Developing green supply chain management strategies: a taxonomic approach

Purpose: The objective of this research is to explore the empirical green supply chain activities found in literature, and to develop a taxonomic framework that can be used for formulating appropriate strategies for green supply chains, based on characteristic dimensions for the green supply chain. Design/methodology/approach: The taxonomic framework is developed through (i) analysis of green supply chain activities found in existing empirical work or case studies recorded in literature, (ii) identification of key dimensions that influence green supply chain management strategies, and (iii) development of a taxonomic scheme for selecting or developing green strategies. Findings: The paper finds that this study yielded: a set of three characteristic dimensions that influence strategic green supply chain management, and a guided structured approach selecting appropriate green strategies, providing managerial insights. Research limitations/implications: This paper shows that future work includes development of specific performance management indices according to the taxonomy of green strategies developed in this study. Practical implications: This research provided a practical guided approach that enhances appropriate formulation of green strategies for green supply chain management, while providing sound managerial insights for the supply chain decision maker. The choice of supply chain strategy directly impacts the overall environmental, economic and operations performance of the supply chain. Originality/value: This study presents to supply chain decision makers a new taxonomic framework that simplifies and enhances the formulation of green strategies, and to researchers a comparative understanding of various strategies applicable to green supply chains.Peer Reviewe

A fuzzy-based particle swarm optimization algorithm for nurse scheduling

The nurse scheduling problem (NSP) has a great impact on the quality and efficiency of health care operations. Healthcare Operations Analysts have to assign daily shifts to nurses over the planning horizon, so that operations costs are minimized, health care quality is improved, and the nursing staff is satisfied. Due to conflicting objectives and a myriad of restrictions imposed by labor laws, company requirements, and other legislative laws, the NSP is a hard problem. In this paper we present a particle swarm optimization-based algorithm that relies on a heuristic mechanism that incorporates hard constraints to improve the computational efficiency of the algorithm. Further, we incorporate soft constraints into objective function evaluation to guide the algorithm. Results from illustrative examples show that the algorithm is effective and efficient, even over large scale problems

A framework for analysis and evaluation of renewable energy policies

The formulation and evaluation of renewable energy policies is a burning subject matter all over the globe. Policy makers seek to cautiously perceive information from the renewable energy market place so as to determine the dynamic factors, variables and policy parameters that influence the design of renewable energy policies. The perceived information is often imprecise or fuzzy, which makes policy formulation difficult. This paper presents a framework for evaluating renewable energy policies based on a fuzzy system dynamics (FSD) paradigm. First, we describe the renewable energy policy problem, with a case study example. Second, we present a framework for FSD modeling. Third, we propose a high-level causal loop analysis to capture the complex dynamic interactions among various energy demand and supply factors, from a fuzzy system dynamics perspective. Fourth, and finally, we propose an FSD model for renewable energy policy formulation and evaluation

Complicating factors in healthcare staff scheduling part 1 : case of nurse rostering

Nurse rostering is a hard problem inundated with inherent complicating features. This paper explores case studies on nurse rostering in order identify complicating factors common in the nurse rostering problem. A taxonomy of complicating factors is then derived. Furthermore, a closer look at the complicating factors and the solution methods applied is performed. Inadequacies of the approaches are identified, and suitable approaches derived. The study recommends future methods that are more intelligent, interactive, making use of techniques such fuzzy theory, fuzzy logic, multi-criteria decision making, and expert systems

System reliability optimization : a fuzzy genetic algorithm approach

System reliability optimization is often faced with imprecise and conflicting goals such as reducing the cost of the system and improving the reliability of the system. The decision making process becomes fuzzy and multi-objective. In this paper, we formulate the problem as a fuzzy multi-objective nonlinear program (FMOOP). A fuzzy multiobjective genetic algorithm approach (FMGA) is proposed for solving the multi-objective decision problem in order to handle the fuzzy goals and constraints. The approach is able flexible and adaptable, allowing for intermediate solutions, leading to high quality solutions. Thus, the approach incorporates the preferences of the decision maker concerning the cost and reliability goals through the use of fuzzy numbers. The utility of the approach is demonstrated on benchmark problems in the literature. Computational results show that the FMGA approach is promising

