Supply chain management: An opportunity for metaheuristics

In today s highly competitive and global marketplace the pressure onorganizations to find new ways to create and deliver value to customersgrows ever stronger. In the last two decades, logistics and supply chainhas moved to the center stage. There has been a growing recognition thatit is through an effective management of the logistics function and thesupply chain that the goal of cost reduction and service enhancement canbe achieved. The key to success in Supply Chain Management (SCM) requireheavy emphasis on integration of activities, cooperation, coordination andinformation sharing throughout the entire supply chain, from suppliers tocustomers. To be able to respond to the challenge of integration there isthe need of sophisticated decision support systems based on powerfulmathematical models and solution techniques, together with the advancesin information and communication technologies. The industry and the academiahave become increasingly interested in SCM to be able to respond to theproblems and issues posed by the changes in the logistics and supply chain.We present a brief discussion on the important issues in SCM. We then arguethat metaheuristics can play an important role in solving complex supplychain related problems derived by the importance of designing and managingthe entire supply chain as a single entity. We will focus specially on theIterated Local Search, Tabu Search and Scatter Search as the ones, but notlimited to, with great potential to be used on solving the SCM relatedproblems. We will present briefly some successful applications.

The crew-scheduling module in the GIST system

The public transportation is gaining importance every year basically duethe population growth, environmental policies and, route and streetcongestion. Too able an efficient management of all the resources relatedto public transportation, several techniques from different areas are beingapplied and several projects in Transportation Planning Systems, indifferent countries, are being developed. In this work, we present theGIST Planning Transportation Systems, a Portuguese project involving twouniversities and six public transportation companies. We describe indetail one of the most relevant modules of this project, the crew-scheduling module. The crew-scheduling module is based on the application of meta-heuristics, in particular GRASP, tabu search and geneticalgorithm to solve the bus-driver-scheduling problem. The metaheuristicshave been successfully incorporated in the GIST Planning TransportationSystems and are actually used by several companies.

Adaptive approach heuristics for the generalized assignment problem

The Generalized Assignment Problem consists in assigning a setof tasks to a set of agents with minimum cost. Each agent hasa limited amount of a single resource and each task must beassigned to one and only one agent, requiring a certain amountof the resource of the agent. We present new metaheuristics forthe generalized assignment problem based on hybrid approaches.One metaheuristic is a MAX-MIN Ant System (MMAS), an improvedversion of the Ant System, which was recently proposed byStutzle and Hoos to combinatorial optimization problems, and itcan be seen has an adaptive sampling algorithm that takes inconsideration the experience gathered in earlier iterations ofthe algorithm. Moreover, the latter heuristic is combined withlocal search and tabu search heuristics to improve the search.A greedy randomized adaptive search heuristic (GRASP) is alsoproposed. Several neighborhoods are studied, including one basedon ejection chains that produces good moves withoutincreasing the computational effort. We present computationalresults of the comparative performance, followed by concludingremarks and ideas on future research in generalized assignmentrelated problems.