Operational Research in Business and Economics

Business process modeling is aimed at the design and documentation of business processes. Business process models are used to analyze processes, to reduce their complexity, to evaluate their performance and finally to assist business process improvement. In this light, a number of modeling architectures, methods and tools have been developed in order to assist scientists and practitioners to model and manage business processes. In addition, supply chain management importance is increasingly being recognized as it integrates and synchronizes business processes across the extended supply chains.

This paper deals with the application of a specific business process modeling architecture in order to design supply chain processes in the case of a SME manufacturing company. The modeling architecture has been developed in the context of the “Odysseus” research project, which deals with the management of demand variability in modern supply chains. The architecture covers different supply chain views such as processes and activities, organization, information systems, risk management and decision making. These views are covered by the modeling architecture using nine selected and interconnected ARIS methods. The architecture is applied in a Greek SME company producing electrical equipment. The production process of the equipment consists of in-house as well as sub-contracted phases performed by Greek and European manufacturers. The coordination of the related supply chain processes is performed by the company under discussion. Due to the extended degree of collaboration, the need for accurate planning, coordination and controlling in the supply chain is highly increased, making business process modeling an ideal enabling approach.

This paper aims to examine how environmental information affects the decisions of managers/owners to incorporate environmental concerns into their supply chain management. A research framework is constructed through a literature review. This is based on certain research hypotheses which are particularly related to the knowledge of managers about environmental impacts of Small and Medium Sized Enterprises (SMEs), and of the barriers and opportunities faced by SMEs when implementing certain practices to green their supply chain management. The findings show that knowledge and environmental information plays a critical role in managers’ decisions to adopt environmental practices across supply chain management, while the economic crisis seems to negatively affect their intention to implement any environmental practice.

Retail Category Management addresses a series of questions and demands decisions for category managers on critical issues such as product assortment and shelf-space planning. Product assortment planning involves listing decisions based on consumer behavior and substitution effects. Shelf space allocation involves facing and replenishment decisions based on space elasticity effects and constraints of limited shelf space and restocking capacity. The complexity of these questions has grown significantly in recent years due to product proliferation and various consumer choice effects in the retail environment. It is an increasingly difficult task for category managers to find an effective assortment due to consumer preferences instability and the extremely large number of possible assortments. This chapter presents an updated review on scientific models that deal with assortment and shelf space planning and other related topics, such as consumer response to stock-outs and consumer perceptions of assortment variety. One of the main objectives of this literature review is to show that shelf space allocation models do not clearly and comprehensively address assortment selection, neglect substitution effects between products, and ignore the stochastic nature of demand. Assortment planning models on the other hand mostly ignore shelf space constraints and neglect space depend demand.

The continuous development of new services is a prerequisite for the development and prosperity of Museums in today’s competitive environment. So far, however, limited research effort has been devoted on the development process of innovative services within cultural and creative industries and particularly Museums. Given the crucial importance of cultural organisations for the dynamic cultural and creative industries, this research work aims at studying the development of new innovative services in the Museums of Athens and their ability and capacity to adopt innovation management (i.e., development, organisational, functional and technological aspects of management) as the basic strategy for diversification. To achieve this task, a systematic desk-top research took place in order to identify and analyse models, practices and processes available on the design and development of new services. Based on the results of this work, a survey was designed aiming to identify the current status with regards to innovative services development and innovation management within Athenian Museums. The fieldwork was carried out in 62 museums in Athens and investigated the development process of 184 different services. The analysis was based on data collected via an in depth structured interview with questionnaires from museum directors knowledgeable about new service development in their organisation. A multicriteria methodology was used to examine the potential of a predictive model for successful new service development projects in the cultural industry.

