We consider a two-level supply chain consisting of a manufacturer and a retailer. The retailer gives a final product to a competitive market with customer sensitive to price. The customer demand is assumed to be constant depending on the price and quality degree of the final product. The manufacturer decides on the quality degree of the product. The optimal values of the major decision variables of the chain are determined for different conditions. The supply chain in centralized condition is considered and the optimal values of the price and the quality degree are found. Chain profit in decentralized condition is optimized and the optimal values of wholesale price and quality degree are determined. Retailer's revenue sharing contract is designed and the optimal values of wholesale price and quality degree are found.Finally, a market segmentation condition in which customers are divided into two categories of quality oriented and price oriented is considered; then, the threshold of the percentage of potential quality oriented customers is determined. A set of numerical examples are designed in order to analyze the optimal values of the decision variables.
Suppliers' evaluation is a subject, which has attracted the attention of many researchers. The performance of potential suppliers is evaluated against multiple criteria rather than considering a single factor such as cost or quality. One of the major objectives of suppliers' evaluation is to determine the optimal quota assigned to each supplier while needing to replenish an order. This problem has been studied by many researchers as a multi-objective problem. The usual objectives are minimizing the purchasing cost, rejected units, and late delivered units. However, in a few researches maximizing the evaluation scores of the selected suppliers is considered as fourth objective. In this paper, we present a model with five objectives including minimizing the transaction costs of purchasing from suppliers as well as the four addressed objectives. We convert the model to a single objective one using the well-known weighting method, solve it utilizing two meta-heuristic algorithms, and analyze the efficiency of the heuristics. The reason why we utilize the metaheuristic algorithms is that the problem is proved to be an NP-hard one.
This paper studies a multi-objective production-distribution system. The objectives are to minimize total costs and maximize the reliability of transportations system. Each transportation system is assumed to be of unique reliability. In the real world, some parameters may be of vagueness; therefore, some tools such as fuzzy logic is applied to tackle with. The problem is formulated using a mixed integer programming model. Commercial software can optimally solve small sized instances. We propose two novel HEURISTICS called ranking genetic algorithm (RGA) and concessive variable neighborhood search (CVNS) in order to solve the large sized instances. RGA utilizes various crossover operators and compares their performances so that better crossover operators are used during the solution process. CVNS applies several neighborhood search structures with a novel learning procedure. The heuristics can recognize which neighborhood structure performs well and applies those more than the others. The results indicated that RGA is of higher performance..
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