Multiobjective Order Acceptance and Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints

Wang, Bailin; Wang, Haifeng

doi:10.1155/2018/6024631

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…The first objective is to maximize the total net profit concerning the revenue, tardiness cost, and production cost and the second is to minimize the makespan, which is a classic scheduling criterion concerning productivity. Due to the conflicting objective to solve the problem, the mathematical model was formulated as multi-objective mixed-integer linear programming and a list scheduling-based multi-objective parthenogenesis algorithm was developed [25]. Another OAS problem on unrelated parallel machines with the objective of the maximization of total net revenue after determining accepting orders and how to arrange the accepted orders in a processing sequence.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Order Acceptance and Scheduling on Identical Parallel Machines

Barak

Koyuncu

2021

Symmetry

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In this study, we focus on the fuzzy order acceptance and scheduling problem in identical parallel machines (FOASIPM), which is a scheduling and optimization problem to decide whether the firm should accept or outsource the order. In general, symmetry is a fundamental property of optimization models used to represent binary relations such as the FOASIPM problem. Symmetry in optimization problems can be considered as an engineering tool to support decision-making. We develop a fuzzy mathematical model (FMM) and a Genetic Algorithm (GA) with two crossover operators. The FOASIPM is formulated as an FMM where the objective is to maximize the total net profit, which includes the revenue, the penalty of tardiness, and the outsourcing. The performance of the proposed methods is tested on the sets of data with orders that are defined by fuzzy durations. We use the signed distance method to handle the fuzzy parameters. While FMM reaches the optimal solution in a reasonable time for datasets with a small number of orders, it cannot find a solution for datasets with a large number of orders due to the NP-hard nature of the problem. Genetic algorithms provide fast solutions for datasets with a medium and large number of orders.

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Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Order Acceptance and Scheduling on Identical Parallel Machines

Barak

Koyuncu

2021

Symmetry

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“…e same problem is studied in Emami et al [6] and solved by Benders decomposition approach. Wang and Wang [7] mainly focus on the biobjective optimization of order acceptance and scheduling on unrelated parallel machines with machine eligibility constraints, where a subset of jobs has to be selected to simultaneously maximize the profit level and to minimize the total completion time. e problem is formulated as a biobjective mixed integer linear programming.…”

Section: 1mentioning

confidence: 99%

“…Constraint (6) computes the completion time for each order. Constraint (7) links variable Y itm and variable X i and is introduced to determine if an order is accepted or not. Constraint (8) calculates the tardiness for each order.…”

Section: Decision Variablesmentioning

confidence: 99%

A Column Generation Algorithm for the Resource-Constrained Order Acceptance and Scheduling on Unrelated Parallel Machines

Song

Xue

Hua

et al. 2021

Mathematical Problems in Engineering

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In this paper, we investigate the resource-constrained order acceptance and scheduling on unrelated parallel machines that arise in make-to-order systems. The objective of this problem is to simultaneously select a subset of orders to be processed and schedule the accepted orders on unrelated machines in such a way that the resources are not overutilized at any time. We first propose two formulations for the problem: mixed integer linear programming formulation and set partitioning. In view of the complexity of the problem, we then develop a column generation approach based on the set partitioning formulation. In the proposed column generation approach, a differential evolution algorithm is designed to solve subproblems efficiently. Extensive numerical experiments on different-sized instances are conducted, and the results demonstrate that the proposed column generation algorithm reports optimal or near-optimal solutions that are evidently better than the solutions obtained by solving the mixed integer linear programming formulation.

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“…Wang and Wang studied the order acceptance and unrelated parallel machines scheduling problem with machine eligibility constraints to maximize total net profit and minimize the makespan. A mathematical model was formulated as multi-objective mixed integer linear programming [31]. Afzalirad and Shafipour designed an efficient genetic algorithm to deal with resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions.…”

mentioning

confidence: 99%

Bi-objective unrelated parallel machines scheduling problem with worker allocation and sequence dependent setup times considering machine eligibility and precedence constraints

Foroutan

Rezaeian

Shafipour

2023

JIMO

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<p style='text-indent:20px;'>In today's competitive world, scheduling problems are one of the most important and vital issues. In this study, a bi-objective unrelated parallel machine scheduling problem with worker allocation, sequence dependent setup times, precedence constraints, and machine eligibility is presented. The objective functions are to minimize the costs of tardiness and hiring workers. In order to formulate the proposed problem, a mixed-integer quadratic programming model is presented. A strategy called repair is also proposed to implement the precedence constraints. Because the problem is NP-hard, two metaheuristic algorithms, a multi-objective tabu search (MOTS) and a multi-objective simulated annealing (MOSA), are presented to tackle the problem. Furthermore, a hybrid metaheuristic algorithm is also developed. Finally, computational experiments are carried out to evaluate different test problems, and analysis of variance is done to compare the performance of the proposed algorithms. The results show that MOTS is doing better in terms of objective values and mean ideal distance (MID) metric, while the proposed hybrid algorithm outperforms in most cases, considering other employed comparison metrics.</p>

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Multiobjective Order Acceptance and Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints

Cited by 4 publications

References 24 publications

Fuzzy Order Acceptance and Scheduling on Identical Parallel Machines

Fuzzy Order Acceptance and Scheduling on Identical Parallel Machines

A Column Generation Algorithm for the Resource-Constrained Order Acceptance and Scheduling on Unrelated Parallel Machines

Bi-objective unrelated parallel machines scheduling problem with worker allocation and sequence dependent setup times considering machine eligibility and precedence constraints

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