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This study aims to propose a new genetic algorithm for solving supply chain scheduling and routing problem in a multi-site manufacturing system. The main research question is: How is the production and transportation scheduled in a multi-site manufacturer? Also the sub-questions are: How is the order assigned to the suppliers? What is the production sequence of the assigned orders to a supplier? How is the order assignment to the vehicles? What are the vehicles routes to convey the orders from the suppliers to the manufacturing centers? The authors’ contributions in this paper are: integration of production scheduling and vehicle routing in multi-site manufacturing supply chain and proposing a new genetic algorithm inspired from the role model concept in sociology.

Considering shared transportation system in production scheduling of a multi-site manufacturer is investigated in this paper. Initially, a mathematical model for the problem is presented. Afterwards, a new genetic algorithm based on the reference group concept in sociology, named Reference Group Genetic Algorithm (RGGA) is introduced for solving the problem. The comparison between RGGA and a developed algorithm of literature closest problem, demonstrates a better performance of RGGA. This comparison is drawn based on many test problems. Moreover, the superiority of RGGA is certificated by comparing it to the optimum solution in the small size problems. Finally, the authors use real data collected from a drug manufacturer in Iran to test the performance of the algorithm. The results show the better performance of RGGA in comparison with obtained outputs from the real case.

The authors presented the mathematical model of the problem and introduced a new genetic algorithm based on the “reference group” concept in sociology. Robert K. Merton is a sociologist who presented the concept of reference groups in society. He believed that some people in each society such as heroes or entertainment artists affect other people. The proposed algorithm uses the reference group concept to the genetic algorithm, namely, RGGA. The comparison of the proposed algorithm with DGA and the optimum solution shows the superiority of RGGA. Finally, the authors implement the algorithm in a real case of drug manufacturing and the results show that the authors’ algorithm gives better outputs than obtained outputs from the real case.

One of the major objectives of supply chains is to create a competitive advantage for the final product. This intension is only achieved when each and every element of the supply chain considers customers’ needs in every function of theirs. This paper studies scheduling in the supply chain of a multi-site manufacturing system. It is assumed that some suppliers produce raw material or initial parts and convey them by a fleet of vehicles to a multi-site manufacturer.

This paper aims to deal with integrated planning and scheduling problem in multi-site manufacturing environment and provides a comprehensive review of literature. Classification schemes and various aspects of planning and scheduling problem in multi-site manufacturing are highlighted.

A structured review methodology is adopted to classify the relevant literature. Taxonomy for classification of the problem is presented, followed by review of modelling approaches, solution strategies and challenges faced in multi-site integrated planning and scheduling problem.

The paper is concluded with interesting research findings and a short view on directions related to modelling approach, solution strategy and technique for further developments in the area of multi-site integrated planning and scheduling.

The findings of this study would be helpful for future researchers and practitioners to provide a knowledge base and to further work in this area.

This study attempts to consolidate the diverse literature available and highlight the various aspects of planning and scheduling in multi-site manufacturing.

The purpose of this study is to investigate the optimization of the scheduling of production and transportation systems while considering delay time (DT) and environmental pollution (EP) concurrently. To this, an integrated multi-site manufacturing process using a cumulative transportation system is investigated. Additionally, a novel multi-society genetic algorithm is developed to reach the best answers.

A bi-objective model is proposed to optimize the production and transportation process with the objectives of minimizing DT and EP. This is solved by a social dynamic genetic algorithm (SDGA), which is a novel multi-society genetic algorithm, in scenarios of equal and unequal impacts of each objective. The impacts of each objective are calculated by the analytical hierarchical process (AHP) using experts’ opinions. Results are compared by dynamic genetic algorithm and optimum solution results.

Results clearly depict the efficiency of the proposed algorithm and model in the scheduling of production and transportation systems with the objectives of minimizing DT and EP concurrently. Although SDGA’s performance is acceptable in all cases, in comparison to other genetic algorithms, it needs more process time which is the cost of reaching better answers. Additionally, SDGA had better performance in variable weights of objectives in comparison to itself and other genetic algorithms.

This research is an improvement which allows both society and industry to elevate the levels of their satisfaction while their social responsibilities have been glorified through assuaging the concerns of customers on distribution networks’ emission, competing more efficient and effective in the global market and having the ability to make deliberate decisions far from bias. Additionally, implications of the developed genetic algorithm help directly to the organizations engaged with intelligent production and/or transportation planning which society will be merited indirectly from their outcomes. It also could be utilitarian for organizations that are engaged with small, medium and big data analysis in their processes and want to use more effective and more efficient tools.

