Integer programming approach to production scheduling for make-to-order manufacturing

AGH University of Science and Technology, Faculty of Management Department of Computer Integrated Manufacturing Al.Mickiewicza 30, 30-059 Krakow, Poland
Mathematical and Computer Modelling (Impact Factor: 1.41). 01/2005; 41(1):99-118. DOI: 10.1016/j.mcm.2003.10.053
Source: DBLP


This paper presents an integer programming approach to production scheduling in make-to-order environment with various due date related performance measures. The proposed formulations incorporate capacity constraints for a hybrid flowshop with multicapacity machines and with batch processing mode. Scheduling of divisible versus indivisible orders are considered. For the proposed integer programming formulations new cutting constraints are identified. Numerical examples modeled after real-world make-to-order assembly system are provided and some computational results with the proposed approach are reported.

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Available from: Tadeusz J Sawik, Jan 22, 2014
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    • "They solved the mentioned problem with branch and bound, heuristics, and genetic algorithms. Sawik [14] proposed more mathematical models for a flexible flow line with tardiness related criteria. Haouari et al. [15] solved two-stage regular HFS (unconstrained number of machines in stages 1 and 2) to minimize makespan criterion with a very effective branch and bound method that produces optimal solutions for problems up to 1000 jobs in size. "
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    ABSTRACT: In previous investigations in the field of flexible flow shop scheduling problem, rework probability for operations was ignored. As these kinds of problems are NPhard, so we presented an enhanced invasive weed optimization (EIWO) metaheuristic algorithm in order to solve the addressed problem with probable rework times, transportation times with a conveyor between two subsequent stages, different ready times and anticipatory sequence dependent setup times. The optimization criterion is to minimize makespan. Although invasive weed optimization (IWO) is an efficient algorithm and has been attracted by many researchers recently, but to increase the capability of IWO, we added mutation operation to enhance the exploration in order to prevent sticking in local optimum. In addition, affinity function is embedded to obstruct premature convergence. With these changes, we balance exploration and exploitation of IWO. Since, the performance of our proposed algorithm depends on parameters values, hence, we applied a popular design of experimental methodology called response surface method (RSM). To evaluate the proposed algorithm, first some random test problems were generated and compared with three benchmark algorithms. The related results were analyzed by statistical tools. The experimental results and statistical analyses demonstrated that the proposed EIWO was effective for the problem.
    Scientia Iranica 10/2014; 21(3). · 1.03 Impact Factor
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    • "Several works treating emergency problem, using reactive approach [6] [7], are inspired from studies achieved on reactive problems in industrial application. Such as (1) the insertion of one or several jobs [20] [22], in the pre-established planning, and (2) the scheduling of activities with uncertain length [1] [23]. Our problem is considered as a flow-shop problem already treated in industrial context [2] [3]. "
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    • "The problem of scheduling jobs with family setup times on parallel machines is also addressed in [25] [14] [15] [29]. A mixed-integer programming approach for scheduling of batch-processing machines is proposed by [8] [23]. "
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    ABSTRACT: In this paper, we present a mixed-integer fuzzy programming model and a genetic algorithm (GA) based solution approach to a scheduling problem of customer orders in a mass customizing furniture industry. Independent job orders are grouped into multiple classes based on similarity in style so that the required number of setups is minimized. The family of jobs can be partitioned into batches, where each batch consists of a set of consecutively processed jobs from the same class. If a batch is assigned to one of available parallel machines, a setup is required at the beginning of the first job in that batch. A schedule defines the way how the batches are created from the independent jobs and specifies the processing order of the batches and that of the jobs within the batches. A machine can only process one job at a time, and cannot perform any processing while undergoing a setup. The proposed formulation minimizes the total weighted flowtime while fulfilling due date requirements. The imprecision associated with estimation of setup and processing times are represented by fuzzy sets.
    International Journal of Approximate Reasoning 01/2009; 50(1-50):117-137. DOI:10.1016/j.ijar.2007.08.013 · 2.45 Impact Factor
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