The equilibrium generalized assignment problem and genetic algorithm

Liu, Linzhong; Mu, Haibo; Song, Yan-Chun; Luo, Haipeng; Li, Xiaojing; Wu, Fang

doi:10.1016/j.amc.2011.12.025

Cited by 12 publications

(7 citation statements)

References 34 publications

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“…When comparing between all the proposed methods, ALNS-2, while using Equation (9) as the acceptance criteria, performs significantly better than the other proposed methods. Equation (9) is the combination of Equations (8) and (10). The advantages of Equations (8) and (10) make it the best proposed method.…”

Section: Computation Framework and Resultsmentioning

confidence: 99%

“…The heuristic and meta-heuristic methods that are available in a variety of ways are widely used to meet various challenges, such as the generalized assignment problem [8], scheduling shipments [9], problems in assigning general work [10], problems concerning assignments and job allocations [11], and problems concerning multi-stage and multi-purpose assignments [12]. Ross [13] first presented problems that are related to general assignments (GAPs), who found that they were difficult problems with NP-hard levels [14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Small problems, or problems with 200 jobs and 20 employees, are the biggest problems that can be solved by the exact method [2]. Therefore, the heuristics method has been used to solve GAPs [8]. In economics, the use of problems that are related to assignment is intended to seek the lowest cost for assignment [15].…”

Section: Literature Reviewmentioning

confidence: 99%

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Adaptive Large Neighborhood Search to Solve Multi-Level Scheduling and Assignment Problems in Broiler Farms

Praseeratasang

Pitakaso

Sethanan

et al. 2019

Journal of Open Innovation: Technology, Market, and Complexity

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This research aimed to present a solution to the problem of production scheduling and assignment in broiler farms, which thus enabled the farms to achieve maximum profit. In the operation of farms, there are many factors that affect profits, such as the number of broilers being consistent with the demand of production plants, including profits from the sales and transportation costs. Therefore, we formulated a mathematical model and tested it while using three problem groups through the Lingo v.11 program. The results indicated that this mathematical model could find a suitable solution. However, finding the best solution had time constraints, which resulted in various other problems that prevented a search for an optimal solution due to time consumption exceeding 72 h. We developed an algorithm using the Adaptive Large Neighborhood Search (ALNS) method in order to find another possible solution using a shorter time period, which consisted of ALNS1, ALNS2, and ALNS3. These algorithms are based on a combination of the method of destruction solutions and methods accepting different solutions. We aimed to effectively solve the problems and ensure that they are appropriate for the case study, a broiler farm in Buriram. When comparing the algorithm efficiency with the Lingo v.11 program, it was found that the ALNS1 algorithm was the most suitable for finding the optimal solution in the shortest time, which resulted in a 5.74% increase in operating profits.

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Section: Computation Framework and Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Large Neighborhood Search to Solve Multi-Level Scheduling and Assignment Problems in Broiler Farms

Praseeratasang

Pitakaso

Sethanan

et al. 2019

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

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“…Branch-and-price algorithm [25], cutting plane algorithm [1], branch-and-cut algorithm [18], branch-andbound [11] have been the significant ones of them in the last two decades. The existing exact algorithms are not effective for more difficult large-scale problems [15].…”

Section: The Generalized Assignment Problemmentioning

confidence: 99%

A New Genetic Algorithm with Agent-Based Crossover for Generalized Assignment Problem

Dörterler

2019

ITC

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Generalized assignment problem (GAP) considers finding minimum cost assignment of n tasks to m agents provided each task should be assigned to one agent only. In this study, a new Genetic Algorithm (GA) with some new methods is proposed to solve GAPs. The agent-based crossover is based on the concept of dominant gene in genotype science and increases fertility rate of feasible solutions. The solutions are classified as infeasible, feasible and mature with reference to their conditions. The new local searches provide not only feasibility in high diversity but high profitability for the solutions. A solution is not given up through maturation-based replacement until it reaches its best. Computational results show that the agent-based crossover has much higher fertility rate compared to classical crossover. Also, the proposed GA creates either optimal or approximately optimal solutions.

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“…In developing the methodology to solve the VRP and GAP problem, many methods have been proposed, such as Simulated Annealing (Osman 1995), Tabu search (Laguna et al 1995;Dıaz and Fernández 2001), BEE algorithm (Özbakir et al 2010), the evolutionary algorithm (Weise et al 2010), and the genetics algorithm (Liu et al 2012). Recently, the differential evolution (DE) algorithms have been successfully applied to solve VRP, GAP, and other combinatorial optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Methodology to Solve the Combination of the Generalized Assignment Problem and the Vehicle Routing Problem: A Case Study in Drug and Medical Instrument Sales and Service

Thongkham

Kaewman

2018

Administrative Sciences

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This article presents algorithms for solving a special case of the vehicle routing problem (VRP). We define our proposed problem of a special VRP case as a combination of two hard problems: the generalized assignment and the vehicle routing problem. The different evolution (DE) algorithm is used to solve the problem. The recombination process of the original DE is modified by adding two more sets of vectors—best vector and random vector—and using two other sets—target vector and trial vector. The linear probability formula is proposed to potentially use one out of the four sets of vectors. This is called the modified DE (MDE) algorithm. Two local searches are integrated into the MDE algorithm: exchange and insert. These procedures create a DE and MDE that use (1) no local search techniques, (2) two local search techniques, (3) only the exchange procedure, and (4) only the insert procedure. This generates four DE algorithms and four MDE algorithms. The proposed methods are tested with 15 tested instances and one case study. The current procedure is compared with all proposed heuristics. The computational result shows that, in the case study, the best DE algorithm (DE-4) has a 1.6% better solution than that of the current practice, whereas the MDE algorithm is 8.2% better. The MDE algorithm that uses the same local search as the DE algorithms generates a maximum 5.814% better solution than that of the DE algorithms.

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The equilibrium generalized assignment problem and genetic algorithm

Cited by 12 publications

References 34 publications

Adaptive Large Neighborhood Search to Solve Multi-Level Scheduling and Assignment Problems in Broiler Farms

Adaptive Large Neighborhood Search to Solve Multi-Level Scheduling and Assignment Problems in Broiler Farms

A New Genetic Algorithm with Agent-Based Crossover for Generalized Assignment Problem

Methodology to Solve the Combination of the Generalized Assignment Problem and the Vehicle Routing Problem: A Case Study in Drug and Medical Instrument Sales and Service

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