Heuristic algorithms for scheduling on uniform parallel machines with heads and tails

This paper considers the uniform parallel machine scheduling problem with unequal release dates and delivery times to minimize the maximum completion time.For this NP-hard problem,the largest sum of release date,processing time and delivery time first rule is designed to determine a certain machine for each job,and the largest difference between delivery time and release date first rule is designed to sequence the jobs scheduled on the same machine,and then a novel algorithm for the scheduling problem is built.To evaluate the performance of the proposed algorithm,a lower bound for the problem is proposed.The accuracy of the proposed algorithm is tested based on the data with problem size varying from 200 jobs to 600 jobs.The computational results indicate that the average relative error between the proposed algorithm and the lower bound is only 0.667%,therefore the solutions obtained by the proposed algorithm are very accurate.

GOOD p-ARY QUASIC-CYCLIC CODES FROM CYCLIC CODES OVER F_p+vF_p

This paper introduces a Gray map from（Fp + uFp）n to Fp2n,and describes the relationship between codes over Fp + vFp and their Gray images.The authors prove that every cyclic code of arbitrary length n over Fp + vFp is principal,and determine its generator polynomial as well as the number of cyclic codes.Moreover,the authors obtain many best-known p-ary quasic-cyclic codes in terms of their parameters via the Gray map.

POLYNOMIAL DYNAMIC PROGRAMMING ALGORITHMS FOR LOT SIZING MODELS WITH BOUNDED INVENTORY AND STOCKOUT AND/OR BACKLOGGING

This paper addresses a dynamic lot sizing problem with bounded inventory and stockout where both no backlogging and backlogging allowed cases are considered. The stockout option means that there is outsourcing in a period only when the inventory level at that period is non-positive. The production capacity is unlimited and production cost functions are linear but with fixed charges. The problem is that of satisfying all demands in the planning horizon at minimal total cost. We show that the no backlogging case can be solved in O（T^2） time with general concave inventory holding and outsourcing cost functions where T is the length of the planning horizon. The complexity can be reduced to O（T） when the inventory holding cost functions are also linear and have some realistic properties, even if the outsourcing cost functions remain general concave functions. When the inventory holding and outsourcing cost functions are linear, the backlogging case can be solved in O（ T^3 logT） time whether the outsourcing level at each period is bounded by the sum of the demand of that period and backlogging level from previous periods, or only by the demand of that period.

Engineering management for high-end equipment intelligent manufacturing

The high-end equipment intelligent manufacturing(HEIM) industry is of strategic importance to national and economic security. Engineering management(EM) for HEIM is a complex, innovative process that integrates natural science, technology, management science, social science, and the human spirit. Newgeneration information technology(IT), including the internet, cloud computing, big data, and artificial intelligence, have made a remarkable influence on HEIM and its engineering management activities, such as product system construction, product life cycle management, manufacturing resources organization, manufacturing model innovation, and reconstruction of the enterprise ecosystem.Engineering management for HEIM is a key topic at the frontier of international academic research. This study systematically reviews the current research on issues pertaining to engineering management for HEIM under the new-generation IT environment. These issues include cross-lifecycle management, network collaboration management, task integration management of innovative development, operation optimization of smart factories,quality and reliability management, information management, and intelligent decision making. The challenges presented by these issues and potential research opportunities are also summarized and discussed.

Application of blockchain in the field of intelligent manufacturing:Theoretical basis,realistic plights,and development suggestions

Blockchain technology is considered one of the promising technologies of the information technology era.The core features of blockchain,such as decentralization,transparency,high security,and tamper-proof nature,bring great convenience for large-scale social cooperation and data sharing.Blockchain has a broad application prospect in the field of intelligent manufacturing.The key issues of this field,such as distributed collaborative production,industrial big data sharing and security,transparent logistics,and supply chain,are naturally consistent with the core characteristics of the blockchain technology.This study aims to analyze the application of blockchain in the field of intelligent manufacturing.First,we introduce the basic connotation and applications of blockchain.Then,we propose the theoretical basis for the application of blockchain in the field of intelligent manufacturing.Finally,we point out the realistic plights and provide some suggestions to promote the application of blockchain in the field of intelligent manufacturing.

