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The contemporary landscape of supply chains necessitates a comprehensive integration of multiple components encompassing production, distribution and customer engagement. The pursuit of supply chain harmony underscores the significance of pricing strategies within the framework of dual-channel distribution, particularly when confronted with the dynamics of asymmetric demand performance.

This paper delves into a nuanced decision-making challenge anchored in a dual-channel distribution context featuring a retailer and two distinct products. Notably, the retailer’s decision-making process employs the computational framework of dual grey numbers, a robust tool for handling uncertainty.

This study revolves around applying game theory to manufacturers. Each manufacturer presents its aggregated price proposition to the retailer. Subsequently, the retailer identifies the optimal pricing configuration among the manufacturers' aggregate prices while adhering to constraints such as spatial classification and inventory costs. This article’s contribution extends to delineating the retailer’s capacity to discern the influence of product market potential and the aggregate product cost on the overall demand.

The model’s innovation lies in its harmonious fusion of spatial classification, pricing strategies and inventory control. Notably, this novel integration provides a platform for unraveling the intricate interplay between non-symmetric market potential, production costs and cross-sensitivity. The investigation is underscored by the utilization of the double interval grey numbers, a powerful computational approach that accommodates the inherent uncertainty pervasive in the domain. This study fills a gap in the existing literature by offering an integrated framework unifying spatial allocation, pricing decisions and inventory optimization.

Introduction The debate on accounting for inflation has dwelt, almost wholly, on the presentation of published accounts, ignoring the implications for management accounting. This is surprising really. The distortions which inflation brings to our measurements are more immediate and decision‐affecting than the once a year public relations exercise of the annual accounts. The subject did get an airing in the Guidance Manual to ED18 but even there it was a technician's manual with no examination of the wider effects. This article (which has no academic pedigree) examines the effect of inflation on the compilation, presentation and interpretation of management information in the multiple retail food industry, i.e. in supermarketing. While not sharing the profundity of the learned debate on ED18 and Hyde, it deals with matters which immediately affect the company's health and where it most certainly does matter that inflation is reckoned with. Accountants in industry and commerce know they must account for inflation in their management accounting—a certainty of purpose lacking in our options in the published accounts. Perhaps if there was more discussion on how inflation affects day to day management decisions—and thereby how it is worked into the information leading to these decisions—then the whole debate might be a bit more real, and not as seems to many accountants in industry, on the edges of their accounting responsibilities.

This paper presents a novel multi-objective optimization model aimed at enhancing the success rate of resource planning (RP) implementation. The model optimization is developed based on the organizational structure types, fit-gap contingency analysis reports, uncertainty optimization problems on implementation schedule time and relative time and budget constraints.

Two pivotal strategies are employed: RP tools redesign through customization and organizational redesign. The synergistic integration of these strategies is essential, recognizing that RP tools implementation success hinges not only on technical aspects but also on aligning the system with organizational structure, culture and practices. In the analysis phase, a committee of experts identifies the initial gaps, which are evaluated through three conflicting objective functions: cost, time and penalty and running by the e-constraint method. In case of uncertainty nature time of RP tools implementation, the Activity-on-Arrow (A-O-A) method has been utilized.

The e-constraint method is utilized to derive the Pareto-optimal front, representing solutions effectively addressing identified gaps. A compromised solution is then proposed using the LP-metric method to strike a balance between conflicting objectives, ultimately improving RP tool implementation by reducing misfits.

To demonstrate and validate the model, a controlled case study is initially presented, illustrating its effectiveness. Subsequently, a real industry case study is provided, further validating the model’s applicability and practical relevance. This comprehensive approach offers valuable insights to optimize RP tool implementation outcomes, a critical concern for organizations undergoing technological transitions.

In Latin America and the Caribbean, the access of students to higher education has presented an extraordinary growth over the past fifteen years. This rapid growth has presented a challenge for increasing the system resources and capabilities while maintaining its quality. As a result, the networked universities (NUs) organized themselves as a collaborative network, and they have become an interesting model for facing the complexity driven by globalization, rapidly changing technology, dynamic growth of knowledge and highly specialized areas of expertise. In this article, we studied the NU named Red Universitaria Mutis (Red Mutis) with the aim of characterizing the collaboration and integration structure of the network.

