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The kitting feeding policy creates kits with the parts of each product to assemble. Each kit contains elements with heterogeneous physical properties imposing heterogeneous logistic facilities and management solutions for storage and handling. The purpose of this paper is to present and apply a two-step procedure to design the part warehouse layout and to assign locations in case of kitting with high-variety part attributes. The proposed procedure aims at reducing the kitting travelled distance, shortening the picker paths, best positioning the components in the warehouse to enhance the possibility of creating kits through a single corridor access. The saturation of the warehouse and the minimization of the required storage space are also considered.

Starting from part categorization, the proposed two-step procedure, of general applicability, designs the component warehouse, sizing the corridors (Step 1) before clustering the kits in terms of part commonality and best-assigning clusters to corridors (Step 2) with the goal of reducing the travelled distance and saturating the available storage space.

A comparison model considers the traditional versus the proposed warehouse layout highlighting the potential saving in the picker travelled distance. A case study taken from the harvesting machine agricultural sector exemplifies the applicability and the practical implications of this research.

Elements of originality are the warehouse design strategy and the assignment model for parts based on their physical attributes and their occurrence in the assembly kits. Finally, the case study taken from industry, with a high number of components and part categories, adds value to the research making the proposed procedure able to address large-scale industrial problems.

Industry 4.0 emerged as the Fourth Industrial Revolution aiming at achieving higher levels of operational efficiency, productivity and automation. In this context, manual assembly systems are still characterized by high flexibility and low productivity, if compared to fully automated systems. Therefore, the purpose of this paper is to propose the design, engineering and testing of a prototypal adaptive automation assembly system, including greater levels of automation to complement the skills and capabilities of human workers.

A lab experimental field-test is presented comparing the assembly process of a full-scale industrial chiller with traditional and adaptive assembly system.

The analysis shows relevant benefits coming from the adoption of the adaptive automation assembly system. In particular, the main findings highlight improvements in the assembly cycle time and productivity, as well as reduction of the operator’s body movements.

The prototype is applied in an Italian mid-size industrial company, confirming its impact in terms of upgrades of the assembly system flexibility and productivity. Thus, the research study proposed in this paper provides valuable knowledge to support companies and industrial practitioners in the shift from traditional to advanced assembly systems matching current industrial and market features.

This paper expands the lacking research on adaptive automation assembly systems design proposing an innovative prototype able to real-time reconfigure its structure according to the product to work, e.g. work cycle, and the operator features.

This paper aims to introduce, apply and validate, through a realistic case study, an analytical cost model to support the design of the tow-train feeding system for mixed-model assembly lines managed according to the just-in-time concept. The fleet size and inventory level, minimizing the total annual cost, are the key model goals, while the tow-train shipping capacity and the service level are the decisional variables to set.

The model computes the material handling, inventory and stockout rising costs of the tow-train feeding system and looks for their minimization. It further computes the expected lead time between consecutive round-trips and the Kanban card number, distinguishing among parts and assembly lines, overcoming the simplifying hypothesis assuming a constant lead time for all parts. The model is validated against a dedicated case study stressing its strengths in terms of cost and inventory-level reduction.

The proposed approach is found to be effective if compared to the standard literature in the field of Kanban system design. The 10.76 per cent cost saving is experienced for the considered case study, and the inventory level is closer to the field-experienced profile.

The model adopts a practical perspective, making it easy and applicable to common operative industries.

The literature neglects to consider the differences in the part consumption when estimating the lead time between tow-train round-trips. The proposed model overcomes such limitations and strengthens the model applicability and performances.

The paper aims to focus on in-house part logistics design and management for assembly systems in which supermarket storage is adopted and coupled with an automated transportation system. In this context, this work aims to assess the transportation mode selection problem to speed up the preliminary design phase.

The paper is divided into two main parts. The first one provides and discusses a new conceptual framework derived from the authors’ experience in the field and from previous published works. The framework aims to support managers in problem comprehension by setting three problem sub-phases, key input parameters and qualitative guidelines without losing sight of the big picture. The second part focuses on the transportation mode selection sub-phase by assessing an analytical study followed by a multi-scenario analysis.

The final outcome of this work is a decision support matrix capable of setting technical guidelines that are helpful to managers and practitioners to speed up the transportation mode selection problem in the preliminary phases.

