A Hybrid Approach to the Optimization of Multiechelon Systems

In freight transportation there are two main distribution strategies: direct shipping and multiechelon distribution. In the direct shipping, vehicles, starting from a depot, bring their freight directly to the destination, while in the multiechelon systems, freight is delivered from the depot to the customers through an intermediate points. Multiechelon systems are particularly useful for logistic issues in a competitive environment. The paper presents a concept and application of a hybrid approach to modeling and optimization of the Multi-Echelon Capacitated Vehicle Routing Problem. Two ways of mathematical programming (MP) and constraint logic programming (CLP) are integrated in one environment. The strengths of MP and CLP in which constraints are treated in a different way and different methods are implemented and combined to use the strengths of both. The proposed approach is particularly important for the discrete decision models with an objective function and many discrete decision variables added up in multiple constraints. An implementation of hybrid approach in the ECLiPSe system using Eplex library is presented. The Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP) and its variants are shown as an illustrative example of the hybrid approach. The presented hybrid approach will be compared with classical mathematical programming on the same benchmark data sets

Integrated Supply Chain Optimization Model Using Mixed Integer Linear Programming

This article presents an integrated approach to optimize the different functions in a supply chain on strategic tactical and operational levels. The integrated supply chain model has been formulated as a cost minimization problem in the form of MILP (Mixed Integer Linear Programming). The costs of production, transport, distribution and environmental protection were adopted as optimization criteria. Timing, volume, capacity and mode of transport were also taken into account. The model was implemented in the LINGO package. The implementation model and the numerical tests are presented and discussed. The numerical experiments were carried out using sample data to show the possibilities of practical decision support and optimization of the supply chai

A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints

This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP) and constraint logic programming (CLP), were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method) helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems). The ECLiPSe system with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent

Integrated Supply Chain Optimization Model Using Mixed Integer Linear Programming

This article presents an integrated approach to optimize the different functions in a supply chain on strategic tactical and operational levels. The integrated supply chain model has been formulated as a cost minimization problem in the form of MILP (Mixed Integer Linear Programming). The costs of production, transport, distribution and environmental protection were adopted as optimization criteria. Timing, volume, capacity and mode of transport were also taken into account. The model was implemented in the LINGO package. The implementation model and the numerical tests are presented and discussed. The numerical experiments were carried out using sample data to show the possibilities of practical decision support and optimization of the supply chain

A Constraint-Based Declarative Programming Framework for Scheduling and Resource Allocation Problems

Scheduling and resource allocation problems are widespread in many areas of today’s technology and management. Their different forms and structures appear in production, logistics, software engineering, computer networks, project and human resources management, services, etc. The literature (problem classification, scheduling and resource allocation models, solutions) is vast and exhaustive. In practice, however, classical scheduling problems with fixed structures and standard constraints (precedence, disjoint, etc.) are rare. Practical scheduling problems include also logical and nonlinear constraints, and they use nonstandard criteria of schedule evaluations. Indeed, in many cases, decision makers are interested in the feasibility and/or optimality of a given schedule for specified conditions formulated as general and/or specific questions. Thus, there is a need to develop a programming framework that will facilitate the modeling and solving of a variety of diverse scheduling problems. The framework should be able to (a) model any types of constraints, (b) ask questions/criteria relating to the schedule execution mode and (c) be highly effective in finding solutions (schedule development). This paper proposes such a constraint-based declarative programming framework for modeling and solving scheduling problems which satisfies the assumptions above. It was built with the Constraint Logic Programming (CLP) environment and supported with Mathematical Programming (MP). The functionality and effectiveness of this framework are presented with the use of an illustrative example for the resource-constrained scheduling problem with additional resources

Proactive Planning of Project Team Members’ Competences

Among the many factors that cause project delays or cancellations are disruptions, that is, unforeseen events occurring during the implementation of a project, which postpone or interrupt the performance of project activities. Examples of disruptions include employee absenteeism, addition of new activities, and others. One way to deal with this type of events is to predict potential disruptions and prepare redundant resources to be used should a disruption occur (proactive approach). The focus of the present paper are human resources, in particular redundant project team competence frameworks, which allow to continue work on a project in the event of a disruption. Previous studies on planning competence frameworks regard insensitivity (robustness) to one type of disruption, caused by employee absenteeism (an absence of one, two, or three employees). The goal of this article is to present a proactive procedure that allows to seek competence frameworks robust to two types of disruptions: absence of one employee and addition of new activities not included in the project plan. Examples are provided to illustrate how the proposed approach can be used in practice

A declarative approach to shop orders optimization

The paper presents the problem of material requirements planning with optimization of load distribution between work centers and workers’ groups. Moreover, it discusses the computational example for shop orders optimization. The data for this example were taken from the relational database. The method of Constraint Logic Programming (CLP) for shop orders optimization has been suggested. Using Constraint Logic Programming, the constraints may be directly introduced to the problem declaration, which is equivalent to the source code of the program. The ECLiPSe-CLP software system has been presented. It allows for solving optimization problems concerning dimensions greater than in the case of the professional mathematical programming solver “LINGO”. The application of ECLiPSe-CLP in accessing data from relational databases has been presented