A Fuzzy Credibility-Based Chance-Constrained Optimization Model for Multiple-Objective Aggregate Production Planning in a Supply Chain under an Uncertain Environment

In this study, a Multiple-Objective Aggregate Production Planning (MOAPP) problem in a supply chain under an uncertain environment is developed. The proposed model considers simultaneously four different conflicting objective functions. To solve the proposed Fuzzy Multiple-Objective Mixed Integer Linear Programming (FMOMILP) model, a hybrid approach has been developed by combining Fuzzy Credibility-based Chance-constrained Programming (FCCP) and Fuzzy Multiple-Objective Programming (FMOP). The FCCP can provide a credibility measure that indicates how much confidence the decision-makers may have in the obtained optimal solutions. In addition, the FMOP, which integrates an aggregation function and a weight-consistent constraint, is capable of handling many issues in making decisions under multiple objectives. The consistency of the ranking of objective’s important weight and satisfaction level is ensured by the weight-consistent constraint. Various compromised solutions, including balanced and unbalanced ones, can be found by using the aggregation function. This methodology offers the decision makers different alternatives to evaluate against conflicting objectives. A case experiment is then given to demonstrate the validity and effectiveness of the proposed formulation model and solution approach. The obtained outcomes can assist to satisfy the decision-makers’ aspiration, as well as provide more alternative strategy selections based on their preferences

Supply chain decisions for an adaptive, decentralized renewable energy system

The need for a more sustainable energy system and the shift to renewable energy and less-polluting fuels causes logistics problems related to the renewable energy supply. In particular, the transition towards more renewables creates problems related to supply-driven energy generation, location differences between energy production and energy demand, and the mismatch in production and demand profiles over time. This leads to curtailment of energy, irregular feed-in to the electricity grid, and transportation challenges related to the distribution of biogas. This thesis is based on the research project entitled “ADAPNER” (Adaptive logistics in a circular economy) which aims to "Determine optimized adaptable and sustainable configurations for different distribution alternatives regarding biomass and biogas in a circular economy”. The objective of this thesis is to determine these configurations for different decentralized renewable energy production, storage, and distribution alternatives. These include wind, photovoltaic (PV), biogas, LNG, and hydrogen.This thesis shows how challenges related to these domains are interrelated and should not be addressed in isolation. By addressing these issues, the results of this thesis contribute to the scientific literature and provide insights on designing the decentralized energy infrastructure in rural areas

Order Acceptance and Scheduling: A Taxonomy and Review

Over the past 20 years, the topic of order acceptance has attracted considerable attention from those who study scheduling and those who practice it. In a firm that strives to align its functions so that profit is maximized, the coordination of capacity with demand may require that business sometimes be turned away. In particular, there is a trade-off between the revenue brought in by a particular order, and all of its associated costs of processing. The present study focuses on the body of research that approaches this trade-off by considering two decisions: which orders to accept for processing, and how to schedule them. This paper presents a taxonomy and a review of this literature, catalogs its contributions and suggests opportunities for future research in this area

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Hacia la mejora del manejo de inventarios caso de estudio en una organización sin fines de lucro : proyecto de investigación

Nowadays, companies around the world, even non-profit ones should keep control of their operations and make improvement efforts in order to remain competitive in a highly demanding market. One of the relevant operations within an organization is the supply chain management, where a significant portion of total costs is associated, specifically the ones relating to inventory management. This paper presents a case of study focused on the inventory management of a nonprofit organization, particularly in one of the bookstores that belong to this organization located throughout Latin America. The actual system and policies for inventory management are analyzed along with the purchasing decisions. A root cause analysis of the principal problems in inventory management is presented, followed by an improvement plan. This plan is compounded by two stages: first, a centralized system for the control of inventories is implemented using available technology combined with Statistic Control Charts; second, a technical forecasting method is employed to help in the decision making process of purchasing. And an inventory management policy is proposed, with a particular focus on the key items sold by the bookstore. Results were validated through interviews with the actors of the process

Designing a supply network for a startup company

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 86-88).Our thesis introduces a supply chain framework catered for startup companies. Startup companies face unique circumstances such as constraints on financial and human resources, and greater uncertainty in demand. From our work with XL Hybrids, a startup company that hybridizes aftermarket vehicles, as well as interviews and literature review, we have attempted to distill supply chain strategies that can be applied to startup companies. To plan XL Hybrids' supply chain, we developed models for the following aspects of their supply chain: production scheduling, capacity planning, inventory policy, and component distribution. By running different demand and pricing scenarios, we gained an understanding of the impact of these variables on the four aspects of XL Hybrid's supply chain. Based on the scenario analysis and supply chain framework that we developed, we recommend that XL Hybrids be conservative with capacity expansion while strategically sourcing key components after considering volume discounts and different distribution methods.by Marcus S. Causton and Jianmin Wu.M.Eng.in Logistic

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

MULTI-OBJECTIVE ROBUST PRODUCTION PLANNING CONSIDERING WORKFORCE EFFICIENCY WITH A METAHEURISTIC SOLUTION APPROACH

Timely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significantly affects production delivery time, which can be achieved through the maximization of workforce efficiency. This issue becomes more complicated when the parameters of the production system are under uncertainty. This paper presents a bi-objective scenario-based robust production planning model considering maximizing workforce efficiency and minimizing costs where the backorder, demand, and costs are uncertain. Also, backorder, raw materials purchasing, inventory control, and manufacturing time capacity are considered. A case study in a faucet manufacturing plant is considered to solve the model. Furthermore, the ε-constraint method, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and the Pareto Envelope-based Selection Algorithm II (PESA-II) are employed to solve the model. Also, the Taguchi method is used to tune the parameters of these algorithms. To compare these algorithms, five indicators are defined. The results show that the SPEA2 is the most time-consuming algorithm and the NSGA-II is the fastest, while their objective function values are nearly the same

A Two-Warehouse Model for Deteriorating Items with Holding Cost under Particle Swarm Optimization

A deterministic inventory model has been developed for deteriorating items and Particle Swarm Optimization (PSO) having a ramp type demands with the effects of inflation with two-warehouse facilities. The owned warehouse (OW) has a fixed capacity of W units; the rented warehouse (RW) has unlimited capacity. Here, we assumed that the inventory holding cost in RW is higher than those in OW. Shortages in inventory are allowed and partially backlogged and Particle Swarm Optimization (PSO) it is assumed that the inventory deteriorates over time at a variable deterioration rate. The effect of inflation has also been considered for various costs associated with the inventory system and Particle Swarm Optimization (PSO). Numerical example is also used to study the behaviour of the model. Cost minimization technique is used to get the expressions for total cost and other parameters