This work is beneficial for supporting managers in understanding the main decisional steps involved in the design of a part-feeding system with a supermarket by discussing the three problem sub-phases and key input parameters and providing both qualitative and quantitative guidelines. Moreover, this study explores the transportation mode selection problem, which is not yet largely explored in the published literature.

The purpose of this paper is to cut down energy consumption and eliminate production waste on mixed-model assembly lines. Therefore, a supermarket integrated dynamic cyclic kitting system with the application of electric vehicles (EVs) is introduced. The system resorts to just-in-time (JIT) and segmented sub-line assignment strategies, with the objectives of minimizing line-side inventory and energy consumption.

Hybrid opposition-based learning and variable neighborhood search (HOVMQPSO), a multi-objective meta-heuristics algorithm based on quantum particle swarm optimization is proposed, which hybridizes opposition-based learning methodology as well as a variable neighborhood search mechanism. Such algorithm extends the search space and is capable of obtaining more high-quality solutions.

Computational experiments demonstrated the outstanding performance of HOVQMPSO in solving the proposed part-feeding problem over the two benchmark algorithms non-dominated sorting genetic algorithm-II and quantum-behaved multi-objective particle swarm optimization. Additionally, using modified real-life assembly data, case studies are carried out, which imply HOVQMPSO of having good stability and great competitiveness in scheduling problems.

The feeding problem is based on static settings in a stable manufacturing system with determined material requirements, without considering the occurrence of uncertain incidents. Current study contributes to assembly line feeding with EV assignment and could be modified to allow cooperation between EVs.

The dynamic cyclic kitting problem with sub-line assignment applying EVs and supermarkets is solved by an innovative HOVMQPSO, providing both novel part-feeding strategy and effective intelligent algorithm for industrial engineering.

With regard to product variety and cost competition, just-in-time (JIT) part-supply has become a critical issue in automobile assembly lines (AALs). This paper aims to investigate a multiple server scheduling problem (MSSP) encountered in the JIT part-supply process of AALs. Parts are stored in boxes and allotted from the JIT-supermarket to consumptive stations with a multiple server system. The schedule is to dispatch and sequence material boxes on each server for minimizing line-side inventory levels.

A mixed integer linear programming (MILP) model is established to formulate the proposed MSSP to pave the way for CPLEX procedure. Considering the high complexity of MSSP, a hybrid ant colony optimization (HACO) approach is developed by integrating basic ant colony optimization (ACO) with local optimizers that comprise of a fast local search and a tailored breadth-first tree search method.

Both CPLEX and HACO approach are capable of solving small-scale instances to optimality within reasonable computation time. The proposed HACO has been well enhanced with the embedded fast local search and tailored breadth-first tree search, and it performs robustly in a statistically significant manner when applied to real-world scale instances.

No stock-outs constraints and weighted line-side inventory level are considered in this paper, and the MSSP is solved satisfactorily to facilitate an efficient JIT part-supply of the AAL. In terms of the algorithm design, a tree search-based local optimizer is embedded into ACO to combine the mechanisms of ACO and problem-specific optimization.

The purpose of this paper is to develop analytical planning models to compare just-in-time (JIT) delivery and line storage (LS) alternatives for a continuous supply of materials to assembly lines.

A mathematical model is developed to size resources and to determine total system costs.

The choice of assembly lines feeding policy requires a thorough economic comparison of alternatives. However, the existing models are often simplistic, neglecting many critical factors which affect the systems’ performances. As a consequence, industries are unsure about which system is best for their environment. This model allows to compare the cost and suitability of two major continuous-supply alternatives in any specific industrial setting. Results of the model application are case-specific and cannot be generalized.

The model is aimed at single-model assembly lines operating in a deterministic environment. Although relevant quantitative cost drivers are included, some context-related qualitative factors are not yet included. The model assumes that the information about product structure and part requirements is known and that a preliminary design of the assembly system has been carried out.

Production managers are given a quantitative decision tool to properly assess the implementation of continuous material supply policies at an early decision stage, and determine which option is the best, also allowing to explore trade-offs between the alternatives.

With respect to previous simplified literature models, this new approach allows to quantify a number of additional factors which are critical for the successful implementation of cost-effective continuous-supply systems, including error costs. No other direct comparison of LS and JIT is available in the literature.