Optimization of EP and DT are considered simultaneously in both model and algorithm in this study. Besides, a novel genetic algorithm, SDGA, is proposed. In this multi-society algorithm, each society is focused on a particular objective; however, in one society all the feasible answers will have been integrated and optimization will have been continued.

The purpose of this paper is to study the information support technologies of integrated production planning control for OEM (original equipment manufacturer) driven networked manufacturing systems, and offer implications to firms for implementing networked manufacturing.

OEM driven networked manufacturing and its operations modes and support technologies are first discussed. Then, integration framework of production planning and control is proposed and relative technologies are discussed. Finally, a case of the application of information support technologies in networked manufacturing is illustrated.

Both theory analysis and case experience show that information integration and sharing are critical for effective operations of OEM driven networked manufacturing and an integrated production planning and control system can benefit firms for successfully operating a networked manufacturing system.

It is valuable to develop and apply integrated production planning and control systems in OEM driven networked manufacturing, Firms should pay more attention to information sharing and communication with partners and utilize advanced information technologies to synchronize the operations of partners.

Integration framework of production planning and control proposed in this paper has originality and the technology strategies are also practical. Managerial ideas, technology framework and application strategies of integrated production planning and control are helpful for firms to implement OEM driven networked manufacturing.

This paper studies the preemptive scheduling problem of independent jobs on identical machines. The purpose of this paper is to minimize the makespan under the imposed constraints, namely, the ones that relate the transportation delays which are required to transport a preempted job from one machine to another. This study considers the case when the transportation delays are variable.

The contribution is twofold. First, this study proposes a new linear programming formulation in real and binary decision variables. Then, this study proposes and implements a solution strategy, which consists of two stages. The goal of the first stage is to obtain the best machines order using a local search strategy. For the second stage, the objective is to determine the best possible sequence of jobs. To solve the preemptive scheduling problem with transportation delays, this study proposes a heuristic and two metaheuristics (simulated annealing and variable neighborhood search), each with two modes of evaluation.

Computational experiments are presented and discussed on randomly generated instances.

The study has implications in various industrial environments when the preemption of jobs is allowed.

This study proposes a new linear programming formulation for the problem with variable transportation delays as well as a corresponding heuristic and metaheuristics.

This paper aims to provide an integrated production-routing model in a three-echelon supply chain containing a two-layer transportation system to minimize the total costs of production, transportation, inventory holding and expired drugs treatment. In the proposed problem, some specifications such as multisite manufacturing, simultaneous pickup and delivery and uncertainty in parameters are considered.

At first, a mathematical model has been proposed for the problem. Then, one possibilistic model and one robust possibilistic model equivalent to the initial model are provided regarding the uncertain nature of the model parameters and the inaccessibility of their probability function. Finally, the performance of the proposed model is evaluated using the real data collected from a pharmaceutical production center in Iran. The results reveal the proper performance of the proposed models.

The results obtained from applying the proposed model to a real-life production center indicated that the number of expired drugs has decreased because of using this model, also the costs of the system were reduced owing to integrating simultaneous drug pickup and delivery operations. Moreover, regarding the results of simulations, the robust possibilistic model had the best performance among the proposed models.

This research considers a two-layer vehicle routing in a production-routing problem with inventory planning. Moreover, multisite manufacturing, simultaneous pickup of the expired drugs and delivery of the drugs to the distribution centers are considered. Providing a robust possibilistic model for tackling the uncertainty in demand, costs, production capacity and drug expiration costs is considered as another remarkable feature of the proposed model.

This paper aims to design an optimal supply chain network and to develop a suitable distribution planning under uncertainty for perishable product's supply chain. The ultimate goal is to help in making decisions under uncertain environments.

In this paper, stochastic programming is used under conditions of demand, supply and process uncertainties, and a non-linear mathematical model is developed for perishable product’s supply chain. Authors’ study considers disruptions in transportation routes and also within the facilities and investigates optimal facility location and shipment decisions while minimising the total supply chain cost. A scenario-based approach is used to model these disruptions. The retailer level uncertainty due to demand-supply mismatch is handled by incorporating the newsvendor model into the last echelon of supply chain network. In this paper, two policies are proposed for making decisions under uncertain environments. In the first one, the expected cost of the supply chain is minimised. To also consider the risk behaviour of the decision maker, authors propose the second policy through a conditional value-at-risk approach.

