scholarly journals A large neighborhood search approach for the paint shop scheduling problem

2021 ◽  
Author(s):  
Felix Winter ◽  
Nysret Musliu
Keyword(s):  
Color Change ◽  
Real Life ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Large Neighborhood Search ◽  
Paint Shop ◽  
Shop Scheduling ◽  
Large Neighborhood ◽  
Benchmark Instances ◽  
Search Approach

AbstractMinimizing the setup costs caused by color changes is one of the main concerns for paint shop scheduling in the automotive industry. Yet, finding an optimized color sequence is a very challenging task, as a large number of exterior systems for car manufacturing need to be painted in a variety of different colors. Therefore, there is a strong need for efficient automated scheduling solutions in this area. Previously, exact and metaheuristic approaches for creating efficient paint shop schedules in the automotive supply industry have been proposed and evaluated on a publicly available set of real-life benchmark instances. However, optimal solutions are still unknown for many of the benchmark instances, and there is still a potential of reducing color change costs for large instances. In this paper, we propose a novel large neighborhood search approach for the paint shop scheduling problem. We introduce innovative exact and heuristic solution methods that are utilized within the large neighborhood search and show that our approach leads to improved results for large real-life problem instances compared to existing techniques. Furthermore, we provide previously unknown upper bounds for 14 benchmark instances using the proposed method.

A metaheuristic technique for energy-efficiency in job-shop scheduling

2016 ◽  
Vol 31 (5) ◽  
pp. 475-485 ◽  
Author(s):  
Joan Escamilla ◽  
Miguel A. Salido ◽  
Adriana Giret ◽  
Federico Barber
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficiency ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Real Life ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Extended Version ◽  
Reasonable Time ◽  
Shop Scheduling

AbstractMany real life problems can be modeled as a scheduling problem. The main objective of these problems is to obtain optimal solutions in terms of processing time, cost and quality. Nowadays, energy-efficiency is also taken into consideration. However, these problems are NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the classical job-shop scheduling problem. In the extended version, each operation has to be executed by one machine and this machine can work at different speed rates. The machines consume different amounts of energy to process tasks at different rates. The evaluation section shows that a powerful commercial tools for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.

scholarly journals Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2250
Author(s):  
Mei Li ◽  
Gai-Ge Wang ◽  
Helong Yu
Keyword(s):  
Artificial Bee Colony ◽  
Flow Shop ◽  
Real Life ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Bee Colony ◽  
Mutation Strategy

In this era of unprecedented economic and social prosperity, problems such as energy shortages and environmental pollution are gradually coming to the fore, which seriously restrict economic and social development. In order to solve these problems, green shop scheduling, which is a key aspect of the manufacturing industry, has attracted the attention of researchers, and the widely used flow shop scheduling problem (HFSP) has become a hot topic of research. In this paper, we study the fuzzy hybrid green shop scheduling problem (FHFGSP) with fuzzy processing time, with the objective of minimizing makespan and total energy consumption. This is more in line with real-life situations. The non-linear integer programming model of FHFGSP is built by expressing job processing times as triangular fuzzy numbers (TFN) and considering the machine setup times when processing different jobs. To address the FHFGSP, a discrete artificial bee colony (DABC) algorithm based on similarity and non-dominated solution ordering is proposed, which allows individuals to explore their neighbors to different degrees in the employed bee phase according to a sequence of positions, increasing the diversity of the algorithm. During the onlooker bee phase, individuals at the front of the sequence have a higher chance of being tracked, increasing the convergence rate of the colony. In addition, a mutation strategy is proposed to prevent the population from falling into a local optimum. To verify the effectiveness of the algorithm, 400 test cases were generated, comparing the proposed strategy and the overall algorithm with each other and evaluating them using three different metrics. The experimental results show that the proposed algorithm outperforms other algorithms in terms of quantity, quality, convergence and diversity.

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