Complicating factors in healthcare staff scheduling part 2 : case of nurse re-rostering

Nurse re-rostering is a highly constrained combinatorial problem characterized with several complicating features. This paper explores recent case studies on nurse re-rostering and identifies the common complicating factors in the nurse re-rostering problem. A taxonomic analysis of complicating factors is then presented. Further, an evaluation of the complicating factors and the solution methods applied, showing the shortfalls of the approaches. A more robust and appropriate approach is realized for the complex problem. Future approaches should be intelligent, interactive, making use of a combination of fuzzy theory, fuzzy logic, multi-criteria decision making, and expert systems techniques

Home healthcare staff scheduling: a clustering particle swarm optimization approach

The home healthcare staff scheduling problem is concerned with the allocation of care tasks to healthcare staff at a minimal cost, subject to healthcare service requirements, labor law, organizational requirements, staff preferences, and other restrictions. Healthcare service providers strive to meet the time window restrictions specified by the patients to improve their service quality. This paper proposes a clustering particle swam optimization methodology (CPSO) for addressing the scheduling problem. The approach utilizes the strengths of unique grouping techniques to efficiently exploit the group structure of the scheduling problem, enabling the algorithm to provide good solutions within reasonable computation times. Computational results obtained in this study demonstrate the efficiency and effectiveness of CPSO approach

Simulated metamorphosis - a novel optimizer

This paper presents a novel metaheuristic algorithm, simulated metamorphosis (SM), inspired by the biological concepts of metamorphosis evolution. The algorithm is motivated by the need for interactive, multi-objective, and fast optimization approaches to solving problems with fuzzy conflicting goals and constraints. The algorithm mimics the metamorphosis process, going through three phases: initialization, growth, and maturation. Initialization involves random but guided generation of a candidate solution. After initialization, the algorithm successively goes through two loops, that is, growth and maturation. Computational tests performed on benchmark problems in the literature show that, when compared to competing metaheuristic algorithms, SM is more efficient and effective, producing better solutions within reasonable computation times

Healthcare staff scheduling in a fuzzy environment : a fuzzy genetic algorithm approach

In the presence of imprecise management targets, staff preferences, and patients’ expectations, the healthcare staff scheduling problem becomes complicated. The goals, preferences, and client expectations, being humanistic, are often imprecise and always evolving over time. We present a Jarosite precipitate (FGA) approach for addressing healthcare staff scheduling problems in fuzzy environments. The proposed FGA-based approach can handle multiple conflicting objectives and constraints. To improve the algorithm, fuzzy set theory is used for fitness evaluations of alternative candidate schedules by modeling the fitness of each alternative solution using fuzzy membership functions. Furthermore, the algorithm is designed to incorporate the decision maker’s choices and preferences, in addition to staff preferences. Rather than prescribing a sing solution to the decision maker, the approach provides a population of alternative solutions from which the decision maker can choose the most satisfactory solution. The FGA-based approach is potential platform upon which useful decision support tools can be developing for solving healthcare staff scheduling problems in a fuzzy environment characterized with multiple conflicting objectives and preference constraints

A taxonomic framework for formulating strategies in green supply chain management

This paper addresses the increasingly important question of formulating appropriate strategies for green supply chain management. Due to increasing attention to green strategies and their impact on the natural environment, the development of a taxonomic framework for selecting appropriate strategies is imperative. In this study, we develop a taxonomic framework for formulating strategies for green supply chains based on characteristic green supply chain dimensions. The practical implication of this work is that the choice of green supply chain strategy impacts environmental and operations performance. The framework developed can be used as a tool for developing green supply chain management strategies, providing sound managerial insights