Decision making of sourcing and order allocation with price discounts

Tsai, Wen-Chin; Wang, Chih-Hsiung

doi:10.1016/j.jmsy.2010.08.002

Cited by 40 publications

(26 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Constraints (11) assure that delays reception rate of each product by each customer in each period for each supplier must be de ned by decision-makers. Constraints (12) and (13) show that price of each product o ered by the suppliers has a discount of sign function type in which a(i; j; k; t; r) are positive variables, and their summation is one. When x is positive in sign function, one is returned; if x is zero, 0 is returned; when x is negative, 1 is returned.…”

Section: Assumptionsmentioning

confidence: 99%

“…Kokangol and Susuz [11], by considering capacity, budget, and discount conditions into consideration, formulated and solved the supplier selection problem by developing a mixed model through mixing hierarchical analysis techniques, nonlinear mathematical programming model, and multiobjective programming model. Tsai and Wang [12] applied a mixed integer programming procedure to solve the problem and allocate order for a multi-source and multi-product case in the supply chain. Their objective functions included cost, minimization of the delay, and wastes from supplier's side.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A fuzzy multi-objective multi-product supplier selection and order allocation problem in supply chain under coverage and price considerations: An urban agricultural case study

2017

View full text Add to dashboard Cite

KEYWORDSMulti-objective supplier selection problem; Coverage; Fuzzy logic; MOPSO; NSGA-II.Abstract. In this paper, a fuzzy multi-objective model is presented to select and allocate order to the suppliers in uncertain conditions, considering multi-period, multisource, and multi-product cases at two levels of a supply chain with pricing considerations. Objective functions considered in this study as the measures to evaluate the suppliers are the purchase, transportation, ordering costs, and timely delivering (or deference shipment quality, or wastages) which are amongst major quality aspects. Partial and general coverage of suppliers with respect to distance and nally suppliers' weights makes the amounts of product orders more realistic. Deference and coverage parameters in the model are considered as uncertain and random triangular fuzzy number. Since the proposed mathematical model is NP-hard, Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is presented. To validate the performance of MOPSO, we applied non-dominated Sorting Genetic Algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms. A practical case study in an agricultural industry is shown to demonstrate that the proposed algorithm can be applied to the real-world problems. The results are analyzed using quantitative criteria, performing parametric, and non-parametric statistical analyses.

show abstract

Section: Assumptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A fuzzy multi-objective multi-product supplier selection and order allocation problem in supply chain under coverage and price considerations: An urban agricultural case study

2017

View full text Add to dashboard Cite

show abstract

“…For this, a well coordination is necessary between manufacturing and marketing functions at strategic and operational levels. Several studies have been concerned with developing efficient mechanisms for improving the operational coordination between marketing and manufacturing [3][4][5]. Central to the resolution of the conflict between manufacturing and marketing functions is the management of capacity and demand.…”

Section: Capacity Management Problem In Make-to-order Manufacturing Pmentioning

confidence: 99%

Determining the capacity and its level of utilization in make-to-order manufacturing: A simple deterministic model for single-machine multiple-product case

Kumru

2011

Journal of Manufacturing Systems

View full text Add to dashboard Cite

“…Adopting multiple sourcing policies can effectively decentralise purchasing risk to avoid the shortage of key component/raw materials (Tsai and Wang 2010). Rosenthal, Zydiak, and Chaudhry (1995) developed a mixed integer programming model to solve SS problem.…”

Section: Introductionmentioning

confidence: 99%

“…all-unit cost, incremental discount and total business volume discount) may be offered by suppliers simultaneously. Tsai and Wang (2010) presented a multi-objective programming model that incorporates the decision-makers' (DMs) preferences to solve order allocation problems when considering multiple suppliers, multiple items and price discounts. Rezaei and Davoodi (2011) formulated a genetic algorithm (GA) to deal with a SS problem in which pricing and lot sizing are considered to be subject to suppliers' capacity, warehouse storage and budget limitations.…”

Section: Introductionmentioning

confidence: 99%

Integrated multi-choice goal programming and multi-segment goal programming for supplier selection considering imperfect-quality and price-quantity discounts in a multiple sourcing environment

Chang

Chen

Zhuang

2013

International Journal of Systems Science

View full text Add to dashboard Cite

Integrated multi-choice goal programming and multi-segment goal programming for supplier selection considering imperfect-quality and pricequantity discounts in a multiple sourcing environmentSupplier selection (SS) is a multi-criteria and multi-objective problem, in which multi-segment (e.g. imperfect-quality discount (IQD) and price-quantity discount (PQD)) and multi-aspiration level problems may be significantly important; however, little attention had been given to dealing with both of them simultaneously in the past. This study proposes a model for integrating multi-choice goal programming and multi-segment goal programming to solve the above-mentioned problems by providing the following main contributions: (1) it allows decision-makers to set multiple aspiration levels on the right-hand side of each goal to suit real-world situations, (2) the PQD and IQD conditions are considered in the proposed model simultaneously and (3) the proposed model can solve a SS problem with n suppliers where each supplier offers m IQD with r PQD intervals, where only n ln m×r ln 2extra binary variables are required. The usefulness of the proposed model is explained using a real case. The results indicate that the proposed model not only can deal with a SS problem with multi-segment and multi-aspiration levels, but also can help the decision-maker to find the appropriate order quantities for each supplier by considering cost, quality and delivery.

show abstract

Decision making of sourcing and order allocation with price discounts

Cited by 40 publications

References 42 publications

A fuzzy multi-objective multi-product supplier selection and order allocation problem in supply chain under coverage and price considerations: An urban agricultural case study

A fuzzy multi-objective multi-product supplier selection and order allocation problem in supply chain under coverage and price considerations: An urban agricultural case study

Determining the capacity and its level of utilization in make-to-order manufacturing: A simple deterministic model for single-machine multiple-product case

Integrated multi-choice goal programming and multi-segment goal programming for supplier selection considering imperfect-quality and price-quantity discounts in a multiple sourcing environment

Contact Info

Product

Resources

About