Network analytic methods (visual analysis, positional analysis and a stochastic network method) were used to characterize the organizational structure and robustness of the network, and to identify what variables or structural tendencies are related to the likelihood that specific areas of a university would collaborate.

Red Mutis is a good example of regional NUs that could take advantage of the strengths, partnerships, information and knowledge of the regional and international universities that form the network. Analyses showed that Red Mutis has a differentiated structure consisting of academic and non-academic university areas with a vertical coordination (by steering and management) of the different university areas.

The methodology could be used as a framework to analyze and strengthen other strategic alliances between universities and as a model for the development of other NU in local and global contexts.

The study assesses the significance of environmental uncertainty and its effects on fishing strategies of small-scale fishermen in Ende, Flores, Indonesia. Periodic environmental cycles such as the moon phase can have important effects on fishing strategies by regulating the behavior of stocks and tides. Traditional lunar calendars are used by subsistence fishermen to decide when and where to go fishing. Environmental uncertainty, specifically unprecedented changes in oceanographic and atmospheric conditions, is threatening the predictability of traditional systems of ecological knowledge.

Methods included ethnographic and observational techniques. Interviews (n = 58) and surveys (n = 132) are qualitatively and quantitatively analyzed. A combination of standard statistical tests, multilevel models, and cluster analysis is applied to long-term repeated observations of fishing events (n = 2,633).

Endenese fishermen emphasized the importance of the traditional lunar calendar to allocate their effort in interviews and surveys. This belief does not coincide with observed behavior. Contrary to expectations from the traditional calendar, the lowest probability of fishing happens in the intermediate phases, with fishing also occurring during the full moon. Differences between individuals play an important role in explaining variability in returns. Finally, based on the consideration of variability, three different fishing strategies are identified that suggest an effect of environmental uncertainty in effort regulation.

The paper underlines the importance of studies of variability to identify behavioral flexibility and adaptation. Results emphasize the value of considering individual traits in the analysis of subsistence practices.

In the present research, location and routing problems, as well as the supply chain, which includes manufacturers, distributor candidate sites and retailers, are explored. The goal of addressing the issue is to reduce delivery times and system costs for retailers so that routing and distributor location may be determined.

By adding certain unique criteria and limits, the issue becomes more realistic. Customers expect simultaneous deliveries and pickups, and retail service start times have soft and hard time windows. Transportation expenses, noncompliance with the soft time window, distributor construction, vehicle purchase or leasing, and manufacturing costs are all part of the system costs. The problem's conceptual model is developed and modeled first, and then General Algebraic Modeling System software (GAMS) and Multiple Objective Particle Swarm Optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGAII) algorithms are used to solve it in small dimensions.

According to the mathematical model's solution, the average error of the two suggested methods, in contrast to the exact answer, is less than 0.7%. In addition, the performance of algorithms in terms of deviation from the GAMS exact solution is pretty satisfactory, with a divergence of 0.4% for the biggest problem (N = 100). As a result, NSGAII is shown to be superior to MOSPSO.

Since this paper deals with two bi-objective models, the priorities of decision-makers in selecting the best solution were not taken into account, and each of the objective functions was given an equal weight based on the weighting procedures. The model has not been compared or studied in both robust and deterministic modes. This is because, with the exception of the variable that indicates traffic mode uncertainty, all variables are deterministic, and the uncertainty character of demand in each level of the supply chain is ignored.

The suggested model's conclusions are useful for any group of decision-makers concerned with optimizing production patterns at any level. The employment of a diverse fleet of delivery vehicles, as well as the use of stochastic optimization techniques to define the time windows, demonstrates how successful distribution networks are in lowering operational costs.

According to a multi-objective model in a three-echelon supply chain, this research fills in the gaps in the link between routing and location choices in a realistic manner, taking into account the actual restrictions of a distribution network. The model may reduce the uncertainty in vehicle performance while choosing a refueling strategy or dealing with diverse traffic scenarios, bringing it closer to certainty. In addition, two modified MOPSO and NSGA-II algorithms are presented for solving the model, with the results compared to the exact GAMS approach for medium- and small-sized problems.

“It is generally accepted that the food industry must be scientifically based to cope with the problems, particularly of public health, which arise as new processes of growing, manufacturing, packaging and preserving food depart even further from traditional ways.”

This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located.

The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms.

According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO.

In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered.

The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs.

This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.