SIMULTANEOUS ROUTING AND LOADING METHOD FOR MILK-RUN USING HYBRID GENETIC SEARCH ALGORITHM

Milk-run methodology is proposed to manage the procurement of orders from suppliers. The heuristic solution methods in the literature generally apply stepwise approach to route and load the vehicles. In this study we propose a hybrid genetic local search algorithm which simultaneous solves vehicle routing and order loading problems. This is the main contribution of the study. We consider volume and weight capacities (multi capacitated) of different types of transportation vehicles (heterogeneous fleet). Because of high adaptability and easy utilization, genetic algorithms are the most preferred approach of meta-heuristics. The chromosome structure of the proposed genetic algorithm is constituted by random numbers to eliminate infeasibility. The best chromosome of each generation is improved using local search method during the algorithm runs. We applied the algorithm to a real manufacturing company that produces welding robots and other process automation equipment. The results showed the effectiveness of the algorithm

Global optimisation for a developed price discrimination model:A signomial geometric programming-based approach

This paper presents a price discrimination model for a manufacturer who acts in two different markets. In order to have a fair price discrimination model and compare monopoly and competitive markets, it is assumed that there is no competitor in the first market (monopoly market) and there is a strong competitor in the other market (competitive market). The manufacturer objective is to maximize the total benefit in both markets. The decision variables are selling price, lot size, marketing expenditure, customer service cost, flexibility and reliability of production process, set up costs and quality of products. The proposed model in this paper is a signomial geometric programming problem which is difficult to solve and find the globally optimal solution. So, this signomial model is converted to a posynomial geometric type and using an iterative method, the globally optimal solution is found. To illustrate the capability of the proposed model, a numerical example is solved and the sensitivity analysis is implemented under different conditions

Global optimisation for a developed price discrimination model:A signomial geometric programming-based approach

This paper presents a price discrimination model for a manufacturer who acts in two different markets. In order to have a fair price discrimination model and compare monopoly and competitive markets, it is assumed that there is no competitor in the first market (monopoly market) and there is a strong competitor in the other market (competitive market). The manufacturer objective is to maximize the total benefit in both markets. The decision variables are selling price, lot size, marketing expenditure, customer service cost, flexibility and reliability of production process, set up costs and quality of products. The proposed model in this paper is a signomial geometric programming problem which is difficult to solve and find the globally optimal solution. So, this signomial model is converted to a posynomial geometric type and using an iterative method, the globally optimal solution is found. To illustrate the capability of the proposed model, a numerical example is solved and the sensitivity analysis is implemented under different conditions