The distributed permutation flowshop scheduling is a critical issue in various industries, involving jobs allocation and scheduling among multiple flowshops. This paper extends the research to explore the Distributed Mixed No-Wait Permutation Flowshop Scheduling Problems (DMNWPFSP) with minimizing makespan. The innovation lies in an optimized mathematical model, hybrid heuristic algorithms, an improved iterated greedy algorithm (IIG), and high-quality solutions. Extensive experimental results demonstrate the effectiveness and superiority of the proposed IIG in terms of scheduling quality, computational efficiency, and robustness compared to existing approaches. The outcomes of this work contribute to the field of distributed flowshop scheduling, providing valuable insights for practitioners seeking to enhance production efficiency and competitiveness.

Most of today's production systems are working with parallel production resources to increase throughput rate due to the increase in high variability in demand and product mix. Effective control and performance evaluation of such systems is of paramount importance to minimize production and inventory-related costs. We examine a production-inventory system featuring parallel production resources capable of producing various products. In many industries such as automotive, white goods, electronics, and paint, multiple/parallel production resources are widely used to produce the ideal amount and satisfy incoming demands for distinct products. In this study, shortage cost is not restricted to only one type and both lost sales and backordering cases are analyzed. In order to analyze the optimal production policies' behavior, we initially formulate dynamic programming models for both lost sales and backordering systems, treating them as Markov Decision Processes. Subsequently, we solve these models using the value iteration algorithm. Given the challenges posed by the curse of dimensionality in the value iteration algorithm, we suggest alternative heuristic production policies. These policies extend the existing ones described for multi-item single-resource make-to-stock (MTS) systems to accommodate multiple resources. We construct simulation models to assess the efficacy of the heuristic policies, conducting comparisons of their performance against both the optimal policy and among one another. To the best of our knowledge, there has been no exploration of scenarios involving multiple production resources concurrently manufacturing distinct products in a MTS environment. Hence, this study serves as an extension to the examination of multi-item, multi-production resource MTS systems.

Competition among seaports has been becoming more and more fierce in current times, which has extended to the contest between transportation chains including seaports and their inland ports. Against this background, this paper studies competitive inland port location and pricing problem for an entering seaport under the condition that the incumbent competitive seaport has construct-ed inland transportation chains inside their overlapping hinterland. Specifically, this paper formulates a mixed-integer nonlinear program for the considered problem, in which we take packaged price and service time as influence factors for the inland transportation chains competition and characterize inland ports choice behaviors for shippers based on logit model. Additionally, this paper designs a hybrid heuristic method by integrating a genetic algorithm and an analytical method to solve location and pricing subproblems, respectively. Based on the computational results and sensitivity analysis, this paper provides some valuable suggestions on how to locate in-land ports and make price decisions for the new entering seaport.

A simple and effective multi-attribute decision-making method, named as BHARAT method, is proposed in Part-1 of this paper and the same method is used now as a multi- and many-objective decision-making method for evaluating the Pareto optimal solutions. The proposed BHARAT method is used to identify the best compromise Pareto solution. Based on their importance for the given optimization problem, the objectives are ranked, and the weights are assigned. The weights of the objectives and the normalized values of the objectives for different Pareto optimal solutions are used to compute the total scores. The total scores are used to differentiate the alternative optimal solutions and an alternative solution that gets the highest total score is suggested as the best compromise solution. Three case studies are presented to illustrate and validate the proposed BHARAT method. The case study 1 is a multi-objective optimization problem related to cloud manufacturing with 3 objectives and 20 alternative solutions; case study 2 is a many-objective optimization problem of electro-discharge machining process with 4 objectives and 50 alternative solutions; case study 3 is a many-objective optimization of milling process parameters with 4 objectives and 100 alternative solutions. The outcomes of the suggested BHARAT method are compared with those of the other popular decision-making approaches for each of the three case studies considered. The suggested simple and more logical BHARAT method can be used in multi- and many-objective optimization problems to select the best compromise solution.

A simple and effective multi-criteria decision-making methodology named as “Best Holistic Adaptable Ranking of Attributes Technique (BHARAT)” is proposed that can be used in single- as well as group decision-making scenarios of the industrial environment. The attributes data for various alternatives can be quantitative or qualitative (i.e., expressed in linguistic terms). This paper proposes to transform the qualitative attributes into quantitative attributes by means of simple linear scales rather than complex fuzzy scales. The proposed BHARAT method normalizes the data with reference to the “best” alternative corresponding to an attribute and the normalization procedure is repeated for all the attributes to get the normalized data. A group of decision-makers or a decision-maker assigns ranks to the attributes according to how important they are deemed to be, and these ranks are then transformed into the proper weights. The total scores of the alternatives are calculated by multiplying the weights of the attributes by the corresponding normalized data of the attributes for different alternatives. Four industrial case studies are presented to illustrate the potential of the suggested BHARAT method. The first case study deals with the problem of an automated warehouse selection for a large industrial plant involving a single decision-maker, 13 attributes, and 4 alternative warehouses; the second case study deals with the problem of sustainable maintenance service provider selection for a large petrochemical plant involving fuzzy group decision-making with 5 decision-makers, 9 attributes, and 4 alternative maintenance service providers; the third case study deals with the problem of alternative strategy selection for implementation of a make-to-order system for passenger car manufacturers involving 6 factors, 18 sub-factors, and 3 alternative strategies; and the fourth case study deals with the problem of process parameters selection in a sustainable high speed turning operation involving 4 attributes and 9 alternative sets of experimental conditions. The results of the proposed decision-making method and its second version are compared with the other popular decision-making methods. The proposed method and its another version are proved simple, effective, powerful, flexible, easy to apply, do not require the use of fuzzy logic, offer logical and consistent procedures to assign weights to the attributes, and are applicable to different decision-making scenarios of the industries. Part-1 of this paper describes the applications of the BHARAT method to multi-attribute decision-making problems and Part-2 describes the evaluation of Pareto solutions using the BHARAT method in multiple objective decision-making problems.

