An integrated mathematical model of crew scheduling

In conditions of air transport companies, the process of planning flight schedules is one of the most important processes each airline has to deal with. The flight schedule planning process consists of several consecutive plans. The first step of the planning process is defining which air routes will be operated, the decision is based on the business plan of the air transport company. Consequently, suitable airplanes have to be assigned to the individual air routes. And finally, on the basis of the pre-vious steps shifts of pilots can be planned, the shifts are usually planned one month in advance. However, with no respect to the created plan some unexpected disruptions of the flying staff, especially of the pilots, may happen in practice due to many reasons. In such cases the original plan has to be modified in order to react to the disruptions. The modifications can represent an optimisation problem – the air transport company has a set of the pilots and on the basis of their qualification and experience the company has to create new aircrews. The pilots can be found in different localities that are different from the airports of the planned flight departures. That means the newly planned aircrews are assigned to the individual flights with respect to costs associated with transportation of the aircrews to the airports of their departure. The problem can be solved by many approaches. One of the possible approaches is a heuristic approach which is based on sequential solving two linear mathematical models. The first model decides about the aircrews (matches the pilots with respect to their compatibility). The second model solves the assignment problem – the air-crews are matched with the individual flights. The article presents an integrated linear model which deals with both problems at the same time.V podmínkách leteckých dopravců je hlavním výsledkem plánovacího procesu letový řád. Samotná tvorba letového řádu je posloupností několika na sebe navazujících dílčích plánů. Prvním krokem v procesu plánování je naplánování linek podle obchodního záměru dopravce, následně se naplánovaným letům přidělí konkrétní typ letadla. Zpravidla s měsíčním předstihem je nutné vytvořit plán práce pro posádky pilotů, kteří budou letouny obsluhovat. Bez ohledu na vytvořený plán práce posádek však může dojít k neočekávaným výpadkům personálu. Potom je nutné operativně upravit připravený plán a posádky přeplánovat. Jedná se tedy o optimalizační problém, kdy dopravce má k dispozici množinu pilotů, z nichž je nutné na základě jejich kvalifikace a zkušeností vytvořit nové posádky. Piloti se mohou nacházet v různých destinacích, které mohou být různé od letišť odletů. Nově vytvořené posádky jsou potom přidělovány konkrétním letadlům v závislosti na velikosti nákladů spojených s přepravou posádek k letadlům. Uvedený problém lze řešit různými způsoby. První způsob je heuristický založený na postupném řešení dvou lineárních modelů. V prvním modelu se rozhoduje o vytvoření posádek. Druhý model vytvořené posádky přiděluje letadlům. Cílem tohoto příspěvku bude prezentovat integrovaný lineární model řešící oba problémy současně

Modeling of Biological Intelligence for SCM System Optimization

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms

Spatial optimization for land use allocation: accounting for sustainability concerns

Land-use allocation has long been an important area of research in regional science. Land-use patterns are fundamental to the functions of the biosphere, creating interactions that have substantial impacts on the environment. The spatial arrangement of land uses therefore has implications for activity and travel within a region. Balancing development, economic growth, social interaction, and the protection of the natural environment is at the heart of long-term sustainability. Since land-use patterns are spatially explicit in nature, planning and management necessarily must integrate geographical information system and spatial optimization in meaningful ways if efficiency goals and objectives are to be achieved. This article reviews spatial optimization approaches that have been relied upon to support land-use planning. Characteristics of sustainable land use, particularly compactness, contiguity, and compatibility, are discussed and how spatial optimization techniques have addressed these characteristics are detailed. In particular, objectives and constraints in spatial optimization approaches are examined

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Models and Algorithms for Production Planning and Scheduling in Foundries – Current State and Development Perspectives

Mathematical programming, constraint programming and computational intelligence techniques, presented in the literature in the field of operations research and production management, are generally inadequate for planning real-life production process. These methods are in fact dedicated to solving the standard problems such as shop floor scheduling or lot-sizing, or their simple combinations such as scheduling with batching. Whereas many real-world production planning problems require the simultaneous solution of several problems (in addition to task scheduling and lot-sizing, the problems such as cutting, workforce scheduling, packing and transport issues), including the problems that are difficult to structure. The article presents examples and classification of production planning and scheduling systems in the foundry industry described in the literature, and also outlines the possible development directions of models and algorithms used in such systems

Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem