A zero-one goal programming approach for information system project selection

This paper presents a multi-objective model that can be used to help information systems (IS) managers decide which IS projects should be selected for development and implementation in a constrained resource environment. Developed to overcome the problems observed in currently used IS selection procedures, the model presented in this paper can be used to make decisions when IS selection goals are conflicting and measured in incommensurable units. To illustrate the use of this multi-objective approach to model IS project selection, a corporate resource allocation problem is developed and solved. Comparative results with currently used scoring and ranking methods reveals the superiority of the proposed multi-objective model.     

Mixed integer (0–1) fractional programming for decision support in paper production industry

This paper presents an effective and efficient method for solving a special class of mixed integer fractional programming (FP) problems. We take a classical reformulation approach for continuous FP as a starting point and extend it for solving a more general class of mixed integer (0–1) fractional programming problems.To stress the practical relevance of the research we focus on a real-life application in paper production industry. The constantly advancing physical knowledge of large scale pulp and paper production did have a substantial impact on an existing DSS in which mixed integer (0–1) fractional programming is introduced. We show that the motivation to solve a real-life fractional programming problem can provide the basis for a new approach in a new context that has an added value of its own, even outside the given application area. We describe the main characteristics of the DSS, the necessity to develop a non-iterative solution procedure and demonstrate both the effectiveness and efficiency of the proposed approach from practical data sets.     

A characterisation of logistics networks for product recovery

Recovery of used products is receiving much attention recently due to growing environmental concern. Efficient implementation requires appropriate logistics structures to be set up for the arising goods flow from users to producers. We investigate the design of such logistics networks. As a basis for our analysis we review recent case studies on logistics network design for product recovery in different industries. We identify general characteristics of product recovery networks and compare them with traditional logistics structures. Moreover, we derive a classification scheme for different types of recovery networks.     

A hybrid inventory management system responding to regular demand and surge demand

This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a given policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.     

A subsidized reverse supply chain in the Chinese electronics industry

We study a special environmental producer responsibility policy for the Chinese electronics industry that is based on awarding a per unit subsidy to qualified returned electronic products and ensuring a minimum producer collection volume while allowing larger collection volumes. Based on a real application from a Chinese electronics company that produces LCD TVs, our paper studies the optimal design of the product’s reverse supply chain when there is flexibility in settling the inspection locations of the returned products and flexibility in the volume of returned products collected. The problem is modeled as a nonlinear mixed-integer program and an efficient outer approximation-based solution approach is proposed. Analytical results and extensive numerical experiments based on this real application are conducted. Observations novel to the reverse logistics literature are related to the testing location decisions (upstream or downstream) and the optimal collection volumes of returned products. Particularly, we show how the government can stimulate the collection amount of returned products by increasing the unit subsidy and we also find that the company’s marginal benefit from improving the subsidy increases in a superlinear fashion. Furthermore, the highest collection volumes may not occur at the highest quality level of returned products for capacitated remanufacturers. The company can also be incentivized to increase the collection of returned products by permitting flexible testing locations. We also observe how the optimal testing locations vary for different levels of unit subsidy and different ratios of qualified and non-qualified returned products. Finally, conclusions and future research directions are provided.     

A heuristic framework for the bi-objective enhanced index tracking problem

The index tracking problem is the problem of determining a portfolio of assets whose performance replicates, as closely as possible, that of a financial market index chosen as benchmark. In the enhanced index tracking problem the portfolio is expected to outperform the benchmark with minimal additional risk. In this paper, we study the bi-objective enhanced index tracking problem where two competing objectives, i.e., the expected excess return of the portfolio over the benchmark and the tracking error, are taken into consideration. A bi-objective Mixed Integer Linear Programming formulation for the problem is proposed. Computational results on a set of benchmark instances are given, along with a detailed out-of-sample analysis of the performance of the optimal portfolios selected by the proposed model. Then, a heuristic procedure is designed to build an approximation of the set of Pareto optimal solutions. We test the proposed procedure on a reference set of Pareto optimal solutions. Computational results show that the procedure is significantly faster than the exact computation and provides an extremely accurate approximation.     

A stochastic programming model for production planning of perishable products with postponement

This study addresses the production planning problem for perishable products, in which the cost and shortage of products are minimised subject to a set of constraints such as warehouse space, labour working time and machine time. Using the concept of postponement, the production process for perishable products is differentiated into two phases to better utilise the resources. A two-stage stochastic programming with recourse model is developed to determine the production loading plan with uncertain demand and parameters. A set of data from a toy company shows the benefits of the postponement strategy: these include lower total cost and higher utilisation of resources. The impact of unit shortage cost under different probability distribution of economic scenarios on the total cost is analyzed. Comparative analysis of solutions with and without postponement strategies is also performed.     

A branch-and-price algorithm for production routing problems with carbon cap-and-trade

This study presents a model for a pollution production-routing problem under carbon cap-and-trade. The aim is to incorporate carbon emissions into production inventory and routing decisions. The model is characterized by an additional flow-related cost structure, which generalizes models for pollution-routing problems and production inventory and routing problems. Correspondingly, we develop a branch-and-price heuristic by incorporating a column-generation formulation based on the Dantzig–Wolfe decomposition. In addition, we design an ad hoc label-setting algorithm to deal with time-slice networks in pricing subproblems. Computational results allow us to provide managerial insights concerning reduction of carbon emissions in supply chains. We prove that the model has the potential to reduce emission levels of carbon dioxide and operational costs.     

A multicriteria decision making model for reverse logistics using analytical hierarchy process

Product recovery activities such as recycling, refurbishing and direct reuse are becoming integral to manufacturing supply chains. This study presents a multicriteria decision making model for reverse logistics using analytical hierarchy process (AHP). The AHP model evaluates a hierarchy of criteria and subcriteria, including costs and business relations, for critical decisions regarding network design. Using sensitivity analysis with AHP, the work provides insights into the preference ordering among eight alternative network configurations. For instance, the choice of test sites is largely dependent on the potential for cost savings on testing procedures and transportation of scrap, and this decision is not sensitive to the importance of business relations. By contrast, the choice of collection sites is largely determined by the relative importance of business relations considerations vs. cost considerations. As well, the processing location decision favors a third-party reprocessor if there is little need to protect proprietary product knowledge and cost savings is very important. The model is demonstrated using three case studies of real-world applications.     

A harmony search-based memetic optimization model for integrated production and transportation scheduling in MTO manufacturing

This paper investigates an integrated production and transportation scheduling problem in an MTO supply chain. A harmony search-based memetic optimization model is developed to handle this problem, in which certain heuristic procedures are proposed to convert the investigated problem into an order assignment problem. A novel improvisation process is also proposed to improve the optimum-seeking performance. The effectiveness of the proposed model is validated by numerical experiments. The experimental results show that (1) the proposed model can solve the investigated problem effectively and that (2) the proposed memetic optimization process exhibits better optimum-seeking performance than genetic algorithm-based and traditional memetic optimization processes.     

A tradeoff model for green supply chain planning:A leanness-versus-greenness analysis

This article presents a tactical supply chain planning model that can be used to investigate tradeoffs between cost and environmental degradation including carbon emissions, energy consumption and waste generation. The proposed model also incorporates other aspects of real world supply chains such as multiple transport lot sizing and flexible holding capacity of warehouses. A solution methodology, the Nested Integrated Cross-Entropy (NICE) method, is developed to solve the proposed mixed-integer nonlinear mathematical model. The application of the model and solution method is investigated in an actual case problem. Analysis of the numerical results focuses on investigating the relationship between lean practices and green outcomes. We find that (1) not all lean interventions at the tactical supply chain planning level result in green benefits, and (2) a flexible supply chain is the greenest and most efficient alternative when compared to strictly lean and centralized situations.     

A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a building energy management system

Electric vehicles (EVs) are becoming an attractive alternative to gasoline vehicles owing to the increase of greenhouse gas emissions and gasoline prices. EVs are also expected to function as battery storages for stabilizing large fluctuations in the power grid through the vehicle-to-grid power system, which requires smart charge and discharge scheduling algorithms. In this paper, we develop a linear programming based heuristic algorithm on a time–space network model for charge and discharge scheduling of EVs. We also develop an improved two-stage heuristic algorithm to cope with uncertain demands and departure times of EVs, and evaluate the effect of the smart charge and discharge scheduling of EVs on a peak load reduction in a building energy management system.