Batch dispersion problem (BDP) restricts batch traceability in large-scale discrete production and negatively impacts batch recall costs. However, previous research has ignored the complexity of the BDP in their analyses. This paper investigates the BDP under the composed bill of materials (BOM) and develops a mathematical model for the BDP with the goal of minimizing the total batch dispersion by utilizing the batch dispersion as a measure of the degree of dispersed usage of part batches. BDP-GAVNS, a hybrid genetic algorithm with variable neighborhood search, is devised for the BDP based on the demonstration that the BDP is an NPC problem. In BDP-GAVNS, memory banks were introduced to increase the diversity of individuals performing crossover operations. Additionally, the encoding method and infeasible solution repair program are designed according to the characteristics of BDP. Numerical experiments validate the viability and effectiveness of BDP-GAVNS in solving BDP. They demonstrate that (1) the optimal combination occurs when the ratio of individuals produced by the three types of population initialization methods, namely global selection (GS), local selection (LS), and random selection (RS), to the population takes values of 0.30, 0.10, and 0.60, respectively; (2) The memory bank enriches the source of individuals required for crossover operations and improves the performance of crossover operations; and (3) The BDP-GAVNS is more effective than the other five heuristic algorithms including genetic algorithms in seeking the optimal solution of BDP.

Introduce government competition policies and technological innovation efforts into the profit game model of the photovoltaic industry supply chain, establish two different supply chain profit models, and study the impact of government competition policies and manufacturers' technological innovation efforts on the profits of each manufacturer and the overall profit of the supply chain. Construct profit models in both competitive and cooperative situations, and empirically analyze the impact of government competition policies and manufacturers' technological innovation efforts on the optimal supply chain profits in both competitive and cooperative situations. Research has shown that: (1) government competition policies are conducive to promoting an increase in manufacturers' profits; (2) The increase in technological innovation efforts can quickly increase manufacturers' profits; (3) Compared to the cooperation situation, the profit of each manufacturer and the total profit of the entire supply chain are higher in the competitive situation.

This paper establishes a location-routing optimization model of the distribution center for emergency supplies with the goals of system reaction time, total cost of consumption, psychological fear of the populace in disaster-affected locations, and material usage rate. Where the excess time, demand, and penalty coefficient are the components of the penalty cost in the total consumption cost, and where the psychological panic of those in the affected area is represented by the psychological perception function of panic developed in accordance with the prospect theory. An improved hybrid multi-objective cuckoo-large-neighborhood search algorithm was then designed to introduce tent mapping, nonlinear inertia weights, elite strategies, congestion operators, and dynamically adjusted discovery probabilities into the standard multi-objective cuckoo optimization algorithm, which generates a new solution using a large-neighborhood search algorithm after discarding part of the solution with the discovery probability, and then accepts the current nondominated solution with dynamic probabilities. The paper uses the improved algorithm to solve Christofides69, an arithmetic example from the standard dataset of the LRP problem, and the results show that the solution provided by the improved algorithm outperforms the solutions provided by the standard multi-objective cuckoo search algorithm and the NSGA-II algorithm in terms of the total cost of dissipation, the level of psychological panic of the people in the affected area, the rate of utilization of the supplies, and the number of distribution centers open. Finally, the improved algorithm was used to analyze cases of different sizes separately, and it was found that the algorithm yielded better results and was therefore able to demonstrate its effectiveness.

This study aims to optimize a hybrid multi-item fabricating-shipping integrated system incorporating scrap, adjustable rate, and postponement. In present-day competitive market environments, there is a clear client demand trend for various goods, shorter lead time, and expected quality. To satisfy the client’s needs, the management of manufacturing firms requires an effective and efficient plan to fabricate various high-quality goods in an expedited period, under limited capacity, and with minimal operating expenses. Inspired by facilitating production management to determine the best fabricating scheme/plan to achieve their operational goals, this work proposes an exploratory postponement model with quality assurance and uptime reduction strategies for their decision-making. By employing a two-phase making scheme, the required standard components are first made in the 1st phase, and multiple finished merchandise is fabricated in the 2nd phase. The study suggests strategies of contracting out a part of the common parts’ batch and adopting an adjusted/expedited making rate in the 2nd phase to considerably reduce both phases’ production uptimes. During both fabricating processes, the screening tasks identify/remove scrapped/faulty goods to ensure each finished batch’s quality. Equal-amount multiple shipments of end merchandise are transported to the clients in fixed time-interval. Optimization methodology and mathematical analyses support us in deriving the model’s expected annual operating cost and deciding the optimal production-transportation policy. A numerical illustration helps verify our model’s applicability and reveals important managerial insights into the studied problem to facilitate management in decision-making.

This research delves into the software testing process and its environmental factors to uncover the core elements influencing software reliability. Specifically, it focuses on the learning and negligent aspects of the software reliability growth model. The learning factor accelerates reliability growth, leading to an S-shaped curve in the mean value function, while the negligent factor highlights the occurrence of imperfect debugging. The study also uses Brownian motion and stochastic differential equations to establish statistical confidence intervals for reliability and costs. These intervals aid software managers in assessing potential release risks at various confidence levels, allowing them to make informed decisions considering resource constraints and desired system reliability levels across different scenarios.