Includes Decision Variables Research Articles

Determining the factor levels for a green supply chain using response surface methodology based discrete event simulation

PurposeThis study aims to optimize the levels of factors for a green supply chain (GSC) while concurrently gaining valuable insights into the dynamic interrelationships among several factors, leading to reductions in CO2 emissions and the maximization of the average service level, thereby enhancing overall supply chain performance.Design/methodology/approachResponse surface methodology (RSM) is employed as a technique for multiple response optimization. This study uses a supply chain simulation model that includes decision variables related to the level of inventory control parameters and vehicle capacity. The desirability approach is adopted to achieve optimization objectives by focusing on minimizing CO2 emissions and maximizing service levels while simultaneously determining the optimum levels of considered decision variables.FindingsThe high R2 values of 97.38% for CO2 and 97.28% for service level, along with adjusted R2 values reasonably close to predicted values, affirm the models' capability to predict responses accurately. Key significant model terms for CO2 encompassed reorder point, order up to quantity, vehicle capacity, and their interaction effects, while service level is notably influenced by reorder point, order up to quantity, and their interaction effects. The study successfully achieved a high level of desirability value of %99.1 and the validated performance levels confirmed that the results fall within the prediction interval.Originality/valueThis study introduces a metamodel framework designed to optimize various design parameters for a GSC combining discrete event simulation (DES) and RSM in the form of a simulation optimization model. In contrast to the literature, the current study offers an exhaustive and in-depth analysis of the structural elements of the supply chain, particularly the inventory control parameters and vehicle capacity, which are crucial for comprehending its performance and environmental impact.

Mixture Optimization of Cementitious Materials Using Machine Learning and Metaheuristic Algorithms: State of the Art and Future Prospects.

The hybrid optimization of modern cementitious materials requires concrete to meet many competing objectives (e.g., mechanical properties, cost, workability, environmental requirements, and durability). This paper reviews the current literature on optimizing mixing ratios using machine learning and metaheuristic optimization algorithms based on past studies on varying methods. In this review, we first discuss the conventional methods for mixing optimization of cementitious materials. Then, the problem expression of hybrid optimization is discussed, including decision variables, constraints, machine learning algorithms for modeling objectives, and metaheuristic optimization algorithms for searching the best mixture ratio. Finally, we explore the development prospects of this field, including, expanding the database by combining field data, considering more influencing variables, and considering more competitive targets in the production of functional cemented materials. In addition, to overcome the limitation of the swarm intelligence-based multi-objective optimization (MOO) algorithm in hybrid optimization, this paper proposes a new MOO algorithm based on individual intelligence (multi-objective beetle antenna search algorithm). The development of computationally efficient robust MOO models will continue to make progress in the field of hybrid optimization. This review is adapted for engineers and researchers who want to optimize the mixture proportions of cementitious materials using machine learning and metaheuristic algorithms.

The Floating-Cuts model: a general and flexible mixed-integer programming model for non-guillotine and guillotine rectangular cutting problems

Cutting and packing problems are challenging combinatorial optimization problems that have many relevant industrial applications and arise whenever a raw material has to be cut into smaller parts while minimizing waste, or products have to be packed, minimizing the empty space. Thus, the optimal solution to these problems has a positive economic and environmental impact.In many practical applications, both the raw material and the cut parts have a rectangular shape, and cutting plans are generated for one raw material rectangle (also known as plate) at a time. This is known in the literature as the (two-dimensional) rectangular cutting problem. Many variants of this problem may arise, led by cutting technology constraints, raw-material characteristics, and different planning goals, the most relevant of which are the guillotine cuts. The absence of the guillotine cuts imposition makes the problem harder to solve to optimality.Based on the Floating-Cuts paradigm, a general and flexible mixed-integer programming model for the general rectangular cutting problem is proposed. To the best of our knowledge, it is the first mixed integer linear programming model in the literature for both non-guillotine and guillotine problems. The basic idea of this model is a tree search where branching occurs by successive first-order non-guillotine-type cuts. The exact position of the cuts is not fixed, but instead remains floating until a concrete small rectangle (also known as item) is assigned to a child node. This model does not include decision variables either for the position coordinates of the items or for the coordinates of the cuts. Under this framework, it was possible to address various different variants of the problem.Extensive computational experiments were run to evaluate the model’s performance considering 16 different problem variants, and to compare it with the state-of-the-art formulations of each variant. The results confirm the power of this flexible model, as, for some variants, it outperforms the state-of-the-art approaches and, for the other variants, it presents results fairly close to the best approaches. But, even more importantly, this is a new way of looking at these problems which may trigger even better approaches, with the consequent economic and environmental benefits.