Freight network planning and the application of distribution innovations are popular fields of research on sustainable urban logistics. However, considerable research on freight network design lacks a comprehensive consideration of the application of distribution innovations. This observation implies that sustainable urban freight research appears highly fragmented in topics of network design and distribution innovations. From the perspective of long-term planning, this situation possibly serves as a barrier to further promotion of sustainability. The objective of this paper is to analyze existing research gaps of literature to further promote the sustainability of urban logistics systems from a future perspective.

This paper employs a systematic literature review (SLR) method, which covers 164 papers and research works published in 2013–2018. The article corpus involved the innovative schemes of freight network design and the emerging delivery concepts in cities. Based on an analysis of articles' relevance, the most significant research contributions on both city logistics network design and exploitation of distribution innovations are detected.

This paper has found four research gaps in aspects of network design and distribution innovations. To respond to these gaps, we propose the research framework of sustainable and flexible future urban freight planning (SFFUFP) based on trends of city development, while discussing further research direction on urban freight planning.

The authors have found four research gaps in aspects of both urban freight network design and distribution innovations in which scholars could be encouraged to contribute. The research framework of SFFUFP can further promote sustainable urban logistics from a view of future management.

The purpose of this paper is to examine the concept of perishable food supply chain quality (PFSCQ) and to suggest a structural model that counts the influence of PFSCQ practices on organizational sustainable performance.

On the basis of comprehensive literature review, PFSCQ highly significant practices were examined and designated. These practices were classified into four dimensions: upstream quality (supplier quality), downstream quality (customer focus), internal quality (process and logistics quality) and support practices (top management leadership and commitment to quality, quality of human resource, quality of information and supply chain integration). The measurement instrument of organizational sustainable performance was also build on, containing three aspects: economic, environmental and social performance.

An inventive conceptual model that specifies a comprehensive image cover up core dimensions of PFSCQ and various aspects of organizational sustainable performance was suggested. This conceptual model can be used as “a directive” for theory developing and measurement instrument development of PFSCQ practices and organizational sustainable performance. More prominently, on the road to achieving additional insight, an extensive structural model that makes out direct and indirect relationships between PFSCQ practices and organizational sustainable performance was also developed. Practitioners can apply this model as “a path plan” for implementing PFSCQ practices to improve organizational sustainable performance.

The integration of quality and supply chain even now remains inadequate in the literature. Consequently, it is required to have a more focused approach in assessing quality issues inside the upstream, internal and downstream of the supply chain. This study concentrates on the practices which make better quality aspects of the supply chain, known as PFSCQ practices. Suggested research models in this paper contribute to conceptual frameworks for theory building in PFSCQ and sustainable organizational performance. It is also expected that this research can suggest a useful direction for determining and implementing PFSCQ practices as well as make possible further studies in this arena.

The food processing industry is growing with retail and catering supply chains. With the rising complexity of food products and the need to address food customization expectations, food processing systems are progressively shifting from production line to job-shops that are characterized by high flexibility and high complexity. A food job-shop system processes multiple items (i.e. raw ingredients, toppings, dressings) according to their working cycles in a typical resource and capacity constrained environment. Given the complexity of such systems, there are divergent goals of process cost optimization and of food quality and safety preservation. These goals deserve integration at both an operational and a strategic decisional perspective. The twofold purpose of this paper is to design a simulation model for food job-shop processing and to build understanding of the extant relationships between food flows and processing equipment through a real case study from the catering industry.

The authors designed a simulation tool enabling the analysis of food job-shop processing systems. A methodology based on discrete event simulation is developed to study the dynamics and behaviour of the processing systems according to an event-driven approach. The proposed conceptual model builds upon a comprehensive set of variables and key performance indicators (KPIs) that describe and measure the dynamics of the food job-shop according to a multi-disciplinary perspective.

This simulation identifies the job-shop bottlenecks and investigates the utilization of the working centres and product queuing through the system. This approach helps to characterize how costs are allocated in a flow-driven approach and identifies the trade-off between investments in equipment and operative costs.

The primary purpose of the proposed model relies on the definition of standard resources and operating patterns that can meet the behaviour of a wide variety of food processing equipment and tasks, thereby addressing the complexity of a food job-shop. The proposed methodology enables the integration of strategic and operative decisions between several company departments. The KPIs enable identification of the benchmark system, tracking the system performance via multi-scenario what-if simulations, and suggesting improvements through short-term (e.g. tasks scheduling, dispatching rules), mid-term (e.g. recipes review), or long-term (e.g. re-layout, working centres number) levers.