Authors discuss the model output through various examples that are provided via a case study from the milk industry. The supply chain design and planning of the disruption-free model are different from those of the resilient model.

Authors’ research benefits the perishable products industries which encounter the disruption problems in their transportation routes as well as in the facilities. Authors have demonstrated the research through a real-life case in a milk industry.

The major contribution of authors’ work is the design of the supply chain network under disruption risks by incorporating aspects of product perishability. This work provides insight into areas such as the simultaneous consideration of demand, supply and process uncertainties. The amalgamation of newsvendor model and the approximation of the non-linearity of retailer level cost function especially in the context of supply chain under uncertainty is the first of its kind. We provide a comprehensive statistical study of uncertainties that are present in the supply chain in a unique manner.

The paper presents results of a pilot programme aimed at efficiency improvement in a multi‐site factories system for automotive component production. Firstly, the company background has been outlined and main problems of the old manufacturing system have been examined. In order to increase competitiveness in global and turbulent markets a renewed organization approach has been proposed based on total manufacturing management and just‐in‐time methodologies. Improvements in set‐up and lead times, work in progress, material handling, product and process quality, environmental effects, have all been assessed, keeping a quite low project cost (around $4 million).

The purpose of this paper is to develop comprehensive risk management tool, Intelligent Risk Mapping and Assessment System (IRMAS™) with a contingency for multi‐site, multi‐partner concurrent engineering projects with the aim of achieving above‐mentioned paradigms. Its unique knowledge warehouse enables the use of organisational knowledge, lessons learnt, captured as well as best practices to minimise risks in project management.

IRMAS is designed to identify, prioritise, analyse and assist project managers to manage perceived sources of concurrent engineering risks. Several knowledge elicitation techniques were used to compile the knowledge used for the intelligent system developed. The core of the research is the reasoning methodology that not only supports the decision‐making process of the user, but also aids the knowledge retrieving, storing, sharing and updating process of manufacturing organisations.

A total of 589 risk items were identified for different project types, as well as information on 4,372 risk items and 136 lessons learnt were gathered. IRMAS is a proactive tool supporting project management activities. It is designed as a web‐based portal compiled in Java facilitating effective and a common communication platform between project partners.

Identification of risks during the complete product design, development and delivery process in a concurrent engineering environment is challenging. It covers the “product value stream” including partners, suppliers, research and development, design and manufacturing, marketing, purchasing, service and support personnel and customers. Within the context of concurrent engineering, the design style must be “Design WITH” approach where collaborative negotiation requires communication, consideration and collaboration. The full validation of IRMAS™ is successfully carried out in two large‐scale new product development projects. It has already been decided to be deployed by a large international aerospace company and is successfully commercialized.

The originality of the paper lies in its uniqueness in these areas: IRMAS provides a systematic engineering approach to risk management of concurrent product and process development based on risk management standards and Project Management Body of Knowledge, to leverage of success factors in manufacturing; concurrencies and relationships between several activities throughout product's life cycle are captured and mapped; the inheritance of risk between several phases are modelled and quantified; the wealth of knowledge stored in the knowledge repository and IRMAS's capability to reuse them for later elicitation in the system's knowledge base; and user‐interactive, unique dynamic risk management software package which will be available in the commercial market.

Multinational business deliver value via multiple sites with similar operational capacities. The age of the Fourth Industrial Revolution (4IR) delivers significant opportunities for the deployment of digital tools for business optimization. Therefore, this study aims to study the Industry 4.0 implementation for multinationals.

The key objective of this research is multi-site systems integration using a reproducible, modular and standardized “Cyber Physical System (CPS) as-a-Service”.

A best practice reference architecture is adopted to guide the design and delivery of a pioneering CPS multi-site deployment. The CPS deployed is a cloud-based platform adopted to enable all manufacturing areas within a multinational energy and petrochemical company. A methodology is developed to quantify the system environmental and sustainability benefits focusing on reduced carbon dioxide (CO₂) emissions and energy consumption. These results demonstrate the benefits of standardization, replication and digital enablement for multinational businesses.

The research illustrates the ability to design a single system, reproducible for multiple sites. This research also illustrates the beneficial impact of system reuse due to reduced environmental impact from lower CO₂ emissions and energy consumption. The paper assists organizations in deploying complex systems while addressing multinational systems implementation constraints and standardization.