Preventive healthcare facility location planning with quality-conscious clients

Pursuing the overarching goal of saving both lives and healthcare costs, we introduce an approach to increase the expected participation in a preventive healthcare program, e.g., breast cancer screening. In contrast to sick people who need urgent medical attention, the clients in preventive healthcare decide whether to go to a specific facility (if this maximizes their utility) or not to take part in the program. We consider clients’ utility functions to include decision variables denoting the waiting time for an appointment and the quality of care. Both variables are defined as functions of a facility’s utilization. We employ a segmentation approach to formulate a mixed-integer linear program. Applying GAMS/CPLEX, we optimally solved instances with up to 400 demand nodes and 15 candidate locations based on both artificial data as well as in the context of a case study based on empirical data within one hour. We found that using a Benders decomposition of our problem decreases computational effort by more than 50%. We observe a nonlinear relationship between participation and the number of established facilities. The sensitivity analysis of the utility weights provides evidence on the optimal participation given a specific application (data set, empirical findings).

Integrated and dynamic energy modelling of a regional system: A cost-optimized approach in the deep decarbonisation of the Province of Trento (Italy)

Since the Kyoto Protocol (1997), the European Union has fought against climate change adopting European, national and regional policies to decarbonise the economy. Moreover, the Paris Agreement (2015) calls 2050 solutions between -80% and -100% of greenhouse gas emissions compared with 1990. Regions have an important role in curbing CO2 emissions, and tailor-made strategies considering local energy demands, savings potentials and renewables must be elaborated factoring in the social and economic context. An “optimized smart energy system” approach is proposed, considering: (I) integration of electricity, thermal and transport sectors, (II) hourly variability of productions and demands, (III) coupling the EnergyPLAN software, to develop integrated and dynamic scenarios, with a multi-objective evolutionary algorithm, to identify solutions optimized both in terms of CO2 emissions and costs, including decision variables for all the three energy sectors simultaneously. The methodology is tested at the regional scale for the Province of Trento (Italy) analyzing a total of 30,000 scenarios. Compared to the Baseline 2016, it is identified: (I) the strategic role of sector coupling among large hydroelectric production and electrification of thermal and transport demands (heat pumps, electric mobility), (II) slight increases in total annual cost, +14% for a -90% of CO2 emissions in 2050.

Weapon Selection and Planning Problems Using MOEA/D with Distance‐Based Divided Neighborhoods

Real‐world multiobjective optimization problems are characterized by multiple types of decision variables. In this paper, we address weapon selection and planning problems (WSPPs), which include decision variables of weapon‐type selection and weapon amount determination. Large solution space and discontinuous, nonconvex Pareto front increase the difficulty of problem solving. This paper solves the addressed problem by means of a multiobjective evolutionary algorithm based on decomposition (MOEA/D). Two mechanisms are designed for the complex combinatorial characteristic of WSPPs. The first is that the neighborhood of each individual is divided as selection and replacement neighborhoods. The second is that the neighborhood size is changing during the evolution by introducing a distance parameter to constrain the search scope of each subproblem. The proposed algorithm is termed as MOEA/D with distance‐based divided neighborhoods (MOEA/D‐DDNs) which can overcome possible drawbacks of original MOEA/D with weighted sum approach for complex combinatorial problems. Benchmark instances are generated to verify the proposed approach. Experimental results suggest the effectiveness of the proposed algorithm.

Optimal sizing and energy management of stand-alone hybrid photovoltaic/wind system based on hydrogen storage considering LOEE and LOLE reliability indices using flower pollination algorithm

In this paper, the optimal design and energy management of the hybrid systems including the photovoltaic (PV) panels, wind turbine (WT) and fuel cell (FC) based on hydrogen storage (HS) (PWFHS) are presented to minimize the total net present cost (TNPC) of northwest region of Iran using intelligent flower pollination algorithm (FPA). The reliability indices that are considered simultaneously as technical constraints are the loss of energy expected (LOEE) and the loss of load expected (LOLE). The FPA performance is compared with well-known optimization methods such as teaching-learning based optimization (TLBO), particle swarm optimization (PSO) and also last researches in hybrid renewable energy designing. The simulation results are presented including decision variables, TNPC, LOEE, LOLE, energy management of generation units in different LOLEmax and LOEEmax and different combination of PWFHS. The results show that the proposed methodology finds the optimal decision variables easily with fast convergence, lower cost and better reliability values in different reliability indices and different PWFHS in comparison to TLBO and PSO.

Raw material inventory optimization for MTO enterprises under price fluctuations

Rapidly changing market environment cause raw materials price fluctuations, it is very difficulty for many manufacturing enterprises to make appropriate purchasing strategy aim at management actual. Accordingly, raw materials inventory optimization has become an important research question for MTO enterprises which is one of special manufacturing enterprises. Current literature researches either assumed all product orders can be met or ignored, which is inconsistent with MTO enterprises actual. It is groping direction of the paper that how to introduce customer order rates into raw material inventory optimization model and to make it between an given interval suitable for MTO enterprise’s actual situation, since the shortcoming appeared in previous researches. The model under raw materials price fluctuations for MTO enterprises with the goal of total costs minimal includes decision variables Q, the constraint. Total costs were composed of fixed order costs, purchasing costs and shortage of punishment costs. Then designed approximate algorithm GA, and analyzed approximation ratio, which showed time complexity is o(n4), where n is the number of raw materials. At last take Xi’an & Control Equipment Co. Ltd. as an example, confirmed the model and algorithm effective.

