Order acceptance using genetic algorithms

Rom, Walter O.; Slotnick, Susan A.

doi:10.1016/j.cor.2008.04.010

Cited by 111 publications

(49 citation statements)

References 69 publications

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“…Slotnick and Morton (2007) formulated the problem of OA decision as an integer program which performs both sequencing and order acceptance jointly. The same problem is later solved by Rom and Slotnick (2009) using genetic algorithm to improve the performance for large size problems. Fabrice and Roel (2011) devised two exact branch-and-bound algorithms to decide the potential orders to retain from the subset of orders for profit maximization.…”

Section: Related Researchmentioning

confidence: 99%

Order Acceptance Decision in Make-to-Order System: Satisfaction Level Based Approach

Piya

Takahashi

Morikawa

2014

OSCM: An Int. Journal

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In make-to-order (MTO) system, deciding which order to accept and which to reject from an incoming orders is one of the most challenging tasks for the decision maker (DM) of manufacturer. When the new order arrives, order acceptance (OA) decision is affected by two kinds of satisfaction level. One is the satisfaction level expected by the DM. This level indicates the acceptance threshold of DM. Next is the satisfaction level that will be generated by the order. In this paper, we propose a method that reflects OA decision as interplay between the DM's acceptance threshold and satisfaction level generated by the order. We investigate this less researched topic in a job shop environment with uncertain scenario. In the proposed method, the DM's acceptance threshold is shown to be the function of workload in the production system and their aggressiveness. While, we consider four criteria to access the satisfaction level generated by the order. They are reputation in terms of delivery reliability, profit both at present and in the future and the utilization of available capacity. We present a numerical analysis by considering various parameters under two different cases of due dates to demonstrate the working mechanism and effectiveness of proposed method.

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Section: Related Researchmentioning

confidence: 99%

Order Acceptance Decision in Make-to-Order System: Satisfaction Level Based Approach

Piya

Takahashi

Morikawa

2014

OSCM: An Int. Journal

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“…They state that the joint approach is far superior to the first. Rom and Slotnick [15] also propose a GA to solve the order acceptance problem with tardiness penalties.…”

Section: Order Acceptance With Static Arrivalsmentioning

confidence: 99%

A branch and price solution approach for order acceptance and capacity planning in make-to-order operations

Mestry

Damodaran

Chen

2011

European Journal of Operational Research

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“…The literature on order acceptance has grown rapidly over the past decade (Slotnick & Morton, 2007). Thus, researchers have attempted to find an optimum system to either accept or reject orders using multitudes of techniques such as Dynamic Programming (Alidaee et al, 2001;Lewis & Slotnick, 2002;Herbots et al, 2007), Mathematics Programming (Guerrero & Kern, 1990;Slotnick & Morton, 1996;Slotnick & Morton, 2007), Simulation (Ten kate, 1994;Ten kate, 1995;Nandi & Rogers, 2003;Nandi & Rogers, 2004;Ebben et al, 2005), Decision theory (Balakrishnan et al,1996;Balakrishnan et al,1999), Heuristics (Ghosh, 1997;Defregger & Kuhn, 2004;Hing et al, 2007), Genetic algorithm (Roundy et al, 2005;Rom & Slotnick, 2009), Neural networks Hing et al, 2002), Neuro-genetics (Snoek, 2000), Markov decision (Kniker & Burman, 2001) etc. References Slotnick and Morton (2007), Ghosh (1997), Jalora (2006) and Ivanescu (2004) provide more detailed information.…”

Section: Introductionmentioning

confidence: 99%

An order acceptance using FAHP and TOPSIS methods: A case study of Iranian vehicle belt production industry

Parsaei¹,

Keramati²,

Zorriassatine³

et al. 2012

ijiec

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Decisions related to acceptance or rejection of orders play an important role in companies engaged in make-to-order production. The incoming orders have a specific delivery date by which the customer expects the due date to be met and the order delivered. In some cases the level of input orders exceeds beyond the existing capacity. In such situations the main concern is to decide which orders must be accepted and which ones rejected taking into account the available production capacity. This paper prioritises the input orders according to a comprehensive and systematic multi criteria decision making (MCDM) model. It then proceeds with making decisions to either accept or reject orders according to the calculated prioritises and production constraints. Ultimately the optimum list of orders for acceptance is determined. The proposed model is a combination of two techniques of Fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this model FAHP is used to determine the weights of criteria and TOPSIS is used for prioritizing the orders. Finally the proposed model is tested for its efficiency by application to a real case.

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Order acceptance using genetic algorithms

Cited by 111 publications

References 69 publications

Order Acceptance Decision in Make-to-Order System: Satisfaction Level Based Approach

Order Acceptance Decision in Make-to-Order System: Satisfaction Level Based Approach

A branch and price solution approach for order acceptance and capacity planning in make-to-order operations

An order acceptance using FAHP and TOPSIS methods: A case study of Iranian vehicle belt production industry

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