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This paper outlines a steady state multi-modal equilibrium transportation model which contains elastic demands and deterministic route-choices. The model may readily be extended to include some stochastic route-choice or mode choice. Capacity constraints and queueing delays are permitted; and signal green-times and prices are explicitly included. The paper shows that, under natural linearity and monotonicity conditions, for fixed control parameters the set of equilibria is the intersection of convex sets. Using this result the paper outlines a method of designing appropriate values for these control parameters; taking account of travellers' choices by supposing that the network is in equilibrium. The method may be applied to non-linear monotone problems by linearising about a current point. An outline justification of the method is given; a rigorous proof of convergence is as yet missing. Thus the method must now be regarded as a heuristic.

Many large organizations employ staff who make the greater part of their careers within the organization. Today's recruits are tomorrow's senior managers and so the planning of the careers of the staff is often undertaken with some care. The planners will want to avoid both shortages of suitably experienced staff for promotion to senior levels, and surpluses with their attendant problems of early retirement and redundancy schemes.

It is of the greatest importance to any organisation to obtain recruits with the right qualifications and abilities both for the immediate tasks and for the years ahead. We will describe a model developed in conjunction with Civil Service Management aimed at assisting them to recruit staff with an appropriate mix of characteristics. The model is an extension of the Camel model described in a previous paper. The data used are based on Civil Service data but have been modified significantly to the extent that the conclusions do not necessarily apply to the Civil Service.

Limited studies in the mobile payment segment have attempted at understanding the factors that resist customers from using financial apps or mobile payment services (MPSs). This study aims at identifying the barriers from online customer reviews and examine how these barriers affect customers’ negative emotions (anger, fear, sadness), customer ratings and recommendation intentions.

This study, divided into three phases, has adopted a text-mining based mixed-method approach on 14,043 reviews present in Google PlayStore or App Store pages about financial apps used in India.

Phase 1 identified barriers like, “bad user experience”, “UPI failure”, “trust issues”, “transaction delays” from the reviews. Phase 2 found that “bad user experience” and “UPI failure” trigger both “anger” and “sadness”. “Transaction delays” and “money lost in transaction” stimulate “fear”. From the IRT stance, in Phase 3 this study has found that barriers like, “transaction error”, “UPI failure” (usage), “bad user experience” (image) and “trust issues” (tradition) have a significant negative impact on both customer ratings and recommendation intention.

The current study contributes to the existing literature on MPSs by identifying barriers from user generated content. Additionally, this study has also examined the impact of the barriers on customers’ negative emotions and recommendation intention.

This paper describes the mathematical models used by the Civil Service Department for manpower planning supply work, their inter‐relationship, and the problems to which they are suited. Mathematical detail is normally not included, the reader being referred to other publications.

Most of optimal design or control engineering problems present conflicting objectives that need to be simultaneously minimized or maximized. Often, however, it is a priori known that some functions have greater importance than other. This paper aims to present a novel multi-surrogate, multi-objective, decision-making (DM) optimization algorithm, which is suitable for time-consuming simulations. Its performances have been compared, on the one hand with a standard decision-making algorithm (iTDEA), on the other with a self-adaptive evolutionary algorithm (AMALGAM*). The comparison concerns numerical tests and an optimal control task in induction heating.

In particular, the algorithm makes use of surrogates (meta-models) to concentrate the field evaluations at the most promising areas of the design space. The effect of the decision-maker is instead to drive the search to given regions of the Pareto front. The synergy between surrogates and the decision-maker leads to a greater effectiveness of the optimization search. For the field analysis of the optimal control task, a coupled electromagnetic-thermal FEM model has been developed.

The novel algorithms outperform both iTDEA and AMALGAM* in all done tests.

The algorithm could be applied to other computationally intensive multi-objective real-life problems whenever a preference between the objectives is known.

The combination of surrogates and a decision-maker is beneficial with time-consuming multi-objective optimization problems.

This paper aims to investigate a multi-objective electric vehicle’s (EV’s) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism to coordinate multiple EVs is proposed to fulfill part feeding tasks.

A chaotic reference-guided multi-objective evolutionary algorithm based on self-adaptive local search (CRMSL) is constructed to deal with the problem. The proposed CRMSL benefits from the combination of reference vectors guided evolutionary algorithm (RVEA) and chaotic search. A novel directional rank sorting procedure and a self-adaptive energy-efficient local search strategy are then incorporated into the framework of the CRMSL to obtain satisfactory computational performance.

The involvement of the chaotic search and self-adaptive energy-efficient local search strategy contributes to obtaining a stronger global and local search capability. The computational results demonstrate that the CRMSL achieves better performance than the other two well-known benchmark algorithms in terms of four performance metrics, which is inspiring for future researches on energy-efficient co-scheduling topics in manufacturing industries.

This research fully considers the cooperation and coordination of handling devices to reduce energy consumption, and an improved multi-objective evolutionary algorithm is creatively applied to solve the proposed engineering problem.

This article makes the case for a ‘racism reduction’ agenda that aims to promote action to reduce racism. It argues for placing this mission and its agenda at the core of agency and community practice. It provides information on the wider national and European context for racism and racist violence, and offers suggestions for practice that can help prevent racial violence and promote race equality. The article draws on recent research carried out for Safer Leeds (Law, 2007).

The purpose of this study is to investigate resource-constrained multi-project scheduling problems (RCMPSP) involving uncertainty in the form of time-dependent renewable resource reliability. A key focus is to minimize the makespan (completion time) of projects when resources can become unavailable or fail over time at non-constant rates. Accounting for realistic resource reliability seeks to provide scheduling solutions that better reflect potential delays in practical multi-project environments.

A new discrete-time binary integer programming formulation of RCMPSP is expanded to include time-dependent resource reliability and simultaneously evaluate the time-dependent failure rate and constant repair rate of a resource. A new hybrid immune genetic algorithm with local search (HIGALS) is developed to solve this NP-hard problem. HIGALS incorporates a new coding mechanism, initialization method and local search operator.

A case study tests the proposed HIGALS approach. The validity of the mathematical model is confirmed by solving small-sized problems with GAMS software. The proposed HIGALS algorithm is validated by solving small-sized problems and comparing its solutions with GAMS. The superiority of HIGALS is demonstrated by comparing its solutions with six basic algorithms on medium- and large-sized problems. Results show that HIGALS outperforms existing algorithms, achieving an average reduction in makespan of over 11.79%, while maintaining the advantages of genetic, immune and local search algorithms and avoiding their disadvantages.

Considering time-dependent resource reliability can help project managers plan for disruptions and delays in resource-critical projects. HIGALS provides decision support for robust multi-project scheduling.

This study contributes to the field by investigating RCMPSP with time-dependent renewable resource reliability, which reflects real-world uncertainty more accurately. HIGALS presents a novel approach to balance intensification and diversification for this challenging problem.