Emel Aktas

Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption

Sustainability considerations in manufacturing scheduling, which is traditionally influenced by service oriented performance metrics, have rarely been adopted in the literature. This paper aims to address this gap by incorporating energy consumption as an explicit criterion in shop floor scheduling. Leveraging the variable speed of machining operations leading to different energy consumption levels, we explore the potential for energy saving in manufacturing. We analyze the trade-off between minimizing makespan, a measure of service level and total energy consumption, an indicator for environmental sustainability of a two-machine sequence dependent permutation flowshop. We develop a mixed integer linear multi-objective optimization model to find the Pareto frontier comprised of makespan and total energy consumption. To cope with combinatorial complexity, we also develop a constructive heuristic for fast trade-off analysis between makespan and energy consumption. We define lower bounds for the two objectives under some non-restrictive conditions and compare the performance of the constructive heuristic with CPLEX through design of experiments. The lower bounds that we develop are valid under realistic assumptions since they are conditional on speed factors. The Pareto frontier includes solutions ranging from expedited, energy intensive schedules to prolonged, energy efficient schedules. It can serve as a visual aid for production and sales planners to consider energy consumption explicitly in making quick decisions while negotiating with customers on due dates. We provide managerial insights by analyzing the areas along the Pareto frontier where energy saving can be justified at the expense of reduced service level and vice versa.

Efficient shift scheduling in the retail sector through two-stage optimization

Efficient workforce scheduling has an important impact on store profit and customer service. Standard scheduling problems do not recognize the effect of staff availability on customer sales, however, even though the latter is an important factor in the retail sector. In this paper a two-stage model is proposed for this purpose. In the first stage a sales response model is used to specify hourly staff requirements. The output of the sales response model is then used as the input of a mixed integer optimization model, which finds an optimum assignment of the staff to daily shifts. Simulations are used to validate the sales response function, and to revise the model for more accurate results. In the simulations, customer arrivals and sales response error values are generated using appropriate distribution functions. As a case study the proposed model is applied to a Turkish retailer in the apparel sector.

Optimizing Fire Station Locations for the Istanbul Metropolitan Municipality

The Istanbul Metropolitan Municipality IMM seeks to determine locations for additional fire stations to build in Istanbul; its objective is to make residences and historic sites reachable by emergency vehicles within five minutes of a fire station’s receipt of a service request. In this paper, we discuss our development of a mathematical model to aid IMM in determining these locations by using data retrieved from its fire incident records. We use a geographic information system to implement the model on Istanbul’s road network, and solve two location models—set-covering and maximal-covering—as what-if scenarios. We discuss 10 scenarios, including the situation that existed when we initiated the project and the scenario that IMM implemented. The scenario implemented increases the city’s fire station coverage from 58.6 percent to 85.9 percent, based on a five-minute response time, with an implementation plan that spans three years.

An analysis of supply chain related graduate programmes in Europe

This is the post-print version of the Article. The official published version of the Article can be accessed from the links below. Copyright @ 2013 Emerald Group Publishing Limited.

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum’s competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey.

Cumulative belief degrees approach for analyzing the competitiveness of the automotive industry

As traditional competition becomes global, businesses fail to take, on their own, the measures that are required to become more competitive. Hence, in a globally competitive environment, national improvement and competitiveness have also become vital. Businesses must utilize and be supported by the international competitiveness of their nations. This study aims to analyze the competitiveness of the automotive industry from a national competitiveness perspective, using a three-stage methodology. For this purpose, a novel cumulative belief degrees (CBD) approach is introduced, to quantify the causal relations among the variables in the system. This methodology is illustrated by the analysis of the Turkish automotive industry for developing suggestions to assist policymakers in their decisions to improve the competitiveness of the industry. Although the findings are country specific, the methodology is applicable to a wider range of industries in developed and developing countries.

A Multi-Criteria Evaluation of Factors Affecting Internet Banking in Turkey

This research is based on quantifying adoption of Internet Banking by using an integrated multi-criteria decision aid approach. The analytic network process model, supported by a Delphi-type group-decision-making procedure, is used to conduct a comprehensive evaluation of the challenges to adoption of the Internet Banking by Turkish customers. This paper details all iterative stages of the decision-making process: structuring the problem, constructing the decision model, and analyzing the problem. Utilizing these stages, the study identifies the factors that influence the adoption of Internet Banking by customers, the effects of these factors on each other, and the assessment of the importance of them from the customers’ point of view. The results indicate that security and reliability appear to be the most important factors for successful adoption of Internet Banking. Infrastructural competencies and user friendliness are other important factors.