Market orientation has always been a critical factor of creating useful business knowledge. As a result, this kind of information is crucial for creating dynamic innovation capabilities. Knowing and affiliating that to their strategy, firms could be able to differentiate easily from their competitors. Having taken into consideration the above framework, they are led to better organizational performance. The only way to achieve such a project is to be always up-to-date and proactive. Added value, competitive advantage, customers’ satisfaction are some of the missions that a business should be able to accomplish. The purpose of this research is to present how SMEs’ could adjust their strategy depending on their customer and competitors’ orientation, innovation capabilities so that organizational performance could be achieved. Differentiation is a weapon which is difficult to use successfully. Most of the researches have shown the importance of innovativeness and performance. However, this research is going to show how differentiation and competitive innovation strategy could affect the organizational performance, not only financially but also non-financially.

This chapter is focused on strategy tools adapted for the needs of the agricultural sector. The increasing complexity and high level of changes in the agricultural context and economic sector lead to the development of new tools and a new specialty, the strategic adviser. An adviser based on specific knowledge, skills and tools offers services at small or very small farm exploitations on strategy issues.

Strategic Analysis is the process of conducting research on the business environment within which an organisation operates and on the organisation itself, in order to formulate strategy. Definitions of strategic analysis often differ, but the following attributes are commonly associated with it, like (a) the identification and evaluation of data relevant to strategy formulation, (b) the definition and analysis of the external and internal environment and (c) a range of analytical methods that can be employed in the analysis.

Commonly used analytical methods in strategic analysis are SWOT, PEST, Porter’s five forces, four corner’s analysis, value chain, early warning scans, war gaming, etc. The subsequent sections of this chapter focus on strategic analysis for farms, as the business context. The PerfEA and the Risk Wheel are presented, as two useful tools for a strategic adviser. The tools are proposed in the European-level Leonardo da Vinci project (STRAT-Training), and are adapted for the farm sector. The tools support the adviser for (a) exploring farm environment, (b) managing risks and (c) setting up an integrated strategy.

In addition, some aspects in the context of decision aiding process are discussed. Thus, as a twofold effort herein, we discuss issues related with complex situation in agro-businesses, like risks handling, and via presenting farm strategy tools we make links with decision science and multi-criteria decision making.

Behavioral credit scoring models are a specific kind of credit scoring models, where time-evolving data about delinquency pattern, outstanding amounts, and account activity, is used. These data have a dynamic nature as they evolve over time in accordance with the economic environment. On the other hand, scoring models are usually static, implicitly assuming that the relationship between the performance characteristics and the subsequent performance of a customer will be the same under the current situation as it was when the information on which the scorecard was built was collected, no matter what economic changes have occurred in that period. In this study we investigate how this assumption affects the predictive power of behavioral scoring models, using a large data set from Greece, where consumer credit has been heavily affected by the economic crisis that hit the country since 2009.

This work presents a new optimization software library which contains a number of financial optimization models. Roughly speaking, the majority of these portfolio allocation models aim to compute the optimal allocation investment weights, and thus they are particularly useful for supporting investment decisions in financial markets. Algebraic modeling languages are very well suited for prototyping and developing optimization models. All the financial optimization models have been implemented in AMPL mathematical programming modeling language and solved using either Gurobi Optimizer or Knitro (for those models having general nonlinear objectives). This proposed software library includes several well-known portfolio allocation models, such as the Markowitz mean-variance model, the Konno-Yamazaki absolute deviation model, the Black-Litterman model, Young’s minimax model and others. These models aim either to minimize the variance of the portfolios, or maximize the expected returns subject to a number of constraints, or include portfolios with a risk-free asset, transaction costs, and others. Furthermore, we also present a literature review of financial optimization software packages and discuss the benefits and drawbacks of our proposed portfolio allocation model library. Since this is a work in progress, new models are still being added to the proposed library.

Genetic Programming (GP) has been used in a variety of fields to solve complicated problems. This paper shows that GP can be applied in the domain of serial production systems for acquiring useful measurements and line characteristics such as throughput. Extensive experimentation has been performed in order to set up the genetic programming implementation and to deal with problems like code bloat or over fitting. We improve previous work on estimation of throughput for three stages and present a formula for the estimation of throughput of production lines with four stations. Further work is needed, but so far, results are encouraging.

