Designing a Transportation-Strategy Decision-Making Process for a Supply Chain: Case of a Pharmaceutical Supply Chain

Including an active participation of stakeholders along the transportation decision-making process is increasingly recognized as a necessary condition for reaching successful and high-quality decisions. This paper presents a framework for deciding on the appropriate transportation strategy for a supply chain from a multistakeholder perspective. It consists of three steps: (1) defining the transportation-strategy decision-making context and the objectives that must be achieved; (2) analyzing the actual transportation strategy regarding its three components: transportation network; transportation mode; and transportation insource/outsource, as well as identifying the stakeholders interested in the study; and (3) conducting a group decision making regarding each transportation strategy’s component, while involving the key stakeholders and taking into account the specificities of transported products. The proposed framework is then applied to a real case of the Moroccan public pharmaceutical supply chain, which has different features that distinguish it from other supply chains including its importance, urgency, and regulation. We employed the DELPHI method to determine the key stakeholders that should be involved in the decisional process. After that, we applied the group AHP method for selecting the appropriate transport-network design option while involving the identified key stakeholders.

Application of Bat Algorithm for Transport Network Design Problem

The requirement of the road services and transportation network development planning came into existence with the development of civilization. In the modern urban transport scenario with the forever mounting amount of vehicles, it is very much essential to tackle network congestion and to minimize the travel time. This work is based on determining the optimal wait time at traffic signals for the microscopic discrete model. The problem is formulated as a bilevel model. The upper layer optimizes the travel time by reducing the wait time at traffic signal and the lower layer solves the stochastic user equilibrium. Soft computing techniques like Genetic Algorithms, Ant Colony Optimization, and many other biologically inspired techniques prove to give good results for bilevel problems. Here this work uses Bat Intelligence to solve the transport network design problem. The results are compared with the existing techniques.