Order acceptance and scheduling decisions in make-to-order systems

̆uz, Ceyda Og; Salman, Serdar; Yalçın, Zehra Bilgintürk

doi:10.1016/j.ijpe.2010.02.002

Cited by 143 publications

(62 citation statements)

References 30 publications

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“…In their study, Oguz et al [8] have proposed two upper bounds for OAS to evaluate the performance of their proposed heuristics [1,8]. The first bound is generated by using their MILP model with the time limit of 3600 seconds while the second bound is obtained by solving the linear programming (LP) relaxation of the MILP strengthened with some additional valid inequalities.…”

Section: Upper Boundmentioning

confidence: 99%

“…The goal of OAS is to optimise the use of manufacturing resources in make-to-order manufacturing systems to improve the total profit and customer satisfaction [1,8]. This paper focuses on the OAS problem in single machine shops [1,[7][8][9]. Particularly, we want to determine whether to accept or reject orders from customers and how the accepted orders can be scheduled.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of this problem is to maximise the total profit TPR = j∈A prt j where A is the set of accepted orders. A Mixed Integer Linear Programming (MILP) model of this problem can be found in [8].…”

Section: Introductionmentioning

confidence: 99%

“…Rom and Slotnick [11] developed a hybrid method in which solutions found by a genetic algorithm (GA) are further improved by a local search heuristic. Oguz et al [8] developed a simulated annealing method (ISFAN) for the OAS problem with dependent setup times in customised packing material producers and showed that their proposed method can find good solutions for large scale problem instances. Cesaret et al [1] proposed a tabu search (TS) method to handle the same OAS problem and the experimental results showed that the proposed TS method outperformed ISFAN in most instances.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, designing efficient exact methods to deal with OAS is very difficult. For the OAS problem in this study, solving the MILP model [1,8] is very time consuming and can only be used to solve small instances (up to 10 orders). For larger instances, MILP usually failed to find the optimal solutions within limited computation times (3600 seconds).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Enhancing Branch-and-Bound Algorithms for Order Acceptance and Scheduling with Genetic Programming

Nguyen

Zhang

Johnston

2014

Lecture Notes in Computer Science

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Abstract. Order acceptance and scheduling (OAS) is an important planning activity in make-to-order manufacturing systems. Making good acceptance and scheduling decisions allows the systems to utilise their manufacturing resources better and achieve higher total profit. Therefore, finding optimal solutions for OAS is desirable. Unfortunately, the exact optimisation approaches previously proposed for OAS are still very time consuming and usually fail to solve the problem even for small instances in a reasonable computational time. In this paper, we develop a new branch-and-bound (B&B) approach to finding optimal solutions for OAS. In order to design effective branching strategies for B&B, a new GP method has been proposed to discover good ordering rules. The results show that the B&B algorithms enhanced by GP can solve the OAS problem more effectively than the basic B&B algorithm and the CPLEX solver on the Mixed Integer Linear Programming model.

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Section: Upper Boundmentioning

confidence: 99%