An optimization approach for traveling purchaser problem with environmental impact of transportation cost

Abstract

Purpose

The traveling purchaser problem (TPP) has gained attention in academics to deal with different variants in real business world. This study aims to study a green TPP with quantity discounts and soft time windows (TPPQS), in which a firm needs to purchase products from a set of available markets and deliver the products to a set of customers.

Design/methodology/approach

Vehicles are available to visit the markets, which offer products at different prices and with different quantity discount schemes. Soft time windows are present for the markets and the customers, and earliness cost and tardiness may incur if a vehicle cannot arrive a market or a customer within the designated time interval. The environmental impact of transportation activities is considered. The objective of this research is to minimize the total cost, including vehicle-assigning cost, vehicle-traveling cost, purchasing cost, emission cost, earliness cost and tardiness cost, while meeting the total demand of the customers and satisfying all the constraints. A mixed integer programming (MIP) model and a genetic algorithm (GA) approach are proposed to solve the TPPQS.

Findings

The results show that both the MIP and the GA can obtain optimal solutions for small-scale cases, and the GA can generate near-optimal solutions for large-scale cases within a short computational time.

Practical implications

The proposed models can help firms increase the performance of customer satisfaction and provide valuable supply chain management references in the service industry.

Originality/value

The proposed models for TPPQS are novel and can facilitate firms to design their green traveling purchasing plans more effectively in today’s environmental conscious and competitive market.

Citation

Kang, H.-Y., Lee, A.H.I. and Yeh, Y.-F. (2021), "An optimization approach for traveling purchaser problem with environmental impact of transportation cost", Kybernetes, Vol. 50 No. 8, pp. 2289-2317. https://doi.org/10.1108/K-04-2020-0237