Hybrid Heuristics for the Territory Alignment Problem

This chapter also illustrates the potential of the proposed approach as a practical and readily implementable management decision aid in the context of a current case that involved the maintenance team of a Portuguese regional office of a worldwide equipment company.

A Hybrid Particle Swarm Algorithm for Resource-Constrained Project Scheduling

The authors present and analyze a particle swarm optimization (PSO) approach for the resource-constrained project scheduling problem (RCPSP). It incorporates well-known procedures such as the serial schedule generation scheme and it is hybridized with forward-backward improvement. The authors investigate the application of PSO in comparison to state-of-the-art methods from the literature. They conduct extensive computational experiments using a benchmark set of problem instances. The reported results demonstrate that the proposed hybrid particle swarm optimization approach is competitive. They significantly improve previous results of PSO for the RCPSP and provide new overall best average results for the medium size data set. Furthermore, the authors provide insights into the importance of crucial components for achieving high-quality results.

HMIP Model for a Territory Design Problem with Capacity and Contiguity Constraints

Small geographic basic units (BU) are grouped into larger geographic territories on a Territory Design Problem (TDP). Proposed approach to solve a TDP is presented through a study case developed on a large soft drinks company which operates in the city of Monterrey, México. Each BU of our TDP is defined by three activity measures: (1) number of customers, (2) sales volume and (3) workload. Some geographic issues about contiguity and compactness for the territories to be constructed are considered. An optimal solution is obtained when the constructed territories are well balanced taking into consideration each activity measure simultaneously. In particular, contiguity is hard to be represented mathematically. All previous research work indicates that this NP-Hard problem is not suitable for solving on large-scale instances. A new strategy which is based on a hybrid-mixed integer programming (HMIP) approach is developed. Specifically, our implementation is based on a Cut-Generation Strategy. We take advantage from territory centers obtained through a relaxation of a P-median based model. This model has a very high degree of connectivity. Thus, small number of iterations to find connected solutions is required. The authors detail out their methodology and then they proceed to its computational implementation. Experimental results show the effectiveness of our method in finding near-optimal solutions for very large instances up to 10,000 BU’s in short computational times (less than 10 minutes). Nowadays, this model is being used by the firm with important economical benefits.

Global Bacteria Optimization Meta-Heuristic

A large number of real-life optimization problems in economics and business are complex and difficult to solve. Hence, using approximate algorithms is a very good alternative to solve this class of problems. Meta-heuristics solution procedures represent general approximate algorithms applicable to a large variety of optimization problems. Most of the meta-heuristics mimic natural metaphors to solve complex optimization problems. This chapter presents a novel procedure based on Bacterial Phototaxis, called Global Bacteria Optimization (GBO) algorithm, to solve combinatorial optimization problems. The algorithm emulates the movement of an organism in response to stimulus from light. The effectiveness of the proposed meta-heuristic algorithm is first compared with the well-known meta-heuristic MOEA (Multi-Objective Evolutionary Algorithm) using mathematical functions. The performance of GBO is also analyzed by solving some single- and multi-objective classical jobshop scheduling problems against state-of-the-art algorithms. Experimental results on well-known instances show that GBO algorithm performs very well and even outperforms existing meta-heuristics in terms of computational time and quality of solution.

Strategies for an Integrated Distribution Problem

Problems arising in the logistics of commercial distribution are complex and involve several players and decision levels. One of the most important decisions is the design of the routes to distribute the products in an efficient and inexpensive way but also satisfying marketing objectives such as customer loyalty. This chapter explores three different distribution routing strategies. The first strategy corresponds to the classical vehicle routing problem where total distance or cost is minimized. This one is usually an objective of the Logistics department. The second strategy is a master route strategy with daily adaptations where customer loyalty is maximized, which is one of the objectives of the Marketing department. The authors propose a third strategy which takes into account the cross-functional planning between the Logistics and the Marketing department through a multi-objective model. All strategies are analyzed in a multi-period scenario. A metaheuristic algorithm based on the Iterated Local Search is proposed and applied to optimize each strategy. An analysis and comparison of the three strategies is presented through a computational experiment. The cross-functional planning strategy leads to solutions that put in practice the coordination between the two functional areas of Marketing and Logistics and better meet business objectives in general.

