Teaching metaheuristics in business schools

In this work we discuss some ideas and opinions related with teaching Metaheuristics in Business Schools. The main purpose of the work is to initiate a discussion and collaboration about this topic,with the final objective to improve the teaching and publicity of the area. The main topics to be discussed are the environment and focus of this teaching. We also present a SWOT analysis which lead us to the conclusion that the area of Metaheuristics only can win with the presentation and discussion of metaheuristics and related topics in Business Schools, since it consists in a excellent Decision Support tools for future potential users.Metaheuristics, Teaching Business

The crew-scheduling module in the GIST system

The public transportation is gaining importance every year basically due the population growth, environmental policies and, route and street congestion. Too able an efficient management of all the resources related to public transportation, several techniques from different areas are being applied and several projects in Transportation Planning Systems, in different countries, are being developed. In this work, we present the GIST Planning Transportation Systems, a Portuguese project involving two universities and six public transportation companies. We describe in detail 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 genetic algorithm to solve the bus-driver-scheduling problem. The metaheuristics have been successfully incorporated in the GIST Planning Transportation Systems and are actually used by several companies.Integrated transportation systems, crew scheduling, metaheuristics

Supply chain management: An opportunity for metaheuristics

In today’s highly competitive and global marketplace the pressure on organizations to find new ways to create and deliver value to customers grows ever stronger. In the last two decades, logistics and supply chain has moved to the center stage. There has been a growing recognition that it is through an effective management of the logistics function and the supply chain that the goal of cost reduction and service enhancement can be achieved. The key to success in Supply Chain Management (SCM) require heavy emphasis on integration of activities, cooperation, coordination and information sharing throughout the entire supply chain, from suppliers to customers. To be able to respond to the challenge of integration there is the need of sophisticated decision support systems based on powerful mathematical models and solution techniques, together with the advances in information and communication technologies. The industry and the academia have become increasingly interested in SCM to be able to respond to the problems 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 argue that metaheuristics can play an important role in solving complex supply chain related problems derived by the importance of designing and managing the entire supply chain as a single entity. We will focus specially on the Iterated Local Search, Tabu Search and Scatter Search as the ones, but not limited to, with great potential to be used on solving the SCM related problems. We will present briefly some successful applications.Supply chain management, metaheuristics, iterated local search, tabu search and scatter search

Optimised search heuristic combining valid inequalities and tabu search

This paper presents an Optimised Search Heuristic that combines a tabu search method with the verification of violated valid inequalities. The solution delivered by the tabu search is partially destroyed by a randomised greedy procedure, and then the valid inequalities are used to guide the reconstruction of a complete solution. An application of the new method to the Job-Shop Scheduling problem is presented.Optimised Search Heuristic, Tabu Search, GRASP, Valid Inequalities, Job Shop Scheduling

A multi-objective model for a multi-period distribution management problem

The problems arising in commercial distribution are complex and involve several players and decision levels. One important decision is related with the design of the routes to distribute the products, in an efficient and inexpensive way. This article deals with a complex vehicle routing problem that can be seen as a new extension of the basic vehicle routing problem. The proposed model is a multi-objective combinatorial optimization problem that considers three objectives and multiple periods, which models in a closer way the real distribution problems. The first objective is cost minimization, the second is balancing work levels and the third is a marketing objective. An application of the model on a small example, with 5 clients and 3 days, is presented. The results of the model show the complexity of solving multi-objective combinatorial optimization problems and the contradiction between the several distribution management objective.Distribution problem, multi-objective models

Adaptive approach heuristics for the generalized assignment problem

The Generalized Assignment Problem consists in assigning a set of tasks to a set of agents with minimum cost. Each agent has a limited amount of a single resource and each task must be assigned to one and only one agent, requiring a certain amount of the resource of the agent. We present new metaheuristics for the generalized assignment problem based on hybrid approaches. One metaheuristic is a MAX-MIN Ant System (MMAS), an improved version of the Ant System, which was recently proposed by Stutzle and Hoos to combinatorial optimization problems, and it can be seen has an adaptive sampling algorithm that takes in consideration the experience gathered in earlier iterations of the algorithm. Moreover, the latter heuristic is combined with local search and tabu search heuristics to improve the search. A greedy randomized adaptive search heuristic (GRASP) is also proposed. Several neighborhoods are studied, including one based on ejection chains that produces good moves without increasing the computational effort. We present computational results of the comparative performance, followed by concluding remarks and ideas on future research in generalized assignment related problems.Metaheuristics, generalized assignment, local search, GRASP, tabu search, ant systems

Inventory-routing model, for a multi-period problem with stochastic and deterministic demand

The need for integration in the supply chain management leads us to consider the coordination of two logistic planning functions: transportation and inventory. The coordination of these activities can be an extremely important source of competitive advantage in the supply chain management. The battle for cost reduction can pass through the equilibrium of transportation versus inventory managing costs. In this work, we study the specific case of an inventory-routing problem for a week planning period with different types of demand. A heuristic methodology, based on the Iterated Local Search, is proposed to solve the Multi-Period Inventory Routing Problem with stochastic and deterministic demand.Inventory-Routing, iterated local search, logistics

A polynomial algorithm for special case of the one-machine scheduling problem with time-lags

The standard one-machine scheduling problem consists in scheduling a set of jobs in one machine which can handle only one job at a time, minimizing the maximum lateness. Each job is available for processing at its release date, requires a known processing time and after finishing the processing, it is delivery after a certain time. There also can exists precedence constraints between pairs of jobs, requiring that the first jobs must be completed before the second job can start. An extension of this problem consists in assigning a time interval between the processing of the jobs associated with the precedence constrains, known by finish-start time-lags. In presence of this constraints, the problem is NP-hard even if preemption is allowed. In this work, we consider a special case of the one-machine preemption scheduling problem with time- lags, where the time-lags have a chain form, and propose a polynomial algorithm to solve it. The algorithm consist in a polynomial number of calls of the preemption version of the Longest Tail Heuristic. One of the applicability of the method is to obtain lower bounds for NP-hard one-machine and job-shop scheduling problems. We present some computational results of this application, followed by some conclusions.One-machine scheduling, polynomial algorithms, lower bounds

A recoverable production planning model

Aware of the importance of developing new alternatives to improve the performance of the companies, our purpose in this paper is to develop a medium term production planning model that deals with the concepts of Partnership and Reverse Logistics. Our model takes advantage of the synergies of integration, developing a model for global production planning that generates the optimal production and purchasing schedule for all the companies integrating a logistic chain. In a second part of the paper we incorporate products returns to the first model proposed, and analyze the implications they have over this model. We use some examples with different configurations of supply chains varying the number of production plants, distribution centers and recovery plants. To solve the model we have combined optimization and simulation procedures.Reverse logistics, production planning, remanufacturing, returns, supply chain management, simulation, optimization

Solving two production scheduling problems with sequence-dependent set-up times

In today�s competitive markets, the importance of good scheduling strategies in manufacturing companies lead to the need of developing efficient methods to solve complex scheduling problems. In this paper, we studied two production scheduling problems with sequence-dependent setups times. The setup times are one of the most common complications in scheduling problems, and are usually associated with cleaning operations and changing tools and shapes in machines. The first problem considered is a single-machine scheduling with release dates, sequence-dependent setup times and delivery times. The performance measure is the maximum lateness. The second problem is a job-shop scheduling problem with sequence-dependent setup times where the objective is to minimize the makespan. We present several priority dispatching rules for both problems, followed by a study of their performance. Finally, conclusions and directions of future research are presented.Production-scheduling, set-up times, priority dispatching rules