scholarly journals Temporary Workforce Planning with Firm Contracts: A Model and a Simulated Annealing Heuristic

2011 ◽  
Vol 2011 ◽  
pp. 1-18 ◽  
Author(s):  
Muhammad Al-Salamah
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Workforce Planning ◽  
Temporary Employment ◽  
Mixed Integer ◽  
Temporary Workers ◽  
Planning Models ◽  
Training Cost ◽  
Holding Cost ◽  
Temporary Staffing

The aim of this paper is to introduce a model for temporary staffing when temporary employment is managed by firm contracts and to propose a simulated annealing-based method to solve the model. Temporary employment is a policy frequently used to adjust the working hour capacity to fluctuating demand. Temporary workforce planning models have been unnecessarily simplified to account for only periodic hiring and laying off; a company can review its workforce requirement every period and make hire-fire decisions accordingly, usually with a layoff cost. We present a more realistic temporary workforce planning model that assumes a firm contract between the worker and the company, which can extend to several periods. The model assumes the traditional constraints, such as inventory balance constraints, worker availability, and labor hour mix. The costs are the inventory holding cost, training cost of the temporary workers, and the backorder cost. The mixed integer model developed for this case has been found to be difficult to solve even for small problem sizes; therefore, a simulated annealing algorithm is proposed to solve the mixed integer model. The performance of the SA algorithm is compared with the CPLEX solution.

A Hybrid Ant Colony Optimization and Simulated Annealing Algorithm for Multi-Objective Scheduling of Cellular Manufacturing Systems

2020 ◽  
Vol 11 (3) ◽  
pp. 1-40 ◽  
Author(s):  
Aidin Delgoshaei ◽  
Ahad Ali
Keyword(s):  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Manufacturing Systems ◽  
Simulated Annealing Algorithm ◽  
Cellular Manufacturing ◽  
Ant Colony ◽  
Mixed Integer ◽  
Production Plant ◽  
Manufacturing Companies ◽  
Multi Objective

During the last 2 decades, there have been many manufacturing companies in various industries that used the advantages of cellular manufacturing layouts. However, determining the best schedule for cellular layouts considering uncertain product demands is a big concern for scientists. In this research, a multi-objective decision-making model is proposed in the process of dynamic cellular production planning where the market demands are uncertain. In this regard, a non-linear mixed integer programming model is developed. The complexity of the model is high to consider the model as NP-hard. Therefore, a hybrid Ant colony Optimization and Simulated Annealing Algorithms are proposed to solve the problem. Then, the Taguchi method is used to estimate appropriate sets of parameters of the proposed algorithm. The results demonstrated that the proposed algorithm can generate the best part-routes of products in terms of time, cost and load variance in a reasonable time. The algorithm is then used for a cellular production plant which is the producer of heavy vehicles parts.

scholarly journals A Mathematical Model and a Simulated Annealing Algorithm for Balancing Multi-manned Assembly Line Problem with Sequence-Dependent Setup Time

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Wucheng Yang ◽  
Wenming Cheng
Keyword(s):  
Simulated Annealing ◽  
Assembly Line ◽  
Simulated Annealing Algorithm ◽  
Assembly Line Balancing ◽  
Setup Time ◽  
Mixed Integer ◽  
Sequence Dependent ◽  
Sequence Dependent Setup Time ◽  
Annealing Algorithm ◽  
Sequence Dependent Setup

Multi-manned assembly lines have been widely applied to the industrial production, especially for large-sized products such as cars, buses, and trucks, in which more than one operator in the same station simultaneously performs different tasks in parallel. This study deals with a multi-manned assembly line balancing problem by simultaneously considering the forward and backward sequence-dependent setup time (MALBPS). A mixed-integer programming is established to characterize the problem. Besides, a simulated annealing algorithm is also proposed to solve it. To validate the performance of the proposed approaches, a set of benchmark instances are tested and the lower bound of the proposed problem is also given. The results demonstrated that the proposed algorithm is quite effective to solve the problem.

scholarly journals A Hybrid Simulated Annealing Heuristic for Multistage Heterogeneous Fleet Scheduling with Fleet Sizing Decisions

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Bing Li ◽  
Xinyu Yang ◽  
Hua Xuan
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Initial Solution ◽  
Mixed Integer ◽  
Greedy Heuristic ◽  
Allocation Scheme ◽  
Heterogeneous Fleet ◽  
Fleet Sizing ◽  
Vehicle Allocation

This paper deals with multistage heterogeneous fleet scheduling with fleet sizing decisions (MHFS-FSD). This MHFS-FSD attempts to integrate vehicles allocation and fleet sizing decisions considering the vehicle routing of multiple vehicle types. The problem is formulated as mixed integer programming model. The matrix formulation denoting vehicle allocation scheme is explored according to the characteristic of this problem. Generating vehicle allocation scheme with greedy heuristic procedure (VA-GHP) as initial solution of problem is presented. The USP-IVA method to update the initial solution generated by VA-GHP approach is developed. And then, incorporating VA-GHP and USP-IVA into simulated annealing algorithm, a novel heuristic called HSAH-GHP&IVA is proposed. Finally, some experiments are designed to test the proposed heuristic and the results show that the heuristic can generate reasonably good solutions in short CPU times.