An integrated decision support model for design and operation of a wind-based hydrogen supply system

We aim to develop a new optimization-based approach for the strategic planning of a renewable hydrogen supply system using onshore and offshore wind energy. To achieve this goal, we develop an optimization model to design and analyze a wind-powered hydrogen supply (WPHS) system using a mixed-integer linear programming technique. In this model, we include decision variables to account for a wide range of issues regarding the proposed system, from the technical selection of the wind turbine and wind farm layout design to strategies for onshore/offshore wind farm and hydrogen supply network development. To illustrate the capability of the proposed approach, we present a case study pertaining to the design of the WPHS system for the road transportation sector of Jeju Island, Korea. Using the proposed approach, we are able to i) identify the configuration and operational practices of an optimal WPHS system, ii) analyze the cost distribution and major cost drivers through economic evaluation, and iii) provide decision-making guidance to stakeholders and policy makers for planning an economically viable and a sustainable hydrogen supply system using various policies to encourage offshore wind farm development.

Annual Distribution Budget in the Beverage Industry: A Case Study

Unicer, a major Portuguese beverage company, improved its tactical distribution planning decisions and study alternative scenarios for its supply strategies and network configuration as result of an operations research (OR)-driven process. In this paper, we present the decision support system responsible for this new methodology. At the core of this system is a mathematical programming-based heuristic that includes decision variables that address transportation and inventory management problems. Unicer runs a set of production and distribution platforms with various characteristics to fulfill customers demand. The main challenge of our work was to develop a tactical distribution plan, which Unicer calls an annual distribution budget, as realistically as possible without jeopardizing the nature of the strategic and tactical tool. The company had a complex tactical distribution planning problem because of the increasing variety of its stock-keeping units and its need for a flexible distribution network to satisfy its customers, who demand a very fragmented set of products. Atypical flows of finished products from Unicer's distribution centers to its production platforms are a major cause of this complexity, which yields an intricate supply chain. The quality of the solutions we provided and the implementation of a user-friendly interface and editable inputs and outputs for our decision support system motivated company practitioners to use it. Unicer saves approximately two million euros annually and provides better information to its decision makers. As a result, these decision makers now view their operations from a more OR-based perspective.

Mathematical programming representations of the dynamics of continuous-flow production systems

This study presents a mathematical programming representation of discrete-event systems with a continuous time and mixed continuous-discrete state space. In particular, continuous material flow production systems are considered. A mathematical programming representation is used to generate simulated sample realizations of the system and also to optimize control parameters. The mathematical programming approach has been used in the literature for performance evaluation and optimization of discrete material flow production systems. In order to show the applicability of the same approach to continuous material flow systems, this article focuses on optimal production flow rate control problems for a continuous material flow system with an unreliable station and deterministic demand. These problems exhibit most of the dynamics observed in various continuous flow productions systems: flow dynamics, machine failures and repairs, changing flow rates due to system status, and control. Moreover, these problems include decision variables related to the control policies and different objective functions. By analyzing the backlog, lost sales, and production and subcontracting rate control problems, it is shown that a mixed-integer linear programming formulation with a linear objective function and linear constraints can be developed to determine the simulated performance of the system. The optimal value of the control policy that optimizes an objective function that includes the estimated expected inventory carrying and backlog cost and also the revenue through sales can also be determined by solving a quadratic integer program with a quadratic objective function and linear constraints. As a result, it is shown that the mathematical programming representation is also a viable method for performance evaluation and optimization of continuous material production systems.

A decision support system for planning biomass-based energy production

Abstract Environmental decision support systems (EDSS) are recognized as valuable tools for environmental planning and management. In this paper, a geographic information system (GIS)-based EDSS for the optimal planning of forest biomass use for energy production is presented. A user-friendly interface allows the creation of Scenarios and the running of the developed decision and environmental models. In particular, the optimization model regards decisions over a long-term period (e.g. years) and includes decision variables related to plant locations, conversion processes (pyrolisis, gasification, combustion), harvested biomass. Moreover, different energy products and different definitions of the harvesting and pre-treatment operations are taken into account. The correct management of the forest is considered through specific constraints, security factors, and procedures for parcel selection. The EDSS features and capabilities are described in detail, with specific reference to a case study. Discussion and further research are reported.

Why the Bass Model Fits without Decision Variables

Over a large number of new products and technological innovations, the Bass diffusion model (Bass 1969) describes the empirical adoption curve quite well. In this study, we generalize the Bass model to include decision variables such as price and advertising. The generalized model reduces to the Bass model as a special case and explains why the Bass model works so well without including decision variables. We compare our generalized Bass model to other approaches from the literature for including decision variables into diffusion models, and our results provide both theoretical and empirical support for the generalized Bass model. We also show how our generalized Bass model can be used for product planning purposes.