Product line optimization with multiples sites

Dávila, Sebastián; Marianov, Vladimir; Ordóñez, Fernando; Semet, Frédéric

doi:10.1016/j.cor.2022.105978

Cited by 3 publications

(6 citation statements)

References 43 publications

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“…Subproblems with bounds that cannot lead to better solutions are discarded, reducing the search space. Branch and Cut [6][7][8][9][10][11][12][13] algorithm extends the branch and bound method by incorporating cutting planes, which are linear inequalities that remove non-optimal solutions from the search space, thus accelerating the convergence to the optimal solution. Dynamic Programming [14][15][16][17][18][19][20] algorithm solves a problem by dividing it into smaller overlapping subproblems and combining their solutions in a systematic manner.…”

Section: Literature Reviewmentioning

confidence: 99%

A mixed-integer linear optimization problem for highway emergency vehicle routing

Wang,

Tian,

et al. 2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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Section: Literature Reviewmentioning

confidence: 99%

A mixed-integer linear optimization problem for highway emergency vehicle routing

Wang,

Tian,

et al. 2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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“…The upperlevel problem consists of maximizing the profit of the company, while the lower-level problem aims to maximize the customers' preferences given by the products bought. Similarly, in Dávila et al (2022), the authors study the product allocation decisions for a chain with multiple stores (coined as the product line optimization problem). In this case, as in the workshop location problem under study, the allocation of products/services to the facilities is an upper-level decision variable, and customers are only interested in acquiring one of them once the allocation has been performed.…”

Section: Facility Location With Customer Preferencesmentioning

confidence: 99%

“…But, contrary to our workshop location problem, where cost minimization is the upper-level objective, the chain is interested in maximizing its profits. Dávila et al (2022) resort to a bi-level formulation to model customer preferences. They propose a single-level reformulation of the problem and use cut-and-branch, branch-and-cut, and Bender's decomposition methods to solve it.…”

Section: Facility Location With Customer Preferencesmentioning

confidence: 99%

“…In the workshop location problem analyzed in this paper, not all schools will offer the same workshops. As in Dávila et al (2022), the opening of workshops at the schools is a decision variable. Furthermore, the total number of workshops opened at a given school is limited by infrastructure and managerial constraints.…”

Section: Facility Location With Multi-product/multiservice Facilitiesmentioning

confidence: 99%

“…Dávila et al. (2022) resort to a bi‐level formulation to model customer preferences. They propose a single‐level reformulation of the problem and use cut‐and‐branch, branch‐and‐cut, and Bender's decomposition methods to solve it.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

A facility location problem for extracurricular workshop planning: bi‐level model and metaheuristics

Polino,

Camacho‐Vallejo,

Villegas

2023

Int Trans Operational Res

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This paper presents a facility location problem with capacitated multiservice facilities in which the allocation of the users is based on their preferences. The problem arises from an educational program, where a government agency or a nongovernmental organization (NGO) offers extracurricular workshops to elementary school students. These workshops promote the development of academic, social, or cultural skills of the students. This problem is formulated as a bi‐level program. The government/NGO is associated with the upper level, which aims to minimize the costs for opening workshops in different schools and the costs of allocating students to each workshop. The lower level is related to the students and seeks to optimize their global preferences for the offered workshops. To solve this problem, we propose a single‐level reformulation and two metaheuristics (a path‐relinking metaheuristic (PRM) and an iterated local search (ILS) procedure). The best performing method is the PRM that generates saturated initial random solutions following a drop heuristic approach. After different phases of improvement (with drop and exchange heuristics), the solutions undergo a path‐relinking procedure. The trajectory between the initial solution and the best solution found so far is explored using a randomized backward path‐relinking strategy that thoroughly explores the neighborhood of the best solution. Computational experimentation indicates that the proposed PRM metaheuristic is effective and efficient. The results obtained provide better upper bounds in shorter running times than the ones obtained by the single‐level reformulation or the ILS procedure. Finally, the application to a realistic case study of Apodaca municipality in Nuevo Leon, Mexico, shows that large‐scale instances can be solved efficiently using the proposed metaheuristic.

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