Joint determination of process mean, price differentiation, and production decisions with demand leakage: A multi-objective approach

Raza, Syed Asif; Abdullakutty, Faseela Chakkalakkal; Rathinam, Sivakumar

doi:10.1016/j.apm.2016.02.043

Cited by 14 publications

(7 citation statements)

References 54 publications

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“…Other models assume that the terminal state is x ≤ U, i.e., rework is needed if x > U, models [31,34,35,38,40,44,[46][47][48]. Finally, there are models where a recurrent state is limited to the case where x < L, models [9,19,20,25,32,36,37,42,43,[49][50][51].…”

Section: Generalized Targeting Modelmentioning

confidence: 99%

“…In the following paragraphs, a discussion of some of the most important models in the literature is provided. [39] Selim and Al-Zu'bi (2011) [40] Darwish et al (2013) [41] Duffuaa and El-Ga'aly (2013a) [42] Duffuaa and El-Ga'aly (2013b) [43] Peng and Khasawneh (2014) [44] Chen et al (2015a) [45] Chen et al (2015b) [46] Chen and Kan (2015) [47] Dodd et al (2015) [48] Duffuaa and El-Ga'aly (2015) [49] Raza and Turiac (2016) [50] Raza et al (2016) [51] This paper (General model)…”

Section: Introductionmentioning

confidence: 99%

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A Generalized Process Targeting Model and an Application Involving a Production Process with Multiple Products

Abdel-Aal

Selim

2019

Mathematics

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This paper presents a generalized targeting model that subsumes most known targeting problems. In this paper, a recurrent state is defined as a condition that requires reprocessing or rework. The generalized model can accommodate one or two specifications limits and can be used for the following quality characteristics: The nominal-the-better, the larger-the-better, and the smaller-the-better. This model can be used to find the optimal mean of a quality characteristic, as well as the optimal specification limits. In addition, the paper studies the conditions under which the solution to the proposed model can provide a global solution. The paper shows that, for some of the special cases and under very general conditions, the optimal lower limit should be zero and the optimal upper limit should be infinity. This paper proves that the expected profits improve for the case where only a lower limit on the quality characteristic is used, if a recurrent state is included by adding an optimized upper limit. A special case of the model is used to study the problem of determining a common mean for multiple products, as well as the optimal upper specification limits for each product. A solution procedure for maximizing the expected profits and obtaining the optimal solution is introduced. A numerical example is presented.

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Section: Generalized Targeting Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Generalized Process Targeting Model and an Application Involving a Production Process with Multiple Products

Abdel-Aal

Selim

2019

Mathematics

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“…Also in the industrial world, planning is needed to produce goods. With the planning of the production of goods, the productivity of the company can be increased [1]. There are several factors that influence the planning of production, including the level of consumer demand, the amount of profit to be achieved, the cost of production, the level of fulfillment of resources that include natural resources and human resources, and procedures for rescheduling production.…”

Section: Introductionmentioning

confidence: 99%

Using Fuzzy Time Series (FTS) and Linear Programming for Production Planning and Planting Pattern Scheduling Red Onion

et al. 2019

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This study discusses the production planning system and scheduling shallots planting patterns using fuzzy time series and linear programming methods. In this study fuzzy time series to predict the number of requests and the results of predictions from fuzzy time series methods become one of the variables in the calculation of linear programming. Using supporting variables, demand data, production data, employment data, land area data, production profit data, data on the number of seedlings and planting time data are indicators used in processing the system. The system provides recommendations for cropping patterns and the number of seeds that must be planted in one period. The age of harvesting onions is ± 3-4 months from the planting process, the number of planting seeds is adjusted to the number of requests that have been predicted by using fuzzy time series and cropping pattern calculation process is carried out using linear programming. The results of this system are recommendations for farmers to plant seedlings, planting schedules, and harvest schedules to meet market demand.

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“…Some literature relating with the planning production, scheduling of planting patterns in plants horticultural: tomato : Rocco dan Morabito [1], citrus : Munhoz and Morabito [8], the difference with this paper is horticulture crops study case is mushroom and this paper use 2 method there is fuzzy time series method and linear programming. Fuzzy time series prediction is very useful, if there is no farm demand to data so that the prediction results in the form of demand data can be one of the constraints variable in linear programming.…”

Section: Introductionmentioning

confidence: 99%

“…Production planning is one of a topic to which is of special attention for the researchers and practitioners academic industry, because it is a problem that was important in the production process, with planning production of good then can increase the company profitability [1]. Several factors that need to be considered in planning the production of is consumer demand, advantage to be achieved, a charge required during the production process, the number of resources that there was either natural resources or human resources and rescheduled, it is rescheduled planting patterns that is the determination of the schedule cropping and harvest the schedule so that it can boost the profitability of agricultural products [2].…”

Section: Introductionmentioning

confidence: 99%

Production Planning and Planting Pattern Scheduling Information System for Horticulture

2018

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Abstract. This paper present the production of planning and planting pattern scheduling faced by horticulture farmer using two methods. Fuzzy time series method use to predict demand on based on sales amount, while linear programming is used to assist horticulture farmers in making production planning decisions and determining the schedule of cropping patterns in accordance with demand predictions of the fuzzy time series method, variable use in this paper is size of areas, production advantage, amount of seeds and age of the plants. This research result production planning and planting patterns scheduling information system with the output is recommendations planting schedule, harvest schedule and the number of seeds will be plant.

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Joint determination of process mean, price differentiation, and production decisions with demand leakage: A multi-objective approach

Cited by 14 publications

References 54 publications

A Generalized Process Targeting Model and an Application Involving a Production Process with Multiple Products

A Generalized Process Targeting Model and an Application Involving a Production Process with Multiple Products

Using Fuzzy Time Series (FTS) and Linear Programming for Production Planning and Planting Pattern Scheduling Red Onion

Production Planning and Planting Pattern Scheduling Information System for Horticulture

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