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Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models to integrate decisions on picker selection, order batching, batch assignment, picker routing, and scheduling. In the first stage, the human factors affecting picker selection are considered as the problem's criteria and the available pickers are treated as alternatives. The fuzzy entropy method and fuzzy COmplex PRoportional ASsessment (COPRAS) are used to weight the factors and rank the pickers, respectively. In the second stage, a three-objective mathematical model is formulated to minimize makespan and the operating costs of picking while maximizing the total scores of the selected pickers. The improved augmented epsilon constraint method (AUGMECON2) and the non-dominated sorting genetic algorithm II (NSGA-II) are applied to solve the proposed model. The performance of the two methods is tested on well-known benchmark instances and a real-world case study. The NSGA-II algorithm can generate optimal results using only about 6.58% of the CPU time required by AUGMECON2 to solve the problem. Our computational experiments show that increasing the number of pickers from 2 to 8 and doubling their capacity reduces the makespan by 2.61% and 2.74%, respectively. [ABSTRACT FROM AUTHOR]

Economic shocks such as pandemics and natural disasters like floods and earthquakes can disrupt the market supply chain. In this study, we address the impact of both negative and positive market disruptions on product demand, and the subsequent need for adaptive strategies by marketing managers. We introduce a novel approach for measuring sales resilience value (SRV), which assists in selecting optimal discount pricing strategies during sales promotion programs. Our approach considers various states of demand disruption and is underpinned by research literature concepts and mathematical methods. A sensitivity analysis was conducted to understand the key drivers of our model, providing valuable insights for decision-making. We examined the effects of positive demand disruption on resiliency by altering the level of demand for a product of online platform in Iran during promotion while keeping other parameters constant. Our findings reveal that an increase in demand due to promotion, decreases the company's resiliency, defined as the ratio of recovery to loss. However, by employing appropriate pricing strategies and promotion policies, companies can adapt to increased demand and improve resiliency over time through actions such as increasing production capacity. This continues until the company reaches its tolerance threshold. These results help offering significant managerial insights for effectively utilizing this concept in real-world applications. [ABSTRACT FROM AUTHOR]

Organizations need to apply resilience and business continuity in industry to protect themselves against the crises and destructive events. Also, the growing expansion of competition in the global market and the increasing crisis in the world have increased the importance of optimal resource allocation. With the optimal resource allocation, huge losses and damages to organizations are prevented. The problem of resource allocation can be raised alongside the criteria of process resilience and continuity. Therefore, organizations change their perspective from focusing solely on reducing vulnerability to increasing resilience and continuity against to accidents in crises and destructive situations. The objective of this paper is proposed a mathematical model for optimal resource allocation with the aim of minimizing the lack of process resilience and maximizing the process continuity. So, the organization can continue to operate with available resources in times of crisis and lack of resources. Also, main activities and processes are not interrupted by crises and destructive events. After solving the model using a case study (textile industry), the results of the model were described and it was found that destructive events were recovered before the tolerance threshold and crisis and destructive events did not interrupt activities and processes. [ABSTRACT FROM AUTHOR]

We address the scheduling problem of a set of non-identical size jobs on a single batch-processing machine (SBPM) wherein the scheduler can make decision whether to schedule a job in batches or not to schedule it with a job-dependent penalty. The processing time of a batch is the greatest job processing time in that batch (parallel batching or p-batching). The scheduler wants to minimize a given objective function f, where f is the total rejection penalties of the rejected jobs (rejection cost) plus the makespan of the scheduled ones. We formulate the aforementioned problem as a 0–1 mixed integer programming model. We also apply an effective dynamic programming algorithm (DPA) to calculate a lower bound (LB) on the optimal cost of the problem. To tackle the problem, we propose a grouping algorithm, based on league championship algorithm (LCA), with new updating equations maintaining the major characteristics of the original updating equations of the LCA and well-suited to the structure of the problem. For small problems, performance of the proposed LCA is compared with GAMS/CPLEX solver. For large-scale instances, a genetic algorithm is adopted as a basis for comparison. Simulated experiments confirm the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

This study attempted to review the articles and explore the gaps and challenges in the areas of resilience, business continuity, risk, and process safety with the aim of providing several directions for future research to understand different research directions in these areas with different perspectives. In addition, in this study, the relationship of articles in these areas with each other was examined. In this research project, related studies were reviewed and reported to identify presented frameworks, models, and methods for them. In the first phase, the articles were divided into three categories according to their similarity, namely “maximizing the value of business continuity and resilience,” “maximizing process safety and the effect of risk and resilience factors,” and “minimizing risk and effect of uncertainty.” In the second phase, the appropriate conceptual frameworks titled “research house” based on resilience, business continuity, and risk categories were created for each category. In the third phase, 22 closed codes were obtained by carefully reviewing the articles, and their co-occurrence network was investigated. The main findings of this article were categorizing the studied articles, providing conceptual frameworks resulting from article analysis, and presenting a conceptual model. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient methodology based on the Monte-Carlo simulation-based bi-objective optimization, to determine base-warranty (BW) and extended warranty (EW) parameters based on the product lifecycle. The first objective, which is from the manufacturer's perspective, maximizes the profit while the second objective minimizes the expected number of failures that occurred during the out-of-warranty period. The manufacturer can rectify failed products via minimal repair, imperfect repair and perfect repair. The optimization model has decision variables including the product price, BW length, EW length, EW price, product failure rate, imperfect repair level, and spare part production rate in each time interval. The structure of the model admits the design of a hybrid method based on the multi-objective optimization search algorithm, Monte-Carlo simulation and an exact Out-Of-Kilter (OOK) algorithm. The nondominated sorting genetic algorithm (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) algorithm are used as search algorithms. The proposed approach consists of three stages, where in the first stage, product price, BW length, EW length, EW price, product failure rate, imperfect repair level are set by NSGA-II/MOPSO. In the second stage, the number of failed products is calculated by the Monte-Carlo simulation and in the third stage, we show that the spare part inventory control sub problem can be transformed to a minimum cost network flow problem which is optimized by the OOK algorithm to attain a unified solution. A Taguchi approach is used to find the optimum level of parameters. The performance of algorithms is compared based on three different metrics. Results on a real-world problem demonstrate that the NSGA-II-OOK algorithm is more effective than the MOPSO-OOK algorithm. Through a sensitivity analysis, we analyze how various levels of planning horizon can affect Pareto-set which indicates valuable managerial insight. [ABSTRACT FROM AUTHOR]

Statistical Process Control (SPC) methods can significantly increase organizational efficiency if appropriately used. The primary goal of process capability studies is to obtain critical information about processes to render them even more effective. This paper proposes a comprehensive framework for proper implementation of SPC studies, including the design of the sampling procedure and intervals as well as process capability indices. Some of the most essential process capability indices in the literature were reviewed to develop a methodology to utilize process capability indices within the SPC framework. The current study presents an efficiency-oriented criterion designed for measuring SPC implementation productivity. The framework is applied to the windshield installation process of an Iranian automobile assembly line. The process was sampled in various sessions. Results verify that the implemented SPC framework could successfully improve the process and that the proposed framework could significantly address bottleneck in the process and enhance the quality level of the process from satisfactory to excellent according to the reference values of process capability indices. Managerial insights are also drawn from results. [ABSTRACT FROM AUTHOR]