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Multi-manned assembly lines are designed to produce large-sized products, such as automobiles. In this paper, a multi-manned assembly line balancing problem (MALBP) is addressed in which a group of workers simultaneously performs different tasks on a workstation. The key idea in this work is to improve the workstation efficiency and worker efficiency of an automobile plant by minimizing the number of workstations, the number of workers, and the cycle time of the MALBP.

A mixed-integer programming formulation for the problem is proposed. The proposed model is solved with benchmark test problems mentioned in research papers. The automobile case study problem is solved in three steps. In the first step, the authors find the task time of all major tasks. The problem is solved in the second step with the objective of minimizing the cycle time for the sub-tasks and major tasks, respectively. In the third step, the output results obtained from the second step are used to minimize the number of workstations using Lingo 16 solver.

The experimental results of the automobile case study show that there is a large improvement in workstation efficiency and worker efficiency of the plant in terms of reduction in the number of workstations and workers; the number of workstations reduced by 24% with a cycle time of 240 s. The reduced number of workstations led to a reduction in the number of workers (32% reduction) working on that assembly line.

For assembly line practitioners, the results of the study can be beneficial where the manufacturer is required to increased workstation efficiency and worker efficiency and reduce resource requirement and save space for assembling the products.

This paper is the first to apply a multi-manned assembly line balancing approach in real life problem by considering the case study of an automobile plant.

Parallel two-sided assembly lines are usually designed to produce large-sized products such as trucks and buses. In parallel two-sided assembly lines, both left and right sides of the line are used for manufacturing one or more products on two or more assembly lines located parallel to each other. The purpose of this paper is to develop a new mathematical model for the parallel two-sided assembly line balancing problem that helps to evaluate and validate the balancing operations of the machines such as removal of tools and fixtures and reallocating the operators.

The proposed approach is explained with the help of an example problem. In all, 22 test problems are formed using the benchmark problems P9, P12, P16 and P24. The results obtained are compared among approaches of the task(s) shared, tool(s) shared and both tool(s) and task(s) shared for effect on efficiency as the performance measure. The solution presented here follows the exact solution procedure that is solved by Lingo 16 solver.

Based on the experiments, line efficiency decreases when only tools are shared and increases when only tasks are shared. Results indicate that by sharing tasks and tools together, better line efficiency is obtained with less cost of tools and fixtures.

According to the industrial aspect, the result of the study can be beneficial for assembly of the products, where tools and tasks are shared between parallel workstations of two or more parallel lines.

According to the author’s best knowledge, this paper is the first to address the tools and tasks sharing between any pair of parallel workstations.

Flights are often delayed owing to emergencies. This paper proposes a cooperative slot secondary assignment (CSSA) model based on a collaborative decision-making (CDM) mechanism, and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time.

Taking passenger delays, transfer delays and flight cancellation delays into account comprehensively, the total delay time is minimized as the objective function. The model is verified by a linear solver and compared with the first come first service (FCFS) method to prove the effectiveness of the method. An improved adaptive partheno-genetic algorithm (IAPGA) using hierarchical serial number coding was designed, combining elite and roulette strategies to find pareto solutions.

Comparing and analyzing the experimental results of various scale examples, the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time, and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality.

Based on the actual situation, this paper considers the operation mode of flight waves. In addition, the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness, which can provide airlines with reasonable decision-making opinions when reassigning slot resources.

Due to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain.

A mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set.

The model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time.

In literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries.

The suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.

The purpose of this paper is to develop a mathematical model for the network design of a reverse supply chain in a multi-product, multi-period environment. The emission cost due to transportation activities is incorporated into the model to reduce the total cost of emission and study the significance of inclusion of emission cost on the network design decisions.

Mixed integer linear programming formulation is used to model the network. The developed model is solved and analysed using the commercial solver LINGO.

The mathematical model provides a unified design of the network for the entire planning horizon comprising of different periods. A reduction in the total cost of emission is achieved. The analysis of the problem environment shows that the network design decisions significantly vary with the consideration of emission cost.

A single mode of transportation is considered in this study. Also, a single type of vehicle is considered for the transportation purpose.

The developed model can aid the decision makers in making better decisions while reducing the total emission cost. The quantification of the emission cost due to transportation activities is presented in an Indian context and can be used for future studies.

An all-encompassing approach for the design of reverse logistics networks with explicit consideration of product structure and emission cost.

The purpose of this paper is to develop a mathematical model for the design of a multi-stage reverse supply chain.

A mixed-integer linear programming formulation is used to model the network. Different data sets are generated randomly. Lingo, an optimisation package is used to solve the model developed.