Managing food security through food waste and loss : small data to big data

Abstract This paper provides a management perspective of organisational factors that contributes to the reduction of food waste through the application of design science principles to explore causal relationships between food distribution (organisational) and consumption (societal) factors. Qualitative data were collected with an organisational perspective from commercial food consumers along with large-scale food importers, distributors, and retailers. Cause-effect models are built and “what-if” simulations are conducted through the development and application of a Fuzzy Cognitive Map (FCM) approaches to elucidate dynamic interrelationships. The simulation models developed provide a practical insight into existing and emergent food losses scenarios, suggesting the need for big data sets to allow for generalizable findings to be extrapolated from a more detailed quantitative exercise. This research offers itself as evidence to support policy makers in the development of policies that facilitate interventions to reduce food losses. It also contributes to the literature through sustaining, impacting and potentially improving levels of food security, underpinned by empirically constructed policy models that identify potential behavioural changes. It is the extension of these simulation models set against a backdrop of a proposed big data framework for food security, where this study sets avenues for future research for others to design and construct big data research in food supply chains. This research has therefore sought to provide policymakers with a means to evaluate new and existing policies, whilst also offering a practical basis through which food chains can be made more resilient through the consideration of management practices and policy decisions.

Supply Chain Flexibility: Managerial Implications

Today’s companies are forced into functioning in a challenging business world with extensive uncertainties. Frontrunners turn out to be those companies that are able to foresee the market swings and react swiftly with minimal adjustment costs and effective response strategies. Hence, developing flexibility in adapting to sudden changes in global markets, resource availabilities, and outbreaks of financial and political crises becomes an integral part of effective management strategy. Supply chain management presents an especially important domain where such flexibility is critical to achieving a consistently successful performance. Earlier research on flexibility in supply chains has focused primarily on manufacturing (e.g., Barad & Nof, 1997; De Toni & Tonchia, 1998; Gupta & Goyal, 1989; Kaighobadi & Venkatesh, 1994; Koste & Malhotra, 1999; Mascarenhas, 1981; Parker & Wirth, 1999; Sethi & Sethi, 1990). In contrast, recent studies have tended to examine a proliferation of different dimensions like volume, launch, and target market flexibilities (Vickery, Calantone & Drőge, 1999); logistics flexibility potentially including flexibilities in postponement, routing, delivery and trans-shipment (Barad & Sapir, 2003; Das & Nagendra, 1997); order quantity and delivery lead time flexibilities (Wang, 2008); sourcing flexibility (Narasimhan & Das, 2000); launch flexibility and access flexibility (Sanchez and Perez, 2005). Firm performance has presented another core theme in recent work, with results pointing to the importance of customer-supplier flexibility capabilities to improve competitiveness (Merschmann & Thonemann, in press; Sanchez and Perez, 2005). Duclos, Vokurka & Lummus (2003) argue for the importance of organizational flexibility and information systems flexibility (in addition to operations system, market, logistics, and supply flexibility) so that the supply chains can function in a seamless succession of efficient processes; while More & Babu (2009) claim that supply chain flexibility is a new strategic tool for management. In thinking about the managerial implications of supply chain flexibility, it is useful to distinguish among ‘flexible competencies’ (internal flexibility issues from the supplier perspective) versus ‘flexible capabilities’ (customer perceptions on external flexibility issues) (Zhang, Vonderembse & Lim, 2003). It is important in this regard to tease out the relevant factors for suppliers and customers using procedures like Delphi (Lummus, Vokurka & Duclos, 2005), where the different attributes could be identified and unified metrics could be developed to enable communication across different perspectives (Gunasekaran, Patel &

Minimizing energy consumption and makespan in a two-machine flowshop scheduling problem

Energy consumption has become a key concern for manufacturing sector because of negative environmental impact of operations. We develop constructive heuristics and multi-objective genetic algorithms (MOGA) for a two-machine sequence-dependent permutation flowshop problem to address the trade-off between energy consumption as a measure of sustainability and makespan as a measure of service level. We leverage the variable speed of operations to develop energy-efficient schedules that minimize total energy consumption and makespan. As minimization of energy consumption and minimization of makespan are conflicting objectives, the solutions to this problem constitute a Pareto frontier. We compare the performance of constructive heuristics and MOGAs with CPLEX and random search in a wide range of problem instances. The results show that MOGAs hybridized with constructive heuristics outperform regular MOGA and heuristics alone in terms of quality and cardinality of Pareto frontier. We provide production planners with new and scalable solution techniques that will enable them to make informed decisions considering energy consumption together with service objectives in shop floor scheduling.