Order Allocation and Purchasing Transportation Planning in the Garment Supply Chain: A Goal-Flexible Planning Approach

The garment supply chain is one of the most common supply chains in the world. In this supply chain, quality and cost are the most important factors that are strongly related to the selection of suppliers and the allocation of orders to them. Accordingly, the purpose of this paper is to integrate decisions for supplier selection, order allocation, and multi- source, multi-mode, multi-product shipping plans with consideration of discounts under uncertainty. For this purpose, a multi-objective mixed-integer mathematical model is presented, including the objectives of minimizing costs and products with delays and maximizing the total purchase value. In this mathematical model, the policy of purchasing materials and determining the number and type of transport equipment are specified. To solve this mathematical model, a goal-flexible programming approach with a utility function is presented. In the solution algorithm, a new possibility-flexible programming method has been developed to deal with the uncertainties in the model, which is based on the expected value method and chance constraint. Finally, using a numerical problem, the establishment of the above model in the garment supply chain is investigated. As indicated by the outcomes, the proposed model was touchy to certain boundaries, including blended leaders’ mentality, a boundary identified with fluffy imperatives, and the degree of certainty characterized by the chief for not exactly equivalent limitations

Optimizing strategic sourcing in the healthcare supply chain with consideration of physician preference and vendor scorecards

This research focuses on the design of a procurement model for expensive medical supplies in a healthcare supply chain. A deterministic optimization model generates recommendations for optimal purchases of products in a given planning period. The model combines common concepts of supply chain procurement such as leveraging tiered pricing, ensuring supply base diversity with phenomena unique to healthcare supply chain such as consideration of physician preference for products. The deterministic optimization model minimizes total spend over a chosen planning period with consideration of four key decision parameters: Physician preference requirements (which are imposed as rules on product substitutability), Upper limits on vendor market share to ensure a suitably diverse supply base Vendors’ performance scores to impose standards for product pricing, quality, service, etc. Quantity discount rebate parameters for bulk purchasing to help contain medical costs The optimization model reveals the extent to which higher product substitutability and lower supply base diversity may help hospitals reduce total procurement costs. Experiments with the optimization model also reveal the potential consequences of rater biases in vendor scorecards on procurement cost. The various parameter combinations listed above may be used in negotiating contracts for better pricing. In summary, this research addresses questions pertinent to healthcare supply chains concerning the possible cost of physician preference for products, the impact of subjective scorecards on procurement costs, the effect of planning period on procurement plans, and the cost of vendor diversity

Wood-based construction project supplier selection under uncertain starting date

There is a growing interest in supply management systems in today's competitive business environment. Importance of implementing supply management systems especially in home construction industry is due to the fact that several risks arising from different sources can adversely affect the project financially or its timely completion. Some risks of construction projects are out of managers' control while other risks such as supply related ones can usually be controlled and directed by effective managerial tactics. In this paper, we address the supplier selection problem (SSP) in wood-based construction industry (housing projects) in the presence of project commencement uncertainties. Based on the suppliers' (vendors') reaction towards these uncertainties in the delivery time, we explore two cases: (a) supplier selection with buyer penalty for a delay (SSPD) where the price of product increases with the delay; (b) supplier selection with quantity reduction for a buyer delay (SSQRD). Three heuristic-based supplier selection approaches are proposed and tested on randomly generated data sets. The proposed approaches show promising result

Integrated FANP-f-MIGP model for supplier selection in the renewable energy sector

The available integrated models for choosing efficient suppliers developed so far are mostly specific to companies with mass production capabilities. However, in some sectors involved in project-type manufacturing, the same decision-making criteria cannot be applied and, plus, there is no point in determining the quantity of orders. For instance, in wind power plant projects, a single turbine supplier needs to be selected for each project. This study proposes an integrated FANP-f-MIGP model that ensures the selection of the optimal supplier for each project by applying the model to an energy firm. The criteria specific to the selection of wind power plant turbine suppliers are established, and the criteria weights are obtained by fuzzy analytic network process (FANP). As a result of the analysis, the most important criterion of all is cost. These weights constitute the coefficients of the f-MIGP model’s objective function. Under the defined constraints, by minimizing cost and risk and maximizing quality and services of the firm, the selection of an optimal wind turbine supplier from three suppliers for each of three projects is ensured. This study contributes to the literature both by the specific criteria it establishes and its proposed integrated model which allows for the selection of the best supplier in wind turbine and similar project-based productions

