scholarly journals A two-agent one-machine multitasking scheduling problem solving by exact and metaheuristics

2021 ◽  
Author(s):  
Chin-Chia Wu ◽  
Ameni Azzouz ◽  
Jia-Yang Chen ◽  
Jianyou Xu ◽  
Wei-Lun Shen ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Completion Time ◽  
Simulated Annealing Algorithm ◽  
Optimal Schedule ◽  
Computational Studies ◽  
Scheduling Problem ◽  
Optimal Solutions ◽  
Large Size ◽  
Annealing Algorithm ◽  
One Machine

AbstractThis paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to find the near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms.

Hybrid Method for Solving Flexible Open Shop Scheduling Problem with Simulated Annealing Algorithm and Multi-Agent Approach

2011 ◽  
Vol 383-390 ◽  
pp. 4612-4619 ◽  
Author(s):  
Tadeusz Witkowski ◽  
Paweł Antczak ◽  
Arkadiusz Antczak
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
The Other ◽  
Scheduling Problem ◽  
Open Shop Scheduling ◽  
Hand Search ◽  
Shop Scheduling ◽  
Multi Agent ◽  
Annealing Algorithm ◽  
Metaheuristic Optimization Algorithm

In this study we propose metaheuristic optimization algorithm, in which simulated annealing, multi agent approach with fuzzy logic are used. On the first level of solution search the multi agent approach is used, and on the second level – the simulated annealing. Two types of routing were considered: a serial and a parallel one. The multi-agent approach emphasizes flexibility rather than the optimality of solutions. On the other hand, search approaches such as simulated annealing, which focus more on the optimality of solutions.

A multiproduct economic order quantity model with simulated annealing application

2017 ◽  
Vol 12 (1) ◽  
pp. 119-142 ◽  
Author(s):  
Valdecy Pereira ◽  
Helder Gomes Costa
Keyword(s):  
Simulated Annealing ◽  
Design Methodology ◽  
Simulated Annealing Algorithm ◽  
Economic Order Quantity ◽  
Economic Order ◽  
Optimal Solutions ◽  
Order Quantity ◽  
Single Product ◽  
Content Type ◽  
Annealing Algorithm

Purpose This paper aims to present a set of five models for the economic order quantity problem. Four models solve problems for a single product: incremental discounts with or without backorders and all-unit discounts with or without backorders, and the last model solves problems for the multiproduct case. Design/methodology/approach A basic integer non-linear model with binary variables is presented, and its flexible structure allows for all five models to be utilised with minor modifications for adaptation to individual situations. The multiproduct model takes into consideration the work of Chopra and Meindl (2012), who studied two types of product aggregations: full and adaptive. To find optimal or near-optimal solutions for the multiproduct case, the authors propose a simulated annealing metaheuristic application. Numerical examples are presented to improve the comprehension of each model, and the authors also present the efficiency of the simulated annealing algorithm through an example that aggregates 50 products, each one with different discount schemes and some allowing backorders. Findings Our model proved to be efficient at finding optimal or near optimal solutions even when confronted with mathematical complexities such as the allowance of backorders and incremental discounts. Originality/value Finally our model can process a mix of products with different discount schemes at the same time, and the simulated annealing metaheuristics could find optimal or near optimal solutions with very few iterations.

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