Energy storage resources management:Planning,operation,and business model

With the acceleration of supply-side renewable energy penetration rate and the increasingly diversified and complex demand-side loads,how to maintain the stable,reliable,and efficient operation of the power system has become a challenging issue requiring investigation.One of the feasible solutions is deploying the energy storage system(ESS)to integrate with the energy system to stabilize it.However,considering the costs and the input/output characteristics of ESS,both theinitial configuration process and the actual operation process require efficient management.This study presents a comprehensive reviewof managing ESs from the perspectives of planning,operation,and business model.First of all,in terms of planning and configuration,it is investigated from capacity planning,location planning,as well as capacity and location combined planning.This process is generally the first step in deploying ESS.Then,it explores operation management of ESS from the perspectives of state assessment and operation optimization.The so-called state assessment refers to the assessment of three aspects:The state of charge(SOC),the state of health(SOH),and the remaining useful life(RUL).The operation optimization includes ESS operation strategy optimization and joint operation optimization.Finally,it discusses the business models of ESS.Traditional business models involve ancillary services and load transfer,while emerging business models include electric vehicle(EV)as energy storage and shared energy storage.

Using Leading Indicators to Improve Project Performance Measurement

Project performance measurement offers a wide range of methods to help project managers monitor projects.However,several critical issues remain in project performance measurement,such as an unbalanced use of indicators due to a lack of prediction-based (leading) indicators with regard to outcome-based (lagging) indicators.For its part,systems engineering measurement,although a more recent discipline,offers a wide variety of indicators,including a set of leading indicators.As our objective is to increase project performance and success rates,this involves improving project performance measurement on which project management decisions are based.This purpose,the proposal put forward in this paper is to extend the number and type of indicators used in project performance measurement by adapting the leading indicators defined in systems engineering measurement.The methodology involves first mapping the systems engineering indicators with the project management activities,resulting in identification of a set of potentially useful indicators for measuring the different activities,then tailoring a selection of these indicators with project-specific data to define a set of the most relevant indicators for a given project.This methodology is illustrated by means of a case study in a manufacturing company.

AN IMPROVED LP MODEL FOR ENERGY OPTIMIZATION OF THE INTEGRATED IRON AND STEEL PLANT WITH A COGENERATION SYSTEM IN CHINA

In an integrated iron and steel plant with a cogeneration system, recycled energy is continuously transported into the cogeneration system and the electricity is continuously generated, and both of them could not be stored for a long time. Moreover, thegeneration and consumption of electricity is irregular, which may bring about more unexpected imbalances. Therefore, it is a crucial issue to schedule the entire energy system by optimizing the operation of energy utilization, which includes the raw energy in the production system, the generation electricity in the cogeneration system and the recycled energy in these two systems. In this paper, an improved Linear Programming model for energy optimization in the integrated iron and steel plant with a cogeneration system is established. The improved model focuses on controlling the whole energy flow and scheduling the whole energy consumption in the entire energy system between the production system and cogeneration system through optimizing all kinds of energy distribution and utilization in an integrated iron and steel plant with a cogeneration system. Case study shows that the improved model offers the optimal operation conditions at the higher energy utilization, lower energy cost and lower pollution emissions.

Comments from young scholars: Can machines completely replace humans in manufacturing processes?

Recently,we asked some fields around the world to this issue: young scholars from different send us their comments about Can machines completely replace humans in manufacturing processes?If so,how should humans deal with this situation?If not,how can humans achieve a harmonious coexistence with intelligent machines?

Special issue:Manufacturing engineering management

Numerous manufacturing engineering management problems have emerged and attracted great attention in academia and practice,especially in high-end equipment manufacturing field with the development of the Internet and big data technology in recent years.Therefore,investigations on manufacturing engineering management technology,theoretical methods,and opportunities and challenges in the new technological environment are essential.The increasing number of relevant studies from theoretical researchers and practical engineers attempted to construct a scientific and reasonable system of manufacturing engineering management based on the Internet and big data technology.