Modeling and solution of maximal covering problem considering gradual coverage with variable radius over multi-periods

2018 ◽  
Vol 52 (4-5) ◽  
pp. 1245-1260 ◽  
Author(s):  
Alireza Eydi ◽  
Javad Mohebi
Keyword(s):  
Simulated Annealing ◽  
Facility Location ◽  
Location Problem ◽  
Simulated Annealing Algorithm ◽  
Mixed Integer ◽  
Multiple Time ◽  
Variable Radius ◽  
Maximal Covering ◽  
Annealing Algorithm ◽  
Time Periods

Facility location is a critical component of strategic planning for public and private firms. Due to high cost of facility location, making decisions for such a problem has become an important issue which have gained a large deal of attention from researchers. This study examined the gradual maximal covering location problem with variable radius over multiple time periods. In gradual covering location problem, it is assumed that full coverage is replaced by a coverage function, so that increasing the distance from the facility decreases the amount of demand coverage. In variable radius covering problems, however, each facility is considered to have a fixed cost along with a variable cost which has a direct impact on the coverage radius. In real-world problems, since demand may change over time, necessitating relocation of the facilities, the problem can be formulated over multiple time periods. In this study, a mixed integer programming model was presented in which not only facility capacity was considered, but also two objectives were followed: coverage maximization and relocation cost minimization. A metaheuristic algorithm was presented to solve the maximal covering location problem. A simulated annealing algorithm was proposed, with its results presented. Computational results and comparisons demonstrated good performance of the simulated annealing algorithm.

scholarly journals Multi-Manned Assembly Line Balancing: Workforce Synchronization for Big Data Sets through Simulated Annealing

2021 ◽  
Vol 11 (6) ◽  
pp. 2523
Author(s):  
Francesco Pilati ◽  
Emilio Ferrari ◽  
Mauro Gamberi ◽  
Silvia Margelli
Keyword(s):  
Simulated Annealing ◽  
Assembly Line ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Assembly Line Balancing ◽  
Task Assignment ◽  
Computation Time ◽  
Mixed Integer ◽  
Solution Quality ◽  
Line Design

The assembly of large and complex products such as cars, trucks, and white goods typically involves a huge amount of production resources such as workers, pieces of equipment, and layout areas. In this context, multi-manned workstations commonly characterize these assembly lines. The simultaneous operators’ activity in the same assembly station suggests considering compatibility/incompatibility between the different mounting positions, equipment sharing, and worker cooperation. The management of all these aspects significantly increases the balancing problem complexity due to the determination of the start/end times of each task. This paper proposes a new mixed-integer programming model to simultaneously optimize the line efficiency, the line length, and the workload smoothness. A customized procedure based on a simulated annealing algorithm is developed to effectively solve this problem. The aforementioned procedure is applied to the balancing of the real assembly line of European sports car manufacturers distinguished by 665 tasks and numerous synchronization constraints. The experimental results present remarkable performances obtained by the proposed procedure both in terms of solution quality and computation time. The proposed approach is the practical reference for efficient multi-manned assembly line design, task assignment, equipment allocation, and mounting position management in the considered industrial fields.

scholarly journals A Simulated Annealing Algorithm for Intermodal Transportation on Incomplete Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4467
Author(s):  
Mustapha Oudani
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Transportation Network ◽  
Sustainable Transportation ◽  
Mixed Integer ◽  
Intermodal Transportation ◽  
Efficient Design ◽  
Road Transportation ◽  
Mixed Integer Linear Program ◽  
Annealing Algorithm

Growing competition in the world enforces the need for an efficient design of transportation networks. Furthermore, a competitive transportation network should also be eco-friendly. As road transportation is responsible for the largest quantities of CO2 emissions, Intermodal Transportation (IT) might be a potential alternative. From this perspective, intermodal terminals location is a cornerstone for building a sustainable transportation network. The purpose of this paper is to study and efficiently solve the Intermodal Terminal Location Problem on incomplete networks. We model this problem as a mixed integer linear program and develop a simulated annealing algorithm to tackle medium and large instances. The computational results show that the obtained solutions using simulated annealing are competitive and close to the exact solutions found by CPLEX solver for small and medium instances. The same developed algorithm outperforms the best found solutions from the literature using heuristics for larger instances.

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