In this paper, a new algorithm is presented which is applied to a real world Vehicle Routing Problem (VRP) of a provision company in the island of Crete in Greece. The company serves 116 customers located in Crete. This real world problem is solved effectively by a hybrid Island Memetic Algorithm (IMA) which employs Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Local Search (ILS). The proposed algorithm is also compared to five other approaches both on the real world problem and on classic benchmark instances from the literature. Methods such as GRASP, local search and Iterated Local Search (ILS) are employed as subroutines with certain probabilities in the algorithms. Furthermore, it is also demonstrated how premature convergence can be prevented by adopting specific strategy. Computational results show the superiority of the proposed hybrid Island Memetic Algorithm.

In our research, we are interested in a practical problem closely related to the three-dimensional multiple-bin-size bin packing problem. We deal with the real word application of cutting mousse blocks proposed by a Tunisian industrial company. First, we present the general context related to this optimization problem. Second, for solving this practical problem, we propose an upper bound based on a MILP formulation (mixed integer linear programming). Finally, computational and comparative results are presented to evaluate the performance of the proposed bound by testing a large instance from the same industrial company.

We develop simple models for understanding how the dynamics of quality may affect customer satisfaction and profitability in make-to-stock manufacturing systems. We study a Markovian, single stage system facing random demand. Any demand not satisfied immediately from stock is lost to competitors. The market is assumed to be finite and comprises both regular and occasional customers. Regular customers have higher mean demand rates than occasional customers. Each outgoing product is inspected and classified as high quality, medium quality, or nonconforming. The customer who purchases an item joins the regular or the occasional class, with corresponding probabilities which depend on the quality level and on past customer state. The higher the quality level, the higher the probability for a customer to remain or become a regular customer. Our goal is to investigate the structure of the optimal production, order satisfaction and quality control policy in order to maximize the average profit rate of the system. We investigate numerically the structure of the optimal policy using stochastic dynamic programming.

In this paper two inventory systems are presented assuming general ramp type demand rate, constant deterioration rate and partial backlogging of unsatisfied demand. Since the capacity of the Owned Warehouse (OW) is usually limited and, under some special circumstances, the procurement of a large amount of items can be decided, a Rented Warehouse (RW) can be used to store the excess quantity. In addition, we assume, that the supplier offers the retailer a credit scheme, which provides a fixed delay period for settling his account. Sales revenue generated during this credit period is deposited to an interest bearing account. At the end of this period the retailer settles the account. Thereafter, capital opportunity cost for the value of items still in stock is charged. The study of the inventory system requires exploring the feasible ordering relations between the time parameters. Consequently the following cases, which lead to two inventory models, must be examined: (1) the offered credit period is less than the time point when the demand rate is stabilized, (2) the offered credit period is longer than the time point when the demand rate is stabilized and less than the planning horizon. The single warehouse inventory problem is also examined as a special case of the model. The optimal replenishment policy for each model is determined. The results obtained are highlighted by suitably selected examples.

Extensive electrification and the use of dc distribution grid are recently proved to be very promising technologies for the development of more efficient and environmentally friendly ships. Onboard dc grids present several advantages such as, improved efficiency, easy integration of different types of power sources, reduced size and rating of switchboard, elimination of reactive power flow, increased reconfiguration capability etc. All electric ship (AES) concept, dc distribution grid and optimal power management can lead to a substantial improvement of ship efficiency and compliance with the environmental constraints. In this paper, a method for optimal demand side management and power generation scheduling is proposed for AES employing dc grid. Demand side management is based on the adjustment of the power consumed by ship electric propulsion motors. Dynamic programming algorithm subject to operation, environmental and travel constraints is used to solve the above problem.