Designing Difficult Office Space Allocation Problem Instances with Mathematical Programming

Ülker, Özgür; Landa-Silva, Dario

doi:10.1007/978-3-642-20662-7_24

Cited by 3 publications

(10 citation statements)

References 14 publications

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“…In our experiments, we used the SVe150 and PNe150 datasets from [4]. These datasets contain 150 entities and 92 rooms and were incrementally generated from initial sets of entity, rooms, and constraints using four different parameters.…”

Section: Methodsmentioning

confidence: 99%

“…Each instance was given 10 runs of 3 minutes each (for a total of 30 minutes execution time per instance). The results produced by the evolutionary local search algorithm were compared to the integer programming (IP) model proposed in [17] and [4]. The IP model had been given a single run of 30 minutes [4] using Gurobi's deterministic IP solver [21].…”

Section: Methodsmentioning

confidence: 99%

“…The results produced by the evolutionary local search algorithm were compared to the integer programming (IP) model proposed in [17] and [4]. The IP model had been given a single run of 30 minutes [4] using Gurobi's deterministic IP solver [21]. In order to compare the new algorithm with the IP model, we took the minimum results obtained after ten runs.…”

Section: Methodsmentioning

confidence: 99%

“…This algorithm combines evolutionary mechanisms of crossover and mutation (to move around the search space) with a fast and extensive local search procedure (to quickly search through a specific segment of the search space). The proposed method is competitive with a mathematical programming approach [4] developed previously for tackling this combinatorial optimisation problem.…”

Section: Introductionmentioning

confidence: 99%

“…This model yielded the best obtained results for the larger dataset instances that were used in [19] and [14]. The model was later improved in [4] with a different not sharing constraint formulation and the addition of three different constraint types that were not present in previous works. The model was reimplemented using Gurobi [21] and improved results over [17] were observed.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary local search for solving the office space allocation problem

Ülker

Landa-Silva

2012

2012 IEEE Congress on Evolutionary Computation

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Abstract-Office Space Allocation (OSA) is the task of correctly allocating the spatial resources of an institution to a set of entities by minimising the wastage of space and the violation of additional constraints. In this paper, an evolutionary local search algorithm is presented to tackle this problem. The evolutionary components of the algorithm include standard crossover and mutation operators and a relatively small population of individuals. The offspring produced by the evolutionary operators are subjected to a short but intense local search process. A very fast cost calculation method tailored for searching a large section of the search space is implemented. Extensive experimentation is carried out related to several parameters of the algorithm: the mutation rate, the population size, the length of the local search procedure after each mutation, hence the balance between the evolutionary and the local search stages, and the level of greediness of the local search process. The final results on 72 different data instances show that this hybrid evolutionary algorithm is very competitive with an integer programming model.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evolutionary local search for solving the office space allocation problem

Ülker

Landa-Silva

2012

2012 IEEE Congress on Evolutionary Computation

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Integer Goal Programming Approach to Optimized Office Assignment in Research and Academic Facilities

Sharif,

Thevasagayam,

Fgaier

et al. 2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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This paper addresses the problem of allocating office space to personnel in an academic institution. The objectives of accommodating everyone, meeting Council of Ontario Universities (COU) guidelines, and proximity of personnel in the same research area are identified. A binary integer goal programming model is created to optimize allocation. To further improve the quality of the allocation, goals are weighted independently, and deviations are disallowed for some goals. The model is illustrated on a case study consisting of 6 research groups, 3 personnel categories, and 108 office spaces. Depending on the personnel category, square footage is assigned which roughly translates to a room capacity of 1 for professors, 2 for post-doctoral fellows and 4 for graduate students. The accommodation goal is met completely (i.e., all individuals are allotted space), there is minimal deviation with the group floor assignment goal (i.e., some individuals are not on the same floor as the rest of the research group) and moderate deviation in meeting the COU space goal (i.e., the room capacity constraints are not completely met).

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A Soft Optimization Model to Solve Space Allocation Problems in Breakbulk Terminals

Kang¹,

Mobtahej²,

Sedaghat³

et al. 2021

CRPASE

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In recent decades, freight transportation systems have been developed rapidly. This development leads to using various policies to enhance system utilization. The studies show that an optimized policy related to space allocation benefits the shareholders in freight transportations. The objective of space allocation problems is to find the best arrangement of cargos in warehouse cells to meet the problem aims. In this paper, inspired by the Office Space Allocation problem, we developed a novel model to minimize the handling costs and to maximize available spaces for the next arriving cargo. We first formulate the optimization model and discuss various constraints. We then present an approach to solve the proposed model. Lastly, we analyze a numerical example derived from the data of Port of Beaumont to illustrate the efficiency of the model.

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Designing Difficult Office Space Allocation Problem Instances with Mathematical Programming

Cited by 3 publications

References 14 publications

Evolutionary local search for solving the office space allocation problem

Evolutionary local search for solving the office space allocation problem

Integer Goal Programming Approach to Optimized Office Assignment in Research and Academic Facilities

A Soft Optimization Model to Solve Space Allocation Problems in Breakbulk Terminals

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