Solving multi-objective supplier selection and quota allocation problem under disruption using a scenario-based approach

Nowadays, experts believe there are abundant sources of risks in a supply chain. An important group of risks against a supply chain is the disruption risks group, which disturbs the flow of material in the chain and may lead to inefficiency in providing the final product in the supply chain. The aim of this article is to investigate the control of costs of disruption in a supply chain by considering the possibility of disruption. In fact, this research focuses on determining the best combination of suppliers and quota allocation with regards to disruption in suppliers. The proposed multi-objective mathematical model in this paper is a mixed-integer programming (MIP) model with objective functions to minimize transaction costs of suppliers, expected costs of purchasing goods, expected percentages of delayed products, expected returned products, and to maximize expected evaluation scores of the selected suppliers. Due to the uncertainty of demand and supplier disruption in the real world, their values are also considered uncertain; the proposed multi-objective model is studied by using a scenario-based stochastic programming (SP) method. In this method, all possible predictions for demand and disruption values are simultaneously included in the model; objective function results have more optimal value than a separate solution of the model for each predicted value

Single Item Supplier Selection and Order Allocation Problem with a Quantity Discount and Transportation Costs

In this paper, we address a single item supplier selection, economic lot-sizing, and order assignment problem under quantity discount environment and transportation costs. A mixed-integer nonlinear program (MINP) model is developed with minimization of cost as its objective, while lead-time, the capacity of the supplier and demand of the product are incorporated as constraints. The total cost considered includes annual inventory holding cost, ordering cost, transportation cost and purchase cost. An efficient and effective genetic algorithm (GA) with problem-specific operators is developed and used to solve the proposed MINP model.  The  model is illustrated through a numerical example and the results show that the GA can solve the model in less than a minute. Moreover, the results of the numerical illustration show that the item cost and transportation cost are the deciding factors in selecting suppliers and allocating orders. Keywords: Supplier selection, Economic Order Quantity, Order allocation, Mixed-integer nonlinear programming

Fuzzy Interactive Approach for a Multi-objective Supplier Selection Problem under Robust Uncertainty

In this paper, the authors proposed a multi-objective Mixed Integer Linear Programming (MILP) model for supplier selection problems. The main aim of the system under the investigation is to plan the companies to supply goods to achieve financial benefit by minimizing the total costs and satisfying the customers with on-time delivery and minimizing rejected items. In this case, some restrictions such as multi-product and multi-period conditions, shortage inventory constraints, and discount circumstances simultaneously are considered. Despite these efforts, due to the uncertainty nature of the problem, some parameters are considering as uncertainty data. For this aim, applying robust counterparts for uncertain parameters plays an essential role in real-world applications of this case. It is concluded that the feasibility and optimality properties of the usual solutions of real-world LPs can be severely affected by small changes of the data and that the robust optimization (RO) methodology can be successfully used to overcome this phenomenon

Designing a clothing supply chain network considering pricing and demand sensitivity to discounts and advertisement

none3These days, clothing companies are becoming more and more developed around the world. Due to the rapid development of these companies, designing an efficient clothing supply chain network can be highly beneficial, especially with the remarkable increase in demand and uncertainties in both supply and demand. In this study, a bi-objective stochastic mixed-integer linear programming model is proposed for designing the supply chain of the clothing industry. The first objective function maximizes total profit and the second one minimizes downside risk. In the presented network, the initial demand and price are uncertain and are incorporated into the model through a set of scenarios. To solve the bi-objective model, weighted normalized goal programming is applied. Besides, a real case study for the clothing industry in Iran is proposed to validate the presented model and developed method. The obtained results showed the validity and efficiency of the current study. Also, sensitivity analyses are conducted to evaluate the effect of several important parameters, such as discount and advertisement, on the supply chain. The results indicate that considering the optimal amount for discount parameter can conceivably enhance total profit by about 20% compared to the time without this discount scheme. When the optimized parameter is taken into account for advertisement, 12% is obtained as total profit.openPaydar M.M.; Olfati M.; Triki C.Paydar, M. M.; Olfati, M.; Triki, C