The model is able to provide optimum solutions regarding the number and location of different facilities to be established in the network. The flow of different items through the network is also obtained. Analysis of the results shows the sensitivity of design decisions with respect to the changes in the input parameter value.

The authors consider only a single-product and single-period situation for this study. Further research can be done by considering a multi-product and multi-period situation. Uncertainty in data can also be included for future research.

The developed model can aid the managers in taking optimum decisions regarding the network design of a reverse supply chain. The analysis of the model for the variations in the input parameter values can also help the decision makers to take better decisions in a reverse supply chain.

The present research simultaneously considers two types of product return, namely, end-of-life and end-of-use product return, in a seven stage supply chain. Different recovery options such as recycling and remanufacturing are also incorporated into the model.

The study attempts to develop a multi-product multi-period (MPMP) aggregate production plan (APP) to fulfill the customers' demand in terms of throughput and lead time for achieving market competence.

This research proposes an integrated Fuzzy analytical hierarchy process (FAHP), multi-objective linear programming (MOLP), and simulation approach. Initially, FAHP is used to select the essential objectives a firm desires to achieve. Adopting the MOLP, an APP is formulated for the firm under study. Later, the simulation model of a firm is created in a discrete-event simulation (DES) software Arena© to evaluate the applicability of the proposed APP. A comparative analysis of the manufacturing performance levels (namely throughput, lead time, and resource utilization) achieved through the implication of an existing production plan and proposed APP is conducted further.

The findings from the study depict that the proposed MOLP-based APP can satisfy the customers' requirement (namely throughput and lead time) and improve the level of resource utilization compared with the firm's existing production plan.

The proposed research facilitates researchers and practitioners to understand the process of developing MOLP-based MPMP APP and analyzing its applicability through simulation technique to be utilized for developing APP at their firm.

An integrated FAHP-MOLP-simulation framework is the novel contribution to the literature on production planning. It can be extended to solve strategic, tactical, and operational problems in different domains like service, healthcare, supply chain, logistics, and project management.

Line–cell conversion and rotation of operators between cells are common in lean production systems. Thus, the purpose of this study is to provide an integrated look at these two practices through integrating job rotation scheduling and line-cell conversion problems, as well as investigating the effect of rotation frequency on flow time of a Seru system.

First, a nonlinear integer programming model of job rotation scheduling problem and line–cell conversion problem (Seru-JRSP) was presented. Then, because Seru-JRSP is NP-hard, an efficient and effective invasive weed optimization (IWO) algorithm was developed. Exploration process of IWO was enhanced by enforcing two shake mechanisms.

Computations of various sample problems showed shorter flow time and less number of assigned operators in a Seru system scheduled through job rotation. Also, nonlinear behavior of flow time versus number of rotation periods was shown. It was demonstrated that, setting number of rotation frequency to one in line with the literature leads to inferior flow time. In addition, ability of developed algorithm to generate clusters of equivalent solutions in terms of flow time was shown.

In this research, integration of job rotation scheduling and line–cell conversion problems was introduced, considering lack of an integrated look at these two practices in the literature. In addition, a new improved IWO equipped with shake enforcement was introduced.

This paper studies a supply chain consisting of multiple suppliers and a single buyer. It considers the case where a set of heterogeneous trucks is used for transporting products, and develops a mathematical model that coordinates the supply chain. The purpose of this paper is to minimise the costs of producing and delivering a product as well as carbon emissions resulting from transportation. In addition, the authors analyse how imposing a tax on carbon emissions impacts the delivery of products from the suppliers to the buyer.

It is assumed that heterogeneous vehicles are used for transporting products, which have different performance and cost attributes. A mathematical model that considers both operating costs and carbon emissions from transportation is developed. The impact of vehicle attributes on lot sizing and routing decisions is studied with the help of numerical examples and a sensitivity analysis.

The analysis shows that considering carbon emissions in coordinating a supply chain leads to changes in the routing of vehicles. It is further shown that if carbon emissions lead to costs, routes are changed in such a way that vehicles travel long distances empty or with a low vehicle load to reduce fuel consumption and therewith emissions.

Several areas for future work are highlighted. The study of alternative supply chain structures, for example structures which include logistics service providers, or the investigation of different functional relationships between vehicle load and emission generation offer possibilities for extending the model.

The paper is one of the first to study the use of heterogeneous vehicles in an inventory model of a supply chain, and one of the few supply chain inventory models that consider ecological aspects.

The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.

A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.

The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO₂ emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.

Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.