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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.

This paper aims to investigate the scheduling and loading problems of tow trains for mixed-model assembly lines (MMALs). An in-plant milk-run delivery model has been formulated to minimize total line-side inventory for all stations over the planning horizon by specifying the departure time, parts quantity of each delivery and the destination station.

An immune clonal selection algorithm (ICSA) combined with neighborhood search (NS) and simulated annealing (SA) operators, which is called the NSICSA algorithm, is developed, possessing the global search ability of ICSA, the ability of SA for escaping local optimum and the deep search ability of NS to get better solutions.

The modifications have overcome the deficiency of insufficient local search and deepened the search depth of the original metaheuristic. Meanwhile, good approximate solutions are obtained in small-, medium- and large-scale instances. Furthermore, inventory peaks are in control according to computational results, proving the effectiveness of the mathematical model.

This study works out only if there is no breakdown of tow trains. The current work contributes to the in-plant milk-run delivery scheduling for MMALs, and it can be modified to deal with similar part feeding problems.

The capacity limit of line-side inventory for workstations as well as no stock-outs rules are taken into account, and the scheduling and loading problems are solved satisfactorily for the part distribution of MMALs.

This paper aims to investigate the just-in-time (JIT) in-house logistics problem for automotive assembly lines. A point-to-point (P2P) JIT distribution model has been formulated to specify the destination station and parts quantity of each delivery for minimizing line-side inventory levels.

An exact backtracking procedure integrating with dominance properties is presented to cope with small-scale instances. As for real-world instances, this study develops a modified discrete artificial bee colony (MDABC) metaheuristic. The neighbor search of MDABC is redefined by a novel differential evolution loop and a breadth-first search.

The backtracking method has efficaciously cut unpromising branches and solved small-scale instances to optimality. Meanwhile, the modifications have enhanced exploitation abilities of the original metaheuristic, and good approximate solutions are obtained for real-world instances. Furthermore, inventory peaks are avoided according to the simulation results which validates the effectiveness of this mathematical model to facilitate an efficient JIT parts supply.

This study is applicable only if the breakdown of transport devices is not considered. The current work has effectively facilitated the P2P JIT logistics scheduling in automotive assembly lines, and it could be modified to tackle similar distribution problems featuring time-varying demands.

Both limited vehicle capacities and no stock-outs constraints are considered, and the combined routing and loading problem is solved satisfactorily for an efficient JIT supply of material in automotive assembly lines.

The purpose of this paper is to investigate the multi-skilled workers assignment problem in complex assembly systems such as aircraft assembly lines. An adaptive binary particle swarm optimization (A-BPSO) algorithm is proposed, which is used to balance the workload of both assembly stations and processes and to minimize the human cost.

Firstly, a cycle time model considering the cooperation of multi-skilled workers is constructed. This model provides a quantitative description of the relationship between the cycle time and multi-skilled workers by means of revising the standard learning curve with the “Partition-And-Accumulate” method. Then, to improve the accuracy and stability of the current heuristic algorithms, an A-BPSO algorithm that suits for the discrete optimization problems is proposed to assign multi-skilled workers to assembly stations and processes based on modified sigmoid limiting function.

The proposed method has been successfully applied to a practical case, and the result justifies its advantage as well as adaptability to both theory and engineering application.

A novel cycle time model considering cooperation of multi-skilled workers is constructed so that the calculation results of cycle time are more accurate and closer to reality. An A-BPSO algorithm is proposed to improve the stability and convergence in dealing with the problems with higher dimensional search space. This research can be used by the project managers and dispatchers on assembly field.

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.

The purpose of this paper is to present a literature review on parts feeding policies and to provide the components of parts feeding systems via a classification structure.

This paper determines the scope and components of parts feeding systems via a classification structure under three main components such as the storage of parts, transport of parts and feeding policy. Afterward, it is focused on parts feeding policies and the related papers are reviewed and analyzed according to their feeding policy types, objectives, solution methodologies and the application types.

A classification structure showing the components and scope of parts feeding systems is provided. Parts feeding policies are handled in detail and feeding policy types, objectives, solution methodologies and application types in the existing studies are presented in this paper. However, the paper highlights the open research areas and advances for academics and presents applied solution methodologies and case studies for practitioners.

This paper reveals the scope of parts feeding systems by presenting a classification structure including three main components and related subcomponents and provides a comprehensive literature review on parts feeding policies.

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.

Driven by sustainable production, mobile robots are introduced as a new clean-energy material handling tool for mixed-model assembly lines (MMALs), which reduces energy consumption and lineside inventory of workstations (LSI). Nevertheless, the previous part feeding scheduling method was designed for conventional material handling tools without considering the flexible spatial layout of the robotic mobile fulfillment system (RMFS). To fill this gap, this paper focuses on a greening mobile robot part feeding scheduling problem with Just-In-Time (JIT) considerations, where the layout and number of pods can be adjusted.

A novel hybrid-load pod (HL-pod) and mobile robot are proposed to carry out part feeding tasks between material supermarkets and assembly lines. A bi-objective mixed-integer programming model is formulated to minimize both total energy consumption and LSI, aligning with environmental and sustainable JIT goals. Due to the NP-hard nature of the proposed problem, a chaotic differential evolution algorithm for multi-objective optimization based on iterated local search (CDEMIL) algorithm is presented. The effectiveness of the proposed algorithm is verified by dealing with the HL-pod-based greening part feeding scheduling problem in different problem scales and compared to two benchmark algorithms. Managerial insights analyses are conducted to implement the HL-pod strategy.

The CDEMIL algorithm's ability to produce Pareto fronts for different problem scales confirms its effectiveness and feasibility. Computational results show that the proposed algorithm outperforms the other two compared algorithms regarding solution quality and convergence speed. Additionally, the results indicate that the HL-pod performs better than adopting a single type of pod.

This study proposes an innovative solution to the scheduling problem for efficient JIT part feeding using RMFS and HL-pods in automobile MMALs. It considers both the layout and number of pods, ensuring a sustainable and environmental-friendly approach to production.

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.

This paper aims to optimize the configuration of assembly lines (ALs) considering the two long-term decision problems within the line balancing and part feeding (PF) contexts, when the supermarket concept is applied in PF.

To this purpose, a bi-level mathematical model is proposed to deal with the assembly line balancing problem (ALBP) and supermarket location problem (SLP) during the strategic decision-making phase of ALs’ configuration. The proposed model is applied on the known test problems taken from the ALBP literature to verify its performance.

The computational results verify that when the proposed structure is applied, the resulting AL configurations are optimized from both ALBP and SLP considerations in terms of the number of stations and line efficiency as well as supermarket transportation and installation costs.

No study has yet dealt with the long-term decision problem of configuring ALs considering both ALBP and SLP. Also, this study validates the effect of the ALBP on the SLP solutions as two long-term interrelated decision problems.