A Hybrid Lagrangian Relaxation and Tabu Search Method for Interdependent-Choice Network Design Problems

Hybridization offers a promising approach in designing and developing improved metaheuristic methods for a variety of complex combinatorial optimization problems. This chapter presents a hybrid Lagrangian relaxation and tabu search method for a class of discrete network design problems with complex interdependent-choice constraints. This method takes advantage of Lagrangian relaxation for problem decomposition and complexity reduction while its algorithmic logic is designed based on the principles of tabu search. The algorithmic advance and solution performance of the method are illustrated by implementing it for solving a network design problem with lane reversal and crossing elimination strategies, arising from urban evacuation planning.

Marriage in Honeybee Optimization to Scheduling Problems

The biological inspired optimization techniques have proven to be powerful tools for solving scheduling problems. Marriage in Honeybee Optimization is a recent biological technique that attempts to emulate the social behavior in a bee colony and although has been applied to only a limited number of problems, it has delivered promising results. By means of this technique in this chapter the authors explore the solution space of scheduling problems by identifying an appropriate representation for each studied case. Two cases were considered: the minimization of earliness-tardiness penalties in a single machine scheduling and the permutation flow shop problem. The performance was evaluated for the first case with 280 instances from the literature. The technique performed quite well for a wide range of instances and achieved an average improvement of 1.4% for all instances. They obtained better solutions than the available upper bound for 141 instances. In the second case, they achieved an average error of 3.5% for the set of 120 test instances.

A Hybrid Genetic Algorithm-Simulated Annealing Approach for the Multi-Objective Vehicle Routing Problem with Time Windows

In this study, the vehicle routing problem with time windows (VRPTW) is investigated and formulated as a multi-objective model. As a solution approach, a hybrid meta-heuristic algorithm is proposed. Proposed algorithm consists of two meta-heuristics: Genetic Algorithm (GA) and Simulated Annealing (SA). In this algorithm, SA is used as an improvement operator in GA. Besides, a hypothetical application is presented to foster the better understanding of the proposed model and algorithm. The validity of the algorithm is tested via some well-known benchmark problems from the literature.

Vehicle Routing Models and Algorithms for Winter Road Spreading Operations

Winter road maintenance operations involve challenging vehicle routing problems that can be addressed using operations research (OR) techniques. Three key problems involve routing trucks and specialized vehicles for spreading chemicals and abrasives on roadways, snow plowing, and snow disposal, all of which are undertaken in a very difficult and dynamic operating environment with stringent level of service constraints. This chapter provides a survey of recent optimization models and solution methodologies for the routing of vehicles for spreading operations. The authors also present a detailed classification scheme for spreader routing models developed over the past 40 years. Key trends in recent model developments include the inclusion of more details of the practical operating constraints, the use of more sophisticated hybrid solution strategies and consideration of more comprehensive models that integrate vehicle routing with models for other related strategic winter maintenance problems. They highlight some factors that may be limiting the application of OR models in practice and discuss promising future research trends.

Routing Solutions for the Service Industry

First introduced by Dantzig and Ramser over 50 years ago, vehicle routing problems (VRP) have drawn the attention of both academic researchers and practitioners due to its difficult-to-solve nature and hence its attractiveness in theoretical research as well as wide applicability in real-world settings. Today VRP is probably one of the most widely encountered types of problems for routing and distribution in the service industry. Examples include furniture delivery to a customer’s address, scheduling of bus service pick-up/drop-off for students or company personnel, or service technician routing. The goal of this chapter is to provide a background, mathematical model and various solution approaches on a more commonly encountered variant of the problem, namely the VRP with Time Windows (VRPTW). The authors also present three case studies from their experience in the service industry that are real applications of VRPTW. For each study, they describe the overall approach and methodology, and the positive contributions to the respective company which has implemented enterprise-scale GIS-based systems around the distribution problem of interest.